index
int64 | repo_name
string | branch_name
string | path
string | content
string | import_graph
string |
|---|---|---|---|---|---|
35,758,898
|
Taospirit/DRL_beta
|
refs/heads/main
|
/drl/algorithm/oac.py
|
import os
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from drl.algorithm import BasePolicy
from drl.utils import ReplayBuffer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class OAC(BasePolicy): # no value network
def __init__(
self,
model,
buffer_size=1000,
batch_size=100,
actor_learn_freq=1,
target_update_freq=5,
target_update_tau=0.01,
learning_rate=1e-3,
discount_factor=0.99,
verbose=False,
update_iteration=10,
act_dim=None
):
super().__init__()
self.lr = learning_rate
self.eps = np.finfo(np.float32).eps.item()
self.tau = target_update_tau
self.actor_learn_freq = actor_learn_freq
self.target_update_freq = target_update_freq
self._gamma = discount_factor
self._target = target_update_freq > 0
self._update_iteration = update_iteration
self._sync_cnt = 0
self._learn_critic_cnt = 0
self._learn_actor_cnt = 0
self._verbose = verbose
self._batch_size = batch_size
self.buffer = ReplayBuffer(buffer_size) # off-policy
self.actor_eval = model.policy_net.to(device).train()
self.critic_eval = model.value_net.to(device).train()
self.actor_target = self.copy_net(self.actor_eval)
self.critic_target = self.copy_net(self.critic_eval)
self.actor_eval_optim = optim.Adam(self.actor_eval.parameters(), lr=self.lr)
self.critic_eval_optim = optim.Adam(self.critic_eval.parameters(), lr=self.lr)
self.criterion = nn.SmoothL1Loss()
self.act_dim = act_dim
self.target_entropy = -torch.tensor(1).to(device)
self.log_alpha = torch.zeros(1, requires_grad=True, device=device)
self.alpha_optim = optim.Adam([self.log_alpha], lr=self.lr)
self.alpha = self.log_alpha.exp()
def choose_action(self, state, test=False, beta_UB=1.0, delta=1.0):
# paper: Better Exploration with Optimistic Actor-Critic, NeurIPS 2019
# pdf: https://arxiv.org/pdf/1910.12807.pdf
# ref: https://github.com/microsoft/oac-explore/blob/master/optimistic_exploration.py
# paper param: beta_UB=4.66 delta=23.53, env_name=humanoid
state = torch.tensor(state, dtype=torch.float32, device=device)
if test:
self.actor_eval.eval()
mean, log_std = self.actor_eval(state)
return mean.detach().cpu().numpy()
assert len(list(state.shape)) == 1 # not batch
mu_T, log_std = self.actor_eval(state)
std = torch.exp(log_std)
# assert len(list(mu_T.shape)) == 1, mu_T
# assert len(list(std.shape)) == 1
mu_T.requires_grad_()
curr_act = torch.tanh(mu_T).unsqueeze(0) # action
state = state.unsqueeze(0)
q1, q2 = self.critic_target(state, curr_act)
mu_q = (q1 + q2) / 2.0
sigma_q = torch.abs(q1 - q2) / 2.0
Q_UB = mu_q + beta_UB * sigma_q
grad = torch.autograd.grad(Q_UB, mu_T)
grad = grad[0]
assert grad is not None
assert mu_T.shape == grad.shape
sigma_T = torch.pow(std, 2)
denom = torch.sqrt(
torch.sum(
torch.mul(torch.pow(grad, 2), sigma_T)
)
) + 10e-6
mu_C = np.sqrt(2.0 * delta) * torch.mul(sigma_T, grad) / denom
assert mu_C.shape == mu_T.shape
mu_E = mu_T + mu_C
assert mu_E.shape == std.shape
normal = Normal(mu_E, std)
z = normal.sample()
action = torch.tanh(z).detach().cpu().numpy()
return action
def learn(self):
pg_loss, q_loss, a_loss = 0, 0, 0
for _ in range(self._update_iteration):
batch = self.buffer.split_batch(self._batch_size)
if self.act_dim is None:
self.act_dim = np.array(batch['a']).shape[-1]
self.target_entropy = -torch.tensor(self.act_dim).to(device)
S = torch.tensor(batch['s'], dtype=torch.float32, device=device)
A = torch.tensor(batch['a'], dtype=torch.float32, device=device).view(-1, 1)
M = torch.tensor(batch['m'], dtype=torch.float32).view(-1, 1)
R = torch.tensor(batch['r'], dtype=torch.float32).view(-1, 1)
S_ = torch.tensor(batch['s_'], dtype=torch.float32, device=device)
# print (f'size S:{S.size()}, A:{A.size()}, M:{M.size()}, R:{R.size()}, S_:{S_.size()}, W:{W.size()}')
with torch.no_grad():
next_A, next_log = self.actor_target.evaluate(S_)
q1_next, q2_next = self.critic_target(S_, next_A)
q_next = torch.min(q1_next, q2_next) - self.alpha * next_log
q_target = R + M * self._gamma * q_next.cpu()
q_target = q_target.to(device)
# q_loss
q1_eval, q2_eval = self.critic_eval(S, A)
critic_loss = self.criterion(q1_eval, q_target) + self.criterion(q2_eval, q_target)
self.critic_eval_optim.zero_grad()
critic_loss.backward()
self.critic_eval_optim.step()
self._learn_critic_cnt += 1
actor_loss = torch.tensor(0)
alpha_loss = torch.tensor(0)
if self._learn_critic_cnt % self.actor_learn_freq == 0:
curr_A, curr_log = self.actor_eval.evaluate(S)
q1_next, q2_next = self.critic_eval(S, curr_A)
q_next = torch.min(q1_next, q2_next)
# pg_loss
actor_loss = (self.alpha * curr_log - q_next).mean()
self.actor_eval_optim.zero_grad()
actor_loss.backward()
self.actor_eval_optim.step()
# alpha loss
alpha_loss = -(self.log_alpha * (curr_log + self.target_entropy).detach()).mean()
self.alpha_optim.zero_grad()
alpha_loss.backward()
self.alpha_optim.step()
self.alpha = float(self.log_alpha.exp().detach().cpu().numpy())
q_loss += critic_loss.item() * 0.5
pg_loss += actor_loss.item()
a_loss += alpha_loss.item()
if self._learn_critic_cnt % self.target_update_freq:
self.soft_sync_weight(self.critic_target, self.critic_eval, self.tau)
self.soft_sync_weight(self.actor_target, self.actor_eval, self.tau)
return pg_loss, q_loss, a_loss
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,899
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/test/test_dqn.py
|
from test_tool import policy_test
import gym
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import namedtuple
# from drl.model import ActorNet, CriticDQN
from drl.algorithm import DQN
# from drl.algorithm import DoubleDQN
env_name = 'CartPole-v0'
env = gym.make(env_name)
env = env.unwrapped
env.seed(1)
torch.manual_seed(1)
# Parameters
state_space = env.observation_space.shape[0]
action_space = env.action_space.n
action_shape = 0 if isinstance(env.action_space.sample(), int) else env.action_space.sample().shape
hidden_dim = 32
episodes = 1000
buffer_size = 5000
batch_size = 100
target_update_freq = 100
max_step = 1000
model_save_dir = 'save/test_dqn_double_'
model_save_dir = os.path.join(os.path.dirname(__file__), model_save_dir)
save_file = model_save_dir.split('/')[-1]
os.makedirs(model_save_dir, exist_ok=True)
class CriticDQN(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim, atoms=51, layer_norm=False):
super().__init__()
self.epsilon = 0.5
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.q_value = nn.Linear(hidden_dim, action_dim)
self.v_value = nn.Linear(hidden_dim, 1)
self.use_dueling = False
self.use_distributional = False
if layer_norm:
layer_norm(self.fc1, std=1.0)
layer_norm(self.q_value, std=1.0)
def forward(self, x):
x = F.relu(self.fc1(x))
if self.use_dueling:
v_value = self.v_value(x)
adv = self.q_value(x)
return v_value + adv - adv.mean()
if self.use_distributional:
self.q_value = nn.Linear(hidden_dim, action_dim*atoms)
x = self.q_value(x)
return F.softmax(x.view(-1, action_dim, atoms), dim=2)
q_value = self.q_value(x)
return q_value
def action(self, state, test=False):
q_values = self.forward(state)
action = q_values.argmax(dim=1).cpu().data.numpy()
action = action[0] if action_shape == 0 else action.reshape(action_shape) # return the argmax index
if test:
self.epsilon = 1.0
if np.random.randn() >= self.epsilon: # epsilon-greedy
action = np.random.randint(0, q_values.size()[-1])
action = action if action_shape == 0 else action.reshape(action_shape)
return action
model = namedtuple('model', ['value_net'])
critic = CriticDQN(state_space, hidden_dim, action_space)
model = model(critic)
policy = DQN(model, buffer_size=buffer_size, batch_size=batch_size, target_update_freq=100)
def sample(env, policy, max_step, test=False):
assert env, 'You must set env for sample'
rewards = 0
state = env.reset()
for step in range(max_step):
action = policy.choose_action(state, test)
next_state, reward, done, info = env.step(action)
# env.render()
# process env callback
if not test:
mask = 0 if done else 1
policy.process(s=state, a=action, r=reward, m=mask, s_=next_state)
rewards += reward
if done:
break
state = next_state
if not test:
policy.learn()
return rewards, step
run_type = ['train', 'eval', 'retrain']
run = run_type[0]
plot_name = 'DQN_Double'
def main():
test = False
if run == 'eval':
global episodes
episodes = 100
test = True
print('Loading model...')
policy.load_model(model_save_dir, save_file, load_actor=True)
elif run == 'train':
print('Saving model setting...')
# save_setting()
policy_test.save_setting(env_name, state_space, action_space, episodes,
max_step, policy, model_save_dir, save_file)
elif run == 'retrain':
print('Loading model...')
policy.load_model(model_save_dir, save_file, load_actor=True, load_critic=True)
else:
print('Setting your run type!')
return 0
live_time = []
for i_eps in range(episodes):
rewards, step = sample(env, policy, max_step, test=test)
if run == 'eval':
print(f'Eval eps:{i_eps+1}, Rewards:{rewards}, Steps:{step+1}')
continue
live_time.append(rewards)
policy_test.plot(live_time, plot_name, model_save_dir)
if i_eps > 0 and i_eps % 100 == 0:
print(f'i_eps is {i_eps}')
policy.save_model(model_save_dir, save_file, save_critic=True)
env.close()
if __name__ == '__main__':
main()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,900
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/tutorial/quick_start.py
|
import gym
import os, sys
import time
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
from torch.utils.tensorboard import SummaryWriter
from collections import namedtuple
import sys
sys.path.append('../..')
from drl.algorithm import A2C
# env
env_name = 'CartPole-v0'
env = gym.make(env_name)
env = env.unwrapped # 还原env的原始设置，env外包了一层防作弊层
# Parameters
state_space = env.observation_space.shape[0]
action_space = env.action_space.n
class ActorNet(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super().__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.action_linear = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = F.relu(self.fc1(x))
action_score = self.action_linear(x)
dist = F.softmax(action_score, dim=-1)
return dist
def action(self, state, test=False):
dist = self.forward(state)
m = Categorical(dist)
action = m.sample()
log_prob = m.log_prob(action)
return action.item(), log_prob
class CriticV(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super().__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.value_linear = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = F.relu(self.fc1(x))
state_value = self.value_linear(x)
return state_value
model = namedtuple('model', ['policy_net', 'value_net'])
actor = ActorNet(state_space, 32, action_space)
critic = CriticV(state_space, 32, 1)
model = model(actor, critic)
policy = A2C(model, buffer_size=1000, learning_rate=1e-2)
def sample(env, policy, max_step):
# reward_avg = 0
rewards = 0
state = env.reset()
for _ in range(max_step):
# step1: policy choose action base state
action, log_prob = policy.choose_action(state)
# feed action into env to step, get next env infomation
next_state, reward, done, info = env.step(action)
# show gym env when trained reward over threhold
env.render()
# step2: policy store transition infomation in Buffer
mask = 0 if done else 1
policy.process(s=state, r=reward, l=log_prob, m=mask)
# record rewards
rewards += reward
if done:
break
state = next_state
return rewards
def main():
for i_eps in range(200):
rewards = sample(env, policy, 1000)
# step3: policy learn from stored transition in Buffer, to update network
pg_loss, v_loss = policy.learn()
reward, pg_loss, v_loss = round(rewards, 3), round(pg_loss, 3), round(v_loss, 3)
print (f'EPS:{i_eps}, reward:{rewards}, pg_loss:{pg_loss}, v_loss:{v_loss}')
env.close()
if __name__ == '__main__':
main()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,901
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/test/test_a2c.py
|
from test_tool import policy_test
import gym
import os
import time
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
from torch.utils.tensorboard import SummaryWriter
from drl.model import ActorNet, CriticV
from drl.algorithm import A2C
from drl.utils import ReplayBuffer as Buffer
from collections import namedtuple
model = namedtuple('model', ['policy_net', 'value_net'])
env_name = 'CartPole-v0'
env = gym.make(env_name)
env = env.unwrapped
env.seed(1)
torch.manual_seed(1)
# Parameters
state_space = env.observation_space.shape[0]
action_space = env.action_space.n
hidden_dim = 32
episodes = 300
max_step = 1000
actor_learn_freq = 1
target_update_freq = 0
model_save_dir = 'save/test_a2c_buffer'
model_save_dir = os.path.join(os.path.dirname(__file__), model_save_dir)
save_file = model_save_dir.split('/')[-1]
os.makedirs(model_save_dir, exist_ok=True)
class ActorNet(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super().__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.action_head = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = F.relu(self.fc1(x))
action_score = self.action_head(x)
dist = F.softmax(action_score, dim=-1)
return dist
def action(self, state, test=False):
if test:
self.actor_eval.eval()
return Categorical(self.actor_eval(state)).sample().item(), 0
dist = self.forward(state)
m = Categorical(dist)
action = m.sample()
log_prob = m.log_prob(action)
return action.item(), log_prob
class CriticV(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layer_norm=False):
super().__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.value_head = nn.Linear(hidden_dim, output_dim)
if layer_norm:
layer_norm(self.fc1, std=1.0)
layer_norm(self.value_head, std=1.0)
def forward(self, x):
x = F.relu(self.fc1(x))
state_value = self.value_head(x)
return state_value
from collections import namedtuple
model = namedtuple('model', ['policy_net', 'value_net'])
actor = ActorNet(state_space, hidden_dim, action_space)
critic = CriticV(state_space, hidden_dim, 1)
model = model(actor, critic)
policy = A2C(model, buffer_size=max_step, actor_learn_freq=actor_learn_freq,
target_update_freq=target_update_freq)
def sample(env, policy, max_step, test=False):
assert env, 'You must set env for sample'
rewards = 0
state = env.reset()
for step in range(max_step):
action, log_prob = policy.choose_action(state, test)
# print (action, type(action))
next_state, reward, done, info = env.step(action)
# env.render()
# process env callback
if not test:
# policy.process(s=state, a=action, s_=next_state, r=reward, d=done)
mask = 0 if done else 1
policy.process(s=state, r=reward, l=log_prob, m=mask)
rewards += reward
if done:
break
state = next_state
if not test:
policy.learn()
return rewards, step
run_type = ['train', 'eval', 'retrain']
run = run_type[0]
plot_name = 'A2C_TwoNet_no_Double'
def main():
test = False
if run == 'eval':
global episodes
episodes = 100
test = True
print('Loading model...')
policy.load_model(model_save_dir, save_file, load_actor=True)
elif run == 'train':
print('Saving model setting...')
# save_setting()
policy_test.save_setting(env_name, state_space, action_space, episodes,
max_step, policy, model_save_dir, save_file)
elif run == 'retrain':
print('Loading model...')
policy.load_model(model_save_dir, save_file, load_actor=True, load_critic=True)
else:
print('Setting your run type!')
return 0
writer = SummaryWriter('./logs/1/')
live_time = []
for i_eps in range(episodes):
rewards, step = sample(env, policy, max_step, test=test)
if run == 'eval':
print(f'Eval eps:{i_eps+1}, Rewards:{rewards}, Steps:{step+1}')
continue
live_time.append(rewards)
policy_test.plot(live_time, plot_name, model_save_dir)
writer.add_scalar('rewards_per_epi', rewards, global_step=i_eps)
if i_eps > 0 and i_eps % 100 == 0:
print(f'i_eps is {i_eps}')
policy.save_model(model_save_dir, save_file, save_actor=True, save_critic=True)
env.close()
writer.close()
if __name__ == '__main__':
main()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,902
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/test/test_pickle.py
|
import os
d = {'mean': [1,2 ,3, 4, 5, 6, 7], 'std': [1, 2, 3, 4, 5, 6, 7]}
path_dir = os.path.abspath(os.path.dirname(__file__))
print (path_dir)
pkl_dir = path_dir + '/test.pkl'
print (pkl_dir)
import pickle
with open(pkl_dir, 'wb') as f:
pickle.dump(d, f, pickle.HIGHEST_PROTOCOL)
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,903
|
Taospirit/DRL_beta
|
refs/heads/main
|
/drl/algorithm/__init__.py
|
from drl.algorithm.base import BasePolicy
from drl.algorithm.dqn import DQN, DoubleDQN, DuelingDQN
from drl.algorithm.a2c import A2C
from drl.algorithm.ddpg import DDPG
from drl.algorithm.ppo import PPO
from drl.algorithm.td3 import TD3
from drl.algorithm.sac import SAC
from drl.algorithm.sac2 import SAC2
from drl.algorithm.sacv import SACV
from drl.algorithm.oac import OAC
__all__ = [
'BasePolicy',
'DQN',
'DoubleDQN',
'DuelingDQN',
'A2C',
'DDPG',
'PPO',
'SAC',
'SAC2',
'SACV',
'OAC'
]
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,904
|
Taospirit/DRL_beta
|
refs/heads/main
|
/drl/backup.py
|
class SegmentTree():
def __init__(self, size):
self.index = 0
self.size = size
self.full = False
self.sum_tree = np.zeros((2 * size - 1, ), dtype=np.float32)
self.memory = []
self.max = 1
self.cnt = 0
def __len__(self):
return len(self.memory)
def _propagate(self, index, value):
parent = (index - 1) // 2
left, right = 2 * parent + 1, 2 * parent + 2
self.sum_tree[parent] = self.sum_tree[left] + self.sum_tree[right]
if parent != 0:
self._propagate(parent, value)
def update(self, index, value):
self.sum_tree[index] = value
self._propagate(index, value)
self.max = max(value, self.max)
def append(self, data, value):
if len(self.memory) < self.size:
self.memory.append(None)
self.memory[self.index] = data
self.update(self.index + self.size - 1, value)
self.index = (self.index + 1) % self.size
self.full = self.full or self.index == 0
self.max = max(value, self.max)
def _retrieve(self, index, value):
left, right = 2 * index + 1, 2 * index + 2
if left >= len(self.sum_tree):
# if self.cnt > 15:
# print (f'GET! at {index} for cnt {self.cnt}')
self.cnt = 0
return index
elif value <= self.sum_tree[left]:
self.cnt += 1
# if self.cnt > 15:
# print (f'search left {left}, size {self.size}')
# print (f'retrieveing at left {self.cnt}')
# print (f'left_index {left} to search')
return self._retrieve(left, value)
else:
self.cnt += 1
# if self.cnt > 15:
# print (f'search right {right}, size {self.size}')
# print (f'retrieveing at right {self.cnt}')
# print (f'right_index {right} to search')
return self._retrieve(right, value - self.sum_tree[left])
def find(self, value):
tree_index = self._retrieve(0, value)
data_index = tree_index - self.size + 1
priority = self.sum_tree[tree_index]
return (priority, data_index, tree_index)
def get(self, data_index):
return self.memory[data_index % self.size]
def total(self):
return self.sum_tree[0]
Transition = namedtuple('Transition', ('timestep', 'state', 'action', 'reward', 'nonterminal'))
# blank_trans = Transition(0, torch.zeros(84, 84, dtype=torch.uint8), None, 0, False) # atari game
blank_trans = Transition(0, None, None, 0, False) # gym
class PriorityReplayBuffer(object):
def __init__(self, capacity):
super().__init__()
self.capacity = capacity
self.discount = 0.99
self.n = 1
self.priority_weight = 0.4
self.priority_exponent = 0.5
self.t = 0
self.StoreTree = SegmentTree(capacity)
self.blank_trans = Transition(0, None, None, 0, False)
def __len__(self):
return len(self.StoreTree)
def append(self, **kwargs):
state, action, reward, terminal = kwargs['s'], kwargs['a'], kwargs['r'], not kwargs['m']
if self.blank_trans.state is None:
blank_state = np.zeros(np.array(state).shape, dtype=np.float32)
self.blank_trans = Transition(0, blank_state, None, 0, False)
self.StoreTree.append(Transition(self.t, state, action, reward, not terminal), self.StoreTree.max)
self.t = 0 if terminal else self.t + 1
def _get_transition(self, idx): # n-step transition
transition = np.array([None] * (self.n + 1))
transition[0] = self.StoreTree.get(idx)
for i in range(1, self.n + 1):
if transition[i - 1].nonterminal:
transition[i] = self.StoreTree.get(idx + i)
else:
transition[i] = blank_trans
return transition
def sample(self, batch_size):
batch_size = min(batch_size, len(self.StoreTree.memory))
def get_sample_from_segment(segment, i):
valid = False
cnt = 0
while not valid:
sample = np.random.uniform(i * segment, (i + 1) * segment)
cnt += 1
if cnt > 30:
sample = np.random.uniform(0, batch_size * segment)
prio, idx, tree_idx = self.StoreTree.find(sample)
forward = (self.StoreTree.index - idx) % self.capacity
if forward > self.n and prio != 0:
valid = True
transition = self._get_transition(idx)
state = [transition[0].state]
next_state = [transition[self.n].state]
action = [transition[0].action]
discounted_reward = [sum(self.discount ** i * transition[i].reward for i in range(self.n))]
nonterminal = [transition[self.n].nonterminal]
return prio, idx, tree_idx, state, action, discounted_reward, next_state, nonterminal
p_total = self.StoreTree.total()
segment = p_total / batch_size
batch = [get_sample_from_segment(segment, i) for i in range(batch_size)]
prios, idxs, tree_idxs, states, actions, returns, next_states, nonterminal = zip(*batch)
states = np.squeeze(states, axis=1)
next_states = np.squeeze(next_states, axis=1)
prios = np.array(prios, dtype=np.float32) / p_total
capacity = self.capacity if self.StoreTree.full else self.StoreTree.index
weights = (capacity * prios) ** -self.priority_weight
return tree_idxs, states, actions, returns, next_states, nonterminal, weights
def update_priorities(self, idxs, priorities):
priorities = np.power(priorities, self.priority_exponent)
[self.StoreTree.update(idx, priority) for idx, priority in zip(idxs, priorities)]
def is_full(self):
return self.StoreTree.full
def size(self):
return self.StoreTree.index
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,905
|
Taospirit/DRL_beta
|
refs/heads/main
|
/drl/algorithm/ddpg.py
|
import os
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from drl.algorithm import BasePolicy
from drl.utils import ReplayBuffer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class DDPG(BasePolicy):
def __init__(
self,
model,
buffer_size=1000,
actor_learn_freq=1,
target_update_freq=1,
target_update_tau=0.005,
learning_rate=1e-4,
discount_factor=0.99,
batch_size=100,
update_iteration=10,
verbose=False,
act_dim=None,
num_episodes=1000,
):
super().__init__()
self.lr = learning_rate
self.end_lr = learning_rate * 0.1
self.eps = np.finfo(np.float32).eps.item()
self.tau = target_update_tau
self.actor_learn_freq = actor_learn_freq
self.target_update_freq = target_update_freq
self._gamma = discount_factor
self._update_iteration = update_iteration
self._sync_cnt = 0
self._learn_critic_cnt = 0
self._learn_actor_cnt = 0
self._verbose = False
self._batch_size = batch_size
self.schedule_adam = True
self.buffer = ReplayBuffer(buffer_size)
self.actor_eval = model.policy_net.to(device).train() # pi(s)
self.critic_eval = model.value_net.to(device).train() # Q(s, a)
self.actor_eval_optim = optim.Adam(self.actor_eval.parameters(), lr=self.lr)
self.critic_eval_optim = optim.Adam(self.critic_eval.parameters(), lr=self.lr)
self.actor_target = self.copy_net(self.actor_eval)
self.critic_target = self.copy_net(self.critic_eval)
self.criterion = nn.MSELoss() # why mse?
self.act_dim = act_dim
self.num_episodes = num_episodes
def learn(self):
loss_actor_avg, loss_critic_avg = 0, 0
for _ in range(self._update_iteration):
batch = self.buffer.split_batch(self._batch_size)
if self.act_dim is None:
self.act_dim = np.array(batch['a']).shape[-1]
S = torch.tensor(batch['s'], dtype=torch.float32, device=device) # [batch_size, state_dim]
# print(batch['a'])
A = torch.tensor(batch['a'], dtype=torch.float32, device=device).view(-1, self.act_dim) # [batch_size, act_dim]
M = torch.tensor(batch['m'], dtype=torch.float32).view(-1, 1) # [batch_size, 1]
R = torch.tensor(batch['r'], dtype=torch.float32).view(-1, 1) # [batch_size, 1]
S_ = torch.tensor(batch['s_'], dtype=torch.float32, device=device) # [batch_size, state_dim]
if self._verbose:
print(f'Shape S:{S.shape}, A:{A.shape}, M:{M.shape}, R:{R.shape}, S_:{S_.shape}')
with torch.no_grad():
q_next = self.critic_target(S_, self.actor_target(S_))
q_target = R + M * self._gamma * q_next.cpu()
q_target = q_target.to(device)
q_eval = self.critic_eval(S, A) # [batch_size, q_value_size]
critic_loss = self.criterion(q_eval, q_target)
self.critic_eval_optim.zero_grad()
critic_loss.backward()
self.critic_eval_optim.step()
loss_critic_avg += critic_loss.item()
self._learn_critic_cnt += 1
if self._verbose:
print(f'=======Learn_Critic_Net, cnt:{self._learn_critic_cnt}=======')
if self._learn_critic_cnt % self.actor_learn_freq == 0:
actor_loss = -self.critic_eval(S, self.actor_eval(S)).mean()
self.actor_eval_optim.zero_grad()
actor_loss.backward()
self.actor_eval_optim.step()
loss_actor_avg += actor_loss.item()
self._learn_actor_cnt += 1
if self._verbose:
print(f'=======Learn_Actort_Net, cnt:{self._learn_actor_cnt}=======')
if self._learn_critic_cnt % self.target_update_freq == 0:
if self._verbose:
print(f'=======Soft_sync_weight of DDPG, tau:{self.tau}=======')
self.soft_sync_weight(self.critic_target, self.critic_eval, self.tau)
self.soft_sync_weight(self.actor_target, self.actor_eval, self.tau)
if self.schedule_adam:
new_lr = self.lr + (self.end_lr - self.lr) / self.num_episodes * self._learn_critic_cnt / self._update_iteration
# set learning rate
# ref: https://stackoverflow.com/questions/48324152/
for g in self.actor_eval_optim.param_groups:
g['lr'] = new_lr
for g in self.critic_eval_optim.param_groups:
g['lr'] = new_lr
loss_actor_avg /= (self._update_iteration/self.actor_learn_freq)
loss_critic_avg /= self._update_iteration
return loss_actor_avg, loss_critic_avg
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,906
|
Taospirit/DRL_beta
|
refs/heads/main
|
/drl/algorithm/sac1.py
|
import os
import numpy as np
from copy import deepcopy
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.distributions import Normal
from drl.algorithm import BasePolicy
from drl.utils import ReplayBuffer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class SAC1(BasePolicy):
def __init__(
self,
model,
buffer_size=1000,
batch_size=100,
actor_learn_freq=1,
target_update_freq=5,
target_update_tau=1e-2,
learning_rate=1e-3,
discount_factor=0.99,
verbose=False,
update_iteration=10,
act_dim=None,
):
super().__init__()
self.lr = learning_rate
self.eps = np.finfo(np.float32).eps.item()
self.tau = target_update_tau
self.actor_learn_freq = actor_learn_freq
self.target_update_freq = target_update_freq
self._gamma = discount_factor
self._target = target_update_freq > 0
self._update_iteration = update_iteration
self._sync_cnt = 0
self._learn_critic_cnt = 0
self._learn_actor_cnt = 0
self._verbose = verbose
self._batch_size = batch_size
self.buffer = ReplayBuffer(buffer_size) # off-policy
self.actor_eval = model.policy_net.to(device).train()
self.critic_eval = model.value_net.to(device).train()
self.value_eval = model.v_net.to(device).train()
self.value_target = self.copy_net(self.value_eval)
self.actor_eval_optim = optim.Adam(self.actor_eval.parameters(), lr=self.lr)
self.critic_eval_optim = optim.Adam(self.critic_eval.parameters(), lr=self.lr)
self.value_eval_optim = optim.Adam(self.value_eval.parameters(), lr=self.lr)
self.criterion = nn.SmoothL1Loss()
self.act_dim = act_dim
def learn(self):
pg_loss, v_loss, q_loss = 0, 0, 0
for _ in range(self._update_iteration):
batch = self.buffer.split_batch(self._batch_size)
if self.act_dim is None:
self.act_dim = np.array(batch['a']).shape[-1]
S = torch.tensor(batch['s'], dtype=torch.float32, device=device)
A = torch.tensor(batch['a'], dtype=torch.float32, device=device).view(-1, self.act_dim)
M = torch.tensor(batch['m'], dtype=torch.float32).view(-1, 1)
R = torch.tensor(batch['r'], dtype=torch.float32).view(-1, 1)
S_ = torch.tensor(batch['s_'], dtype=torch.float32, device=device)
new_A, log_prob = self.actor_eval.evaluate(S)
# V_value loss
with torch.no_grad():
new_q1_value, new_q2_value = self.critic_eval(S, new_A)
next_value = torch.min(new_q1_value, new_q2_value) - log_prob
value = self.value_eval(S)
value_loss = self.criterion(value, next_value)
# Soft q loss
with torch.no_grad():
target_value = self.value_target(S_)
target_q_value = R + M * self._gamma * target_value.cpu()
target_q_value = target_q_value.to(device)
q1_value, q2_value = self.critic_eval(S, A)
loss1 = self.criterion(q1_value, target_q_value)
loss2 = self.criterion(q2_value, target_q_value)
q_value_loss = 0.5 * (loss1 + loss2)
# policy loss
policy_loss = (log_prob - torch.min(new_q1_value, new_q2_value)).mean()
# policy_loss = (log_prob - torch.min(new_q1_value, new_q2_value).detach()).mean()
# update V
self.value_eval_optim.zero_grad()
value_loss.backward()
self.value_eval_optim.step()
# update soft Q
self.critic_eval_optim.zero_grad()
q_value_loss.backward()
self.critic_eval_optim.step()
self._learn_critic_cnt += 1
# update policy
self.actor_eval_optim.zero_grad()
policy_loss.backward()
self.actor_eval_optim.step()
pg_loss += policy_loss.item()
v_loss += value_loss.item()
q_loss += q_value_loss.item()
if self._learn_critic_cnt % self.target_update_freq:
self.soft_sync_weight(self.value_target, self.value_eval, self.tau)
return pg_loss, q_loss, v_loss
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,907
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/tutorial/tutorial_a2c.py
|
import gym
import os
import time
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical
from torch.utils.tensorboard import SummaryWriter
from collections import namedtuple
import sys
sys.path.append('../..')
from drl.algorithm import A2C
from drl.utils import plot
from tqdm import tqdm
# env
env_name = 'CartPole-v0'
env = gym.make(env_name)
env = env.unwrapped
env.seed(1)
torch.manual_seed(1)
# Parameters
state_space = env.observation_space.shape[0]
action_space = env.action_space.n
class ActorNet(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super().__init__()
self.net = nn.Sequential(nn.Linear(input_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, output_dim), )
def forward(self, x):
action_sorce = self.net(x)
dist = F.softmax(action_sorce, dim=-1)
return dist
def action(self, state, test=False):
dist = self.forward(state)
m = Categorical(dist)
act = m.sample()
log_prob = m.log_prob(act)
return act.item(), log_prob
class CriticV(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super().__init__()
self.net = nn.Sequential(nn.Linear(input_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, output_dim), )
def forward(self, x):
return self.net(x)
episodes = 200
max_step = 300
hidden_dim = 32
model = namedtuple('model', ['policy_net', 'value_net'])
actor = ActorNet(state_space, hidden_dim, action_space)
critic = CriticV(state_space, hidden_dim, 1)
model = model(actor, critic)
policy = A2C(model, buffer_size=max_step, learning_rate=1e-2, num_episodes=episodes)
model_save_dir = 'save/a2c'
model_save_dir = os.path.join(os.path.dirname(__file__), model_save_dir)
save_file = model_save_dir.split('/')[-1]
os.makedirs(model_save_dir, exist_ok=True)
writer = SummaryWriter(os.path.dirname(model_save_dir)+'/logs/a2c_1')
TRAIN = True
PLOT = True
WRITER = False
def sample(env, policy, max_step):
rewards = 0
state = env.reset()
for step in range(max_step):
#==============choose_action==============
action, log_prob = policy.choose_action(state)
next_state, reward, done, info = env.step(action)
if TRAIN:
mask = 0 if done else 1
#==============process==============
policy.process(s=state, r=reward, l=log_prob, m=mask)
else:
env.render()
rewards += reward
if done:
break
state = next_state
return rewards
def main():
if not TRAIN:
policy.load_model(model_save_dir, save_file, load_actor=True)
live_time = []
for i_eps in range(episodes):
rewards = sample(env, policy, max_step)
if not TRAIN:
print (f'EPS:{i_eps + 1}, reward:{round(rewards, 3)}')
else:
#==============learn==============
pg_loss, v_loss = policy.learn()
if PLOT:
live_time.append(rewards)
plot(live_time, 'A2C_'+env_name, model_save_dir, 100)
if WRITER:
writer.add_scalar('reward', rewards, global_step=i_eps)
writer.add_scalar('loss/pg_loss', pg_loss, global_step=i_eps)
writer.add_scalar('loss/v_loss', v_loss, global_step=i_eps)
if i_eps % 5 == 0:
print (f'EPS:{i_eps}, reward:{round(rewards, 3)}, pg_loss:{round(pg_loss, 3)}, v_loss:{round(v_loss, 3)}')
if i_eps % 200 == 0:
policy.save_model(model_save_dir, save_file, save_actor=True, save_critic=True)
writer.close()
env.close()
if __name__ == '__main__':
main()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,908
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/test_oac.py
|
# from test_tool import policy_test
#region
import gym
import os
from os.path import abspath, dirname
import time
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Categorical, Normal
from torch.utils.tensorboard import SummaryWriter
from collections import namedtuple
import numpy as np
import sys
sys.path.append('..')
from drl.algorithm import OAC
from utils.plot import plot
from utils.config import config
#config
config = config['sac']
env_name = config['env_name']
buffer_size = config['buffer_size']
actor_learn_freq = config['actor_learn_freq']
update_iteration = config['update_iteration']
target_update_freq = config['target_update_freq']
batch_size = config['batch_size']
hidden_dim = config['hidden_dim']
episodes = config['episodes'] + 10
max_step = config['max_step']
target_update_tau = config['target_update_tau']
lr = config['lr']
LOG_DIR = config['LOG_DIR']
SAVE_DIR = config['SAVE_DIR'] + env_name
POLT_NAME = config['POLT_NAME'] + env_name
PKL_DIR = config['PKL_DIR'] + env_name
file_path = abspath(dirname(__file__))
pkl_dir = file_path + PKL_DIR
model_save_dir = file_path + SAVE_DIR
save_file = model_save_dir.split('/')[-1]
writer_path = model_save_dir + LOG_DIR
try:
os.makedirs(model_save_dir)
except FileExistsError:
import shutil
shutil.rmtree(model_save_dir)
os.makedirs(model_save_dir)
env = gym.make(env_name)
env = env.unwrapped
# Parameters
state_space = env.observation_space.shape[0]
action_space = env.action_space.shape[0]
action_max = env.action_space.high[0]
action_scale = (env.action_space.high - env.action_space.low) / 2
action_bias = (env.action_space.high + env.action_space.low) / 2
#endregion
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def layer_norm(layer, std=1.0, bias_const=1e-6):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
class ActorModel(nn.Module):
def __init__(self, state_dim, hidden_dim, output_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.mean = nn.Linear(hidden_dim, output_dim)
self.log_std = nn.Linear(hidden_dim, output_dim)
layer_norm(self.fc1, std=1.0)
layer_norm(self.fc2, std=1.0)
layer_norm(self.mean, std=1.0)
layer_norm(self.log_std, std=1.0)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
mean = self.mean(x)
log_std = self.log_std(x)
log_std = torch.clamp(log_std, min=-20, max=2)
return mean, log_std
def action(self, state, test=False):
mean, log_std = self.forward(state)
if test:
return mean.detach().cpu().numpy()
std = log_std.exp()
normal = Normal(mean, std)
z = normal.sample()
action = torch.tanh(z).detach().cpu().numpy()
return action
# Use re-parameterization tick
def evaluate(self, state, epsilon=1e-6):
mean, log_std = self.forward(state)
std = log_std.exp()
normal = Normal(mean, std)
noise = Normal(0,1)
z = noise.sample()
action = torch.tanh(mean + std*z.to(device))
log_prob = normal.log_prob(mean + std*z.to(device)) - torch.log(1 - action.pow(2) + epsilon)
return action, log_prob
class CriticModel(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim):
super().__init__()
self.net1 = nn.Sequential(nn.Linear(state_dim + action_dim , hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, 1), )
self.net2 = nn.Sequential(nn.Linear(state_dim + action_dim , hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, 1), )
def forward(self, state, action):
x = torch.cat((state, action), dim=1)
q1_value = self.net1(x)
q2_value = self.net2(x)
return q1_value, q2_value
class ValueModel(nn.Module):
def __init__(self, state_dim, init_w=3e-3):
super().__init__()
self.fc1 = nn.Linear(state_dim, 256)
self.fc2 = nn.Linear(256, 256)
self.fc3 = nn.Linear(256, 1)
layer_norm(self.fc1, std=1.0)
layer_norm(self.fc2, std=1.0)
layer_norm(self.fc3, std=1.0)
self.fc3.weight.data.uniform_(-init_w, init_w)
self.fc3.bias.data.uniform_(-init_w, init_w)
def forward(self, state):
x = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
TRAIN = True
PLOT = True
WRITER = False
def map_action(action):
if isinstance(action, torch.Tensor):
action = action.item()
return action * action_scale + action_bias
def sample(env, policy, max_step, warm_up=False):
rewards = []
state = env.reset()
for step in range(max_step):
#==============choose_action==============
action = policy.choose_action(state) if not warm_up else env.action_space.sample()
next_state, reward, done, info = env.step(map_action(action))
if TRAIN:
mask = 0 if done else 1
#==============process==============
policy.process(s=state, a=action, r=reward, m=mask, s_=next_state)
else:
env.render()
rewards.append(reward)
if done:
break
state = next_state
return rewards
def train():
model = namedtuple('model', ['policy_net', 'value_net', 'v_net'])
actor = ActorModel(state_space, hidden_dim, action_space)
critic = CriticModel(state_space, hidden_dim, action_space)
v_net = ValueModel(state_space)
rl_agent = model(actor, critic, v_net)
policy = OAC(rl_agent, buffer_size=buffer_size, actor_learn_freq=actor_learn_freq,
update_iteration=update_iteration, target_update_freq=target_update_freq,
target_update_tau=target_update_tau, batch_size=batch_size, learning_rate=lr)
writer = SummaryWriter(writer_path)
if not TRAIN:
policy.load_model(model_save_dir, save_file, load_actor=True)
mean, std = [], []
live_time = []
# while policy.warm_up():
# sample(env, policy, max_step, warm_up=True)
# print (f'Warm up for buffer {policy.buffer.size()}', end='\r')
for i_eps in range(episodes):
rewards = sample(env, policy, max_step)
reward_mean = np.mean(rewards)
reward_std = np.std(rewards)
mean.append(reward_mean)
std.append(reward_std)
if not TRAIN:
print (f'EPS:{i_eps + 1}, reward:{round(reward_mean, 3)}')
else:
#==============learn==============
pg_loss, q_loss, v_loss = policy.learn()
if PLOT:
live_time.append(reward_mean)
plot(live_time, POLT_NAME, model_save_dir, 100)
if WRITER:
writer.add_scalar('reward', reward_mean, global_step=i_eps)
writer.add_scalar('loss/pg_loss', pg_loss, global_step=i_eps)
writer.add_scalar('loss/q_loss', q_loss, global_step=i_eps)
writer.add_scalar('loss/v_loss', v_loss, global_step=i_eps)
if i_eps % 5 == 0:
print (f'EPS:{i_eps}, reward_mean:{round(reward_mean, 3)}, pg_loss:{round(pg_loss, 3)}, q_loss:{round(q_loss, 3)}, alpha_loss:{round(v_loss, 3)}')
if i_eps % 200 == 0:
policy.save_model(model_save_dir, save_file, save_actor=True, save_critic=True)
writer.close()
env.close()
return mean, std
if __name__ == '__main__':
env.seed(1)
torch.manual_seed(1)
#only for logistic test
train()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,909
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/test/test_seaborn.py
|
import pickle
import os
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
def getdata():
basecond = [[18, 20, 19, 18, 13, 4, 1],
[20, 17, 12, 9, 3, 0, 0],
[20, 20, 20, 12, 5, 3, 0]]
cond1 = [[18, 19, 18, 19, 20, 15, 14],
[19, 20, 18, 16, 20, 15, 9],
[19, 20, 20, 20, 17, 10, 0],
[20, 20, 20, 20, 7, 9, 1]]
cond2 = [[20, 20, 20, 20, 19, 17, 4],
[20, 20, 20, 20, 20, 19, 7],
[19, 20, 20, 19, 19, 15, 2]]
cond3 = [[20, 20, 20, 20, 19, 17, 12],
[18, 20, 19, 18, 13, 4, 1],
[20, 19, 18, 17, 13, 2, 0],
[19, 18, 20, 20, 15, 6, 0]]
return basecond, cond1, cond2, cond3
def smooth(data, sm=1):
if sm > 1:
smooth_data = []
for d in data:
y = np.ones(sm)*1.0/sm
d = np.convolve(y, d, "same")
smooth_data.append(d)
else:
smooth_data = data
return smooth_data
data = getdata()
fig = plt.figure()
xdata = np.array([0, 1, 2, 3, 4, 5, 6])/5
linestyle = ['-', '--', ':', '-.']
color = ['r', 'g', 'b', 'k']
label = ['algo1', 'algo2', 'algo3', 'algo4']
sns.set(style="darkgrid", font_scale=1)
# for i in range(4):
# sns.tsplot(time=xdata, data=data[i], color=color[i], linestyle=linestyle[i], condition=label[i])
data1 = [[18, 20, 19, 18, 13, 4, 1],
[20, 17, 12, 9, 3, 0, 0],
[20, 20, 20, 12, 5, 3, 0]]
data2 = [[17, 20, 19, 18, 13, 4, 1],
[17, 17, 12, 9, 3, 0, 0],
[17, 20, 20, 12, 5, 3, 0]]
base = np.array(data2[0])
def ran():
return np.random.uniform(-1, 1, size=(7,))
print(ran())
data3= [base,
base + ran(),
base + ran()]
sns.tsplot(time=xdata, data=data1, color=color[0], linestyle=linestyle[0], condition=label[0])
data2 = smooth(data1, sm=5)
sns.tsplot(time=xdata, data=data2, color=color[1], linestyle=linestyle[0], condition=label[1])
# sns.tsplot(time=xdata, data=data3, color=color[2], linestyle=linestyle[0], condition=label[2])
# plt.ylabel("Success Rate", fontsize=25)
# plt.xlabel("Iteration Number", fontsize=25)
# plt.title("Awesome Robot Performance", fontsize=30)
plt.ylabel("Success Rate")
plt.xlabel("Iteration Number")
plt.title("Awesome Robot Performance")
plt.show()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,910
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/test/test_ppo.py
|
from test_tool import policy_test
import gym
import os
import time
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import namedtuple
from drl.model import ActorPPO, CriticV
from drl.algorithm import PPO
from drl.utils import ZFilter
env_name = 'Pendulum-v0'
env = gym.make(env_name)
env = env.unwrapped
env.seed(1)
torch.manual_seed(1)
# Parameters
state_space = env.observation_space.shape[0]
action_space = env.action_space.shape[0]
action_max = env.action_space.high[0]
hidden_dim = 100
episodes = 2000
max_step = 200
buffer_size = 32
actor_learn_freq = 1
target_update_freq = 0
batch_size = 100
model_save_dir = 'save/test_ppo_adv_reward_tanh'
model_save_dir = os.path.join(os.path.dirname(__file__), model_save_dir)
save_file = model_save_dir.split('/')[-1]
os.makedirs(model_save_dir, exist_ok=True)
class ActorPPO(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim, layer_norm=False):
super().__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.mu_head = nn.Linear(hidden_dim, 1)
self.sigma_head = nn.Linear(hidden_dim, 1)
# if layer_norm:
# layer_norm(self.fc1, std=1.0)
# layer_norm(self.mu_head, std=1.0)
# layer_norm(self.sigma_head, std=1.0)
def forward(self, state):
x = torch.tanh(self.fc1(state))
# x = F.relu(self.fc1(state))
mu = 2.0 * torch.tanh(self.mu_head(x)) # test for gym_env: 'Pendulum-v0'
sigma = F.softplus(self.sigma_head(x))
return mu, sigma
def action(self, state, test=False):
with torch.no_grad():
mu, sigma = self.actor_eval(state)
dist = Normal(mu, sigma)
action = dist.sample()
# print (f'mu:{mu}, sigma:{sigma}, dist: {dist}, action sample before clamp: {action}')
action = action.clamp(-action_max, action_max)
# print (f'action after clamp {action}')
log_prob = dist.log_prob(action)
assert abs(action.item()) <= action_max, f'ERROR: action out of {action}'
return action.item(), log_prob.item()
class CriticV(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layer_norm=False):
super().__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.value_head = nn.Linear(hidden_dim, output_dim)
if layer_norm:
layer_norm(self.fc1, std=1.0)
layer_norm(self.value_head, std=1.0)
def forward(self, x):
x = F.relu(self.fc1(x))
state_value = self.value_head(x)
return state_value
model = namedtuple('model', ['policy_net', 'value_net'])
actor = ActorPPO(state_space, hidden_dim, action_space)
critic = CriticV(state_space, hidden_dim, action_space)
model = model(actor, critic)
policy = PPO(model, buffer_size=32, actor_learn_freq=1, target_update_freq=0)
def sample(env, policy, max_step, i_episode=0, num_episode=100, test=False):
assert env, 'You must set env for sample'
reward_avg = 0
running_state = ZFilter((state_space,), clip=10.0)
state = env.reset()
# test state norm
# state = running_state(state)
for step in range(max_step):
action, log_prob = policy.choose_action(state, test)
# action = action.clip(-1, 1) * env.action_space.high[0]
next_state, reward, done, info = env.step([action])
env.render()
# test state norm
# next_state = running_state(next_state)
# process env callback
if not test:
policy.process(s=state, a=action, s_=next_state, r=(reward + 8) / 8, l=log_prob)
policy.learn(i_episode, num_episode)
reward_avg += reward
if done:
break
state = next_state
return reward_avg/(step+1), step
run_type = ['train', 'eval', 'retrain']
run = run_type[1]
plot_name = 'Training_PPO_TwoNet_'+model_save_dir.split('/')[-1]
def main():
test = False
if run == 'eval':
global episodes
episodes = 100
test = True
print('Loading model...')
policy.load_model(model_save_dir, save_file, load_actor=True)
elif run == 'train':
print('Saving model setting...')
policy_test.save_setting(env_name, state_space, action_space, episodes,
max_step, policy, model_save_dir, save_file)
elif run == 'retrain':
print('Loading model...')
policy.load_model(model_save_dir, save_file, load_actor=True, load_critic=True)
else:
print('Setting your run type!')
return 0
live_time = []
for i_eps in range(episodes):
rewards, step = sample(env, policy, max_step, i_episode=i_eps, num_episode=episodes, test=test)
if run == 'eval':
print(f'Eval eps:{i_eps+1}, Rewards:{rewards}, Steps:{step+1}')
continue
live_time.append(rewards)
policy_test.plot(live_time, plot_name, model_save_dir)
if i_eps > 0 and i_eps % 100 == 0:
print(f'i_eps is {i_eps}')
policy.save_model(model_save_dir, save_file, save_actor=True, save_critic=True)
env.close()
if __name__ == '__main__':
main()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,911
|
Taospirit/DRL_beta
|
refs/heads/main
|
/baidu.py
|
class yimian1():
def __init__(self):
pass
def get_index(self, num_list, target):
l, r = 0, len(num_list) - 1
while l != r:
if num_list[l] + num_list[r] > target:
r -= 1
if num_list[l] + num_list[r] < target:
l += 1
if num_list[l] + num_list[r] == target:
return l, r
return 0, 0
class yimian2():
def __init__(self):
pass
def get_num(self, num_list):
a = 0
for item in num_list:
a ^= item
return a
class ermian():
def __init__(self):
self.choose = []
self.get = False
def backsum(self, num_list, m, target):
if self.get or len(self.choose) > m:
return
if sum(self.choose) == target and len(self.choose) == m:
print (self.choose)
self.get = True
return
for i in range(len(num_list)):
self.choose.append(num_list[i])
self.backsum(num_list[:i] + num_list[i+1:], m, target)
self.choose.pop()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,912
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/test/test_ddpg.py
|
from test_tool import policy_test
import gym
import os
import time
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from torch.distributions import Categorical
from collections import namedtuple
# from drl.model import ActorDPG, CriticQ
from drl.algorithm import DDPG
env_name = 'Pendulum-v0'
env = gym.make(env_name)
env = env.unwrapped
env.seed(1)
torch.manual_seed(1)
# Parameters
state_space = env.observation_space.shape[0]
action_space = env.action_space.shape[0]
action_max = env.action_space.high[0]
action_scale = (env.action_space.high - env.action_space.low) / 2
action_bias = (env.action_space.high + env.action_space.low) / 2
episodes = 5000
buffer_size = 50000
hidden_dim = 32
# max_step = 200
actor_learn_freq = 1
target_update_freq = 10
# batch_size = 1000
# hidden_dim = 256 # mush slow, even cannt shoulian
max_step = 300
# actor_learn_freq = 5 # 非常震荡、不稳定
# target_update_freq = 20 # 收敛非常慢
batch_size = 128
model_save_dir = 'save/test_ddpg_6'
model_save_dir = os.path.join(os.path.dirname(__file__), model_save_dir)
save_file = model_save_dir.split('/')[-1]
os.makedirs(model_save_dir, exist_ok=True)
action_scale = (env.action_space.high - env.action_space.low) / 2
action_bias = (env.action_space.high + env.action_space.low) / 2
class ActorDPG(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim):
super().__init__()
self.net = nn.Sequential(nn.Linear(state_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, action_dim), nn.Tanh(), )
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def forward(self, state):
action = self.net(state)
return action
def action(self, state, noise_std=0.2, noise_clip=0.5):
action = self.net(state)
if noise_std:
noise_norm = torch.ones_like(action).data.normal_(0, noise_std).to(self.device)
action += noise_norm.clamp(-noise_clip, noise_clip)
action = action.clamp(-action_max, action_max)
return action.item()
class CriticQ(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim):
super().__init__()
# inpur_dim = state_dim + action_dim,
self.net = nn.Sequential(nn.Linear(state_dim + action_dim , hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, 1), )
# self.net = build_critic_network(state_dim, hidden_dim, action_dim)
def forward(self, state, action):
x = torch.cat((state, action), dim=1)
q_value = self.net(x)
return q_value
model = namedtuple('model', ['policy_net', 'value_net'])
actor = ActorDPG(state_space, hidden_dim, action_space)
critic = CriticQ(state_space, hidden_dim, action_space)
model = model(actor, critic)
policy = DDPG(model, buffer_size=buffer_size, actor_learn_freq=actor_learn_freq, target_update_freq=target_update_freq, batch_size=batch_size)
def map_action(action):
return action * action_scale + action_bias
def sample(env, policy, max_step, test=False):
assert env, 'You must set env for sample'
reward_avg = 0
state = env.reset()
for step in range(max_step):
action = policy.choose_action(state, test)
# action *= env.action_space.high[0]
next_state, reward, done, info = env.step(map_action(action))
if test:
env.render()
# process env callback
if not test:
mask = 0 if done else 1
policy.process(s=state, a=action, r=reward, m=mask, s_=next_state)
reward_avg += reward
if done:
break
state = next_state
if not test:
pg_loss, v_loss = policy.learn()
return reward_avg/(step+1), step, pg_loss, v_loss
return reward_avg/(step+1), step, 0, 0
run_type = ['train', 'eval']
run = run_type[0]
plot_name = 'DDPG_TwoNet_Double'
writer = SummaryWriter('./logs/ddpg')
def main():
test = False
if run == 'eval':
global episodes
episodes = 100
test = True
print('Loading model...')
policy.load_model(model_save_dir, save_file, load_actor=True)
elif run == 'train':
print('Saving model setting...')
# save_setting()
# woc, 这句写的太丑了
policy_test.save_setting(env_name, state_space, action_space, episodes,
max_step, policy, model_save_dir, save_file)
else:
print('Setting your run type!')
return 0
# while policy.warm_up():
# sample(env, policy, max_step)
# print (f'Warm up for buffer {policy.buffer.size()}', end='\r')
live_time = []
for i_eps in range(episodes):
rewards, step, pg_loss, v_loss = sample(env, policy, max_step)
if run == 'eval':
print(f'Eval eps:{i_eps+1}, Rewards:{rewards}, Steps:{step+1}')
continue
live_time.append(rewards)
policy_test.plot(live_time, plot_name, model_save_dir)
writer.add_scalar('reward', rewards, global_step=i_eps)
writer.add_scalar('loss/pg', pg_loss, global_step=i_eps)
writer.add_scalar('loss/v', v_loss, global_step=i_eps)
if i_eps > 0 and i_eps % 100 == 0:
print(f'i_eps is {i_eps}')
policy.save_model(model_save_dir, save_file, save_actor=True, save_critic=True)
env.close()
if __name__ == '__main__':
main()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,913
|
Taospirit/DRL_beta
|
refs/heads/main
|
/beta/tutorial/tutorial_sac.py
|
# from test_tool import policy_test
from os.path import abspath, dirname
import os
import gym
import time
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
from torch.utils.tensorboard import SummaryWriter
from collections import namedtuple
import numpy as np
# from drl.model import ActorModel, CriticQ
# from drl.algorithm import SAC
from drl.algorithm import SAC2 as SAC
from utils.plot import plot
from utils.config import config
# config
config = config['sac']
env_name = config['env_name']
buffer_size = config['buffer_size']
actor_learn_freq = config['actor_learn_freq']
target_update_freq = config['target_update_freq']
batch_size = config['batch_size']
hidden_dim = config['hidden_dim']
episodes = config['episodes'] + 10
max_step = config['max_step']
POLT_NAME = config['POLT_NAME'] + env_name
SAVE_DIR = config['SAVE_DIR'] + env_name
LOG_DIR = config['LOG_DIR']
model_save_dir = abspath(dirname(__file__)) + SAVE_DIR
save_file = model_save_dir.split('/')[-1]
writer_path = model_save_dir + LOG_DIR
try:
os.makedirs(model_save_dir)
except FileExistsError:
import shutil
shutil.rmtree(model_save_dir)
os.makedirs(model_save_dir)
env = gym.make(env_name)
env = env.unwrapped
env.seed(1)
torch.manual_seed(1)
# Parameters
state_space = env.observation_space.shape[0]
action_space = env.action_space.shape[0]
action_scale = (env.action_space.high - env.action_space.low) / 2
action_bias = (env.action_space.high + env.action_space.low) / 2
def layer_norm(layer, std=1.0, bias_const=1e-6):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
class ActorModel(nn.Module):
def __init__(self, state_dim, hidden_dim, output_dim):
super().__init__()
self.fc1 = nn.Linear(state_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.mean = nn.Linear(hidden_dim, output_dim)
self.log_std = nn.Linear(hidden_dim, output_dim)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
layer_norm(self.fc1, std=1.0)
layer_norm(self.fc2, std=1.0)
layer_norm(self.mean, std=1.0)
layer_norm(self.log_std, std=1.0)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
mean = self.mean(x)
log_std = self.log_std(x)
log_std = torch.clamp(log_std, min=-20, max=2)
return mean, log_std
def action(self, state, test=False):
mean, log_std = self.forward(state)
if test:
return mean.detach().cpu().numpy()
std = log_std.exp()
normal = Normal(mean, std)
z = normal.sample()
action = torch.tanh(z).detach().cpu().numpy()
action = action * action_scale + action_bias
return action
# Use re-parameterization tick
def evaluate(self, state, epsilon=1e-6):
mean, log_std = self.forward(state)
std = log_std.exp()
normal = Normal(mean, std)
noise = Normal(0,1)
z = noise.sample()
action = torch.tanh(mean + std*z.to(self.device))
log_prob = normal.log_prob(mean + std*z.to(self.device)) - torch.log(1 - action.pow(2) + epsilon)
return action, log_prob
class CriticModel(nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim):
super().__init__()
# inpur_dim = state_dim + action_dim,
self.net1 = nn.Sequential(nn.Linear(state_dim + action_dim , hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, 1), )
self.net2 = nn.Sequential(nn.Linear(state_dim + action_dim , hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim), nn.ReLU(),
nn.Linear(hidden_dim, 1), )
# self.net = build_critic_network(state_dim, hidden_dim, action_dim)
def forward(self, state, action):
input = torch.cat((state, action), dim=1)
q1_value = self.net1(input)
q2_value = self.net2(input)
return q1_value, q2_value
class ValueModel(nn.Module):
def __init__(self, state_dim, edge=3e-3):
super().__init__()
self.linear1 = nn.Linear(state_dim, 256)
self.linear2 = nn.Linear(256, 256)
self.linear3 = nn.Linear(256, 1)
layer_norm(self.linear1, std=1.0)
layer_norm(self.linear2, std=1.0)
layer_norm(self.linear3, std=1.0)
# self.linear3.weight.data.uniform_(-edge, edge)
# self.linear3.bias.data.uniform_(-edge, edge)
def forward(self, state):
x = F.relu(self.linear1(state))
x = F.relu(self.linear2(x))
x = self.linear3(x)
return x
model = namedtuple('model', ['policy_net', 'value_net', 'v_net'])
actor = ActorModel(state_space, hidden_dim, action_space)
critic = CriticModel(state_space, hidden_dim, action_space)
v_net = ValueModel(state_space)
model = model(actor, critic, v_net)
policy = SAC(model, buffer_size=buffer_size, actor_learn_freq=actor_learn_freq,
target_update_freq=target_update_freq, batch_size=batch_size)
writer = SummaryWriter(writer_path)
TRAIN = True
PLOT = True
WRITER = False
def sample(env, policy, max_step):
# rewards = 0
rewards = []
state = env.reset()
for step in range(max_step):
#==============choose_action==============
action = policy.choose_action(state)
next_state, reward, done, info = env.step(action)
if TRAIN:
mask = 0 if done else 1
#==============process==============
policy.process(s=state, a=action, r=reward, m=mask, s_=next_state)
else:
env.render()
rewards.append(reward)
if done:
break
state = next_state
return rewards
def main():
if not TRAIN:
policy.load_model(model_save_dir, save_file, load_actor=True)
live_time = []
while policy.warm_up():
sample(env, policy, max_step)
print (f'Warm up for buffer {policy.buffer.size()}', end='\r')
for i_eps in range(episodes):
rewards = sample(env, policy, max_step)
reward_mean = np.mean(rewards)
if not TRAIN:
print (f'EPS:{i_eps + 1}, reward:{round(reward_mean, 3)}')
else:
#==============learn==============
pg_loss, q_loss, a_loss = policy.learn()
if PLOT:
live_time.append(reward_mean)
plot(live_time, POLT_NAME, model_save_dir, 100)
if WRITER:
writer.add_scalar('reward', reward_mean, global_step=i_eps)
writer.add_scalar('loss/pg_loss', pg_loss, global_step=i_eps)
writer.add_scalar('loss/q_loss', q_loss, global_step=i_eps)
writer.add_scalar('loss/alpha_loss', a_loss, global_step=i_eps)
if i_eps % 5 == 0:
print (f'EPS:{i_eps}, reward_mean:{round(reward_mean, 3)}, pg_loss:{round(pg_loss, 3)}, q_loss:{round(q_loss, 3)}, alpha_loss:{round(a_loss, 3)}')
if i_eps % 200 == 0:
policy.save_model(model_save_dir, save_file, save_actor=True, save_critic=True)
writer.close()
env.close
if __name__ == '__main__':
main()
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,758,914
|
Taospirit/DRL_beta
|
refs/heads/main
|
/setup.py
|
from setuptools import setup
setup(
name='drl1',
version='0.0.1',
packages=setuptools.find_packages(),
description='a drl package',
author='lintao',
author_email='lintao209@outlook.com',
install_requires=['numpy', 'torch'],
python_requires='>=3.6',
)
|
{"/beta/backup/backup_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/backup/backup_sac_per.py": ["/drl/algorithm/__init__.py"], "/beta/play_atari/Pong-v0.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/rainbow.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_pong.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_dqn.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/quick_start.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_a2c.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/__init__.py": ["/drl/algorithm/dqn.py", "/drl/algorithm/a2c.py", "/drl/algorithm/ddpg.py", "/drl/algorithm/oac.py"], "/drl/algorithm/ddpg.py": ["/drl/algorithm/__init__.py"], "/drl/algorithm/sac1.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_a2c.py": ["/drl/algorithm/__init__.py"], "/beta/test_oac.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ppo.py": ["/drl/algorithm/__init__.py"], "/beta/test/test_ddpg.py": ["/drl/algorithm/__init__.py"], "/beta/tutorial/tutorial_sac.py": ["/drl/algorithm/__init__.py"]}
|
35,785,313
|
xiaoyu-work/text
|
refs/heads/main
|
/torchtext/functional.py
|
import torch
from torch import Tensor
from torch.nn.utils.rnn import pad_sequence
from typing import List, Optional
__all__ = [
'to_tensor',
'truncate',
'add_token',
]
def to_tensor(input: List[List[int]], padding_value: Optional[int] = None) -> Tensor:
if padding_value is None:
output = torch.tensor(input, dtype=torch.long)
return output
else:
output = pad_sequence(
[torch.tensor(ids, dtype=torch.long) for ids in input],
batch_first=True,
padding_value=float(padding_value)
)
return output
def truncate(input: List[List[int]], max_seq_len: int) -> List[List[int]]:
output: List[List[int]] = []
for ids in input:
output.append(ids[:max_seq_len])
return output
def add_token(input: List[List[int]], token_id: int, begin: bool = True) -> List[List[int]]:
output: List[List[int]] = []
if begin:
for ids in input:
output.append([token_id] + ids)
else:
for ids in input:
output.append(ids + [token_id])
return output
|
{"/torchtext/models/roberta/bundler.py": ["/torchtext/_download_hooks.py", "/torchtext/models/roberta/model.py"], "/torchtext/models/roberta/__init__.py": ["/torchtext/models/roberta/model.py", "/torchtext/models/roberta/bundler.py"]}
|
35,785,314
|
xiaoyu-work/text
|
refs/heads/main
|
/test/experimental/test_datasets.py
|
import hashlib
import json
from torchtext.experimental.datasets import sst2
from ..common.case_utils import skipIfNoModule
from ..common.torchtext_test_case import TorchtextTestCase
class TestDataset(TorchtextTestCase):
@skipIfNoModule("torchdata")
def test_sst2_dataset(self):
split = ("train", "dev", "test")
train_dataset, dev_dataset, test_dataset = sst2.SST2(split=split)
# verify datasets objects are instances of SST2Dataset
for dataset in (train_dataset, dev_dataset, test_dataset):
self.assertTrue(isinstance(dataset, sst2.SST2Dataset))
# verify hashes of first line in dataset
self.assertEqual(
hashlib.md5(
json.dumps(next(iter(train_dataset)), sort_keys=True).encode("utf-8")
).hexdigest(),
sst2._FIRST_LINE_MD5["train"],
)
self.assertEqual(
hashlib.md5(
json.dumps(next(iter(dev_dataset)), sort_keys=True).encode("utf-8")
).hexdigest(),
sst2._FIRST_LINE_MD5["dev"],
)
self.assertEqual(
hashlib.md5(
json.dumps(next(iter(test_dataset)), sort_keys=True).encode("utf-8")
).hexdigest(),
sst2._FIRST_LINE_MD5["test"],
)
|
{"/torchtext/models/roberta/bundler.py": ["/torchtext/_download_hooks.py", "/torchtext/models/roberta/model.py"], "/torchtext/models/roberta/__init__.py": ["/torchtext/models/roberta/model.py", "/torchtext/models/roberta/bundler.py"]}
|
35,785,315
|
xiaoyu-work/text
|
refs/heads/main
|
/torchtext/models/roberta/bundler.py
|
import os
from dataclasses import dataclass
from functools import partial
from typing import Optional, Callable
from torchtext._download_hooks import load_state_dict_from_url
from torch.nn import Module
import logging
logger = logging.getLogger(__name__)
from .model import (
RobertaEncoderParams,
RobertaModel,
_get_model,
)
from .transforms import get_xlmr_transform
from torchtext import _TEXT_BUCKET
@dataclass
class RobertaModelBundle:
"""
Example - Pretrained encoder
>>> import torch, torchtext
>>> xlmr_base = torchtext.models.XLMR_BASE_ENCODER
>>> model = xlmr_base.get_model()
>>> transform = xlmr_base.transform()
>>> model_input = torch.tensor(transform(["Hello World"]))
>>> output = model(model_input)
>>> output.shape
torch.Size([1, 4, 768])
>>> input_batch = ["Hello world", "How are you!"]
>>> from torchtext.functional import to_tensor
>>> model_input = to_tensor(transform(input_batch), padding_value=transform.pad_idx)
>>> output = model(model_input)
>>> output.shape
torch.Size([2, 6, 768])
Example - Pretrained encoder attached to un-initialized classification head
>>> import torch, torchtext
>>> xlmr_large = torchtext.models.XLMR_LARGE_ENCODER
>>> classifier_head = torchtext.models.RobertaClassificationHead(num_classes=2, input_dim = xlmr_large.params.embedding_dim)
>>> classification_model = xlmr_large.get_model(head=classifier_head)
>>> transform = xlmr_large.transform()
>>> model_input = torch.tensor(transform(["Hello World"]))
>>> output = classification_model(model_input)
>>> output.shape
torch.Size([1, 2])
"""
_params: RobertaEncoderParams
_path: Optional[str] = None
_head: Optional[Module] = None
transform: Optional[Callable] = None
def get_model(self, head: Optional[Module] = None, *, dl_kwargs=None) -> RobertaModel:
if head is not None:
input_head = head
if self._head is not None:
logger.log("A custom head module was provided, discarding the default head module.")
else:
input_head = self._head
model = _get_model(self._params, input_head)
dl_kwargs = {} if dl_kwargs is None else dl_kwargs
state_dict = load_state_dict_from_url(self._path, **dl_kwargs)
if input_head is not None:
model.load_state_dict(state_dict, strict=False)
else:
model.load_state_dict(state_dict, strict=True)
return model
@property
def params(self) -> RobertaEncoderParams:
return self._params
XLMR_BASE_ENCODER = RobertaModelBundle(
_path=os.path.join(_TEXT_BUCKET, "xlmr.base.encoder.pt"),
_params=RobertaEncoderParams(vocab_size=250002),
transform=partial(get_xlmr_transform,
vocab_path=os.path.join(_TEXT_BUCKET, "xlmr.vocab.pt"),
spm_model_path=os.path.join(_TEXT_BUCKET, "xlmr.sentencepiece.bpe.model"),
)
)
XLMR_LARGE_ENCODER = RobertaModelBundle(
_path=os.path.join(_TEXT_BUCKET, "xlmr.large.encoder.pt"),
_params=RobertaEncoderParams(vocab_size=250002, embedding_dim=1024, ffn_dimension=4096, num_attention_heads=16, num_encoder_layers=24),
transform=partial(get_xlmr_transform,
vocab_path=os.path.join(_TEXT_BUCKET, "xlmr.vocab.pt"),
spm_model_path=os.path.join(_TEXT_BUCKET, "xlmr.sentencepiece.bpe.model"),
)
)
|
{"/torchtext/models/roberta/bundler.py": ["/torchtext/_download_hooks.py", "/torchtext/models/roberta/model.py"], "/torchtext/models/roberta/__init__.py": ["/torchtext/models/roberta/model.py", "/torchtext/models/roberta/bundler.py"]}
|
35,785,316
|
xiaoyu-work/text
|
refs/heads/main
|
/torchtext/models/roberta/model.py
|
import math
from dataclasses import dataclass, asdict
from typing import Optional
from torch.nn import Module
import torch
from torch import Tensor
import torch.nn as nn
from .modules import (
TransformerEncoder,
)
@dataclass
class RobertaEncoderParams:
vocab_size: int = 50265
embedding_dim: int = 768
ffn_dimension: int = 3072
padding_idx: int = 1
max_seq_len: int = 514
num_attention_heads: int = 12
num_encoder_layers: int = 12
dropout: float = 0.1
scaling: Optional[float] = None
normalize_before: bool = False
class RobertaEncoder(Module):
def __init__(
self,
vocab_size: int,
embedding_dim: int,
ffn_dimension: int,
padding_idx: int,
max_seq_len: int,
num_attention_heads: int,
num_encoder_layers: int,
dropout: float = 0.1,
scaling: Optional[float] = None,
normalize_before: bool = False,
):
super().__init__()
if not scaling:
head_dim = embedding_dim // num_attention_heads
scaling = 1.0 / math.sqrt(head_dim)
self.transformer = TransformerEncoder(
vocab_size=vocab_size,
embedding_dim=embedding_dim,
padding_idx=padding_idx,
max_seq_len=max_seq_len,
ffn_dimension=ffn_dimension,
num_encoder_layers=num_encoder_layers,
num_attention_heads=num_attention_heads,
dropout=dropout,
normalize_before=normalize_before,
scaling=scaling,
)
def forward(self, tokens: Tensor, mask: Optional[Tensor] = None) -> Tensor:
all_layers = self.transformer(tokens)
last_layer = all_layers[-1].transpose(1, 0)
if mask is not None:
last_layer = last_layer[mask.to(torch.bool), :]
return last_layer
# TODO: Add Missing quant noise and spectral norm from latest Roberta head in fairseq repo
class RobertaClassificationHead(nn.Module):
def __init__(self, num_classes, input_dim, inner_dim: Optional[int] = None, dropout: float = 0.1, activation=nn.ReLU):
super().__init__()
if not inner_dim:
inner_dim = input_dim
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
self.activation_fn = activation()
def forward(self, features):
x = features[:, 0, :]
x = self.dropout(x)
x = self.dense(x)
x = self.activation_fn(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class RobertaModel(Module):
def __init__(self, encoder: Module, head: Optional[Module] = None):
super().__init__()
self.encoder = encoder
self.head = head
def forward(self, tokens: Tensor) -> Tensor:
features = self.encoder(tokens)
if self.head is None:
return features
x = self.head(features)
return x
def _get_model(params: RobertaEncoderParams, head: Module) -> RobertaModel:
encoder = RobertaEncoder(**asdict(params))
return RobertaModel(encoder, head)
|
{"/torchtext/models/roberta/bundler.py": ["/torchtext/_download_hooks.py", "/torchtext/models/roberta/model.py"], "/torchtext/models/roberta/__init__.py": ["/torchtext/models/roberta/model.py", "/torchtext/models/roberta/bundler.py"]}
|
35,785,317
|
xiaoyu-work/text
|
refs/heads/main
|
/torchtext/models/roberta/__init__.py
|
from .model import (
RobertaEncoderParams,
RobertaClassificationHead,
)
from .bundler import (
RobertaModelBundle,
XLMR_BASE_ENCODER,
XLMR_LARGE_ENCODER,
)
__all__ = [
"RobertaEncoderParams",
"RobertaClassificationHead",
"RobertaModelBundle",
"XLMR_BASE_ENCODER",
"XLMR_LARGE_ENCODER",
]
|
{"/torchtext/models/roberta/bundler.py": ["/torchtext/_download_hooks.py", "/torchtext/models/roberta/model.py"], "/torchtext/models/roberta/__init__.py": ["/torchtext/models/roberta/model.py", "/torchtext/models/roberta/bundler.py"]}
|
35,785,318
|
xiaoyu-work/text
|
refs/heads/main
|
/test/models/test_models.py
|
import torchtext
import torch
from ..common.torchtext_test_case import TorchtextTestCase
from ..common.assets import get_asset_path
class TestModels(TorchtextTestCase):
def test_xlmr_base_output(self):
asset_name = "xlmr.base.output.pt"
asset_path = get_asset_path(asset_name)
xlmr_base = torchtext.models.XLMR_BASE_ENCODER
model = xlmr_base.get_model()
model = model.eval()
model_input = torch.tensor([[0, 43523, 52005, 3647, 13293, 113307, 40514, 2]])
actual = model(model_input)
expected = torch.load(asset_path)
torch.testing.assert_close(actual, expected)
def test_xlmr_base_jit_output(self):
asset_name = "xlmr.base.output.pt"
asset_path = get_asset_path(asset_name)
xlmr_base = torchtext.models.XLMR_BASE_ENCODER
model = xlmr_base.get_model()
model = model.eval()
model_jit = torch.jit.script(model)
model_input = torch.tensor([[0, 43523, 52005, 3647, 13293, 113307, 40514, 2]])
actual = model_jit(model_input)
expected = torch.load(asset_path)
torch.testing.assert_close(actual, expected)
def test_xlmr_large_output(self):
asset_name = "xlmr.large.output.pt"
asset_path = get_asset_path(asset_name)
xlmr_base = torchtext.models.XLMR_LARGE_ENCODER
model = xlmr_base.get_model()
model = model.eval()
model_input = torch.tensor([[0, 43523, 52005, 3647, 13293, 113307, 40514, 2]])
actual = model(model_input)
expected = torch.load(asset_path)
torch.testing.assert_close(actual, expected)
def test_xlmr_large_jit_output(self):
asset_name = "xlmr.large.output.pt"
asset_path = get_asset_path(asset_name)
xlmr_base = torchtext.models.XLMR_LARGE_ENCODER
model = xlmr_base.get_model()
model = model.eval()
model_jit = torch.jit.script(model)
model_input = torch.tensor([[0, 43523, 52005, 3647, 13293, 113307, 40514, 2]])
actual = model_jit(model_input)
expected = torch.load(asset_path)
torch.testing.assert_close(actual, expected)
def test_xlmr_transform(self):
xlmr_base = torchtext.models.XLMR_BASE_ENCODER
transform = xlmr_base.transform()
test_text = "XLMR base Model Comparison"
actual = transform([test_text])
expected = [[0, 43523, 52005, 3647, 13293, 113307, 40514, 2]]
torch.testing.assert_close(actual, expected)
def test_xlmr_transform_jit(self):
xlmr_base = torchtext.models.XLMR_BASE_ENCODER
transform = xlmr_base.transform()
transform_jit = torch.jit.script(transform)
test_text = "XLMR base Model Comparison"
actual = transform_jit([test_text])
expected = [[0, 43523, 52005, 3647, 13293, 113307, 40514, 2]]
torch.testing.assert_close(actual, expected)
|
{"/torchtext/models/roberta/bundler.py": ["/torchtext/_download_hooks.py", "/torchtext/models/roberta/model.py"], "/torchtext/models/roberta/__init__.py": ["/torchtext/models/roberta/model.py", "/torchtext/models/roberta/bundler.py"]}
|
35,803,983
|
Emekaborisama/EndSars-twitter-bot
|
refs/heads/main
|
/mention.py
|
import tweepy as tp
import logging
import os
from time import sleep
logger = logging.getLogger()
auth = tp.OAuthHandler('pIZw54XtvVuGZM4TTJYHZrFX1', '6x1ohdPHRdFnSrUGUo6qbEup3O7eiVk5mMNpnFXcWrLQPNVhTC')
auth.set_access_token('1305611268447928320-MA3D0D1Ay41wjefcRmrxKr08Ub12VM', 's0EAbYg9gKIM8qoq0XjDpCCA3mToGPvQxkntPv6AWbtVI')
api = tp.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
try:
api.verify_credentials()
print("Authentication OK")
except:
print("Error during authentication")
def tweet_mention():
tweets= api.mentions_timeline(tweet_mode = 'extended')
try:
for tweet in tweets:
api.update_status('#EndSWAT #5for5 #SarsMustEnd', in_reply_to_status_id = tweet.id, auto_populate_reply_metadata = True)
print('replied the mentioned')
#success = True
except:
print('We pass')
pass
#return success
|
{"/.ipynb_checkpoints/endsarsbot-checkpoint.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"], "/endsarsbot.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"]}
|
35,803,984
|
Emekaborisama/EndSars-twitter-bot
|
refs/heads/main
|
/like_retweet.py
|
import tweepy as tp
import logging
import os
from time import sleep
logger = logging.getLogger()
auth = tp.OAuthHandler('pIZw54XtvVuGZM4TTJYHZrFX1', '6x1ohdPHRdFnSrUGUo6qbEup3O7eiVk5mMNpnFXcWrLQPNVhTC')
auth.set_access_token('1305611268447928320-MA3D0D1Ay41wjefcRmrxKr08Ub12VM', 's0EAbYg9gKIM8qoq0XjDpCCA3mToGPvQxkntPv6AWbtVI')
api = tp.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
try:
api.verify_credentials()
print("Authentication OK")
except:
print("Error during authentication")
def like_rt():
for tweet in tp.Cursor(api.search, q = '#EndSars').items(1):
print('Tweet by: @' + tweet.user.screen_name)
try:
tweet.retweet()
tweet.favorite()
print('retweeted')
if not tweet.user.following:
tweet.user.follow()
print('Followed the user')
##success = True
except tp.TweepError as e:
print(e.reason)
#return success
|
{"/.ipynb_checkpoints/endsarsbot-checkpoint.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"], "/endsarsbot.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"]}
|
35,803,985
|
Emekaborisama/EndSars-twitter-bot
|
refs/heads/main
|
/config.py
|
import tweepy as tp
import logging
import os
logger = logging.getLogger()
def create_api():
auth = tp.OAuthHandler('pIZw54XtvVuGZM4TTJYHZrFX1', '6x1ohdPHRdFnSrUGUo6qbEup3O7eiVk5mMNpnFXcWrLQPNVhTC')
auth.set_access_token('1305611268447928320-MA3D0D1Ay41wjefcRmrxKr08Ub12VM', 's0EAbYg9gKIM8qoq0XjDpCCA3mToGPvQxkntPv6AWbtVI')
api = tp.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
try:
api.verify_credentials()
print("Authentication OK")
except:
print("Error during authentication")
|
{"/.ipynb_checkpoints/endsarsbot-checkpoint.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"], "/endsarsbot.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"]}
|
35,803,986
|
Emekaborisama/EndSars-twitter-bot
|
refs/heads/main
|
/endsarsbot.py
|
import tweepy as tp
import logging
import os
from time import sleep
logger = logging.getLogger()
auth = tp.OAuthHandler('pIZw54XtvVuGZM4TTJYHZrFX1', '6x1ohdPHRdFnSrUGUo6qbEup3O7eiVk5mMNpnFXcWrLQPNVhTC')
auth.set_access_token('1305611268447928320-MA3D0D1Ay41wjefcRmrxKr08Ub12VM', 's0EAbYg9gKIM8qoq0XjDpCCA3mToGPvQxkntPv6AWbtVI')
api = tp.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
try:
api.verify_credentials()
print("Authentication OK")
except:
print("Error during authentication")
import comment_bot as c
import like_retweet as lrt
import mention as m
def main():
usersname = ['@NigeriaGov', '@NGRPresident',
'@NigEducation', '@fmaviationng', '@fccpcnigeria', '@NCDCgov', '@Nigeria', '@PowerMinNigeria', '@NigeriaMFA', '@NgComCommission', '@FinMinNigeria', '@firsNigeria', '@followlasg', '@FMoCDENigeria', '@LSMOH', '@OfficialAPCNg', '@followlastma', '@PwC_Nigeria', '@NGRSenate', '@SPNigeria', '@nassnigeria', '@FRSCNigeria', '@PoliceNG', '@inecnigeria', '@MBuhari', '@drobafemihamzat', '@ProfOsinbajo', '@toluogunlesi', '@jidesanwoolu', '@Laurestar', '@tundefashola', '@femigbaja']
c.comment(usersname = usersname)
lrt.like_rt()
m.tweet_mention()
success = True
return success
while True:
main()
sleep(10)
|
{"/.ipynb_checkpoints/endsarsbot-checkpoint.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"], "/endsarsbot.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"]}
|
35,803,987
|
Emekaborisama/EndSars-twitter-bot
|
refs/heads/main
|
/comment_bot.py
|
import tweepy as tp
import logging
import os
from time import sleep
auth = tp.OAuthHandler('pIZw54XtvVuGZM4TTJYHZrFX1', '6x1ohdPHRdFnSrUGUo6qbEup3O7eiVk5mMNpnFXcWrLQPNVhTC')
auth.set_access_token('1305611268447928320-MA3D0D1Ay41wjefcRmrxKr08Ub12VM', 's0EAbYg9gKIM8qoq0XjDpCCA3mToGPvQxkntPv6AWbtVI')
api = tp.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
try:
api.verify_credentials()
print("Authentication OK")
except:
print("Error during authentication")
def comment(usersname):
for user in usersname:
time_line = api.user_timeline(user, count = 1)
users = api.get_user(user)
for tweet in time_line:
id = tweet.id
print(f"{tweet.user.name} said {tweet.text} with {id}" )
try:
api.update_status("#EndSWAT #5for5 #SarsMustEnd", in_reply_to_status_id = tweet.id, auto_populate_reply_metadata=True)
api.update_status(usersname + ' ' + "Just tweeted")
#print(users)
#success = True
except:
pass
|
{"/.ipynb_checkpoints/endsarsbot-checkpoint.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"], "/endsarsbot.py": ["/comment_bot.py", "/like_retweet.py", "/mention.py"]}
|
35,835,907
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/utils/database_functions.py
|
from datetime import datetime, timedelta, date
from dateutil.relativedelta import relativedelta
import calendar
import pyodbc
from typing import Dict, List, Tuple
def connect_to_database():
"""This function returns a connection to the database.
Returns:
pyodbc connection: A connection to the relevant battery database.
"""
return pyodbc.connect('Driver={SQL Server};'
'Server=localhost\SQLEXPRESS;'
'Database=BEEnterprise;'
'Trusted_Connection=yes;')
def string_record_id_to_intertier_cells_monitor():
""" Returns a dictionary { StringRecordID: ([intertier_cells], monitortype) }
"""
ms_conn = connect_to_database()
ms_cursor = ms_conn.cursor()
query = """
SELECT s.StringRecordID, dt.DeviceTypeName FROM BEEnterprise.dbo.strings s
INNER JOIN
BEEnterprise.dbo.deviceTypes dt
on s.StringDeviceTypeRecordID = dt.DeviceTypeRecordID;
"""
ms_cursor.execute(query)
sr_dt_to_icm = dict()
for row in ms_cursor:
intertier_cell_list = get_intertier_cells(row[0])
sr_dt_to_icm[row[0]] = (intertier_cell_list, row[1])
return sr_dt_to_icm
def get_string_record_id_to_name_dict():
""" Queries the database and returns a dictionary(mapping) of StringRecordID to name
Returns:
dict: Dictionary in the format {StringRecordID: ()}
"""
ms_conn = connect_to_database()
ms_cursor = ms_conn.cursor()
query = """SELECT s.StringRecordID, s.name, dt.DeviceTypeName FROM BEEnterprise.dbo.strings s
INNER JOIN
BEEnterprise.dbo.deviceTypes dt
on s.StringDeviceTypeRecordID = dt.DeviceTypeRecordID;"""
ms_cursor.execute(query)
string_record_id_name_dict = {}
for row in ms_cursor:
string_record_id_name_dict.update({row[0]: (row[1], row[2])})
return string_record_id_name_dict
def get_intertier_cells(string_record_id: int)->List[int]:
"""
Returns:
List[str]: List of the cells that are intertier cells
"""
conn = connect_to_database()
ms_cursor = conn.cursor()
query = F"""SELECT CellNumber FROM BEEnterpriseHistory.dbo.CellReadingsHistory
where ReadingTypeRecordID = 10 and StringRecordID = {string_record_id}"""
ms_cursor.execute(query)
intertier_cell_list = []
for row in ms_cursor:
if row[0] not in intertier_cell_list:
intertier_cell_list.append(row[0])
return intertier_cell_list
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,908
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/email_manager/email_lib.py
|
import substation_readings_manager.email_manager.email_manager as em
import configparser
import json
email_manager_primary_receipient_list = [
'knliao@southernco.com'
]
primary_receipient_list = [
'jrstewar@southernco.com',
'EBBRAY@southernco.com',
'JONJAMES@southernco.com',
'RJSCHNEI@southernco.com',
'BCYOUMAN@southernco.com',
'CALITTON@southernco.com',
'TFETTERL@southernco.com'
]
def send_email(filepath):
receipient_list = email_manager_primary_receipient_list
email_manager = em.EmailManager()
for email in receipient_list:
email_manager.add_primary_recipient(email)
email_manager.add_text(text="")
email_manager.add_attachment(filepath)
#TODO: Need to add something here that refers to which Month
email_manager.send(subject="MPC Battery Report")
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,909
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/app.py
|
"""This module queries the MPC Battery Data Database and checks for missing values."""
from datetime import date
import calendar
import xlsxwriter
import substation_readings_manager.utils.utils as df
import substation_readings_manager.email_manager.email_lib as el
from substation_readings_manager.excel_file.excel_file import ExcelFile
def main():
reading_type_dict = {6: "Cell Impedance", 8: "Cell Voltage",
9:"Strap Impedance"}
string_record_id_name_dict = df.get_string_record_id_to_name_dict()
workbook_name = df.workbook_name()
excel_file = ExcelFile(workbook_name)
for reading_type in reading_type_dict:
for string_record_id in string_record_id_name_dict:
reading_count = 0
for cell_number in range(1, 60):
print(df.program_status(string_record_id_name_dict.get(string_record_id), reading_type_dict.get(reading_type), cell_number))
readings = df.check_database_values(string_record_id, reading_type,
cell_number)
if (reading_count <= 59):
if (reading_type == 6):
excel_file.write_to_cell_impedance(string_record_id_name_dict.get(string_record_id),
reading_count, 59)
if (reading_type == 8):
excel_file.write_to_cell_voltage(string_record_id_name_dict.get(string_record_id),
reading_count, 59)
if (reading_type == 9):
excel_file.write_to_strap_impedance(string_record_id_name_dict.get(string_record_id),
reading_count, 59)
excel_file.close()
el.send_email(df.workbook_name())
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,910
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/utils/workbook.py
|
"""[summary]
Returns:
[type]: [description]
"""
from datetime import datetime, timedelta, date
from dateutil.relativedelta import relativedelta
import calendar
import pyodbc
from typing import Dict, List, Tuple
import database_functions as database_func
import date_functions as date_func
def check_substation(string_record_id: int,
reading_type: int, day: date) -> bool:
reading_type_dict = {6: "Cell Impedance", 8: "Cell Voltage",
9: "Strap Impedance", 10: "Intertier Impedance"}
if (reading_type_dict.get(reading_type) == "Cell Impedance" or
reading_type_dict.get(reading_type) == "Strap Impedance" or
reading_type_dict.get(reading_type) == "Intertier Impedance"):
return len(reading_list) < 1
if reading_type_dict.get(reading_type) == "Cell Voltage":
first_day, last_day = date_func.get_date_range_one_month(day)
query_date_list = []
for reading in reading_list:
query_date_list.append(reading)
days_between_two_dates = date_func.get_all_days_between_two_dates(
first_day, last_day)
return set(query_date_list) == set(days_between_two_dates)
def workbook_name(day: date = date_func.get_last_month_date())->str:
"""This function returns a workbook name given a date
Args:
day (date): Date that the report is being run on
Returns:
string: Name in the format "October 2020 MPC Battery Report.xlsx"
"""
month = str(calendar.month_name[day.month])
year = str(day.year)
workbook_name = month + " " + year + ' MPC Battery Report' + '.xlsx'
return workbook_name
def program_status(name: str, reading_type: str, cell_number: int)->str:
"""This functions prints the current value that the program is on
Args:
name (str): Name of the substation
reading_type (str): Reading type that the substation is on
cell_number (int): What Cell Number of the substation that the program is on
Returns:
string: "Substation x reading_type x Cell Number: x"
"""
status = "Substation " + name + " " + reading_type + "Cell:" + cell_number
return status
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,911
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/app_copy.py
|
"""This module queries the MPC Battery Data Database and checks for missing values."""
from datetime import date
import calendar
import substation_readings_manager.utils.database_functions as df
import substation_readings_manager.email_manager.email_lib as el
from substation_readings_manager.excel_file.excel_file import ExcelFile
from substation_readings_manager.substation.substation import Substation
import substation_readings_manager.utils.workbook as wb
def main():
workbook_name = wb.workbook_name(date(2020,8,4))
workbook = ExcelFile(workbook_name)
x = df.string_record_id_to_intertier_cells_monitor()
for string_record_id, value in x.items():
sub = Substation(string_record_id, value[0], value[1])
sub.print_values()
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,912
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/utils/deprecated.py
|
def query_database_cell_values(string_record_id: str, reading_type: int, cell_number: int,
day: date = get_last_month_date()) -> List:
""" This function queries the database for the readings for a specific StringID,
reading type and cell number
Args:
string_record_id (str): The StringRecordID of the substation
reading_type (int): The Reading Type being queried
cell_number (int): The Cell Number being queried
day (date, optional): Any day within the month that will be queried. Functions use this date
to return the first of the month. Defaults to get_last_month_date().
Returns:
reading_list (List): reading_list is the list of readings from the database.
"""
ms_conn = connect_to_database()
ms_cursor = ms_conn.cursor()
first_day, last_day = get_date_range_one_month(day)
query = F"""SELECT CellReadingsRecordID, ReadingDateTime,
ReadingValue FROM BEEnterpriseHistory.dbo.CellReadingsHistory where
StringRecordID = {string_record_id} and
ReadingTypeRecordID = {reading_type} and cellNumber = {cell_number} and
ReadingDateTime between '{first_day}' and '{last_day}'"""
ms_cursor.execute(query)
reading_list = []
for row in ms_cursor:
reading_list.append(row[1].date())
return reading_list
def check_database_values(string_record_id: str, reading_list: List[date],
reading_type: int, cell_number: int, day: date) -> bool:
"""This function checks the results of query_database_cell_values to see if the correct
number of readings is in the database for the specific reading type and string_record_id.
Returns:
bool(not_enough_readings) : True if not enough, False if enough readings
"""
reading_type_dict = {6: "Cell Impedance", 8: "Cell Voltage",
9: "Strap Impedance", 10: "Intertier Impedance"}
reading_list = query_database_cell_values(string_record_id, reading_type, cell_number)
if (reading_type_dict.get(reading_type) == "Cell Impedance" or
reading_type_dict.get(reading_type) == "Strap Impedance" or
reading_type_dict.get(reading_type) == "Intertier Impedance"):
return len(reading_list) < 1
if reading_type_dict.get(reading_type) == "Cell Voltage":
first_day, last_day = get_date_range_one_month(day)
query_date_list = []
for reading in reading_list:
query_date_list.append(reading)
days_between_two_dates = get_all_days_between_two_dates(
first_day, last_day)
return set(query_date_list) == set(days_between_two_dates)
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,913
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/main.py
|
from substation_readings_manager.app_copy import main
main()
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,914
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/substation/substation.py
|
from enum import Enum
from datetime import date
from typing import List, Dict, Tuple
import substation_readings_manager.utils.database_functions as df
import substation_readings_manager.utils.date_functions as date_func
class DataReading():
reading_type_dict = {6: "Cell Impedance", 8: "Cell Voltage",
9:"Strap Impedance", 10: "Intertier Impedance"}
def __init__(self, database_id):
self.reading_type: tuple = (database_id, self.reading_type_dict.get(database_id))
self.readings: list = None
def set_readings(self, string_record_id: int, day: date)->None:
ms_conn = df.connect_to_database()
ms_cursor = ms_conn.cursor()
first_day, last_day = date_func.get_date_range_one_month(day)
query = F"""SELECT CellReadingsRecordID, ReadingDateTime,
ReadingValue FROM BEEnterpriseHistory.dbo.CellReadingsHistory where
StringRecordID = {string_record_id} and
ReadingTypeRecordID = {self.reading_type[0]} and
ReadingDateTime between '{first_day}' and '{last_day}'"""
ms_cursor.execute(query)
reading_list = []
for row in ms_cursor:
reading_list.append(row)
self.readings = reading_list
class Substation:
def __init__(self, string_record_id: int, intertier_cells: List[int], monitor_type:str):
self.string_record_id: int = string_record_id
self.intertier_cells: list = intertier_cells
self.monitor_type: str = monitor_type
self.cell_voltage_readings = DataReading(6)
self.cell_impedance_readings = DataReading(8)
self.strap_impedance_readings = DataReading(9)
self.intertier_impedance_readings = DataReading(10)
self.cell_voltage_readings.set_readings(string_record_id, date(2020,8,4))
self.cell_impedance_readings.set_readings(string_record_id, date(2020,8,4))
self.strap_impedance_readings.set_readings(string_record_id, date(2020,8,4))
self.intertier_impedance_readings.set_readings(string_record_id, date(2020,8,4))
def print_values(self):
print(self.string_record_id, self.intertier_cells, self.monitor_type)
def get_cell_voltage_readings(self, string_record_id: int, day: date):
"""
Need to execute a sql statement here is what I suspect
"""
query = F"""SELECT CellReadingsRecordID, ReadingDateTime,
ReadingValue FROM BEEnterpriseHistory.dbo.CellReadingsHistory where
StringRecordID = {string_record_id} and
ReadingTypeRecordID = {reading_type} and cellNumber = {cell_number} and
ReadingDateTime between '{first_day}' and '{last_day}' """
# def check_cell_impedance(self):
# #Needs a reading once a month
# """ This funciton checks to make sure that a substation has at least one cell impedance reading for each of its cells
# Args:
# day (date, optional): Any day within the month that will be queried. Functions use this date
# to return the first of the month. Defaults to get_last_month_date().
# Returns:
# Tuple[int, int]:(The amount of readings in the database, the amount of readings that there should have been)
# """
# first_day, last_day = utils.get_date_range_one_month(day)
# db_conn = utils.connect_to_database()
# cursor = db_conn.cursor()
# query = F"""SELECT CellNumber, ReadingDateTime, ReadingValue FROM BEEnterpriseHistory.dbo.CellReadingsHistory
# where StringRecordID = {self.string_record_id}
# and ReadingTypeRecordID = 6
# and ReadingDateTime between {first_day} and {last_day}"""
# cursor.execute(query)
# #This is a simple check for now, it should be upgraded in the future so that it actually looks
# #at the cell readings. This is to catch scenarios like one cell being read more than once.
# if (len(self.intertier_cells) == len(cursor)):
# return(True, len(self.intertier_cells),len(cursor))
# return (False, len(self.intertier_cells), len(cursor))
# def check_strap_impedance(self):
# #Needs a reading once a amonth
# #Needs a reading once a month
# """ This funciton checks to make sure that a substation has at least one intertier impedance reading for each of its cells
# in the intertier_cells attribute
# Args:
# day (date, optional): Any day within the month that will be queried. Functions use this date
# to return the first of the month. Defaults to get_last_month_date().
# Returns:
# Tuple[int, int]:(The amount of readings in the database, the amount of readings that there should have been)
# """
# first_day, last_day = utils.get_date_range_one_month(day)
# db_conn = utils.connect_to_database()
# cursor = db_conn.cursor()
# query = F"""SELECT CellNumber, ReadingDateTime, ReadingValue FROM BEEnterpriseHistory.dbo.CellReadingsHistory
# where StringRecordID = {self.string_record_id}
# and ReadingTypeRecordID = 9
# and ReadingDateTime between {first_day} and {last_day}"""
# cursor.execute(query)
# #This is a simple check for now, it should be upgraded in the future so that it actually looks
# #at the cell readings. This is to catch scenarios like one cell being read more than once.
# if (len(self.intertier_cells) == len(cursor)):
# return(True, len(self.intertier_cells),len(cursor))
# return (False, len(self.intertier_cells), len(cursor))
# def check_intertier_impedance(self, day: date = utils.get_last_month_date())->Tuple[bool, int, int]:
# #Needs a reading once a month
# """ This funciton checks to make sure that a substation has at least one intertier impedance reading for each of its cells
# in the intertier_cells attribute
# Args:
# day (date, optional): Any day within the month that will be queried. Functions use this date
# to return the first of the month. Defaults to get_last_month_date().
# Returns:
# Tuple[int, int]:(The amount of readings in the database, the amount of readings that there should have been)
# """
# first_day, last_day = utils.get_date_range_one_month(day)
# db_conn = utils.connect_to_database()
# cursor = db_conn.cursor()
# query = F"""SELECT CellNumber, ReadingDateTime, ReadingValue FROM BEEnterpriseHistory.dbo.CellReadingsHistory
# where StringRecordID = {self.string_record_id}
# and ReadingTypeRecordID = 10
# and ReadingDateTime between {first_day} and {last_day}"""
# cursor.execute(query)
# #This is a simple check for now, it should be upgraded in the future so that it actually looks
# #at the cell readings. This is to catch scenarios like one cell being read more than once.
# if (len(self.intertier_cells) == len(cursor)):
# return(True, len(self.intertier_cells),len(cursor))
# return (False, len(self.intertier_cells), len(cursor))
# def check_cell_voltage(self):
# #Needs a reading once a month
# """ This funciton checks to make sure that a substation has at least one intertier impedance reading for each of its cells
# in the intertier_cells attribute
# Args:
# day (date, optional): Any day within the month that will be queried. Functions use this date
# to return the first of the month. Defaults to get_last_month_date().
# Returns:
# Tuple[int, int]:(The amount of readings in the database, the amount of readings that there should have been)
# """
# first_day, last_day = utils.get_date_range_one_month(day)
# db_conn = utils.connect_to_database()
# cursor = db_conn.cursor()
# query = F"""SELECT CellNumber, ReadingDateTime, ReadingValue FROM BEEnterpriseHistory.dbo.CellReadingsHistory
# where StringRecordID = {self.string_record_id}
# and ReadingTypeRecordID = 8
# and ReadingDateTime between {first_day} and {last_day}"""
# cursor.execute(query)
# #This is a simple check for now, it should be upgraded in the future so that it actually looks
# #at the cell readings. This is to catch scenarios like one cell being read more than once.
# if (len(self.intertier_cells) == len(cursor)):
# return(True, len(self.intertier_cells),len(cursor))
# return (False, len(self.intertier_cells), len(cursor)
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,915
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/excel_file/excel_file.py
|
"""This class is responsible for creating the excel sheet and writing to it.
"""
import xlsxwriter
from substation_readings_manager.substation.substation import Substation
class ExcelFile:
def __init__(self, workbook_name):
self.workbook = xlsxwriter.Workbook(workbook_name)
self.key = self.workbook.add_worksheet('Key')
self.overall = self.workbook.add_worksheet('Overall')
self.overall_row = 1
self.format()
def format(self):
impedance_format = self.workbook.add_format()
impedance_format.set_bg_color('red')
voltage_format = self.workbook.add_format()
voltage_format.set_bg_color('blue')
both_format = self.workbook.add_format()
both_format.set_bg_color('purple')
self.key.write('A1', 'Key')
self.key.write('A2','', impedance_format)
self.key.write('A3', '', voltage_format)
self.key.write('A4', '', both_format)
self.key.write('B2', 'Indicates Impedance Value Missing')
self.key.write('B3', 'Indicates a certain percentage of Voltage Values missing')
self.key.write('B4', 'Both')
def close(self):
self.workbook.close()
def analyze_substation(self, substation: Substation):
if len(substation.cell_voltage_readings.readings < 30):
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,916
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/utils/utils_test.py
|
import unittest
from datetime import date
import pyodbc
import utils as df
class LastMonthDate(unittest.TestCase):
def test_connect_to_database(self):
self.assertIs(type(df.connect_to_database()), pyodbc.Connection)
def test_get_last_month_date(self):
self.assertIs(type(df.get_last_month_date()), date)
def test_get_string_record_id_to_name_dict(self):
#There is potentially an issue here, I've seen the database values
#change sometimes
self.assertIs(type(df.get_string_record_id_to_name_dict()), dict)
def test_check_substation(self):
pass
def test_workbook_name(self):
self.assertEqual(df.workbook_name(date(2020, 10, 29)), "October 2020 MPC Battery Report.xlsx")
def test_program_status(self):
self.assertIs(df.program_status("Malborn", "Cell Voltage",6),
"Substation Malborn Cell Voltage Cell: 6")
def test_query_database_cell_values(self):
#What do I need to assert on this funciton? I need to assert whether or not,for a known value in the database?
# Yes, I can call a query maybe on a known value
#
#
if __name__ == '__main__':
unittest.main()
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,917
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/utils/date_functions.py
|
from datetime import datetime, timedelta, date
from dateutil.relativedelta import relativedelta
import calendar
import pyodbc
from typing import Dict, List, Tuple
def get_last_month_date() -> date:
""" This function returns the date one month ago. This is for get_date_range_one_month
which then takes the first of the month.
Returns:
date: One month ago from the current date
"""
now = datetime.now()
one_month_ago = now - relativedelta(months=1)
return one_month_ago.date()
def get_all_days_between_two_dates(s_date: date, e_date: date) -> List[date]:
"""This function returns a list of days in between two days, inclusive of start and end
Args:
s_date (date): start date
e_date (date): end date
Returns:
List[date]: List that contains a date object for every day in a month
"""
delta = e_date - s_date
date_list = []
for i in range(delta.days):
day = s_date + timedelta(days=i)
date_list.append(day)
return date_list
def get_month_and_year_string(day: date) -> Tuple[int, int]:
"""[summary]
Args:
day (date): Date that we want month and year of
Returns:
Tuple[int, int]: Month(1-12), Year(Int)
"""
date_obj = datetime.strptime(day, "%m-%d-%Y")
return (date_obj.month, date_obj.year)
def get_date_range_one_month(day_of_month: date = get_last_month_date()) -> Tuple[date, date]:
"""This function returns the first day of the month of day_of_month and
the first of the next month in a tuple.
The first day of the next month is chosen because when iterating through the date list,
the last day is exclusive.
This function by default in the code, just uses the current date but can use any date with
the day_of_month argument.
Args:
day_of_month (date, optional): [description]. Defaults to get_last_month_date().
Returns:
tuple: tuple that contains first day of month and first day of next month
"""
first_day = day_of_month.replace(day=1)
last_day = first_day + relativedelta(months=1)
return (first_day, last_day)
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,835,918
|
knliao-southernco/substation_readings_manager
|
refs/heads/master
|
/substation_readings_manager/excel_file/test_excel_file.py
|
import os
import unittest
from ExcelFile import ExcelFile
class ExcelFileTest(unittest.TestCase):
def test_create_excel_file(self):
excel_file = ExcelFile("Excel_Test.xlsx")
excel_file.close()
self.assertTrue(os.path.exists("./Excel_Test.xlsx"))
if __name__ == '__main__':
unittest.main()
|
{"/substation_readings_manager/app_copy.py": ["/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/excel_file/excel_file.py", "/substation_readings_manager/substation/substation.py", "/substation_readings_manager/utils/workbook.py"], "/main.py": ["/substation_readings_manager/app.py", "/substation_readings_manager/app_copy.py"], "/substation_readings_manager/app.py": ["/substation_readings_manager/utils/utils.py", "/substation_readings_manager/email_manager/email_lib.py", "/substation_readings_manager/excel_file/excel_file.py"], "/substation_readings_manager/substation/substation.py": ["/substation_readings_manager/utils/database_functions.py", "/substation_readings_manager/utils/date_functions.py"]}
|
35,897,976
|
Yefimenko1Kirillallay/gitHomeworkPython
|
refs/heads/master
|
/work.py
|
import pygame
import sys
# Переменные для установки ширины и высоты окна
SCREEN_WIDTH = 800
SCREEN_HEIGHT = 600
mainSurface = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT), 0, 32)
# Подключение фото для заднего фона
# Здесь лишь создание переменной, вывод заднего фона ниже в коде
bg = pygame.image.load('nugno/fon.jpg')
pygame.mixer.init(frequency=22050, size=-16, channels=2, buffer=4096)
f = pygame.mixer.Sound('nugno/фоновая музика.mp3')
# Класс, описывающий поведение главного игрока
class Player(pygame.sprite.Sprite) :
# Изначально игрок смотрит вправо, поэтому эта переменная True
right = True
# Методы
def __init__(self) :
# Стандартный конструктор класса
# Нужно ещё вызывать конструктор родительского класса
super().__init__()
# Создаем изображение для игрока
# Изображение находится в этой же папке проекта
self.image = pygame.image.load('nugno/plaer.png')
# Установите ссылку на изображение прямоугольника
self.rect = self.image.get_rect()
global change_x
global change_y
# Задаем вектор скорости игрока
self.change_x = 0
self.change_y = 0
def update(self) :
# В этой функции мы передвигаем игрока
# Сперва устанавливаем для него гравитацию
self.calc_grav()
# Передвигаем его на право/лево
# change_x будет меняться позже при нажатии на стрелочки клавиатуры
self.rect.x += self.change_x
# Следим ударяем ли мы какой-то другой объект, платформы, например
block_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)
# Перебираем все возможные объекты, с которыми могли бы столкнуться
for block in block_hit_list :
# Если мы идем направо,
# устанавливает нашу правую сторону на левой стороне предмета, которого мы ударили
if self.change_x > 0 :
self.rect.right = block.rect.left
elif self.change_x < 0 :
# В противном случае, если мы движемся влево, то делаем наоборот
self.rect.left = block.rect.right
# Передвигаемся вверх/вниз
self.rect.y += self.change_y
# То же самое, вот только уже для вверх/вниз
block_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)
for block in block_hit_list :
# Устанавливаем нашу позицию на основе верхней / нижней части объекта, на который мы попали
if self.change_y > 0 :
self.rect.bottom = block.rect.top
elif self.change_y < 0 :
self.rect.top = block.rect.bottom
# Останавливаем вертикальное движение
self.change_y = 0
blockP_hit_list = pygame.sprite.spritecollide(self, self.levelP.portal_list, False)
for block in blockP_hit_list :
if self.change_x > 0 :
self.rect.right = block.rect.left
NewLevel()
elif self.change_x < 0:
self.rect.left = block.rect.right
NewLevel()
elif self.change_y > 0 :
self.rect.bottom = block.rect.top
NewLevel()
elif self.change_y < 0 :
self.rect.top = block.rect.bottom
NewLevel()
def calc_grav(self) :
# Здесь мы вычисляем как быстро объект будет
# падать на землю под действием гравитации
if self.change_y == 0 :
self.change_y = 1
else :
self.change_y += 0.95
# Если уже на земле, то ставим позицию Y как 0
if self.rect.y >= SCREEN_HEIGHT - self.rect.height and self.change_y >= 0 :
self.change_y = 0
self.rect.y = SCREEN_HEIGHT - self.rect.height
def jump(self) :
# Обработка прыжка
# Нам нужно проверять здесь, контактируем ли мы с чем-либо
# или другими словами, не находимся ли мы в полете.
# Для этого опускаемся на 10 единиц, проверем соприкосновение и далее поднимаемся обратно
self.rect.y += 10
platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)
self.rect.y -= 10
# Если все в порядке, прыгаем вверх
if len(platform_hit_list) > 0 or self.rect.bottom >= SCREEN_HEIGHT :
self.change_y = -16
# Передвижение игрока
def go_left(self) :
# Сами функции будут вызваны позже из основного цикла
self.change_x = -10 # Двигаем игрока по Х
if (self.right) : # Проверяем куда он смотрит и если что, то переворачиваем его
self.flip()
self.right = False
def go_right(self) :
# то же самое, но вправо
self.change_x = 10
if (not self.right) :
self.flip()
self.right = True
def stop(self) :
# вызываем этот метод, когда не нажимаем на клавиши
self.change_x = 0
def flip(self) :
# переворот игрока (зеркальное отражение)
self.image = pygame.transform.flip(self.image, True, False)
#*------------------------------------------*-----------------------------*------------------------*--------------------*-------------------*
# Класс для описания платформы
class Platform(pygame.sprite.Sprite) :
def __init__(self, width, height) :
# Конструктор платформ
super().__init__()
# Также указываем фото платформы
self.image = pygame.image.load('nugno/platform.png')
# Установите ссылку на изображение прямоугольника
self.rect = self.image.get_rect()
# Класс для расстановки платформ на сцене
class Level(object) :
def __init__(self, player) :
# Создаем группу спрайтов (поместим платформы различные сюда)
self.platform_list = pygame.sprite.Group()
# Ссылка на основного игрока
self.player = player
# Чтобы все рисовалось, то нужно обновлять экран
# При вызове этого метода обновление будет происходить
def update(self) :
self.platform_list.update()
# Метод для рисования объектов на сцене
def draw(self, screen,current) :
# Рисуем задний фон
screen.blit(bg, (0, 0))
# Рисуем все платформы из группы спрайтов
self.platform_list.draw(screen)
class Level_00(Level) :
def __init__(self, player) :
# Вызываем родительский конструктор
Level.__init__(self, player)
# Класс, что описывает где будут находится все платформы
# на определенном уровне игры
class Level_01(Level) :
def __init__(self, player) :
# Вызываем родительский конструктор
Level.__init__(self, player)
# Массив с данными про платформы. Данные в таком формате:
# ширина, высота, x и y позиция
level = [[210, 32, 300, 500],]
# Перебираем массив и добавляем каждую платформу в группу спрайтов - platform_list
for platform in level :
block = Platform(platform [0], platform [1])
block.rect.x = platform [2]
block.rect.y = platform [3]
block.player = self.player
self.platform_list.add(block)
class Level_02(Level) :
def __init__(self, player) :
# Вызываем родительский конструктор
Level.__init__(self, player)
# Массив с данными про платформы. Данные в таком формате:
# ширина, высота, x и y позиция
level = [
[210, 32, 500, 500],
[210, 32, 500, 400],
[210, 32, 500, 300],
[210, 32, 500, 200],
[210, 32, 500, 100],
[210, 32, 200, 500],
[210, 32, 0, 390],
[210, 32, 300, 270],
[210, 32, 90, 150],
]
# Перебираем массив и добавляем каждую платформу в группу спрайтов - platform_list
for platform in level :
block = Platform(platform [0], platform [1])
block.rect.x = platform [2]
block.rect.y = platform [3]
block.player = self.player
self.platform_list.add(block)
#*------------------------------------------*-----------------------------*------------------------*--------------------*-------------------*
class Portals(pygame.sprite.Sprite):
def __init__(self, width, height) :
# Конструктор платформ
super().__init__()
# Также указываем фото платформы
self.image = pygame.image.load('nugno/portal.png')
# Установите ссылку на изображение прямоугольника
self.rect = self.image.get_rect()
class LevelP(object) :
def __init__(self, player) :
# Создаем группу спрайтов (поместим платформы различные сюда)
self.image = pygame.image.load('nugno/portal.png')
self.portal_list = pygame.sprite.Group()
# Ссылка на основного игрока
self.player = player
# Чтобы все рисовалось, то нужно обновлять экран
# При вызове этого метода обновление будет происходить
def update(self) :
self.portal_list.update()
# Метод для рисования объектов на сцене
def draw(self, blockP) :
global mainSurface
mainSurface.blit(self.image, (blockP[0], blockP[1]))
class LevelP_00(LevelP) :
def __init__(self, player) :
LevelP.__init__(self, player)
self.portals = [
[28, 70, SCREEN_WIDTH-28, SCREEN_HEIGHT-70], ]
for portal in self.portals :
blockP = Portals(portal [0], portal [1])
blockP.rect.x = portal [2]
blockP.rect.y = portal [3]
blockP.player = self.player
self.portal_list.add(blockP)
class LevelP_01(LevelP) :
def __init__(self, player) :
LevelP.__init__(self, player)
self.portals2 = [
[28, 70, SCREEN_WIDTH-28, SCREEN_HEIGHT-70], ]
for portal in self.portals2 :
blockP = Portals(portal [0], portal [1])
blockP.rect.x = portal [2]
blockP.rect.y = portal [3]
blockP.player = self.player
self.portal_list.add(blockP)
class LevelP_02(LevelP) :
def __init__(self, player) :
LevelP.__init__(self, player)
self.portals3 = [
[28, 70, SCREEN_WIDTH-28, SCREEN_HEIGHT-70],]
for portal in self.portals3 :
block = Portals(portal [0], portal [1])
block.rect.x = portal [2]
block.rect.y = portal [3]
block.player = self.player
self.portal_list.add(block)
#*------------------------------------------*-----------------------------*------------------------*--------------------*-------------------*
# Основная функция прогарммы
def main() :
# Инициализация
pygame.init()
# Установка высоты и ширины
size = [SCREEN_WIDTH, SCREEN_HEIGHT]
screen = pygame.display.set_mode(size)
# Создаем игрока
global player, xy
player = Player()
# Создаем все уровни
global current_level_no
current_level_no = 0
global level_list
global portal_list
level_list = [Level_00(player), Level_01(player), Level_02(player)]
portal_list = [LevelP_00(player),LevelP_01(player),LevelP_02(player)]
# Устанавливаем текущий уровень
global active_sprite_list
global level
current_level =level_list [current_level_no]
current_levelP = portal_list [current_level_no]
active_sprite_list = pygame.sprite.Group()
player.level = current_level
player.levelP = current_levelP
player.rect.x = 0
player.rect.y = SCREEN_HEIGHT - player.rect.height
active_sprite_list.add(player)
# Цикл будет до тех пор, пока пользователь не нажмет кнопку закрытия
done = False
# Используется для управления скоростью обновления экрана
clock = pygame.time.Clock()
# Основной цикл программы
while not done :
f.play(loops=0, maxtime=0, fade_ms=0)
level=LevelP(player)
# Название игры
pygame.display.set_caption("Платформер Level " + str(current_level_no + 1))
# Отслеживание действий
for event in pygame.event.get() :
if event.type == pygame.QUIT : # Если закрыл программу, то останавливаем цикл
done = True
# Если нажали на стрелки клавиатуры, то двигаем объект
if event.type == pygame.KEYDOWN :
if event.key == pygame.K_LEFT or event.key == pygame.K_a :
player.go_left()
if event.key == pygame.K_RIGHT or event.key == pygame.K_d :
player.go_right()
if event.key == pygame.K_UP or event.key == pygame.K_SPACE or event.key == pygame.K_w :
player.jump()
if event.type == pygame.KEYUP :
if event.key == pygame.K_LEFT and player.change_x < 0 or event.key == pygame.K_a and player.change_x < 0:
player.stop()
if event.key == pygame.K_RIGHT and player.change_x > 0 or event.key == pygame.K_d and player.change_x > 0 :
player.stop()
# Обновляем игрока
active_sprite_list.update()
# Обновляем объекты на сцене
# current_levelP.update()
current_level.update()
current_levelP.update()
# Если игрок приблизится к правой стороне, то дальше его не двигаем
if player.rect.right > SCREEN_WIDTH :
player.rect.right = SCREEN_WIDTH
# Если игрок приблизится к левой стороне, то дальше его не двигаем
if player.rect.left < 0 :
player.rect.left = 0
xy=[SCREEN_WIDTH-28, SCREEN_HEIGHT-70]
current_level = level_list [current_level_no]
current_levelP = portal_list [current_level_no]
# Рисуем объекты на окне
# current_levelP.draw(screen)
current_level.draw(screen,current_level_no)
current_levelP.draw(xy)
active_sprite_list.draw(screen)
# Устанавливаем количество фреймов
clock.tick(30)
# Обновляем экран после рисования объектов
pygame.display.flip()
# Корректное закртытие программы
pygame.quit()
def NewLevel():
global current_level_no
if current_level_no<2:
current_level_no += 1
current_level = level_list [current_level_no]
current_levelP = level_list [current_level_no]
active_sprite_list = pygame.sprite.Group()
player.level = current_level
player.rect.x = 0
player.rect.y = SCREEN_HEIGHT - player.rect.height
active_sprite_list.add(player)
|
{"/main.py": ["/work.py"]}
|
35,897,977
|
Yefimenko1Kirillallay/gitHomeworkPython
|
refs/heads/master
|
/main.py
|
import pygame
import work
work.main()
|
{"/main.py": ["/work.py"]}
|
35,958,008
|
aalnobel/CS361Project
|
refs/heads/master
|
/GitHubTest/models.py
|
from django.db import models
class MyUserLogin(models.Model):
username = models.CharField(max_length=20)
password = models.CharField(max_length=20)
class MyUser(models.Model):
login = models.ForeignKey(MyUserLogin, on_delete=models.CASCADE)
type = models.CharField(max_length=1) # T=TA I=Instructor A=Admin
lastName = models.CharField(max_length=20)
firstName = models.CharField(max_length=20)
officeHours = models.CharField(max_length=20)
officeNumber = models.CharField(max_length=20)
email = models.CharField(max_length=20)
phoneNumber = models.CharField(max_length=20)
class MyCourse(models.Model):
courseName = models.CharField(max_length=50)
instructor = models.ForeignKey(MyUser, on_delete=models.CASCADE)
class MySection(models.Model):
sectionNumber = models.IntegerField()
course = models.ForeignKey(MyCourse, on_delete=models.CASCADE)
description = models.CharField(max_length=20)
teachingAssistant = models.ForeignKey(MyUser, on_delete=models.CASCADE)
class MySyllabus(models.Model):
course = models.ForeignKey(MyCourse, on_delete=models.CASCADE)
instructor = models.ForeignKey(MyUser, on_delete=models.CASCADE)
courseName = models.CharField(max_length=10)
courseDescription = models.CharField(max_length=200)
def __str__(self):
return self.courseName
class MySyllabusComponent(models.Model):
syllabus = models.ForeignKey(MySyllabus, on_delete=models.CASCADE)
name = models.CharField(max_length=50)
content = models.CharField(max_length=200)
|
{"/GitHubTest/tests.py": ["/GitHubTest/models.py"], "/GitHubTest/views.py": ["/GitHubTest/models.py"], "/GitHubTest/admin.py": ["/GitHubTest/models.py"]}
|
35,958,009
|
aalnobel/CS361Project
|
refs/heads/master
|
/GitHubTest/views.py
|
from django.core.exceptions import ObjectDoesNotExist
from django.shortcuts import render, redirect
from django.views import View
from GitHubTest.models import MySyllabus, MyUser, MyUserLogin, MyCourse, MySection, MySyllabusComponent
class Home(View):
def get(self, request):
request.session["current"] = ""
syllabi = list(MySyllabus.objects.all())
return render(request, "login.html", {"syllabi": syllabi, "error_msg": ""})
def post(self, request):
msg = "Something went wrong. Please try again."
if "login" in request.POST:
try:
login = MyUserLogin.objects.get(username=request.POST['username'], password=request.POST['password'])
my_user = MyUser.objects.get(login=login)
request.session["current"] = my_user.id
return redirect("/Administrator/" if my_user.type == 'A' else "/PersonalInfo/")
except ObjectDoesNotExist:
msg = "Incorrect username or password."
if "guest" in request.POST:
return redirect("/Syllabus/" + request.POST["course"])
syllabi = list(MySyllabus.objects.all())
return render(request, "login.html", {"syllabi": syllabi,
"error_msg": msg})
def build_course_info(course_objects):
courses = []
for course in course_objects:
sections = []
section_objects = MySection.objects.filter(course=course)
for s in section_objects:
sections.append(str(s.sectionNumber) + " " + s.description + ", " +
s.teachingAssistant.firstName + " " + s.teachingAssistant.lastName)
course = {
"name": course.courseName,
"instructor": course.instructor,
"sections": sections
}
courses.append(course)
return courses
class AdminView(View):
def get(self, request):
return render(request, "admin.html", get_admin_template_data())
def post(self, request):
if "create_user" in request.POST:
new_login = MyUserLogin(username=request.POST["username"], password=request.POST["password"])
new_login.save()
new_user = MyUser(login=new_login, type=request.POST["type"],
lastName=request.POST["lastName"], firstName=request.POST["firstName"],
officeHours="", officeNumber="", email="", phoneNumber="")
new_user.save()
elif "create_course" in request.POST:
new_course = MyCourse(courseName=request.POST["course_name"],
instructor=MyUser.objects.get(id=request.POST["instructor"]))
new_course.save()
elif "create_section" in request.POST:
new_section = MySection(sectionNumber=request.POST["section_number"],
description=request.POST["description"],
course=MyCourse.objects.get(id=request.POST["course"]),
teachingAssistant=MyUser.objects.get(id=request.POST["teaching_assistant"]))
new_section.save()
return render(request, "admin.html", get_admin_template_data())
def get_admin_template_data():
return {
"tas": list(MyUser.objects.filter(type="T")),
"instructors": list(MyUser.objects.filter(type="I")),
"courses": list(MyCourse.objects.all().values()),
"course_info": build_course_info(MyCourse.objects.all())
}
class PersonalInfoView(View):
def get(self, request):
user = MyUser.objects.get(id=request.session["current"])
return render(request, "personal_info.html", {"user_info": user})
def post(self, request):
user = MyUser.objects.get(id=request.session["current"])
user.officeHours = request.POST["officeHours"]
user.email = request.POST["email"]
user.phoneNumber = request.POST["phoneNumber"]
user.officeNumber = request.POST["officeNumber"]
user.save()
return render(request, "personal_info.html", {"user_info": user})
class InstructorView(View):
def get(self, request):
return render(request, "instructor.html", get_instructor_template_data(request.session["current"]))
def post(self, request):
if "create_syllabus" in request.POST:
new_syllabus = MySyllabus(course=MyCourse.objects.get(courseName=request.POST["course"]),
instructor=MyUser.objects.get(id=request.session["current"]),
courseName=request.POST["course"],
courseDescription=request.POST["description"])
new_syllabus.save()
elif "delete_syllabus" in request.POST:
syllabus = MySyllabus.objects.get(id=request.POST["syllabus"])
syllabus.delete()
elif "add_component" in request.POST:
new_component = MySyllabusComponent(syllabus=MySyllabus.objects.get(id=request.POST["syllabus"]),
name=request.POST["name"],
content=request.POST["contents"])
new_component.save()
elif "update_components" in request.POST:
return update_instructor_components(request)
elif "delete_component" in request.POST:
component = MySyllabusComponent.objects.get(id=request.POST["component"])
component.delete()
return render(request, "instructor.html", get_instructor_template_data(request.session["current"]))
def get_instructor_template_data(session_id):
user = MyUser.objects.get(id=session_id)
available_courses = []
for i in list(MyCourse.objects.filter(instructor=user)):
try:
MySyllabus.objects.get(course=i)
except ObjectDoesNotExist:
available_courses.append(i)
return {
"syllabi": list(MySyllabus.objects.filter(instructor=user).values()),
"available_courses": available_courses,
"selected_syllabus": "...",
"components": []
}
def update_instructor_components(request):
data = get_instructor_template_data(request.session["current"])
selected_syllabus = MySyllabus.objects.get(id=request.POST["update_components"])
data["selected_syllabus"] = selected_syllabus.courseName
data["components"] = list(MySyllabusComponent.objects.filter(syllabus=selected_syllabus))
return render(request, "instructor.html", data)
def render_syllabus(request, course):
syllabus = MySyllabus.objects.get(courseName=course)
sections = list(MySection.objects.filter(course=MyCourse.objects.get(courseName=course)))
components = list(MySyllabusComponent.objects.filter(syllabus=syllabus).values())
return render(request, "syllabus.html", {"syllabus": syllabus,
"instructor": syllabus.instructor,
"sections": sections,
"components": components})
|
{"/GitHubTest/tests.py": ["/GitHubTest/models.py"], "/GitHubTest/views.py": ["/GitHubTest/models.py"], "/GitHubTest/admin.py": ["/GitHubTest/models.py"]}
|
35,958,010
|
aalnobel/CS361Project
|
refs/heads/master
|
/GitHubTest/migrations/0001_initial.py
|
# Generated by Django 3.1.3 on 2020-12-13 00:27
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='MyCourse',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('courseName', models.CharField(max_length=20)),
],
),
migrations.CreateModel(
name='MySyllabus',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('courseName', models.CharField(max_length=10)),
('courseDescription', models.CharField(max_length=200)),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='GitHubTest.mycourse')),
],
),
migrations.CreateModel(
name='MyUserLogin',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('username', models.CharField(max_length=20)),
('password', models.CharField(max_length=20)),
],
),
migrations.CreateModel(
name='MyUser',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('type', models.CharField(max_length=1)),
('lastName', models.CharField(max_length=20)),
('firstName', models.CharField(max_length=20)),
('officeHours', models.CharField(max_length=20)),
('officeNumber', models.CharField(max_length=20)),
('email', models.CharField(max_length=20)),
('phoneNumber', models.CharField(max_length=20)),
('login', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='GitHubTest.myuserlogin')),
],
),
migrations.CreateModel(
name='MySyllabusComponent',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=50)),
('description', models.CharField(max_length=200)),
('syllabus', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='GitHubTest.mysyllabus')),
],
),
migrations.AddField(
model_name='mysyllabus',
name='instructor',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='GitHubTest.myuser'),
),
migrations.CreateModel(
name='MySection',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('sectionNumber', models.IntegerField()),
('course', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='GitHubTest.mycourse')),
('teachingAssistant', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='GitHubTest.myuser')),
],
),
migrations.AddField(
model_name='mycourse',
name='instructor',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='GitHubTest.myuser'),
),
]
|
{"/GitHubTest/tests.py": ["/GitHubTest/models.py"], "/GitHubTest/views.py": ["/GitHubTest/models.py"], "/GitHubTest/admin.py": ["/GitHubTest/models.py"]}
|
35,958,011
|
aalnobel/CS361Project
|
refs/heads/master
|
/GitHubTest/tests.py
|
from django.test import TestCase, Client
from .models import MySyllabus, MyUser, MyCourse, MySection, MyUserLogin
from . import views
import unittest
# Create your tests here.
class TestLogin(TestCase):
def setUp(self):
self.client = Client()
self.user_admin = MyUser(login=MyUserLogin(username="noman", password="1234"),
type="A")
self.user_instructor = MyUser(login=MyUserLogin(username="Nick", password="5678"),
type="I")
self.user_ta = MyUser(login=MyUserLogin(username="Santha", password="1234"),
type="T")
def test_login(self):
client = Client()
# verify that a user can log in
response = client.post("/", {"name": "login", "username": "noman", "password": "1234"}, follow=True)
self.assertEqual(response.status_code, 200)
class TestPersonalInfo(TestCase):
def test_officeNumber1(self):
self.user.officeNumber = "205"
self.assertTrue(self.user.officeNumber.isnumeric())
def test_officeNumber2(self):
self.user.officeNumber = "205"
self.assertEqual(int(self.user.officeNumber), 205)
def test_officeNumber3(self):
self.user.officeNumber = "0"
self.assertFalse(int(self.user.officeNumber), "officeNumber can't be 0")
def test_phoneNumber(self):
self.user.phoneNumber = "4145445978"
self.assertTrue(self.user.phoneNumber.isnumeric())
def test_phoneNumber2(self):
self.user.phoneNumber = "4145445978"
self.assertEqual(int(self.user.phoneNumber), 4145445978)
def test_phoneNumber3(self):
self.user.phoneNumber = "0"
self.assertFalse(int(self.user.phoneNumber), "Phone numbers can't be 0")
def test_email(self):
self.user.email = "dakito@uwm.edu"
self.assertTrue(self.user.email.find("@uwm.edu") > 0)
def test_email2(self):
self.user.email = "dakito@uwm.edu23"
x = self.user.email.split(7, 16)
self.assertNotEqual(x, "@uwm.edu", "There can not be characters after @uwm.edu")
def test_email3(self):
self.user.email = "@uwm.edu"
self.assertFalse(self.user.email.index("@") == 0, "There has to be characters before the @ symbol")
def test_email4(self):
self.user.email = "34@45@uwm.edu"
x = self.user.email.find("@")
y = self.user.email.split(x, 13)
z = y.find("@")
self.assertEqual(z, -1, "There cannot be multiple @ in an email")
class TestSyllabus(TestCase):
def test_syllabus_course(self):
syllabus1 = MySyllabus(course="CS361", instructor="John")
self.assertNotEquals(syllabus1.course, (MySyllabus(course="CS361")), "Can't have duplicate class syllabi")
self.assertEquals(syllabus1.course, (MySyllabus(instructor="John")), "Instructors can teach multiple classes")
class TestSections(TestCase):
def test_sections(self):
syllabus1 = MySection(sectionNumber=802, course="CS361")
self.assertNotEquals(syllabus1.sectionNumber, (MySection(sectionNumber=802)), "Can't have duplicate sections")
self.assertEquals(syllabus1.course, (MySection(course="CS361")), "Can have multiple sections")
#suite = unittest.TestSuite()
#suite.addTests(TestStuffStr(), TestPersonalInfo(), TestSyllabus(), TestSections())
|
{"/GitHubTest/tests.py": ["/GitHubTest/models.py"], "/GitHubTest/views.py": ["/GitHubTest/models.py"], "/GitHubTest/admin.py": ["/GitHubTest/models.py"]}
|
35,958,012
|
aalnobel/CS361Project
|
refs/heads/master
|
/GitHubTest/admin.py
|
from django.contrib import admin
from GitHubTest.models import MyUserLogin, MyUser, MySyllabus, MyCourse, MySection, MySyllabusComponent
admin.site.register(MyUserLogin)
admin.site.register(MyUser)
admin.site.register(MySyllabus)
admin.site.register(MySyllabusComponent)
admin.site.register(MyCourse)
admin.site.register(MySection)
# Register your models here.
|
{"/GitHubTest/tests.py": ["/GitHubTest/models.py"], "/GitHubTest/views.py": ["/GitHubTest/models.py"], "/GitHubTest/admin.py": ["/GitHubTest/models.py"]}
|
35,958,013
|
aalnobel/CS361Project
|
refs/heads/master
|
/GitHubTest/apps.py
|
from django.apps import AppConfig
class GithubtestConfig(AppConfig):
name = 'GitHubTest'
|
{"/GitHubTest/tests.py": ["/GitHubTest/models.py"], "/GitHubTest/views.py": ["/GitHubTest/models.py"], "/GitHubTest/admin.py": ["/GitHubTest/models.py"]}
|
36,001,401
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/main.py
|
from imutils.video import VideoStream
from imutils import face_utils
from threading import Thread
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2
import playsound
from drowsy import *
from utils import *
import headpose_utils as headpose
from scipy.spatial import distance
from PIL import Image
import tensorflow as tf
from phone.phone_detection import detect
from tensorflow.python.saved_model import tag_constants
import os
import time
model_path = os.path.join(os.getcwd() +'/phone/checkpoints/','yolov4_tiny_416')
def sound_alarm(path):
# play an alarm sound
playsound.playsound(path)
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor.dat')
saved_model_loaded = tf.saved_model.load(model_path, tags=[tag_constants.SERVING])
infer = saved_model_loaded.signatures['serving_default']
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
frame_counter=0
frame_counter_head=0
alarm = False
vs = VideoStream(src=0).start()
time.sleep(1.0)
# loop over frames from the video stream
while True:
frame = vs.read()
frame = imutils.resize(frame, width=450)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
rects = detector(gray, 0)
#function to detect if there's a phone in the screen and raises alarm immediatly
phone_found = detect(frame,infer)
if phone_found:
cv2.putText(frame, "Put Phone Away" , (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
t = Thread(target=sound_alarm, args=['alarm.mp3'])
t.deamon = True
t.start()
faces = face_cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
#flags=cv2.cv.CV_HAAR_SCALE_IMAGE
)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
for rect in rects:
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
shape_marks = predictor(gray, rect)
shape = shape_to_np(shape_marks)
#detect the angle of the face, and if the driver isn't looking forward for an extended period of time raise an alarm
angles ,pose_alarm, frame_counter_head = headpose.HeadPoseAngles(shape_marks, frame , frame_counter_head)
# extract the left and right eye coordinates, then use the coordinates to compute the eye aspect ratio for both eyes
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_ascpect_ratio(leftEye)
rightEAR = eye_ascpect_ratio(rightEye)
# average the eye aspect ratio together for both eyes
ear = (leftEAR + rightEAR) / 2.0
# compute the convex hull for eyes, and draw it
leftEyeHull = cv2.convexHull(leftEye)
rightEyeHull = cv2.convexHull(rightEye)
cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
#function to count the frames in which the eye is closed, and rasies alarm if it exceeds the max_no_of_frames
frame_counter, alarm = drowsy_detect(ear,frame_counter)
#alarm for eyes closed
if alarm:
cv2.putText(frame, "WAKE UP!", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
t = Thread(target=sound_alarm, args=['alarm.mp3'])
t.deamon = True
t.start()
#alarm for not looking forward
if pose_alarm:
cv2.putText(frame, "Please Look Forward" , (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
t = Thread(target=sound_alarm, args=['alarm.mp3'])
t.deamon = True
t.start()
cv2.imshow('frame',frame)
#close the window when q is pressed
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,402
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/detection.py
|
from scipy.spatial import distance
from imutils.video import VideoStream
from imutils import face_utils
from threading import Thread
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2
import playsound
#cap = cv2.VideoCapture(0)
ALARM_ON = False
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
def eye_ascpect_ratio(points):
A = distance.euclidean(points[1], points[5])
B = distance.euclidean(points[2], points[4])
C = distance.euclidean(points[0], points[3])
EAR = (A+B)/(2*C)
return EAR
def sound_alarm(path):
# play an alarm sound
playsound.playsound(path)
# def detect():
# detector = dlib.get_frontal_face_detector()
# return detector
# def predict():
# predictor = dlib.shape_predictor('shape_predictor.dat')
# return predictor
def sound_alarm(path):
# play an alarm sound
playsound.playsound(path)
def drowsy_detect(ear,frame,frame_counter):
threshold = 0.3
#frame_counter=0
max_no_of_frames = 48
ALARM_ON= False
if ear < threshold:
#global frame_counter
frame_counter += 1
if frame_counter >= max_no_of_frames:
if not ALARM_ON:
alarm = True
t =Thread(target=sound_alarm('Alarm.mp3'),args="Alarm.mp3")
t.deamon = True
t.start()
cv2.putText(frame, "TAKE CARE!", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
else:
frame_counter = 0
alarm = False
# ap = argparse.ArgumentParser()
# ap.add_argument("-p", "--shape-predictor", required=True,
# help="path to facial landmark predictor")
# ap.add_argument("-a", "--alarm", type=str, default="",
# help="path alarm .WAV file")
# ap.add_argument("-w", "--webcam", type=int, default=0,
# help="index of webcam on system")
# args = vars(ap.parse_args())
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 48
###############################
vs = VideoStream(src=0).start()
time.sleep(1.0)
# loop over frames from the video stream
while True:
# grab the frame from the threaded video file stream, resize
# it, and convert it to grayscale
# channels)
frame = vs.read()
frame = imutils.resize(frame, width=450)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
detector = dlib.get_frontal_face_detector()
rects = detector(gray, 0)
faces = face_cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
)
# draw rectangle around the face
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
for rect in rects:
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
predictor = dlib.shape_predictor('shape_predictor.dat')
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
# extract the left and right eye coordinates, then use the
# coordinates to compute the eye aspect ratio for both eyes
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_ascpect_ratio(leftEye)
rightEAR = eye_ascpect_ratio(rightEye)
# average the eye aspect ratio together for both eyes
ear = (leftEAR + rightEAR) / 2.0
# compute the convex hull for the left and right eye, then
# visualize each of the eyes
leftEyeHull = cv2.convexHull(leftEye)
rightEyeHull = cv2.convexHull(rightEye)
cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
#Alarm#
#qqqqqdrowsy_detect(ear,frame,0)
# if ear < EYE_AR_THRESH:
# frame_counter += 1
# if frame_counter >= EYE_AR_CONSEC_FRAMES:
# if not ALARM_ON:
# ALARM_ON = True
# # check to see if an alarm file was supplied,
# # and if so, start a thread to have the alarm
# # sound played in the background
# if not ALARM_ON:
# t = Thread(target=sound_alarm,args=["Alarm.mp3"])
# t.deamon = True
# t.start()
# # draw an alarm on the frame
# cv2.putText(frame, "Take Care!", (10, 30),
# cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# else:
# frame_counter = 0
# ALARM_ON = False
cv2.imshow('frame',frame)
key = cv2.waitKey(1) & 0xFF
#To quit and exit
if key == ord("q"):
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,403
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/phone/phone_detection.py
|
import cv2
import time
import numpy as np
from PIL import Image
import tensorflow as tf
from .helper import *
from tensorflow.python.saved_model import tag_constants
def detect (frame,infer): #infer model instance
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(frame)
frame_size = frame.shape[:2]
image_data = cv2.resize(frame, (416, 416))
image_data = image_data / 255.
image_data = image_data[np.newaxis, ...].astype(np.float32)
#start_time = time.time()
phone_found = False
batch_data = tf.constant(image_data)
pred_bbox = infer(batch_data)
for key, value in pred_bbox.items():
boxes = value[:, :, 0:4]
pred_conf = value[:, :, 4:]
boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
boxes=tf.reshape(boxes, (tf.shape(boxes)[0], -1, 1, 4)),
scores=tf.reshape(
pred_conf, (tf.shape(pred_conf)[0], -1, tf.shape(pred_conf)[-1])),
max_output_size_per_class=50,
max_total_size=50,
iou_threshold=0.45,
score_threshold=0.25
)
for i in range(valid_detections[0]):
if int(classes[0][i] == 67):
phone_found = True
pred_bbox = [boxes.numpy(), scores.numpy(), classes.numpy(), valid_detections.numpy()]
# image = draw_bbox(frame, pred_bbox)
#fps = 1.0 / (time.time() - start_time)
#print("FPS: %.2f" % fps)
# result = np.asarray(image)
# # cv2.namedWindow("result", cv2.WINDOW_AUTOSIZE)
# result = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
return phone_found
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,404
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/headpose_main.py
|
import cv2
import dlib
import numpy as np
import headpose_utils as headpose
from utils import *
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor.dat')
vid = cv2.VideoCapture(0)
while(True):
ret, image = vid.read()
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# detect faces in the grayscale image
rects = detector(gray, 1)
# loop over the face detections
for (i, rect) in enumerate(rects):
marks = predictor(gray, rect)
marks_np = shape_to_np(marks)
(x, y, w, h) = rect_to_bb(rect)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
distortion_coeff = np.zeros((4,1))
h,w,c = headpose.getimageshape(image)
imagePoints_2d = headpose.Imagepoints_2D_Matrix(marks)
cameraMatrix = headpose.CameraMatrix(w, (h/2,w/2))
successmsg , rotationVector , translationVector = headpose.SolvePnp(headpose.FaceModel_3D_Matrix(),
imagePoints_2d , cameraMatrix,distortion_coeff)
# angles = headpose.CalcEulerAngles(rotationVector) #btl3 warning w error m3 enha zay lta7t bzbt
rmat, jac = cv2.Rodrigues(rotationVector)
angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(rmat)
if angles[1] < -35:
GAZE = "Looking: Left"
elif angles[1] > 35:
GAZE = "Looking: Right"
else:
GAZE = "Forward"
cv2.putText(image, GAZE, (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 80), 2)
cv2.imshow("Head Pose", image)
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
vid.release()
cv2.destroyAllWindows()
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,405
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/UIandMain.py
|
# import
import sys
import PyQt5.QtWidgets as qwd
from PyQt5 import QtGui as gui
import datetime
from imutils.video import VideoStream
from imutils import face_utils
from threading import Thread
import numpy as np
import argparse
import imutils
import time
import dlib
import cv2
import playsound
from drowsy import *
from utils import *
import headpose_utils as headpose
from scipy.spatial import distance
from PIL import Image
import tensorflow as tf
from phone.phone_detection import detect
from tensorflow.python.saved_model import tag_constants
import os
import time
# fileName=""
videoPath = ""
# MACRO DEFINITIONS
WINDOW_TITLE = "Drowsy Driver"
WINDOW_SIZE_X = 1280 # 1920
WINDOW_SIZE_Y = 720 # 1080
QDIALOG_IMAGE_PREFIX_TYPE = "*.png *.jpg *.jpeg *.JPG"
QDIALOG_IAMGE_SELECTION_DIALOG_NAME = "Select image"
model_path = os.path.join(os.getcwd() +'/phone/checkpoints/','yolov4_tiny_416')
class PicButton(qwd.QAbstractButton):
def __init__(self, pixmap, parent=None):
super(PicButton, self).__init__(parent)
self.pixmap = pixmap
def paintEvent(self, event):
painter = gui.QPainter(self)
painter.drawPixmap(event.rect(), self.pixmap)
def sizeHint(self):
return self.pixmap.size()
class firstGui(qwd.QDialog):
def __init__(self, parent=None):
super(firstGui, self).__init__(parent)
self.setWindowTitle(WINDOW_TITLE)
self.setGeometry(0, 0, 600, 600)
self.InitUI()
def InitUI(self):
# Label OpenImg
logData("creating main UI")
self.setStyleSheet(""" firstGui{ background-repeat: no-repeat;
background-position: center;}""")
btnClose = qwd.QPushButton('Close', self)
btnClose.setToolTip("Close Program")
btnClose.clicked.connect(self.BtnCloseSystem)
btnClose.setStyleSheet("QPushButton"
"{"
"background-color : red ;color : white; border-radius: 10 ; border-color : black; border-width: 2px; border-bottom: 2px solid ;padding : 8;"
"}"
"QPushButton::pressed"
"{"
"}"
)
# button open video
btnOpenVideo = qwd.QPushButton("Start", self)
btnOpenVideo.clicked.connect(self.startScript)
btnOpenVideo.setStyleSheet("QPushButton"
"{"
"background-color : lightblue ;color : black; border-radius: 10 ; border-color : black; border-width: 2px; border-bottom: 2px solid ;padding : 8;"
"}"
"QPushButton::pressed"
"{"
"}"
)
# layouts
lytQHBtns = qwd.QHBoxLayout()
lytQHBtns.addWidget(btnOpenVideo)
lytQHBtns.addWidget(btnClose)
# Box Layout
# image
lytQBoxImg = qwd.QBoxLayout(qwd.QBoxLayout.TopToBottom, parent=None)
# lytQBoxImg.addWidget(self.lblOpenImg)
lytQVImg = qwd.QVBoxLayout()
lytQVImg.addLayout(lytQBoxImg)
# Line Edit + Lbl Layout
lytQVLnedsAndLabels = qwd.QVBoxLayout()
# Grid Layout
lytQGridMain = qwd.QGridLayout()
# lytQGridMain.addLayout(lytQVImg, 1, 0, 6, 1)
# lytQGridMain.addLayout(lytQVLnedsAndLabels, 1, 0)
# lytQGridMain.addLayout(lytQBoxTxt, 8, 0, 6, 1)
lytQGridMain.addLayout(lytQHBtns, 7, 0)
self.setLayout(lytQGridMain)
def startScript(self):
# put here the python file that will execute all stuff
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor.dat')
saved_model_loaded = tf.saved_model.load(model_path, tags=[tag_constants.SERVING])
infer = saved_model_loaded.signatures['serving_default']
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
frame_counter=0
frame_counter_head=0
alarm = False
vs = VideoStream(src=0).start()
time.sleep(1.0)
# loop over frames from the video stream
while True:
frame = vs.read()
frame = imutils.resize(frame, width=450)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
rects = detector(gray, 0)
#function to detect if there's a phone in the screen and raises alarm immediatly
phone_found = detect(frame,infer)
if phone_found:
cv2.putText(frame, "Put Phone Away" , (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
t = Thread(target=sound_alarm, args=['alarm.mp3'])
t.deamon = True
t.start()
faces = face_cascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
#flags=cv2.cv.CV_HAAR_SCALE_IMAGE
)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
for rect in rects:
# determine the facial landmarks for the face region, then
# convert the facial landmark (x, y)-coordinates to a NumPy
# array
shape_marks = predictor(gray, rect)
shape = shape_to_np(shape_marks)
#detect the angle of the face, and if the driver isn't looking forward for an extended period of time raise an alarm
angles ,pose_alarm, frame_counter_head = headpose.HeadPoseAngles(shape_marks, frame , frame_counter_head)
# extract the left and right eye coordinates, then use the coordinates to compute the eye aspect ratio for both eyes
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_ascpect_ratio(leftEye)
rightEAR = eye_ascpect_ratio(rightEye)
# average the eye aspect ratio together for both eyes
ear = (leftEAR + rightEAR) / 2.0
# compute the convex hull for eyes, and draw it
leftEyeHull = cv2.convexHull(leftEye)
rightEyeHull = cv2.convexHull(rightEye)
cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
#function to count the frames in which the eye is closed, and rasies alarm if it exceeds the max_no_of_frames
frame_counter, alarm = drowsy_detect(ear,frame_counter)
#alarm for eyes closed
if alarm:
cv2.putText(frame, "WAKE UP!", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
t = Thread(target=sound_alarm, args=['alarm.mp3'])
t.deamon = True
t.start()
#alarm for not looking forward
if pose_alarm:
cv2.putText(frame, "Please Look Forward" , (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
t = Thread(target=sound_alarm, args=['alarm.mp3'])
t.deamon = True
t.start()
cv2.imshow('frame',frame)
#close the window when q is pressed
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
def BtnCloseSystem(self):
sys.exit()
def SetSystemSearchedFlag(self, flag):
self.isSystemSearched = flag
def logData(data):
print("[" + str(datetime.datetime.now())+"] " + data)
def main():
cv2.__version__
app = qwd.QApplication(sys.argv)
# main gui
ex = firstGui()
ex.show()
sys.exit(app.exec_())
if __name__ == '__main__':
main()
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,406
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/mainYolo.py
|
import os
import cv2
import time
import numpy as np
from PIL import Image
import tensorflow as tf
from phone.phone_detection import detect
from tensorflow.python.saved_model import tag_constants
model_path = os.path.join(os.getcwd() +'/phone/checkpoints/','yolov4_tiny_416')
def main():
saved_model_loaded = tf.saved_model.load(model_path, tags=[tag_constants.SERVING])
infer = saved_model_loaded.signatures['serving_default']
# begin video capture
vid = cv2.VideoCapture(0)
while True:
_, frame = vid.read()
result,phone_found = detect(frame,infer)
# create instance from your function
cv2.imshow("result", result)
if cv2.waitKey(1) & 0xFF == ord('q'): break
cv2.destroyAllWindows()
if __name__ == '__main__':
try:
main()
except SystemExit:
pass
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,407
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/UI.py
|
# import
import os
import sys
import cv2 as cv
import PyQt5.QtWidgets as qwd
from PyQt5 import QtGui as gui
import datetime
from imutils.video import VideoStream
import imutils
import cv2
# fileName=""
videoPath = ""
# MACRO DEFINITIONS
WINDOW_TITLE = "Drowsy Driver"
WINDOW_SIZE_X = 1280 # 1920
WINDOW_SIZE_Y = 720 # 1080
QDIALOG_IMAGE_PREFIX_TYPE = "*.png *.jpg *.jpeg *.JPG"
QDIALOG_IAMGE_SELECTION_DIALOG_NAME = "Select image"
class PicButton(qwd.QAbstractButton):
def __init__(self, pixmap, parent=None):
super(PicButton, self).__init__(parent)
self.pixmap = pixmap
def paintEvent(self, event):
painter = gui.QPainter(self)
painter.drawPixmap(event.rect(), self.pixmap)
def sizeHint(self):
return self.pixmap.size()
class firstGui(qwd.QDialog):
def __init__(self, parent=None):
super(firstGui, self).__init__(parent)
self.setWindowTitle(WINDOW_TITLE)
self.setGeometry(0, 0, 600, 600)
self.InitUI()
def InitUI(self):
# Label OpenImg
logData("creating main UI")
self.setStyleSheet(""" firstGui{ background-repeat: no-repeat;
background-position: center;}""")
btnClose = qwd.QPushButton('Close', self)
btnClose.setToolTip("Close Program")
btnClose.clicked.connect(self.BtnCloseSystem)
btnClose.setStyleSheet("QPushButton"
"{"
"background-color : red ;color : white; border-radius: 10 ; border-color : black; border-width: 2px; border-bottom: 2px solid ;padding : 8;"
"}"
"QPushButton::pressed"
"{"
"}"
)
# button open video
btnOpenVideo = qwd.QPushButton("Start Script", self)
btnOpenVideo.clicked.connect(self.startScript)
btnOpenVideo.setStyleSheet("QPushButton"
"{"
"background-color : lightblue ;color : black; border-radius: 10 ; border-color : black; border-width: 2px; border-bottom: 2px solid ;padding : 8;"
"}"
"QPushButton::pressed"
"{"
"}"
)
# layouts
lytQHBtns = qwd.QHBoxLayout()
lytQHBtns.addWidget(btnOpenVideo)
lytQHBtns.addWidget(btnClose)
# Box Layout
# image
lytQBoxImg = qwd.QBoxLayout(qwd.QBoxLayout.TopToBottom, parent=None)
# lytQBoxImg.addWidget(self.lblOpenImg)
lytQVImg = qwd.QVBoxLayout()
lytQVImg.addLayout(lytQBoxImg)
# Line Edit + Lbl Layout
lytQVLnedsAndLabels = qwd.QVBoxLayout()
# Grid Layout
lytQGridMain = qwd.QGridLayout()
# lytQGridMain.addLayout(lytQVImg, 1, 0, 6, 1)
# lytQGridMain.addLayout(lytQVLnedsAndLabels, 1, 0)
# lytQGridMain.addLayout(lytQBoxTxt, 8, 0, 6, 1)
lytQGridMain.addLayout(lytQHBtns, 7, 0)
self.setLayout(lytQGridMain)
def startScript(self):
# put here the python file that will execute all stuff
os.system("python main.py")
def BtnCloseSystem(self):
sys.exit()
def SetSystemSearchedFlag(self, flag):
self.isSystemSearched = flag
def logData(data):
print("[" + str(datetime.datetime.now())+"] " + data)
def main():
cv.__version__
app = qwd.QApplication(sys.argv)
# main gui
ex = firstGui()
ex.show()
sys.exit(app.exec_())
if __name__ == '__main__':
main()
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,408
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/headpose_utils.py
|
import numpy as np
# import dlib
import cv2
def HeadPoseAngles(marks,image,frame_counter_head):
#marks : 68 facial landmarks
#frame_counter_head : counter for num of frames to detect if the driver looked away to turn alarm on
distortion_coeff = np.zeros((4,1)) #no camera length distortion
h,w,c = getimageshape(image)
imagePoints_2d = Imagepoints_2D_Matrix(marks)
cameraMatrix =CameraMatrix(w, (h/2,w/2))
#head pose estimation using solvepnp using iterative algorithm
#estimate the orientation(projection) of a 3D object in a 2D image
successmsg , rotationVector , translationVector = SolvePnp(FaceModel_3D_Matrix(),
imagePoints_2d , cameraMatrix,distortion_coeff)
rmat, jac = cv2.Rodrigues(rotationVector) #must change from vector to matrix
angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(rmat)
max_no_of_frames = 45 #num of frames to detect if the driver looked away to turn alarm on
alarm = False
if angles[1] < -10: #looking left
frame_counter_head += 1
if frame_counter_head >= max_no_of_frames:
if not alarm:
alarm = True
GAZE = "Please Look Forward"
elif angles[1] > 25: #looking right
frame_counter_head += 1
if frame_counter_head >= max_no_of_frames:
if not alarm:
alarm = True
GAZE = "Please Look Forward"
else:
frame_counter_head = 0
GAZE = "............ "
alarm = False
return angles , alarm , frame_counter_head
def FaceModel_3D_Matrix():
#[x y z 1] 3d model of face (World Coordinates)
#3D location of the 2D feature image points
modelPoints_3D = [[0.0, 0.0, 0.0], # Nose tip
[0.0, -330.0, -65.0],# Chin
[-225.0, 170.0, -135.0],# Left eye left corner
[225.0, 170.0, -135.0],# Right eye right corner
[-150.0, -150.0, -125.0],# Left Mouth corner
[150.0, -150.0, -125.0]] # Right mouth corner
modelpoints = np.array(modelPoints_3D, dtype=np.float64)
return modelpoints
def Imagepoints_2D_Matrix(landmarks):
#s[u v t] 2d image taken by camera
# get from the 68 facial landmarks from predictor (from dlib library )(shape) the x,y coordinates for some landmarks
imagePoints_2D = [[landmarks.part(30).x, landmarks.part(30).y], #Nose Tip
[landmarks.part(8).x, landmarks.part(8).y], #Chin
[landmarks.part(36).x, landmarks.part(36).y], #Left corner of the left eye
[landmarks.part(45).x, landmarks.part(45).y], #Right corner of the right eye
[landmarks.part(48).x, landmarks.part(48).y], #Left corner of the mouth
[landmarks.part(54).x, landmarks.part(54).y]] #Right corner of the mouth
imagePoints = np.array(imagePoints_2D, dtype=np.float64)
return imagePoints
def CameraMatrix(focal_l, center):
#[[fx, γ , u0], f(x, y) elfocal lenthgs bt3 lcamera , (u0,v0) elcenter of 2d image
# [0, fy, v0,
# [0, 0, 1]]
#calibrating camera
cameraMatrix = [[focal_l, 1, center[0]],
[0, focal_l, center[1]],
[0, 0, 1]]
return np.array(cameraMatrix, dtype=np.float)
def SolvePnp(facemodel_3D, imagePoints_2d, cameraMatrix, distortion_coeff):
# calculating rotation and translation vector using solvePnP
successmsg, rotationVector, translationVector = cv2.solvePnP(facemodel_3D,imagePoints_2d, cameraMatrix, distortion_coeff)
return successmsg , rotationVector , translationVector
def getimageshape(image):
#get the h,w,c of image
height, width, channels = image.shape
return height , width , channels
def CalcEulerAngles(RotationVector):
# calculating angle
RotationMatrix,jac = cv2.Rodrigues(RotationVector) #lazm ageebha matrix msh vector
three_angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(RotationMatrix)
return three_angles
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,409
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/phone/__init__.py
|
from .checkpoints import yolov4_tiny_416
from .helper import draw_bbox
from .phone_detection import detect
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,001,410
|
reemnasserr/Drowsy-Driver-Detection-using-OpenCV
|
refs/heads/main
|
/drowsy.py
|
import cv2
from scipy.spatial import distance
#this fucntion takes the average of eye ascpect ratio of both eyes and the current frame counter
#and starts counting the number of frames the driver is closing their eyes in. Once it reaches the max_no_of_frames
#the alarm is turned to true and if the the eye is open, meaning the driver is looking forward, frame counter
#goes back to zero and alarm goesback to false.
def drowsy_detect(ear,frame_counter):
threshold = 0.3
max_no_of_frames = 45
alarm = False
if ear < threshold:
frame_counter += 1
if frame_counter >= max_no_of_frames:
if not alarm:
alarm = True
return frame_counter, alarm
else:
frame_counter = 0
alarm = False
return frame_counter,alarm
#This function calculate the eye ascpect ratio according to it's rule
def eye_ascpect_ratio(points):
A = distance.euclidean(points[1], points[5])
B = distance.euclidean(points[2], points[4])
C = distance.euclidean(points[0], points[3])
EAR = (A+B)/(2*C)
return EAR
|
{"/main.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/headpose_main.py": ["/headpose_utils.py"], "/UIandMain.py": ["/drowsy.py", "/headpose_utils.py", "/phone/phone_detection.py"], "/mainYolo.py": ["/phone/phone_detection.py"], "/phone/__init__.py": ["/phone/phone_detection.py"]}
|
36,018,404
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/teste.py
|
vowels = ['a', 'e', 'i', 'o', 'i', 'u']
# index of 'e' in vowels
index = vowels.index('e')
print('The index of e:', index)
# element 'i' is searched
# index of the first 'i' is returned
index = vowels.index('i')
print('The index of i:', index)
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,405
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/urls.py
|
from django.urls import path
from . import views
urlpatterns = [
path('', views.index, name='index'),
path('orcamento', views.OrcamentoListView.as_view(), name='orcamento'),
path(
'orcamento/<int:pk>',
views.OrcamentoDetailView.as_view(),
name='orcamento-id',
),
path(
'orcamento/<int:pk>/update/',
views.OrcamentoUpdateView.as_view(),
name='orcamento_update',
),
path(
'orcamento/<int:pk>/delete/',
views.OrcamentoDeleteView.as_view(),
name='orcamento_delete',
),
path('cliente', views.ClienteListView.as_view(), name='cliente'),
path(
'cliente/create/',
views.ClienteCreateView.as_view(),
name='cliente_create',
),
path(
'cliente/<int:pk>',
views.ClienteDetailView.as_view(),
name='cliente-id',
),
path(
'cliente/<int:pk>/update/',
views.ClienteUpdateView.as_view(),
name='cliente_update',
),
path(
'cliente/<int:pk>/delete/',
views.ClienteDeleteView.as_view(),
name='cliente_delete',
),
path('dimensao_bulk', views.DimensaoBulk.as_view(), name='dimensao_bulk'),
]
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,406
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/models.py
|
from django.db import models
from django.contrib.auth.models import User
from django.urls import (
reverse,
) # Used to generate URLs by reversing the URL patterns
from django.utils import timezone
from .customclass.estruturas.dimensao import Dimensao
from .customclass.objetos.filtro import Filtro
from .customclass.objetos.motor import Motor
class ClienteModel(models.Model):
"""Modelo representando as informações de cadastro do cliente."""
nome = models.CharField(max_length=30)
sobrenome = models.CharField(max_length=30)
estado = models.CharField(max_length=15, blank=True)
cidade = models.CharField(max_length=20, blank=True)
bairro = models.CharField(max_length=20, blank=True)
rua = models.CharField(max_length=100, blank=True)
numero_casa = models.CharField(max_length=10, blank=True)
cep = models.CharField(max_length=20, blank=True)
telefone = models.IntegerField(blank=True, default=0)
email = models.EmailField(
max_length=50, blank=True, help_text='Ex. clinte@gmail.com'
)
@property
def nome_completo(self):
return self.nome + ' ' + self.sobrenome
class Meta:
ordering = ['nome', 'sobrenome']
def get_absolute_url(self):
"""Returns the url to access a particular instance."""
return reverse('cliente-id', args=[str(self.id)])
def __str__(self):
"""String representando o objeto."""
return f'{self.nome}, {self.sobrenome}'
class DimensaoModel(models.Model):
cliente = models.ForeignKey(
ClienteModel, on_delete=models.SET_NULL, null=True
)
usuario = models.ForeignKey(User, on_delete=models.SET_NULL, null=True)
"""INPUT da piscina."""
comprimento = models.FloatField(
max_length=3, null=False, blank=False, help_text='Ex. 8.00', default=0
)
largura = models.FloatField(
max_length=5, null=False, blank=False, help_text='Ex. 4.00', default=0
)
prof_inicial = models.FloatField(max_length=5)
prof_final = models.FloatField(
max_length=3, null=False, blank=False, help_text='Ex. 1.40', default=0
)
largura_calcada = models.FloatField(
max_length=3, null=False, blank=True, default=1, help_text='Ex. 1.00'
)
espessura = models.CharField(max_length=3)
fornecedor = models.CharField(max_length=8)
"""DIMENSÕES"""
profundidade_media = models.CharField(
max_length=25, null=False, blank=False, default=0
)
area_calcada = models.CharField(max_length=25)
perimetro = models.CharField(max_length=25)
m2_facial = models.CharField(max_length=25)
m2_parede = models.CharField(max_length=25)
m2_total = models.CharField(max_length=25)
m3_total = models.CharField(max_length=25)
m3_real = models.CharField(max_length=25)
"""CONJUNTO FILTRANTE"""
filtro = models.CharField(max_length=30)
motobomba = models.CharField(max_length=30)
tampa_casa_maquinas = models.CharField(max_length=30)
sacos_areia = models.CharField(max_length=30)
# BUDGET_STATUS = (('Em negociação', 'Em negociação'), ('Contrato', 'Contrato'), ('Encerrado', 'Encerrado'),)
status = models.CharField(
max_length=15,
choices=(
('Em negociação', 'Em negociação'),
('Contrato', 'Contrato'),
('Encerrado', 'Encerrado'),
),
blank=True,
default='Em negociação',
help_text='Status do Orçamento',
)
data = models.DateTimeField(blank=True, null=True)
def prof_media(self, *args, **kwargs):
dimensoes = Dimensao(
self.largura,
self.comprimento,
self.prof_inicial,
self.prof_final,
self.largura_calcada,
)
filtro = Filtro(dimensoes)
motor = Motor(dimensoes)
self.profundidade_media = dimensoes.profundidade_media()
self.area_calcada = dimensoes.area_da_calcada()
self.perimetro = dimensoes.perimetro()
self.m2_facial = dimensoes.m2facial()
self.m2_parede = dimensoes.m2parede()
self.m2_total = dimensoes.m2total()
self.m3_total = dimensoes.m3total()
self.m3_real = dimensoes.m3real()
self.filtro = (
filtro.dimensionamento_filtro_grupo()['marca'].title()
+ ' '
+ filtro.dimensionamento_filtro_grupo()['modelo']
)
self.motobomba = (
motor.dimensionamento_motobomba_grupo()['marca'].title()
+ ' - '
+ motor.dimensionamento_motobomba_grupo()['modelo']
)
self.tampa_casa_maquinas = (
filtro.dimensionamento_tampa_casa_de_maquinas_grupo()['modelo']
)
self.sacos_areia = filtro.quantidade_de_areia_no_filtro()
super(DimensaoModel, self).save(*args, **kwargs)
return self.profundidade_media
def publish(self):
self.data = timezone.now()
self.save()
def get_absolute_url(self):
"""Returns the url to access a detailed."""
return reverse('orcamento-id', args=[str(self.id)])
def __str__(self):
"""String representando o objeto."""
return f'{self.comprimento}, {self.largura}, {self.prof_inicial},{self.prof_final},{self.largura_calcada}'
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,407
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/actions.py
|
def negociacao(modeladmin, request, queryset):
queryset.update(status='Em negociação')
negociacao.short_description = 'Em negociação'
def contrato(modeladmin, request, queryset):
queryset.update(status='Contrato')
contrato.short_description = 'Contrato'
def encerrado(modeladmin, request, queryset):
queryset.update(status='Encerrado')
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,408
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/migrations/0001_initial.py
|
# Generated by Django 4.0.3 on 2022-03-22 00:33
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='ClienteModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('nome', models.CharField(max_length=30)),
('sobrenome', models.CharField(max_length=30)),
('estado', models.CharField(blank=True, max_length=15)),
('cidade', models.CharField(blank=True, max_length=20)),
('bairro', models.CharField(blank=True, max_length=20)),
('rua', models.CharField(blank=True, max_length=100)),
('numero_casa', models.CharField(blank=True, max_length=10)),
('cep', models.CharField(blank=True, max_length=20)),
('telefone', models.IntegerField(blank=True, default=0)),
('email', models.EmailField(blank=True, help_text='Ex. clinte@gmail.com', max_length=50)),
],
options={
'ordering': ['nome', 'sobrenome'],
},
),
migrations.CreateModel(
name='DimensaoModel',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('comprimento', models.FloatField(default=0, help_text='Ex. 8.00', max_length=3)),
('largura', models.FloatField(default=0, help_text='Ex. 4.00', max_length=5)),
('prof_inicial', models.FloatField(max_length=5)),
('prof_final', models.FloatField(default=0, help_text='Ex. 1.40', max_length=3)),
('largura_calcada', models.FloatField(blank=True, default=1, help_text='Ex. 1.00', max_length=3)),
('espessura', models.CharField(max_length=3)),
('fornecedor', models.CharField(max_length=8)),
('profundidade_media', models.CharField(default=0, max_length=25)),
('area_calcada', models.CharField(max_length=25)),
('perimetro', models.CharField(max_length=25)),
('m2_facial', models.CharField(max_length=25)),
('m2_parede', models.CharField(max_length=25)),
('m2_total', models.CharField(max_length=25)),
('m3_total', models.CharField(max_length=25)),
('m3_real', models.CharField(max_length=25)),
('filtro', models.CharField(max_length=30)),
('motobomba', models.CharField(max_length=30)),
('tampa_casa_maquinas', models.CharField(max_length=30)),
('sacos_areia', models.CharField(max_length=30)),
('status', models.CharField(blank=True, choices=[('Em negociação', 'Em negociação'), ('Contrato', 'Contrato'), ('Encerrado', 'Encerrado')], default='Em negociação', help_text='Status do Orçamento', max_length=15)),
('data', models.DateTimeField(blank=True, null=True)),
('cliente', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='dimensoes.clientemodel')),
('usuario', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)),
],
),
]
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,409
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/customclass/objetos/vinil.py
|
# -*- coding: utf-8 -*-
from .dbs.database import Database
class Vinil:
def __init__(self, espessura, fornecedor):
self.espessura = espessura
self.fornecedor = fornecedor
self.config = {}
self.config['vinil'] = Database('vinils').lista()
def vinil_grupo(self):
return
def escolha_fornecedor(self):
fornecedor = self.fornecedor
variavel = []
for chave in self.config['vinil']:
if chave['fornecedor'] == fornecedor:
variavel.append(chave)
return variavel
def choose_thickness(self):
espessura = self.espessura
for chave in self.escolha_fornecedor():
if chave['espessura'] == espessura:
return chave
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,410
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/customclass/objetos/precificacao.py
|
from .dbs.database import Database
from .vinil import Vinil
from .filtro import Filtro
import locale
class Precificacao:
def __init__(self, dimensao):
self.dimensao = dimensao
self.config = {}
self.config['vinil'] = Database('vinils').lista()
self.config['perfil_rigido'] = Database('perfil_rigido').lista()
self.config['areia'] = Database('areia').lista()
self.config['manta_revestimento'] = Database(
'manta_revestimento'
).lista()
self.config['mao_de_obra'] = Database('mao_de_obra')
def moeda(self, valor):
valor = valor
locale.setlocale(locale.LC_ALL, 'pt_BR.UTF-8')
valor = locale.currency(valor, grouping=True, symbol=None)
return 'R$: %s' % valor
# *** PRODUTOS ***
def get_preco_produto(self, produto):
for chave in self.config[produto]:
return chave['preco']
def preco_do_vinil_dimensionado(self, espessura, fornecedor):
vinil = Vinil(espessura, fornecedor)
dimensao = self.dimensao
choose = vinil.choose_thickness()
return dimensao.m2total() * choose['preco']
def preco_perfil_rigido_dimensionado(self):
dimensao = self.dimensao
for chave in self.config['perfil_rigido']:
return dimensao.perimetro() * chave['preco']
def preco_manta_de_revestimento_dimensionado(self):
dimensao = self.dimensao
for chave in self.config['manta_revestimento']:
return dimensao.m2facial() * chave['preco']
def preco_tampa_casa_maquinas(self):
dimensao = self.dimensao
filtro = Filtro(dimensao)
return filtro.dimensionamento_tampa_casa_de_maquinas_grupo()['preco']
# *** MÃO DE OBRA***
def get_preco_mao_de_obra(self, item):
return self.config['mao_de_obra'].listaitem(item)['preco']
def preco_escavacao_dimensionado(self):
dimensao = self.dimensao
return self.get_preco_mao_de_obra('escavacao') * dimensao.m3total()
def preco_mao_de_obra_construcao_dimensionado(self):
dimensao = self.dimensao
return (
self.get_preco_mao_de_obra('construcao_piscina')
* dimensao.m2total()
)
def preco_remocao_de_terra_dimensionado(self):
dimensao = self.dimensao
return (
self.get_preco_mao_de_obra('remocao_de_terra') * dimensao.m3total()
)
def preco_construcao_calcada(self):
dimensao = self.dimensao
return (
self.get_preco_mao_de_obra('construcao_calcada')
* dimensao.area_da_calcada()
)
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,411
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/customclass/objetos/filtro.py
|
from .dbs.database import Database
class Filtro:
def __init__(self, dimensao):
self.dimensao = dimensao
self.config = {}
self.config['filtros'] = Database('filtros')
self.config['tampa_casa_maquinas'] = Database('tampa_casa_maquinas')
def dimensionamento_filtro_grupo(
self,
): # Dimensiona o filtro, baseado no volume dágua, e retorna o ID.
dimensao = self.dimensao
filtros = self.config['filtros'].lista()
for filtro in filtros:
if dimensao.m3real() < filtro['capacidade maxima']:
return filtro
return (
'Nao existe filtro com a capacidade adequada cadastrado no sistema'
)
def dimensionamento_tampa_casa_de_maquinas_grupo(
self,
): # Dimensiona a tampa da casa de máquinas baseado no ID do Filtro.
dimensao = self.dimensao
tampa_casa_maquinas = self.config['tampa_casa_maquinas'].lista()
filtro = self.dimensionamento_filtro_grupo()
for chave in tampa_casa_maquinas:
if filtro['id'] <= 5:
if chave['id'] == 1:
return chave
elif filtro['id'] >= 6:
if chave['id'] == 2:
return chave
else:
return 'Não foi encontrado uma tampa de casa de máquinas adequada para este filtro'
def quantidade_de_areia_no_filtro(
self,
): # refatorar para possibilitar a inclusão de outros filtros
dimensao = self.dimensao
dimensionamento_filtro = self.dimensionamento_filtro_grupo()
if dimensionamento_filtro['id'] <= 2:
return 1 # Incluir o atributo "areia" no filtro
elif dimensionamento_filtro['id'] == 3:
return 2
elif dimensionamento_filtro['id'] == 4:
return 3
elif dimensionamento_filtro['id'] == 5:
return 5
elif dimensionamento_filtro['id'] == 6:
return 8
elif dimensionamento_filtro['id'] == 7:
return 12
elif dimensionamento_filtro['id'] == 8:
return 21
else:
return 'Error'
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,412
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/customclass/estruturas/__init__.py
|
from .dimensao import Dimensao
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,413
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/admin.py
|
from django.contrib import admin
from .models import ClienteModel, DimensaoModel
from .actions import negociacao, contrato, encerrado
# Register your models here.
@admin.register(ClienteModel)
class ClienteAdmin(admin.ModelAdmin):
list_display = (
'nome_completo',
'cidade',
'rua',
'numero_casa',
'telefone',
'email',
)
list_filter = (
'nome',
'cidade',
'bairro',
) #'status'
@admin.register(DimensaoModel)
class DimensaoAdmin(admin.ModelAdmin):
readonly_fields = ('data', 'cliente')
fieldsets = (
('Cabeçalho', {'fields': ('cliente', 'data')}),
(
'Formulário',
{
'fields': (
'espessura',
'fornecedor',
'comprimento',
'largura',
'prof_inicial',
'prof_final',
'largura_calcada',
)
},
),
(
'Dimensões Calculadas',
{
'classes': ('collapse',),
'fields': (
'profundidade_media',
'area_calcada',
'perimetro',
'm2_facial',
'm2_parede',
'm2_total',
'm3_total',
'm3_real',
),
},
),
(
'Conjunto Filtrante',
{
'classes': ('collapse',),
'fields': (
'filtro',
'motobomba',
'tampa_casa_maquinas',
'sacos_areia',
),
},
),
)
list_display = ('cliente', 'data', 'medidas')
actions = [negociacao, contrato, encerrado]
def medidas(self, obj):
if obj.comprimento and obj.largura and obj.profundidade_media:
return (
str(round(float(obj.comprimento), 1))
+ ' x '
+ str(round(float(obj.largura), 1))
+ ' x '
+ str(round(float(obj.profundidade_media), 1))
)
else:
return 'Sem medidas'
medidas.short_description = 'Medidas'
list_filter = (
'usuario',
'cliente',
'cliente__bairro',
'data',
) #'status'
search_fields = ('cliente',)
admin.site.site_header = 'Painel de Controle'
admin.site.index_title = 'Configurações'
admin.site.site_title = ' '
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,414
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/customclass/objetos/motor.py
|
# -*- coding: utf-8 -*-
from .filtro import Filtro
from .dbs.database import Database
class Motor:
def __init__(self, dimensao):
self.dimensao = dimensao
self.materiais = []
self.config = {}
self.config['motores'] = Database('motores')
def add_materiais(self, material):
self.materiais.append(material)
def dimensionamento_motobomba_grupo(
self,
): # Relaciona a motobomba adequada para o filtro, baseado no ID do filtro.
dimensao = self.dimensao
motobomba = self.config['motores'].lista()
filtro = Filtro(dimensao)
dime_filtro = filtro.dimensionamento_filtro_grupo()
for chave in motobomba:
if dime_filtro['id'] <= 2:
if chave['id'] == 1:
return chave
elif dime_filtro['id'] == 3:
if chave['id'] == 2:
return chave
elif dime_filtro['id'] == 4:
if chave['id'] == 3:
return chave
elif dime_filtro['id'] == 5:
if chave['id'] == 4:
return chave
elif dime_filtro['id'] == 6:
if chave['id'] == 5:
return chave
elif dime_filtro['id'] == 7:
if chave['id'] == 6:
return chave
elif dime_filtro['id'] == 8:
if chave['id'] == 7:
return chave
else:
return 'Nao existe motobomba com a capacidade adequada cadastrado no sistema'
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,415
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/schema.py
|
import graphene
from graphene_django.types import DjangoObjectType, ObjectType
from .models import ClienteModel, DimensaoModel
# Create a GraphQL type for the actor model
class ClienteType(DjangoObjectType):
nome_completo = graphene.String(source='nome_completo')
class Meta:
model = ClienteModel
fields = '__all__'
# Create a GraphQL type for the movie model
class DimensaoType(DjangoObjectType):
class Meta:
model = DimensaoModel
# Create a Query type
class Query(ObjectType):
cliente = graphene.Field(ClienteType, id=graphene.Int())
dimensao = graphene.Field(DimensaoType, id=graphene.Int())
clientes = graphene.List(ClienteType)
dimensoes = graphene.List(DimensaoType)
def resolve_cliente(self, info, **kwargs):
id = kwargs.get('id')
if id is not None:
return ClienteModel.objects.get(pk=id)
return None
def resolve_dimensao(self, info, **kwargs):
id = kwargs.get('id')
if id is not None:
return DimensaoModel.objects.get(pk=id)
return None
def resolve_clientes(self, info, **kwargs):
return ClienteModel.objects.all()
def resolve_dimensoes(self, info, **kwargs):
return DimensaoModel.objects.all()
class ClienteInput(graphene.InputObjectType):
id = graphene.ID()
nome = graphene.String()
sobrenome = graphene.String()
estado = graphene.String()
cidade = graphene.String()
bairro = graphene.String()
rua = graphene.String()
numero_casa = graphene.String()
cep = graphene.String()
telefone = graphene.String()
email = graphene.String()
class DimensaoInput(graphene.InputObjectType):
id = graphene.ID()
comprimento = graphene.Float()
largura = graphene.Float()
prof_inicial = graphene.Float()
prof_final = graphene.Float()
largura_calcada = graphene.Float()
cliente = graphene.List(ClienteInput)
espessura = graphene.String()
fornecedor = graphene.String()
# Create mutations for actors
class CreateCliente(graphene.Mutation):
class Arguments:
input = ClienteInput(required=True)
ok = graphene.Boolean()
cliente = graphene.Field(ClienteType)
@staticmethod
def mutate(root, info, input=None):
ok = True
cliente_instance = ClienteModel(
nome=input.nome,
sobrenome=input.sobrenome,
estado=input.estado,
cidade=input.cidade,
bairro=input.bairro,
rua=input.rua,
numero_casa=input.numero_casa,
cep=input.cep,
telefone=input.telefone,
email=input.email,
)
cliente_instance.save()
return CreateCliente(ok=ok, cliente=cliente_instance)
class UpdateCliente(graphene.Mutation):
class Arguments:
id = graphene.Int(required=True)
input = ClienteInput(required=True)
ok = graphene.Boolean()
cliente = graphene.Field(ClienteType)
@staticmethod
def mutate(root, info, id, input=None):
ok = False
cliente_instance = ClienteModel.objects.get(pk=id)
if cliente_instance:
ok = True
cliente_instance.nome = input.nome
cliente_instance.save()
return UpdateCliente(ok=ok, cliente=cliente_instance)
return UpdateCliente(ok=ok, cliente=None)
# Create mutations for movies
class CreateDimensao(graphene.Mutation):
class Arguments:
input = DimensaoInput(required=True)
ok = graphene.Boolean()
dimensao = graphene.Field(DimensaoType)
@staticmethod
def mutate(root, info, input=None):
ok = True
clientes = []
for cliente_input in input.clientes:
cliente = ClienteModel.objects.get(pk=cliente_input.id)
if cliente is None:
return CreateDimensao(ok=False, dimensao=None)
clientes.append(cliente)
dimensao_instance = DimensaoModel(
comprimento=input.comprimento, largura=input.largura
)
dimensao_instance.save()
dimensao_instance.cliente.set(clientes)
return CreateDimensao(ok=ok, dimensao=dimensao_instance)
class UpdateDimensao(graphene.Mutation):
class Arguments:
id = graphene.Int(required=True)
input = DimensaoInput(required=True)
ok = graphene.Boolean()
dimensao = graphene.Field(DimensaoType)
@staticmethod
def mutate(root, info, id, input=None):
ok = False
dimensao_instance = DimensaoModel.objects.get(pk=id)
if dimensao_instance:
ok = True
clientes = []
for cliente_input in input.dimensoes:
cliente = ClienteModel.objects.get(pk=cliente_input.id)
if cliente is None:
return UpdateDimensao(ok=False, dimensao=None)
clientes.append(cliente)
dimensao_instance.comprimento = input.title
dimensao_instance.largura = input.year
dimensao_instance.save()
dimensao_instance.cliente.set(clientes)
return UpdateDimensao(ok=ok, dimensao=dimensao_instance)
return UpdateDimensao(ok=ok, dimensao=None)
class Mutation(graphene.ObjectType):
create_cliente = CreateCliente.Field()
update_cliente = UpdateCliente.Field()
create_dimensao = CreateDimensao.Field()
update_dimensao = UpdateDimensao.Field()
schema = graphene.Schema(query=Query, mutation=Mutation)
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,416
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/views.py
|
from django.http import HttpResponse
from django.views import generic
from django.shortcuts import render
from django.urls import reverse_lazy
from django.utils import timezone
from django.shortcuts import redirect
from .models import ClienteModel, DimensaoModel
from .forms import DimensaoForm, OrcamentoUpdateForm, ClienteForm
def index(request):
if request.method == 'POST':
dimensao_form = DimensaoForm(request.POST)
cliente_form = ClienteForm(request.POST)
if dimensao_form.is_valid() and cliente_form.is_valid():
post = dimensao_form.save(commit=False)
cliente_post = cliente_form.save()
post.cliente = cliente_post
post.usuario = request.user
post.data = timezone.now()
post.save()
return redirect('orcamento-id', pk=post.pk)
else:
return HttpResponse('Erro de operação')
else:
dimensao_form = DimensaoForm()
cliente_form = ClienteForm()
return render(
request,
'index.html',
{'dimensao_form': dimensao_form, 'cliente_form': cliente_form},
)
class DimensaoBulk(generic.View):
def get(self, request):
produtos = ['Banana', 'Maca', 'Limao', 'Laranja', 'Pera', 'Melancia']
list_produtos = []
for produto in produtos:
p = DimensaoModel(produto=produto, preco=10)
list_produtos.append(p)
DimensaoModel.objects.bulk_create(list_produtos)
return HttpResponse('Funcionou')
# -----------------------ORÇAMENTO------------------------------#
class OrcamentoListView(generic.ListView):
model = DimensaoModel
paginate_by = 30 # Exibe uma lista de no máximo 10 itens por vez
context_object_name = 'lista_orcamento' # Nome do objeto
template_name = (
'dimensoes/orcamento_list.html' # Nome e caminho do template
)
def get_object(self, queryset=None):
pk = self.kwargs.get(self.pk_url_kwarg)
return DimensaoModel.objects.select_related('cliente').get(id=pk)
# ---------------------------------------------------------
class OrcamentoDetailView(generic.DetailView):
model = DimensaoModel
context_object_name = 'lista_orcamento'
template_name = 'dimensoes/orcamento_detail.html'
def get_object(
self, queryset=None
): # Evita o a consulta no BD das foreign-key 'cliente' e 'usuario'
pk = self.kwargs.get(self.pk_url_kwarg)
return DimensaoModel.objects.select_related('cliente', 'usuario').get(
id=pk
)
class OrcamentoUpdateView(
generic.UpdateView
): # todo: Testar o fields com '__all__'
"""
------------DEBUG----------------
import pdb; pdb.set_trace()
l (mostra o ponto de verificação)
n (next)
c (continue)
---------------------------------
"""
model = DimensaoModel
form_class = OrcamentoUpdateForm
context_object_name = 'update_orcamento'
template_name = 'dimensoes/orcamento_update.html'
def post(self, *args, **kwargs):
formulario = self.get_form()
print('**********')
print(formulario.errors)
print('**********')
return super().post(*args, **kwargs)
class OrcamentoDeleteView(generic.DeleteView):
model = DimensaoModel
fields = '__all__'
success_url = reverse_lazy(
'orcamento'
) # Se confirmado o delete, redireciona para a página orcamento.
context_object_name = 'delete_orcamento'
# template_name = 'dimensoes/orcamento_list.html'
def get(self, *args, **kwargs):
return self.post(*args, **kwargs)
# -----------------------CLIENTE------------------------------#
class ClienteListView(generic.ListView):
model = ClienteModel
paginate_by = 10
context_object_name = 'lista_cliente'
template_name = 'dimensoes/cliente_list.html'
class ClienteCreateView(generic.CreateView):
model = ClienteModel
fields = '__all__'
context_object_name = 'create_cliente'
template_name = 'dimensoes/cliente_create.html'
class ClienteDetailView(generic.DetailView):
model = ClienteModel
context_object_name = 'lista_cliente'
template_name = 'dimensoes/cliente_detail.html'
class ClienteUpdateView(generic.UpdateView):
model = ClienteModel
fields = '__all__'
context_object_name = 'update_cliente'
template_name = 'dimensoes/cliente_update.html'
class ClienteDeleteView(generic.DeleteView):
model = ClienteModel
fields = '__all__'
# success_url = reverse_lazy('cliente')
context_object_name = 'delete_cliente'
template_name = 'dimensoes/cliente_confirm_delete.html'
def get_success_url(self): # Permite personalizar a visualização
return reverse_lazy('cliente')
def get(self, *args, **kwargs):
return self.post(*args, **kwargs)
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,417
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/customclass/estruturas/dimensao.py
|
# -*- coding: utf-8 -*-
# Estrutura de Dados de Dimensão
class Dimensao:
def __init__(
self,
rec_largura,
rec_comprimento,
rec_prof_inicial,
rec_prof_final,
rec_largura_da_calcada,
):
self.largura = rec_largura
self.comprimento = rec_comprimento
self.prof_inicial = rec_prof_inicial
self.prof_final = rec_prof_final
self.largura_da_calcada = rec_largura_da_calcada
# ******** FUNÇÕES DAS DIMENSÕES DA PISCINA ********#
def profundidade_media(self):
return (self.prof_inicial + self.prof_final) / 2
def area_da_calcada(self):
return (self.perimetro() * self.largura_da_calcada) + (
(self.largura_da_calcada * self.largura_da_calcada) * 4
)
def perimetro(self):
return (self.comprimento + self.comprimento) + (
self.largura + self.largura
)
def m2facial(self):
return self.comprimento * self.largura
def m2parede(self):
return self.profundidade_media() * self.perimetro()
def m2total(self):
return self.m2facial() + self.m2parede()
def m3total(self):
return self.profundidade_media() * self.m2facial()
def m3real(self):
return (self.profundidade_media() - 0.1) * self.m2facial()
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,418
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/customclass/objetos/dbs/database.py
|
import ast
import json
class Database:
def __init__(self, tabela):
self.file = open(
'dimensoes/customclass/objetos/dbs/' + tabela + '.json', 'r'
)
self.table = tabela
self.db = json.dumps(ast.literal_eval(self.file.read()))
def lista(self):
return json.loads(self.db)[self.table]
def listaitem(self, item):
return json.loads(self.db)[item]
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,018,419
|
leopesi/pool_budget
|
refs/heads/main
|
/projeto/dimensoes/forms.py
|
from django import forms
from django.forms import ModelForm
from .models import DimensaoModel, ClienteModel
class DimensaoForm(ModelForm):
espessura = forms.CharField(
widget=forms.RadioSelect(
choices=[
['0.6', '0.6 mm'],
['0.7', '0.7 mm'],
['0.8', '0.8 mm'],
]
)
)
fornecedor = forms.ChoiceField(
widget=forms.RadioSelect(),
choices=[
['sodramar', 'Sodramar'],
['viniplas', 'Viniplas'],
],
)
comprimento = forms.FloatField(
label='Comprimento',
help_text='Ex. 8.00',
required=True,
)
largura = forms.FloatField(
label='Largura',
help_text='Ex. 4.00',
required=True,
)
prof_inicial = forms.FloatField(
label='Prof. Inicial',
help_text='Ex. 1.20',
required=True,
)
prof_final = forms.FloatField(
label='Prof. Final',
help_text='Ex. 1.60',
required=True,
)
largura_calcada = forms.FloatField(
label='Calçada',
help_text='Ex. 1.00',
required=True,
)
class Meta:
model = DimensaoModel
fields = (
'comprimento',
'largura',
'prof_inicial',
'prof_final',
'largura_calcada',
'espessura',
'fornecedor',
)
class OrcamentoUpdateForm(ModelForm):
class Meta:
model = DimensaoModel
exclude = ('cliente', 'preco', 'produto')
class ClienteForm(ModelForm):
numero_casa = forms.FloatField(
label='Número',
required=False,
)
class Meta:
model = ClienteModel
fields = (
'nome',
'sobrenome',
'estado',
'cidade',
'bairro',
'rua',
'numero_casa',
'cep',
'telefone',
'email',
)
|
{"/projeto/dimensoes/customclass/estruturas/__init__.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py"], "/projeto/dimensoes/models.py": ["/projeto/dimensoes/customclass/estruturas/dimensao.py", "/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/motor.py"], "/projeto/dimensoes/customclass/objetos/vinil.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/customclass/objetos/precificacao.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py", "/projeto/dimensoes/customclass/objetos/vinil.py", "/projeto/dimensoes/customclass/objetos/filtro.py"], "/projeto/dimensoes/customclass/objetos/filtro.py": ["/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/admin.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/actions.py"], "/projeto/dimensoes/customclass/objetos/motor.py": ["/projeto/dimensoes/customclass/objetos/filtro.py", "/projeto/dimensoes/customclass/objetos/dbs/database.py"], "/projeto/dimensoes/schema.py": ["/projeto/dimensoes/models.py"], "/projeto/dimensoes/views.py": ["/projeto/dimensoes/models.py", "/projeto/dimensoes/forms.py"], "/projeto/dimensoes/forms.py": ["/projeto/dimensoes/models.py"]}
|
36,053,109
|
kalyanteja/device-monitor
|
refs/heads/master
|
/ImageReader.py
|
import os, io
from typing import Dict
from google.cloud import vision
import pandas as pd
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = r"google_api_key.json"
client = vision.ImageAnnotatorClient()
class ImageReadingService:
@classmethod
def fetch_readings_from_image(cls, file_name: str = 4) -> Dict[str, str]:
image_location = f"/Users/kalyan.teja-tatineni/Documents/code/fun/device-monitor/test/test{file_name}.HEIC"
with io.open(image_location, "rb") as img:
image_content = img.read()
image = vision.Image(content=image_content)
# annotate Image Response
response = client.text_detection(image=image)
df = pd.DataFrame(columns=["locale", "description"])
texts = response.text_annotations
readings = []
for text in texts:
readings.append(text.description)
df = df.append(
dict(locale=text.locale, description=text.description), ignore_index=True
)
print(df["description"][0])
return {'readings': readings}
|
{"/api.py": ["/ImageReader.py"]}
|
36,053,110
|
kalyanteja/device-monitor
|
refs/heads/master
|
/api.py
|
import flask
from flask import jsonify
from ImageReader import ImageReadingService
app = flask.Flask(__name__)
app.config["DEBUG"] = True
@app.route('/', methods=['GET', 'POST'])
def home():
# todo: get image from FE and pass along
df = ImageReadingService.fetch_readings_from_image(4)
return jsonify(df)
app.run()
|
{"/api.py": ["/ImageReader.py"]}
|
36,141,953
|
sapdeleon/python_lecture
|
refs/heads/master
|
/hello.py
|
print("Hello, World!")
# array in python
names = ["Harry", "Ron", "Tess", "Meghan"]
# loop in python
for name in names:
print(name)
|
{"/imports.py": ["/classes.py"]}
|
36,141,954
|
sapdeleon/python_lecture
|
refs/heads/master
|
/inputs.py
|
import sys
# using exceptions
try:
num = int(input("Enter a number: "))
except ValueError:
print("Error: Invalid number!")
sys.exit(1)
# using conditions
if (num > 0):
print("Number is positive.")
elif (num < 0):
print("Number is negative.")
else:
print("Number is zero...")
|
{"/imports.py": ["/classes.py"]}
|
36,141,955
|
sapdeleon/python_lecture
|
refs/heads/master
|
/classes.py
|
class Flight():
def __init__(self, capacity):
self.capacity = capacity
self.passengers = []
def add_passenger(self, name):
if not self.open_seats(): # no seats available
return False
self.passengers.append(name)
return True
def open_seats(self):
return self.capacity - len(self.passengers)
|
{"/imports.py": ["/classes.py"]}
|
36,141,956
|
sapdeleon/python_lecture
|
refs/heads/master
|
/imports.py
|
import classes
people = ["Harry", "Ron", "James", "Dave"]
# create new Flight object with 3 maximum capacity
flight = classes.Flight(3);
for person in people:
if flight.add_passenger(person):
print(f"Added {person} to flight successfully!")
else:
print(f"Sorry, no available seat for {person}.")
|
{"/imports.py": ["/classes.py"]}
|
36,161,503
|
minglii1998/simple-classification-frame
|
refs/heads/main
|
/lib/utils/fileutils.py
|
import os
import numpy as np
import cv2
mapping_dict_weight2id = {}
mapping_dict_weight2id['25'] = 1
mapping_dict_weight2id['5'] = 2
mapping_dict_weight2id['75'] = 3
mapping_dict_weight2id['10'] = 4
mapping_dict_weight2id['125'] = 5
mapping_dict_weight2id['15'] = 6
mapping_dict_weight2id['175'] = 7
mapping_dict_weight2id['20'] = 8
mapping_dict_id2weight = {}
mapping_dict_id2weight[0] = 'None'
mapping_dict_id2weight[1] = '2.5'
mapping_dict_id2weight[2] = '5'
mapping_dict_id2weight[3] = '7.5'
mapping_dict_id2weight[4] = '10'
mapping_dict_id2weight[5] = '12.5'
mapping_dict_id2weight[6] = '15'
mapping_dict_id2weight[7] = '17.5'
mapping_dict_id2weight[8] = '20'
def get_files(img_dir):
imgs, masks, xmls = list_files(img_dir)
return imgs, masks, xmls
def list_files(in_path):
img_files = []
mask_files = []
gt_files = []
for (dirpath, dirnames, filenames) in os.walk(in_path):
for file in filenames:
filename, ext = os.path.splitext(file)
ext = str.lower(ext)
if ext == '.jpg' or ext == '.jpeg' or ext == '.gif' or ext == '.png' or ext == '.pgm':
img_files.append(os.path.join(dirpath, file))
elif ext == '.bmp':
mask_files.append(os.path.join(dirpath, file))
elif ext == '.xml' or ext == '.gt' or ext == '.txt':
gt_files.append(os.path.join(dirpath, file))
elif ext == '.zip':
continue
# img_files.sort()
# mask_files.sort()
# gt_files.sort()
return img_files, mask_files, gt_files
def weight2id(raw_label):
# input is str type
raw_label = raw_label.replace('.','')
if raw_label in mapping_dict_weight2id.keys():
id = mapping_dict_weight2id[raw_label]
else:
id = 0
return id
def id2weight(id):
# input is int
if id in mapping_dict_id2weight.keys():
weight = mapping_dict_id2weight[id]
else:
weight = 0
return weight
|
{"/lib/evaluators.py": ["/lib/evaluation_metrics/__init__.py"], "/main.py": ["/config.py", "/lib/datasets/dataset_classification.py", "/lib/trainers.py", "/lib/evaluators.py"], "/lib/trainers.py": ["/lib/evaluation_metrics/__init__.py"]}
|
36,183,352
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parlacards/serializers/cards/person/recent_activity.py
|
from django.db.models import Value
from parlacards.pagination import create_paginator
from parlacards.serializers.common import PersonScoreCardSerializer
from parlacards.serializers.recent_activity import EventSerializer
from parladata.models.ballot import Ballot
from parladata.models.question import Question
from parladata.models.speech import Speech
from parladata.models.common import Mandate
class RecentActivityCardSerializer(PersonScoreCardSerializer):
def get_results(self, person):
# this is implemented in to_representation for pagination
return None
def to_representation(self, person):
parent_data = super().to_representation(person)
mandate = Mandate.get_active_mandate_at(self.context['request_date'])
from_timestamp, to_timestamp = mandate.get_time_range_from_mandate(self.context['request_date'])
# get all events (questions, ballots, speeches) for person
# Important: bacause we join all of them in a union, they all need to
# have the same number and types of values {id, timestamp, type}
questions = Question.objects.filter(
person_authors=person,
timestamp__range=(from_timestamp, to_timestamp),
).values('id', 'timestamp').annotate(type=Value('question'))
ballots = Ballot.objects.filter(
personvoter=person,
vote__timestamp__range=(from_timestamp, to_timestamp),
).values('id', 'vote__timestamp').annotate(type=Value('ballot'))
speeches = Speech.objects.filter_valid_speeches(
self.context['request_date']
).filter(
speaker=person,
start_time__range=(from_timestamp, to_timestamp),
).values('id', 'start_time').annotate(type=Value('speech'))
# create a union of all events and sort them by timestamp (unions get
# column names from first queryset, e.g. `timestamp` is from questions)
ordered_event_ids = questions.union(ballots).union(speeches).order_by('-timestamp', 'id')
paged_event_ids, pagination_metadata = create_paginator(self.context.get('GET', {}), ordered_event_ids)
# we need full db objects for serialization
db_objects = {
'question': Question.objects.filter(id__in=[o['id'] for o in paged_event_ids if o['type'] == 'question']),
'ballot': Ballot.objects.filter(id__in=[o['id'] for o in paged_event_ids if o['type'] == 'ballot']),
'speech': Speech.objects.filter(id__in=[o['id'] for o in paged_event_ids if o['type'] == 'speech']),
}
# map from {id, timestamp, type} to full Question, Ballot, or Speech object
paged_object_list = map(
lambda id_object: next(db_object for db_object in db_objects[id_object['type']] if db_object.id == id_object['id']),
paged_event_ids,
)
event_serializer = EventSerializer(
paged_object_list,
many=True,
context=self.context,
)
return {
**parent_data,
**pagination_metadata,
'results': event_serializer.data,
}
|
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"/parlacards/utils.py", "/parladata/models/__init__.py"], "/parlacards/serializers/cards/cards.py": ["/parladata/models/agenda_item.py", "/parladata/models/ballot.py", "/parladata/models/media.py", "/parladata/models/vote.py", "/parladata/models/question.py", "/parladata/models/memberships.py", "/parladata/models/legislation.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/common.py", "/parladata/models/public_question.py", "/parlacards/models.py", "/parlacards/serializers/media.py", "/parlacards/serializers/person.py", "/parlacards/serializers/organization.py", "/parlacards/serializers/legislation.py", "/parlacards/serializers/ballot.py", "/parlacards/serializers/voting_distance.py", "/parlacards/serializers/membership.py", "/parlacards/serializers/style_scores.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/tfidf.py", 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"/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", 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["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,353
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/update_helpers/methods.py
|
from django.utils.module_loading import import_string
from django.conf import settings
def get_helper_method(lib, name):
language_code = settings.LEGISLATION_RESOLVER_LANGUAGE_CODE
mathod_path_string = f'parladata.update_helpers.{language_code}.{lib}.{name}'
try:
method = import_string(mathod_path_string)
except:
print(f'method {name} for language code {language_code} are missing')
print(f'check method {mathod_path_string}')
method = lambda:None
return method
def set_legislation_results():
method = get_helper_method('legislation', 'set_legislation_result')
method()
|
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"/parlacards/models.py", "/parladata/models/memberships.py"], "/parladata/models/task.py": ["/parladata/behaviors/models.py"], "/parladata_project/settings/local.py": ["/parladata_project/settings/defaults.py"], "/parlacards/serializers/question.py": ["/parlacards/serializers/common.py"], "/parlacards/scores/deviation_from_group.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/update_helpers/sl/legislation.py": ["/parladata/models/__init__.py"], "/parladata/admin/filters.py": ["/parladata/models/memberships.py", "/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/legislation.py"], "/parlacards/management/commands/upload_speeches_to_solr.py": ["/parladata/models/speech.py", "/parlacards/solr.py"], "/tests/conftest.py": ["/parladata/models/organization.py", "/parladata/models/person.py"], 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"/parladata/models/common.py", "/parladata/admin/filters.py", "/parladata/admin/link.py"], "/parlacards/serializers/facets.py": ["/parlacards/serializers/common.py"], "/export/resources/person.py": ["/parladata/models/__init__.py", "/parlacards/models.py", "/export/resources/common.py"], "/parlacards/management/commands/set_tfidf_for_sessions.py": ["/parlacards/scores/tfidf.py", "/parlacards/utils.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/speech.py"], "/parladata/models/common.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py"], "/export/views/group.py": ["/export/resources/group.py", "/export/views/common.py"], "/tests/scores/test_style.py": ["/parladata/models/speech.py", "/parlacards/scores/style.py", "/tests/fixtures/common.py"], "/parlacards/serializers/area.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/person.py": ["/parladata/models/link.py", "/parlacards/serializers/common.py", "/parlacards/serializers/area.py"], "/parladata/models/agenda_item.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/link.py": ["/parladata/models/link.py"], "/parlacards/serializers/session.py": ["/parlacards/serializers/common.py", "/parladata/models/agenda_item.py", "/parladata/models/vote.py"], "/parlacards/admin/__init__.py": ["/parlacards/admin/number_of_spoken_words.py", "/parlacards/admin/discord.py", "/parlacards/admin/admin.py"], "/parlacards/serializers/motion.py": ["/parlacards/serializers/common.py"], "/parlacards/management/commands/cache_votes.py": ["/parladata/models/vote.py", "/parlacards/serializers/vote.py"], "/tests/scores/test_group_attenadance_on_session.py": ["/parlacards/scores/session_attendance.py", "/tests/fixtures/common.py"], "/parladata/models/person.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parladata/exceptions.py"], "/parlacards/serializers/cards/__init__.py": ["/parlacards/serializers/cards/misc/last_session.py", "/parlacards/serializers/cards/misc/members.py", "/parlacards/serializers/cards/misc/groups.py", "/parlacards/serializers/cards/misc/legislation.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/cards/person/questions.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/group/questions.py", "/parlacards/serializers/cards/group/votes.py", "/parlacards/serializers/cards/session/single.py", "/parlacards/serializers/cards/session/tfidf.py", "/parlacards/serializers/cards/session/legislation.py", "/parlacards/serializers/cards/search/votes.py", "/parlacards/serializers/cards/search/usage_through_time.py", "/parlacards/serializers/cards/cards.py"], "/parladata/management/commands/create_sl_education_levels.py": ["/parladata/models/common.py"], "/test/data_test.py": ["/parladata/models/__init__.py"], 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"/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,354
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parlacards/management/commands/upload_votes_to_solr.py
|
import json
import requests
from django.core.management.base import BaseCommand, CommandError
from django.conf import settings
from parladata.models.vote import Vote
from parlacards.serializers.vote import SessionVoteSerializer
from datetime import datetime, timedelta
from parlacards.solr import delete_from_solr, commit_to_solr
def delete_invalid_votes(vote_ids_in_solr):
vote_ids = list(Vote.objects.all().values_list('id', flat=True))
ids_to_delete = list(set(vote_ids_in_solr) - set(vote_ids))
if bool(ids_to_delete):
solr_ids_to_delete = ['vote_' + str(i) for i in ids_to_delete]
delete_from_solr(solr_ids_to_delete)
class Command(BaseCommand):
help = 'Uploads all votes to solr'
def handle(self, *args, **options):
# get IDs from SOLR so we don't upload
# vote already there
yesterday = datetime.now() - timedelta(days=1)
url = settings.SOLR_URL + '/select?wt=json&q=type:vote&fl=vote_id&rows=100000000'
self.stdout.write(f'Getting all IDs from {url} ...')
solr_response = requests.get(url)
try:
docs = solr_response.json()['response']['docs']
ids_in_solr = [
doc['vote_id'] for
doc in
solr_response.json()['response']['docs'] if
'vote_id' in doc
]
except:
ids_in_solr = []
# delete invalid votes
self.stdout.write('Deleting invalid votes ...')
delete_invalid_votes(ids_in_solr)
votes = Vote.objects.exclude(id__in=ids_in_solr).prefetch_related('motion', 'motion__session')
updated_votes = Vote.objects.filter(updated_at__gte=yesterday).prefetch_related('motion', 'motion__session')
votes = set(list(votes) + list(updated_votes))
self.stdout.write(f'Uploading {len(votes)} votes to solr ...')
output = []
for i, vote in enumerate(votes):
output.append({
'term': vote.motion.session.mandate.id,
'type': 'vote',
'id': 'vote_' + str(vote.id),
'vote_id': vote.id,
'session_id': vote.motion.session.id,
'org_id': vote.motion.session.organization.id,
'start_time': vote.timestamp.isoformat(),
'content': vote.name,
#'results_json': SessionVoteSerializer(vote).data,
})
if (i > 0) and (i % 100 == 0):
commit_to_solr(self, output)
output = []
if bool(output):
commit_to_solr(self, output)
|
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"/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,355
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/management/commands/send_daily_notifications.py
|
from django.core.management.base import BaseCommand, CommandError
from parladata.update_utils import notify_editors_for_new_data
class Command(BaseCommand):
help = 'Send daily notifications'
def handle(self, *args, **options):
self.stdout.write('Checking for new data')
notify_editors_for_new_data()
|
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|
36,183,356
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/compass.py
|
# -*- coding: utf-8 -*-
from parladata.models import *
def removekey(d, key):
r = dict(d)
del r[key]
return r
def getVotingArray():
persons = Person.objects.all()
votes = Vote.objects.all()
results = {}
# for each person
for i, person in enumerate(persons):
# generate new line
# results.append([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) # 100 zeroes
# generate voting record
record = []
print()'Processing votes for ' + person.name)
# for each vote that happened
for vote in votes:
# check if person voted
if len(vote.ballots.filter(voter=person)) != 0:
record.append(vote.ballots.filter(voter=person)[0].option)
else:
record.append(0)
results[person.name] = record
# compare with all other people
# otherpersons = list(persons)
# otherpersons.remove(person)
# for k, other in enumerate(otherpersons):
# if ballot.option == other.ballots.all().order_by('vote')[j].option:
# results[i][k] = results[i][k] + 1
print('Finished processing votes.')
return results
def getScores(results):
scores = {}
# for all people
for i, person in enumerate(results):
print('Processing person ' + person)
# make new person score
scores[person] = []
#for every other person
# otherpeople = removekey(results, person)
for other in results:
print('Other ' + other)
score = 0
# for every vote
for j, vote in enumerate(results[person]):
# print 'Vote ' + str(j)
# if vote is the same
if results[other][j] == vote:
# add to score
score = score + 1
# when done comparing votes, add score to person
scores[person].append(score)
return scores
def getAll():
return getScores(getVotingArray())
|
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|
36,183,357
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/migrations/0036_auto_20210802_1314.py
|
# Generated by Django 3.2.5 on 2021-08-02 13:14
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('parladata', '0035_auto_20210715_1704'),
]
operations = [
migrations.AlterField(
model_name='personmembership',
name='role',
field=models.TextField(choices=[('member', 'member'), ('voter', 'voter'), ('persident', 'president'), ('deputy', 'deputy')], default='member', help_text='The role that the person fulfills in the organization', verbose_name='role'),
),
migrations.CreateModel(
name='OrganizationEmail',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('valid_from', models.DateTimeField(blank=True, default=None, help_text='row valid from', null=True)),
('valid_to', models.DateTimeField(blank=True, default=None, help_text='row valid to', null=True)),
('value', models.TextField()),
('owner', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='parladata.organization')),
],
options={
'abstract': False,
},
),
]
|
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"/parladata/models/ballot.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/common.py"], "/parlacards/management/commands/upload_votes_to_solr.py": ["/parladata/models/vote.py", "/parlacards/serializers/vote.py", "/parlacards/solr.py"], "/parladata/management/commands/send_daily_notifications.py": ["/parladata/update_utils.py"], "/tests/views/test_card_views.py": ["/tests/fixtures/common.py", "/parlacards/views.py"], "/parladata/models/question.py": ["/parladata/behaviors/models.py"], "/parladata/management/commands/delete_all_sessions.py": ["/parladata/models/__init__.py"], "/tests/scores/test_number_of_questions.py": ["/parlacards/scores/number_of_questions.py", "/tests/fixtures/common.py"], "/parladata/management/commands/startparlameter.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/run_tasks.py": ["/parladata/models/task.py", "/parladata/update_utils.py"], "/parladata/admin/motion.py": ["/parladata/models/__init__.py", 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"/parladata/update_utils.py"], "/parlacards/serializers/voting_distance.py": ["/parlacards/serializers/common.py"], "/parladata/management/commands/mergepeople.py": ["/parladata/models/__init__.py"], "/parlacards/lemmatizers/ua/lemmatizer.py": ["/parlacards/lemmatizers/ua/stop_words.py"], "/parladata/apps.py": ["/parladata/signals.py"], "/parlacards/management/commands/upload_legislation_to_solr.py": ["/parladata/models/legislation.py", "/parlacards/solr.py"], "/parlacards/scores/tfidf.py": ["/parladata/models/session.py", "/parladata/models/common.py", "/parladata/models/speech.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/utils.py", "/parlacards/scores/common.py"], "/tests/resources/test_misc_resources.py": ["/tests/fixtures/common.py", "/export/resources/misc.py"], "/parladata/admin/link.py": ["/parladata/models/link.py", "/parladata/admin/filters.py"], "/parlacards/serializers/cards/misc/groups.py": ["/parlacards/serializers/common.py", 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"/parlacards/utils.py", "/parladata/models/__init__.py"], "/parlacards/serializers/cards/cards.py": ["/parladata/models/agenda_item.py", "/parladata/models/ballot.py", "/parladata/models/media.py", "/parladata/models/vote.py", "/parladata/models/question.py", "/parladata/models/memberships.py", "/parladata/models/legislation.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/common.py", "/parladata/models/public_question.py", "/parlacards/models.py", "/parlacards/serializers/media.py", "/parlacards/serializers/person.py", "/parlacards/serializers/organization.py", "/parlacards/serializers/legislation.py", "/parlacards/serializers/ballot.py", "/parlacards/serializers/voting_distance.py", "/parlacards/serializers/membership.py", "/parlacards/serializers/style_scores.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/tfidf.py", "/parlacards/serializers/facets.py", "/parlacards/serializers/question.py", "/parlacards/serializers/agenda_item.py", "/parlacards/serializers/common.py", "/parlacards/serializers/public_question.py", "/parlacards/solr.py", "/parlacards/pagination.py", "/parlacards/recaptcha.py"], "/parladata/utils.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/agenda_item.py"], "/tests/scores/test_voting_distance.py": ["/parlacards/scores/voting_distance.py", "/tests/fixtures/common.py"], "/parladata/models/versionable_properties.py": ["/parladata/behaviors/models.py", "/parladata/models/common.py"], "/parladata/models/legislation.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/set_tfidf.py": ["/parladata/models/__init__.py", "/parlacards/scores/tfidf.py", "/parlacards/utils.py"], "/export/resources/group.py": ["/export/resources/common.py", "/parlacards/models.py", "/parladata/models/__init__.py", "/parladata/models/common.py"], "/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": 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"/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,358
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/migrations/0045_auto_20210913_1845.py
|
# Generated by Django 3.2.7 on 2021-09-13 18:45
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('parladata', '0044_auto_20210911_1135'),
]
operations = [
migrations.RemoveField(
model_name='organization',
name='mandate',
),
migrations.AlterField(
model_name='law',
name='status',
field=models.TextField(blank=True, choices=[('in_procedure', 'in_procedure'), ('enacted', 'enacted'), ('submitted', 'submitted'), ('rejected', 'rejected'), ('adopted', 'adopted'), ('received', 'received'), ('retracted', 'retracted')], help_text='Status of law', null=True),
),
migrations.AlterField(
model_name='organizationmembership',
name='mandate',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='organizationmemberships', to='parladata.mandate', verbose_name='Mandate'),
),
migrations.AlterField(
model_name='personmembership',
name='mandate',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='personmemberships', to='parladata.mandate', verbose_name='Mandate'),
),
]
|
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"/parladata/update_utils.py"], "/parlacards/serializers/voting_distance.py": ["/parlacards/serializers/common.py"], "/parladata/management/commands/mergepeople.py": ["/parladata/models/__init__.py"], "/parlacards/lemmatizers/ua/lemmatizer.py": ["/parlacards/lemmatizers/ua/stop_words.py"], "/parladata/apps.py": ["/parladata/signals.py"], "/parlacards/management/commands/upload_legislation_to_solr.py": ["/parladata/models/legislation.py", "/parlacards/solr.py"], "/parlacards/scores/tfidf.py": ["/parladata/models/session.py", "/parladata/models/common.py", "/parladata/models/speech.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/utils.py", "/parlacards/scores/common.py"], "/tests/resources/test_misc_resources.py": ["/tests/fixtures/common.py", "/export/resources/misc.py"], "/parladata/admin/link.py": ["/parladata/models/link.py", "/parladata/admin/filters.py"], "/parlacards/serializers/cards/misc/groups.py": ["/parlacards/serializers/common.py", "/parlacards/models.py", "/parladata/models/memberships.py"], "/parladata/models/task.py": ["/parladata/behaviors/models.py"], "/parladata_project/settings/local.py": ["/parladata_project/settings/defaults.py"], "/parlacards/serializers/question.py": ["/parlacards/serializers/common.py"], "/parlacards/scores/deviation_from_group.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/update_helpers/sl/legislation.py": ["/parladata/models/__init__.py"], "/parladata/admin/filters.py": ["/parladata/models/memberships.py", "/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/legislation.py"], "/parlacards/management/commands/upload_speeches_to_solr.py": ["/parladata/models/speech.py", "/parlacards/solr.py"], "/tests/conftest.py": ["/parladata/models/organization.py", "/parladata/models/person.py"], 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"/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/solr.py", "/parlacards/pagination.py"], "/parlacards/serializers/cards/session/tfidf.py": ["/parlacards/models.py", "/parlacards/serializers/tfidf.py", "/parlacards/serializers/common.py"], "/parlacards/serializers/tfidf.py": ["/parlacards/serializers/common.py"], "/parladata/management/commands/deletepeople.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/reset_order_with_agenda_items.py": ["/parladata/update_utils.py"], "/parlacards/serializers/cards/misc/last_session.py": ["/parladata/models/__init__.py", "/parlacards/models.py", "/parlacards/serializers/common.py", "/parlacards/serializers/group_attendance.py", "/parlacards/serializers/session.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/cards/session/tfidf.py", "/parlacards/pagination.py"], "/parlacards/lemmatizers/sl/lemmatizer.py": ["/tagger/lemmatiser.py"], "/parladata/models/area.py": ["/parladata/behaviors/models.py"], "/parladata/management/commands/fetch_members_articles.py": ["/parladata/models/media.py", "/parladata/models/common.py"], "/parladata/models/public_question.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/speech.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parladata/models/vote.py"], "/parladata/management/commands/import_sentio_data.py": ["/parladata/models/__init__.py", "/parladata/models/common.py"], "/parladata/models/document.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/flush_solr.py": ["/parlacards/solr.py"], "/parladata/admin/question.py": ["/parladata/admin/filters.py", "/parladata/models/question.py", "/parladata/admin/link.py"], "/parladata/models/memberships.py": ["/parladata/behaviors/models.py"], "/parlacards/pagination.py": ["/parlacards/solr.py"], "/parlacards/serializers/common.py": ["/parladata/models/person.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/utils.py", "/parladata/exceptions.py"], "/parladata/management/commands/set_session_to_votes_ua.py": ["/parladata/update_utils.py"], "/parladata/migrations/0001_initial.py": ["/parladata/models/__init__.py"], "/parlacards/admin/admin.py": ["/parlacards/models.py", "/parladata/models/memberships.py", "/parladata/admin/filters.py", "/parlacards/admin/filters.py"], "/parlacards/utils.py": ["/parladata/models/memberships.py"], "/parlacards/management/commands/seed_scores.py": ["/parladata/models/organization.py", "/parlacards/scores/seed.py"], "/tests/scores/test_monthly_attendance.py": ["/parlacards/scores/monthly_attendance.py", "/tests/fixtures/common.py"], "/parlacards/serializers/cards/misc/legislation.py": ["/parlacards/pagination.py", "/parlacards/serializers/common.py", "/parlacards/serializers/legislation.py", "/parladata/models/legislation.py"], "/parlacards/serializers/cards/person/questions.py": ["/parlacards/pagination.py", "/parlacards/serializers/common.py", "/parlacards/serializers/question.py", "/parladata/models/question.py", "/parladata/models/common.py"], "/export/views/misc.py": ["/export/resources/misc.py", "/export/views/common.py"], "/export/resources/session.py": ["/export/resources/common.py", "/parladata/models/__init__.py"], "/parlacards/scores/voting_distance.py": ["/parladata/models/ballot.py", "/parladata/models/vote.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/tests/fixtures/common.py": ["/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/common.py"], "/parlacards/management/commands/upload_agenda_items_to_solr.py": ["/parladata/models/agenda_item.py", "/parlacards/solr.py"], "/parladata/signals.py": ["/parladata/models/__init__.py"], "/parlacards/serializers/style_scores.py": ["/parladata/models/person.py", "/parladata/models/organization.py", "/parlacards/serializers/common.py", "/parlacards/models.py"], "/parlacards/admin_views.py": ["/parlacards/utils.py", "/parladata/models/task.py", "/parladata/models/session.py"], "/tests/scores/test_number_of_spoken_words.py": ["/parladata/models/speech.py", "/parlacards/scores/number_of_spoken_words.py", "/tests/fixtures/common.py"], "/parlacards/scores/vocabulary_size.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/tests/resources/test_person_resources.py": ["/tests/fixtures/common.py", "/export/resources/person.py"], "/parlacards/serializers/cards/session/legislation.py": ["/parlacards/pagination.py", "/parlacards/serializers/cards/misc/legislation.py", "/parlacards/serializers/common.py", "/parlacards/serializers/legislation.py"], "/parlacards/management/commands/lemmatize_speeches.py": ["/parladata/models/speech.py"], "/export/views/person.py": ["/export/resources/person.py", "/export/views/common.py"], "/export/views/session.py": ["/export/views/common.py", "/export/resources/session.py"], "/parladata/models/motion.py": ["/parladata/behaviors/models.py"], "/parlacards/admin/discord.py": ["/export/resources/group.py", "/parlacards/models.py", "/parlacards/admin/common.py"], "/parladata/tasks.py": ["/parladata/models/__init__.py", "/parlacards/solr.py"], "/parladata/management/commands/prepare_test_database.py": ["/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/vote.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py"], "/tests/scores/test_deviation_from_group.py": ["/parlacards/scores/deviation_from_group.py", "/tests/fixtures/common.py"], "/parladata/models/speech.py": ["/parladata/behaviors/models.py", "/parlacards/scores/common.py"], "/parladata/admin/public_question.py": ["/parladata/models/public_question.py"], "/parlacards/views.py": ["/parladata/models/person.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/vote.py", "/parladata/models/legislation.py", "/parladata/models/agenda_item.py", "/parlacards/models.py", "/parlacards/serializers/cards/__init__.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/group_attendance.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/public_question.py"], "/parladata/admin/agenda_item.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/admin/admin.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/versionable_properties.py", "/parladata/models/common.py", "/parladata/admin/filters.py", "/parladata/admin/link.py"], "/parlacards/serializers/facets.py": ["/parlacards/serializers/common.py"], "/export/resources/person.py": ["/parladata/models/__init__.py", "/parlacards/models.py", "/export/resources/common.py"], "/parlacards/management/commands/set_tfidf_for_sessions.py": ["/parlacards/scores/tfidf.py", "/parlacards/utils.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/speech.py"], "/parladata/models/common.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py"], "/export/views/group.py": ["/export/resources/group.py", "/export/views/common.py"], "/tests/scores/test_style.py": ["/parladata/models/speech.py", "/parlacards/scores/style.py", "/tests/fixtures/common.py"], "/parlacards/serializers/area.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/person.py": ["/parladata/models/link.py", "/parlacards/serializers/common.py", "/parlacards/serializers/area.py"], "/parladata/models/agenda_item.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/link.py": ["/parladata/models/link.py"], "/parlacards/serializers/session.py": ["/parlacards/serializers/common.py", "/parladata/models/agenda_item.py", "/parladata/models/vote.py"], "/parlacards/admin/__init__.py": ["/parlacards/admin/number_of_spoken_words.py", "/parlacards/admin/discord.py", "/parlacards/admin/admin.py"], "/parlacards/serializers/motion.py": ["/parlacards/serializers/common.py"], "/parlacards/management/commands/cache_votes.py": ["/parladata/models/vote.py", "/parlacards/serializers/vote.py"], "/tests/scores/test_group_attenadance_on_session.py": ["/parlacards/scores/session_attendance.py", "/tests/fixtures/common.py"], "/parladata/models/person.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parladata/exceptions.py"], "/parlacards/serializers/cards/__init__.py": ["/parlacards/serializers/cards/misc/last_session.py", "/parlacards/serializers/cards/misc/members.py", "/parlacards/serializers/cards/misc/groups.py", "/parlacards/serializers/cards/misc/legislation.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/cards/person/questions.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/group/questions.py", "/parlacards/serializers/cards/group/votes.py", "/parlacards/serializers/cards/session/single.py", "/parlacards/serializers/cards/session/tfidf.py", "/parlacards/serializers/cards/session/legislation.py", "/parlacards/serializers/cards/search/votes.py", "/parlacards/serializers/cards/search/usage_through_time.py", "/parlacards/serializers/cards/cards.py"], "/parladata/management/commands/create_sl_education_levels.py": ["/parladata/models/common.py"], "/test/data_test.py": ["/parladata/models/__init__.py"], "/parlacards/serializers/organization.py": ["/parladata/models/link.py", "/parlacards/serializers/common.py"], "/parladata/models/organization.py": ["/parladata/models/link.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/ballot.py", "/parladata/behaviors/models.py", "/parladata/exceptions.py", "/parladata/models/versionable_properties.py"], "/parlacards/serializers/vote.py": ["/parladata/models/ballot.py", "/parlacards/serializers/session.py", "/parlacards/serializers/link.py", "/parlacards/serializers/common.py"], "/scripts/pl_parser.py": ["/sandbox/import_pl.py"], "/parladata/management/commands/pair_votes_and_speeches.py": ["/parladata/update_utils.py"], "/parladata/models/vote.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py", "/parladata/models/ballot.py"], "/parladata_project/settings/k8s.py": ["/parladata_project/settings/defaults.py"], "/parlacards/tasks.py": ["/parlacards/scores/tfidf.py", "/parlacards/utils.py", "/parladata/models/__init__.py"], "/parlacards/serializers/cards/cards.py": ["/parladata/models/agenda_item.py", "/parladata/models/ballot.py", "/parladata/models/media.py", "/parladata/models/vote.py", "/parladata/models/question.py", "/parladata/models/memberships.py", "/parladata/models/legislation.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/common.py", "/parladata/models/public_question.py", "/parlacards/models.py", "/parlacards/serializers/media.py", "/parlacards/serializers/person.py", "/parlacards/serializers/organization.py", "/parlacards/serializers/legislation.py", "/parlacards/serializers/ballot.py", "/parlacards/serializers/voting_distance.py", "/parlacards/serializers/membership.py", "/parlacards/serializers/style_scores.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/tfidf.py", "/parlacards/serializers/facets.py", "/parlacards/serializers/question.py", "/parlacards/serializers/agenda_item.py", "/parlacards/serializers/common.py", "/parlacards/serializers/public_question.py", "/parlacards/solr.py", "/parlacards/pagination.py", "/parlacards/recaptcha.py"], "/parladata/utils.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/agenda_item.py"], "/tests/scores/test_voting_distance.py": ["/parlacards/scores/voting_distance.py", "/tests/fixtures/common.py"], "/parladata/models/versionable_properties.py": ["/parladata/behaviors/models.py", "/parladata/models/common.py"], "/parladata/models/legislation.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/set_tfidf.py": ["/parladata/models/__init__.py", "/parlacards/scores/tfidf.py", "/parlacards/utils.py"], "/export/resources/group.py": ["/export/resources/common.py", "/parlacards/models.py", "/parladata/models/__init__.py", "/parladata/models/common.py"], "/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,359
|
danesjenovdan/parladata
|
refs/heads/dev
|
/tests/views/test_card_views.py
|
from datetime import datetime
import pytest
from tests.fixtures.common import *
from rest_framework.test import APIClient
from parlacards.views import *
client = APIClient()
def single_test_url(url, params, status_code=200):
response = client.get(url, params)
assert response.status_code == status_code
def run_test_urls(url, fixtures):
for fixture in fixtures:
single_test_url(url, fixture)
def run_test_for_bicameral_system(url, result_length, params_1, params_2):
response2 = client.get(url, params_1)
response1 = client.get(url, params_2)
assert response1.status_code == 200
assert response2.status_code == 200
assert len(response1.json().get('results', [])) == result_length
assert len(response2.json().get('results', [])) == result_length
# tests
@pytest.mark.django_db()
def test_misc_members():
response = client.get('/v3/cards/misc/members/?id=1&members:per_page=10&members:page=1&text=&order_by=name&preferred_pronoun=she')
assert response.status_code == 200
@pytest.mark.django_db()
def test_misc_groups(current_root_org, first_root_org):
run_test_urls('/v3/cards/misc/groups/', [first_root_org, current_root_org])
@pytest.mark.django_db()
def test_misc_sessions(first_mandate_params, current_mandate_params):
run_test_urls('/v3/cards/misc/sessions/', [first_mandate_params, current_mandate_params])
@pytest.mark.django_db()
def test_misc_legislation(first_mandate_params, current_mandate_params):
run_test_urls('/v3/cards/misc/legislation/', [first_mandate_params, current_mandate_params])
@pytest.mark.django_db()
def test_misc_last_session(first_root_org, current_root_org):
run_test_urls('/v3/cards/misc/last-session/', [first_root_org, current_root_org])
@pytest.mark.django_db()
def test_misc_search(first_mandate_params, current_mandate_params):
run_test_urls('/v3/cards/misc/search/', [first_mandate_params, current_mandate_params])
@pytest.mark.django_db()
def test_misc_menu_search(first_mandate_params, current_mandate_params):
run_test_urls('/v3/cards/misc/menu-search/', [first_mandate_params, current_mandate_params])
@pytest.mark.django_db()
def test_misc_basic_information(first_mandate_params, current_mandate_params):
run_test_urls('/v3/cards/misc/basic-information/', [first_mandate_params, current_mandate_params])
@pytest.mark.django_db()
def test_person_basic_information(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/basic-information/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_vocabulary_size(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/vocabulary-size/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_votes(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/votes/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_questions(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/questions/', [first_mandate_member, all_mandate_member])
# TODO we should also test the questions aren't all the same
@pytest.mark.django_db()
def test_person_memberships(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/memberships/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_most_votes_in_common(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/most-votes-in-common/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_least_votes_in_common(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/least-votes-in-common/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_least_votes_in_common_for_bicameral_systems(
bicameral_person_1,
bicameral_person_2
):
run_test_for_bicameral_system(
'/v3/cards/person/least-votes-in-common/',
5,
bicameral_person_1,
bicameral_person_2
)
@pytest.mark.django_db()
def test_person_most_votes_in_common_for_bicameral_systems(
bicameral_person_1,
bicameral_person_2
):
run_test_for_bicameral_system(
'/v3/cards/person/most-votes-in-common/',
5,
bicameral_person_1,
bicameral_person_2
)
@pytest.mark.django_db()
def test_person_deviation_from_group(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/deviation-from-group/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_average_number_of_speeches_per_session(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/average-number-of-speeches-per-session/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_questions(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/number-of-questions/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_vote_attendance(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/vote-attendance/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_recent_activity(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/recent-activity/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_monthly_vote_attendance(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/monthly-vote-attendance/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_monthly_vote_attendance_for_bicameral_systems(
bicameral_person_1,
bicameral_person_2
):
run_test_for_bicameral_system(
'/v3/cards/person/monthly-vote-attendance/',
1,
bicameral_person_1,
bicameral_person_2
)
@pytest.mark.django_db()
def test_person_style_scores(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/style-scores/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_number_of_spoken_words(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/number-of-spoken-words/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_tfidf(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/tfidf/', [first_mandate_member, all_mandate_member])
@pytest.mark.django_db()
def test_person_media_reports(first_mandate_member, all_mandate_member):
run_test_urls('/v3/cards/person/media-reports/', [first_mandate_member, all_mandate_member])
# TODO: needs settings.SOLR_URL
# @pytest.mark.django_db()
# def test_person_speeches():
# response = client.get('/v3/cards/person/speeches/?id=19')
# assert response.status_code == 200
@pytest.mark.django_db()
def test_group_basic_information(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/basic-information/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_members(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/members/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_vocabulary_size(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/vocabulary-size/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_number_of_questions(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/number-of-questions/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_monthly_vote_attendance(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/monthly-vote-attendance/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_monthly_vote_attendance_for_bicameral_systems(
bicameral_org_1,
bicameral_org_2
):
run_test_for_bicameral_system(
'/v3/cards/group/monthly-vote-attendance/',
1,
bicameral_org_1,
bicameral_org_2
)
@pytest.mark.django_db()
def test_group_questions(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/questions/', [first_mandate_party, all_mandate_party])
# TODO we should also test the questions aren't all the same
@pytest.mark.django_db()
def test_group_vote_attendance(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/vote-attendance/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_votes(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/votes/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_most_votes_in_common(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/most-votes-in-common/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_least_votes_in_common(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/least-votes-in-common/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_deviation_from_group(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/deviation-from-group/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_tfidf(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/tfidf/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_style_scores(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/style-scores/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_discord(first_mandate_party, all_mandate_party):
run_test_urls('/v3/cards/group/discord/', [first_mandate_party, all_mandate_party])
@pytest.mark.django_db()
def test_group_discord_with_nonexistent_id():
response = client.get('/v3/cards/group/discord/?id=12000')
assert response.status_code == 404
@pytest.mark.django_db()
def test_group_media_reports():
response = client.get('/v3/cards/group/media-reports/?id=19')
assert response.status_code == 200
# TODO: needs settings.SOLR_URL
# @pytest.mark.django_db()
# def test_group_speeches():
# response = client.get('/v3/cards/group/speeches/?id=19')
# assert response.status_code == 200
@pytest.mark.django_db()
def test_session_legislation():
response = client.get('/v3/cards/session/legislation/?id=3782')
assert response.status_code == 200
@pytest.mark.django_db()
def test_session_speeches():
response = client.get('/v3/cards/session/speeches/?id=3782')
assert response.status_code == 200
@pytest.mark.django_db()
def test_session_votes():
response = client.get('/v3/cards/session/votes/?id=3782')
assert response.status_code == 200
@pytest.mark.django_db()
def test_session_votes():
response = client.get('/v3/cards/session/tfidf/?id=3782')
assert response.status_code == 200
@pytest.mark.django_db()
def test_single_speech():
response = client.get('/v3/cards/speech/single/?id=68974')
assert response.status_code == 200
@pytest.mark.django_db()
def test_single_vote():
response = client.get('/v3/cards/vote/single/?id=10229')
assert response.status_code == 200
@pytest.mark.django_db()
def test_single_vote_with_only_anonymous_ballots():
response = client.get('/v3/cards/vote/single/?id=10229')
assert response.status_code == 200
@pytest.mark.django_db()
def test_single_vote_with_some_anonymous_ballots():
response = client.get('/v3/cards/vote/single/?id=10229')
assert response.status_code == 200
@pytest.mark.django_db()
def test_single_vote_without_ballots():
response = client.get('/v3/cards/vote/single/?id=10229')
assert response.status_code == 200
@pytest.mark.django_db()
def test_single_session():
response = client.get('/v3/cards/session/single/?id=3782')
assert response.status_code == 200
@pytest.mark.django_db()
def test_session_agenda_items():
response = client.get('/v3/cards/session/agenda-items/?id=3782')
assert response.status_code == 200
@pytest.mark.django_db()
def test_session_minutes():
response = client.get('/v3/cards/session/minutes/?id=3782')
assert response.status_code == 200
@pytest.mark.django_db()
def test_single_law():
response = client.get('/v3/cards/legislation/single/?id=1403')
assert response.status_code == 200
@pytest.mark.django_db()
def test_single_minutes():
response = client.get('/v3/cards/minutes/single/?id=28813')
assert response.status_code == 200
# TODO: needs settings.SOLR_URL
# @pytest.mark.django_db()
# def test_search_speeches():
# response = client.get('/v3/cards/search/speeches/?id=1')
# assert response.status_code == 200
# TODO: needs settings.SOLR_URL
# @pytest.mark.django_db()
# def test_search_most_used_by_people():
# response = client.get('/v3/cards/search/most-used-by-people/?id=1')
# assert response.status_code == 200
# TODO: needs settings.SOLR_URL
# @pytest.mark.django_db()
# def test_search_usage_by_group():
# response = client.get('/v3/cards/search/usage-by-group/?id=1')
# assert response.status_code == 200
# TODO: needs settings.SOLR_URL
# @pytest.mark.django_db()
# def test_search_usage_through_time():
# response = client.get('/v3/cards/search/usage-through-time/?id=1')
# assert response.status_code == 200
@pytest.mark.django_db()
def test_search_votes():
response = client.get('/v3/cards/search/votes/?id=1')
assert response.status_code == 200
@pytest.mark.django_db()
def test_search_legislation():
response = client.get('/v3/cards/search/legislation/?id=1')
assert response.status_code == 200
@pytest.mark.django_db()
def test_create_and_get_quote():
response = client.post(
'/v3/cards/speech/quote/',
{
'speech': 68974,
'start_index': 6,
'end_index': 9,
'quote_content': 'dan'
}
)
assert response.status_code == 201
response = client.get('/v3/cards/speech/quote/?id=1')
assert response.status_code == 200
@pytest.mark.django_db()
def test_public_person_question():
response = client.post(
'/v3/cards/person/public-questions/?id=240',
{
'recaptcha': 'token',
'author_email': 'ivan@email.com',
'text': 'what what what',
'recipient_person': 240
}
)
assert response.status_code == 201
response = client.get('/v3/cards/person/public-questions/?id=240')
assert response.status_code == 200
# new question is not approved
assert len(response.json()['results']) == 0
@pytest.mark.django_db()
def test_validation_error_on_create_quote():
response = client.post(
'/v3/cards/speech/quote/',
{
'speech': 68974,
'start_index': 6,
'end_index': 10,
'quote_content': 'ivan'
}
)
assert response.status_code == 400
assert 'quote_content' in response.json().keys()
|
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"/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,360
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/models/question.py
|
from django.db import models
from parladata.behaviors.models import Timestampable
QUESTION_TYPES = [
('question', 'question'),
('initiative', 'initiative'),
('unknown', 'unknown'),
]
class Question(Timestampable):
"""All questions from members of parlament."""
session = models.ForeignKey(
'Session',
blank=True,
null=True,
on_delete=models.CASCADE,
help_text='The session this question belongs to.'
)
mandate = models.ForeignKey(
'Mandate',
blank=True,
null=True,
related_name='questions',
on_delete=models.SET_NULL,
help_text='The mandate of this question.'
)
timestamp = models.DateTimeField(
blank=True,
null=True,
help_text='Date of the question.'
)
answer_timestamp = models.DateTimeField(
blank=True,
null=True,
help_text='Date of answer the question.'
)
title = models.TextField(
blank=True,
null=True,
help_text='Title name as written on dz-rs.si'
)
person_authors = models.ManyToManyField(
'Person',
blank=True,
related_name='authored_questions',
help_text='The persons (MP) who asked the question.'
)
organization_authors = models.ManyToManyField(
'Organization',
blank=True,
help_text='Recipient organization (if it\'s an organization).',
related_name='questions_org_author'
)
recipient_people = models.ManyToManyField(
'Person',
blank=True,
help_text='Recipient person (if it\'s a person).',
related_name='received_questions'
)
recipient_organizations = models.ManyToManyField(
'Organization',
blank=True,
help_text='Recipient organization (if it\'s an organization).',
related_name='questions_org'
)
recipient_text = models.TextField(
blank=True,
null=True,
help_text='Recipient name as written on dz-rs.si'
)
type_of_question = models.TextField(
blank=True,
null=True,
choices=QUESTION_TYPES,
)
gov_id =models.TextField(
blank=True,
null=True,
help_text='Unique identifier of question on government site.'
)
def __str__(self):
person_author_names = " ".join([author.name for author in self.person_authors.all()])
organization_author_names = " ".join([author.name for author in self.organization_authors.all()])
author = person_author_names if person_author_names else organization_author_names
return f'{self.type_of_question}: {self.title} - {author}'
|
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"/parlacards/serializers/facets.py", "/parlacards/serializers/question.py", "/parlacards/serializers/agenda_item.py", "/parlacards/serializers/common.py", "/parlacards/serializers/public_question.py", "/parlacards/solr.py", "/parlacards/pagination.py", "/parlacards/recaptcha.py"], "/parladata/utils.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/agenda_item.py"], "/tests/scores/test_voting_distance.py": ["/parlacards/scores/voting_distance.py", "/tests/fixtures/common.py"], "/parladata/models/versionable_properties.py": ["/parladata/behaviors/models.py", "/parladata/models/common.py"], "/parladata/models/legislation.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/set_tfidf.py": ["/parladata/models/__init__.py", "/parlacards/scores/tfidf.py", "/parlacards/utils.py"], "/export/resources/group.py": ["/export/resources/common.py", "/parlacards/models.py", "/parladata/models/__init__.py", "/parladata/models/common.py"], "/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,361
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/migrations/0053_auto_20211201_1150.py
|
# Generated by Django 3.2.9 on 2021-12-01 11:50
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('parladata', '0052_auto_20211130_1124'),
]
operations = [
migrations.CreateModel(
name='LegislationStatus',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_at', models.DateTimeField(auto_now_add=True, db_index=True)),
('updated_at', models.DateTimeField(auto_now=True, db_index=True)),
('name', models.TextField(blank=True, help_text='Status of legisaltion', null=True)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='Procedure',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_at', models.DateTimeField(auto_now_add=True, db_index=True)),
('updated_at', models.DateTimeField(auto_now=True, db_index=True)),
('type', models.TextField(blank=True, help_text='Name of procedure type', null=True)),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='ProcedurePhase',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_at', models.DateTimeField(auto_now_add=True, db_index=True)),
('updated_at', models.DateTimeField(auto_now=True, db_index=True)),
('name', models.TextField(blank=True, help_text='Name of procedure phase', null=True)),
('procedure', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='parladata.procedure')),
],
options={
'abstract': False,
},
),
migrations.CreateModel(
name='LegislationConsideration',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_at', models.DateTimeField(auto_now_add=True, db_index=True)),
('updated_at', models.DateTimeField(auto_now=True, db_index=True)),
('timestamp', models.DateTimeField(blank=True, help_text='Date of the law.', null=True)),
('legislation', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='parladata.law')),
('organization', models.ForeignKey(blank=True, help_text='Organization in which consideration happened', null=True, on_delete=django.db.models.deletion.CASCADE, to='parladata.organization')),
('procedure_phase', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='parladata.procedurephase')),
],
options={
'abstract': False,
},
),
migrations.AddField(
model_name='law',
name='considerations',
field=models.ManyToManyField(blank=True, help_text='Consideration of legislation', through='parladata.LegislationConsideration', to='parladata.ProcedurePhase'),
),
migrations.AddField(
model_name='link',
name='legislation_consideration',
field=models.ForeignKey(blank=True, help_text='The legislation consideration this link belongs to.', null=True, on_delete=django.db.models.deletion.CASCADE, related_name='links', to='parladata.legislationconsideration'),
),
migrations.AlterField(
model_name='law',
name='status',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='parladata.legislationstatus'),
),
]
|
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"/parladata/update_utils.py"], "/parlacards/serializers/voting_distance.py": ["/parlacards/serializers/common.py"], "/parladata/management/commands/mergepeople.py": ["/parladata/models/__init__.py"], "/parlacards/lemmatizers/ua/lemmatizer.py": ["/parlacards/lemmatizers/ua/stop_words.py"], "/parladata/apps.py": ["/parladata/signals.py"], "/parlacards/management/commands/upload_legislation_to_solr.py": ["/parladata/models/legislation.py", "/parlacards/solr.py"], "/parlacards/scores/tfidf.py": ["/parladata/models/session.py", "/parladata/models/common.py", "/parladata/models/speech.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/utils.py", "/parlacards/scores/common.py"], "/tests/resources/test_misc_resources.py": ["/tests/fixtures/common.py", "/export/resources/misc.py"], "/parladata/admin/link.py": ["/parladata/models/link.py", "/parladata/admin/filters.py"], "/parlacards/serializers/cards/misc/groups.py": ["/parlacards/serializers/common.py", "/parlacards/models.py", "/parladata/models/memberships.py"], "/parladata/models/task.py": ["/parladata/behaviors/models.py"], "/parladata_project/settings/local.py": ["/parladata_project/settings/defaults.py"], "/parlacards/serializers/question.py": ["/parlacards/serializers/common.py"], "/parlacards/scores/deviation_from_group.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/update_helpers/sl/legislation.py": ["/parladata/models/__init__.py"], "/parladata/admin/filters.py": ["/parladata/models/memberships.py", "/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/legislation.py"], "/parlacards/management/commands/upload_speeches_to_solr.py": ["/parladata/models/speech.py", "/parlacards/solr.py"], "/tests/conftest.py": ["/parladata/models/organization.py", "/parladata/models/person.py"], "/parlacards/serializers/agenda_item.py": ["/parladata/models/agenda_item.py", "/parladata/models/link.py", "/parladata/models/vote.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parlacards/serializers/session.py"], "/parladata/admin/speech.py": ["/parladata/models/speech.py", "/parladata/models/motion.py", "/parladata/admin/filters.py"], "/tests/scores/test_attendance.py": ["/parlacards/scores/attendance.py", "/tests/fixtures/common.py"], "/parladata/models/__init__.py": ["/parladata/models/versionable_properties.py", "/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parladata/models/area.py", "/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/motion.py", "/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/legislation.py", "/parladata/models/agenda_item.py", "/parladata/models/question.py", "/parladata/models/public_question.py", "/parladata/models/link.py", "/parladata/models/document.py", "/parladata/models/media.py"], "/parladata/models/media.py": ["/parladata/behaviors/models.py"], "/parladata/admin/__init__.py": ["/parladata/admin/admin.py", "/parladata/admin/person.py", "/parladata/admin/organization.py", "/parladata/admin/session.py", "/parladata/admin/speech.py", "/parladata/admin/agenda_item.py", "/parladata/admin/vote.py", "/parladata/admin/motion.py", "/parladata/admin/memberships.py", "/parladata/admin/media.py", "/parladata/admin/legislation.py", "/parladata/admin/question.py", "/parladata/admin/public_question.py"], "/export/resources/misc.py": ["/export/resources/common.py", "/parladata/models/__init__.py", "/parlacards/models.py", "/parladata/models/person.py"], "/parladata/views.py": ["/parladata/forms.py", "/parladata/utils.py", "/parladata/models/person.py", "/parladata/models/ballot.py", "/parladata/models/vote.py", "/parladata/models/task.py", "/parladata/models/common.py"], "/parladata/models/link.py": ["/parladata/behaviors/models.py"], "/parladata/management/commands/create_editor_group.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/models/common.py", "/parlacards/models.py"], "/parlacards/scores/monthly_attendance.py": ["/parladata/models/ballot.py", "/parladata/models/vote.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/urls.py": ["/export/views/person.py", "/export/views/misc.py", "/export/views/group.py", "/export/views/session.py"], "/parladata/management/commands/min_seed.py": ["/parladata/models/versionable_properties.py", "/parladata/models/__init__.py"], "/parlacards/serializers/cards/misc/sessions.py": ["/parlacards/pagination.py", "/parlacards/serializers/common.py", "/parlacards/serializers/session.py", "/parladata/models/organization.py", "/parladata/models/session.py", 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"/parlacards/serializers/facets.py", "/parlacards/serializers/question.py", "/parlacards/serializers/agenda_item.py", "/parlacards/serializers/common.py", "/parlacards/serializers/public_question.py", "/parlacards/solr.py", "/parlacards/pagination.py", "/parlacards/recaptcha.py"], "/parladata/utils.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/agenda_item.py"], "/tests/scores/test_voting_distance.py": ["/parlacards/scores/voting_distance.py", "/tests/fixtures/common.py"], "/parladata/models/versionable_properties.py": ["/parladata/behaviors/models.py", "/parladata/models/common.py"], "/parladata/models/legislation.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/set_tfidf.py": ["/parladata/models/__init__.py", "/parlacards/scores/tfidf.py", "/parlacards/utils.py"], "/export/resources/group.py": ["/export/resources/common.py", "/parlacards/models.py", "/parladata/models/__init__.py", "/parladata/models/common.py"], "/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,362
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/management/commands/delete_all_sessions.py
|
from django.core.management.base import BaseCommand, CommandError
from parladata.models import Session
class Command(BaseCommand):
help = 'Merges people together'
def handle(self, *args, **options):
self.stdout.write('I am about to delete all sessions')
self.stdout.write('\n')
Session.objects.all().delete()
self.stdout.write('\n')
self.stdout.write('DONE')
|
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"/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,363
|
danesjenovdan/parladata
|
refs/heads/dev
|
/tests/scores/test_number_of_questions.py
|
import pytest
from parlacards.scores.number_of_questions import calculate_number_of_questions_from_person
from tests.fixtures.common import *
@pytest.mark.django_db()
def test_calculate_number_of_questions_from_person(
first_person,
second_person,
last_person,
ending_date_of_first_mandate
):
number_of_questions = calculate_number_of_questions_from_person(first_person, ending_date_of_first_mandate)
assert number_of_questions == 0
number_of_questions = calculate_number_of_questions_from_person(second_person, ending_date_of_first_mandate)
assert number_of_questions == 1
number_of_questions = calculate_number_of_questions_from_person(last_person, ending_date_of_first_mandate)
assert number_of_questions == 0
|
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"/export/views/person.py": ["/export/resources/person.py", "/export/views/common.py"], "/export/views/session.py": ["/export/views/common.py", "/export/resources/session.py"], "/parladata/models/motion.py": ["/parladata/behaviors/models.py"], "/parlacards/admin/discord.py": ["/export/resources/group.py", "/parlacards/models.py", "/parlacards/admin/common.py"], "/parladata/tasks.py": ["/parladata/models/__init__.py", "/parlacards/solr.py"], "/parladata/management/commands/prepare_test_database.py": ["/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/vote.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py"], "/tests/scores/test_deviation_from_group.py": ["/parlacards/scores/deviation_from_group.py", "/tests/fixtures/common.py"], "/parladata/models/speech.py": ["/parladata/behaviors/models.py", "/parlacards/scores/common.py"], "/parladata/admin/public_question.py": ["/parladata/models/public_question.py"], "/parlacards/views.py": ["/parladata/models/person.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/vote.py", "/parladata/models/legislation.py", "/parladata/models/agenda_item.py", "/parlacards/models.py", "/parlacards/serializers/cards/__init__.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/group_attendance.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/public_question.py"], "/parladata/admin/agenda_item.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/admin/admin.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/versionable_properties.py", "/parladata/models/common.py", "/parladata/admin/filters.py", "/parladata/admin/link.py"], "/parlacards/serializers/facets.py": ["/parlacards/serializers/common.py"], "/export/resources/person.py": ["/parladata/models/__init__.py", "/parlacards/models.py", "/export/resources/common.py"], "/parlacards/management/commands/set_tfidf_for_sessions.py": ["/parlacards/scores/tfidf.py", "/parlacards/utils.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/speech.py"], "/parladata/models/common.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py"], "/export/views/group.py": ["/export/resources/group.py", "/export/views/common.py"], "/tests/scores/test_style.py": ["/parladata/models/speech.py", "/parlacards/scores/style.py", "/tests/fixtures/common.py"], "/parlacards/serializers/area.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/person.py": ["/parladata/models/link.py", "/parlacards/serializers/common.py", "/parlacards/serializers/area.py"], "/parladata/models/agenda_item.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/link.py": ["/parladata/models/link.py"], "/parlacards/serializers/session.py": ["/parlacards/serializers/common.py", "/parladata/models/agenda_item.py", "/parladata/models/vote.py"], "/parlacards/admin/__init__.py": ["/parlacards/admin/number_of_spoken_words.py", "/parlacards/admin/discord.py", "/parlacards/admin/admin.py"], "/parlacards/serializers/motion.py": ["/parlacards/serializers/common.py"], "/parlacards/management/commands/cache_votes.py": ["/parladata/models/vote.py", "/parlacards/serializers/vote.py"], "/tests/scores/test_group_attenadance_on_session.py": ["/parlacards/scores/session_attendance.py", "/tests/fixtures/common.py"], "/parladata/models/person.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parladata/exceptions.py"], "/parlacards/serializers/cards/__init__.py": ["/parlacards/serializers/cards/misc/last_session.py", "/parlacards/serializers/cards/misc/members.py", "/parlacards/serializers/cards/misc/groups.py", "/parlacards/serializers/cards/misc/legislation.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/cards/person/questions.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/group/questions.py", "/parlacards/serializers/cards/group/votes.py", "/parlacards/serializers/cards/session/single.py", "/parlacards/serializers/cards/session/tfidf.py", "/parlacards/serializers/cards/session/legislation.py", "/parlacards/serializers/cards/search/votes.py", "/parlacards/serializers/cards/search/usage_through_time.py", "/parlacards/serializers/cards/cards.py"], "/parladata/management/commands/create_sl_education_levels.py": ["/parladata/models/common.py"], "/test/data_test.py": ["/parladata/models/__init__.py"], 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"/parlacards/serializers/facets.py", "/parlacards/serializers/question.py", "/parlacards/serializers/agenda_item.py", "/parlacards/serializers/common.py", "/parlacards/serializers/public_question.py", "/parlacards/solr.py", "/parlacards/pagination.py", "/parlacards/recaptcha.py"], "/parladata/utils.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/agenda_item.py"], "/tests/scores/test_voting_distance.py": ["/parlacards/scores/voting_distance.py", "/tests/fixtures/common.py"], "/parladata/models/versionable_properties.py": ["/parladata/behaviors/models.py", "/parladata/models/common.py"], "/parladata/models/legislation.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/set_tfidf.py": ["/parladata/models/__init__.py", "/parlacards/scores/tfidf.py", "/parlacards/utils.py"], "/export/resources/group.py": ["/export/resources/common.py", "/parlacards/models.py", "/parladata/models/__init__.py", "/parladata/models/common.py"], "/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,364
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/migrations/0065_task_module_name.py
|
# Generated by Django 3.2.12 on 2022-11-21 17:17
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('parladata', '0064_auto_20220908_1825'),
]
operations = [
migrations.AddField(
model_name='task',
name='module_name',
field=models.TextField(default='parladata.tasks', help_text='Name of task'),
),
]
|
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"/parlacards/models.py", "/parladata/models/memberships.py"], "/parladata/models/task.py": ["/parladata/behaviors/models.py"], "/parladata_project/settings/local.py": ["/parladata_project/settings/defaults.py"], "/parlacards/serializers/question.py": ["/parlacards/serializers/common.py"], "/parlacards/scores/deviation_from_group.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/update_helpers/sl/legislation.py": ["/parladata/models/__init__.py"], "/parladata/admin/filters.py": ["/parladata/models/memberships.py", "/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/legislation.py"], "/parlacards/management/commands/upload_speeches_to_solr.py": ["/parladata/models/speech.py", "/parlacards/solr.py"], "/tests/conftest.py": ["/parladata/models/organization.py", "/parladata/models/person.py"], 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["/parladata/behaviors/models.py"], "/parladata/management/commands/fetch_members_articles.py": ["/parladata/models/media.py", "/parladata/models/common.py"], "/parladata/models/public_question.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/speech.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parladata/models/vote.py"], "/parladata/management/commands/import_sentio_data.py": ["/parladata/models/__init__.py", "/parladata/models/common.py"], "/parladata/models/document.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/flush_solr.py": ["/parlacards/solr.py"], "/parladata/admin/question.py": ["/parladata/admin/filters.py", "/parladata/models/question.py", "/parladata/admin/link.py"], "/parladata/models/memberships.py": ["/parladata/behaviors/models.py"], "/parlacards/pagination.py": ["/parlacards/solr.py"], "/parlacards/serializers/common.py": ["/parladata/models/person.py", 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["/parladata/models/person.py", "/parladata/models/organization.py", "/parlacards/serializers/common.py", "/parlacards/models.py"], "/parlacards/admin_views.py": ["/parlacards/utils.py", "/parladata/models/task.py", "/parladata/models/session.py"], "/tests/scores/test_number_of_spoken_words.py": ["/parladata/models/speech.py", "/parlacards/scores/number_of_spoken_words.py", "/tests/fixtures/common.py"], "/parlacards/scores/vocabulary_size.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/tests/resources/test_person_resources.py": ["/tests/fixtures/common.py", "/export/resources/person.py"], "/parlacards/serializers/cards/session/legislation.py": ["/parlacards/pagination.py", "/parlacards/serializers/cards/misc/legislation.py", "/parlacards/serializers/common.py", "/parlacards/serializers/legislation.py"], "/parlacards/management/commands/lemmatize_speeches.py": ["/parladata/models/speech.py"], "/export/views/person.py": ["/export/resources/person.py", "/export/views/common.py"], "/export/views/session.py": ["/export/views/common.py", "/export/resources/session.py"], "/parladata/models/motion.py": ["/parladata/behaviors/models.py"], "/parlacards/admin/discord.py": ["/export/resources/group.py", "/parlacards/models.py", "/parlacards/admin/common.py"], "/parladata/tasks.py": ["/parladata/models/__init__.py", "/parlacards/solr.py"], "/parladata/management/commands/prepare_test_database.py": ["/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/vote.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py"], "/tests/scores/test_deviation_from_group.py": ["/parlacards/scores/deviation_from_group.py", "/tests/fixtures/common.py"], "/parladata/models/speech.py": ["/parladata/behaviors/models.py", "/parlacards/scores/common.py"], "/parladata/admin/public_question.py": ["/parladata/models/public_question.py"], "/parlacards/views.py": ["/parladata/models/person.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/vote.py", "/parladata/models/legislation.py", "/parladata/models/agenda_item.py", "/parlacards/models.py", "/parlacards/serializers/cards/__init__.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/group_attendance.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/public_question.py"], "/parladata/admin/agenda_item.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/admin/admin.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/versionable_properties.py", "/parladata/models/common.py", "/parladata/admin/filters.py", "/parladata/admin/link.py"], "/parlacards/serializers/facets.py": ["/parlacards/serializers/common.py"], "/export/resources/person.py": ["/parladata/models/__init__.py", "/parlacards/models.py", "/export/resources/common.py"], "/parlacards/management/commands/set_tfidf_for_sessions.py": ["/parlacards/scores/tfidf.py", "/parlacards/utils.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/speech.py"], "/parladata/models/common.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py"], "/export/views/group.py": ["/export/resources/group.py", "/export/views/common.py"], "/tests/scores/test_style.py": ["/parladata/models/speech.py", "/parlacards/scores/style.py", "/tests/fixtures/common.py"], "/parlacards/serializers/area.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/person.py": ["/parladata/models/link.py", "/parlacards/serializers/common.py", "/parlacards/serializers/area.py"], "/parladata/models/agenda_item.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/link.py": ["/parladata/models/link.py"], "/parlacards/serializers/session.py": ["/parlacards/serializers/common.py", "/parladata/models/agenda_item.py", "/parladata/models/vote.py"], "/parlacards/admin/__init__.py": ["/parlacards/admin/number_of_spoken_words.py", "/parlacards/admin/discord.py", "/parlacards/admin/admin.py"], "/parlacards/serializers/motion.py": ["/parlacards/serializers/common.py"], "/parlacards/management/commands/cache_votes.py": ["/parladata/models/vote.py", "/parlacards/serializers/vote.py"], "/tests/scores/test_group_attenadance_on_session.py": ["/parlacards/scores/session_attendance.py", "/tests/fixtures/common.py"], "/parladata/models/person.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parladata/exceptions.py"], "/parlacards/serializers/cards/__init__.py": ["/parlacards/serializers/cards/misc/last_session.py", "/parlacards/serializers/cards/misc/members.py", "/parlacards/serializers/cards/misc/groups.py", "/parlacards/serializers/cards/misc/legislation.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/cards/person/questions.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/group/questions.py", "/parlacards/serializers/cards/group/votes.py", "/parlacards/serializers/cards/session/single.py", "/parlacards/serializers/cards/session/tfidf.py", "/parlacards/serializers/cards/session/legislation.py", "/parlacards/serializers/cards/search/votes.py", "/parlacards/serializers/cards/search/usage_through_time.py", "/parlacards/serializers/cards/cards.py"], "/parladata/management/commands/create_sl_education_levels.py": ["/parladata/models/common.py"], "/test/data_test.py": ["/parladata/models/__init__.py"], "/parlacards/serializers/organization.py": ["/parladata/models/link.py", "/parlacards/serializers/common.py"], "/parladata/models/organization.py": ["/parladata/models/link.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/ballot.py", "/parladata/behaviors/models.py", "/parladata/exceptions.py", "/parladata/models/versionable_properties.py"], "/parlacards/serializers/vote.py": ["/parladata/models/ballot.py", "/parlacards/serializers/session.py", "/parlacards/serializers/link.py", "/parlacards/serializers/common.py"], "/scripts/pl_parser.py": ["/sandbox/import_pl.py"], "/parladata/management/commands/pair_votes_and_speeches.py": ["/parladata/update_utils.py"], "/parladata/models/vote.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py", "/parladata/models/ballot.py"], "/parladata_project/settings/k8s.py": ["/parladata_project/settings/defaults.py"], "/parlacards/tasks.py": ["/parlacards/scores/tfidf.py", "/parlacards/utils.py", "/parladata/models/__init__.py"], "/parlacards/serializers/cards/cards.py": ["/parladata/models/agenda_item.py", "/parladata/models/ballot.py", "/parladata/models/media.py", "/parladata/models/vote.py", "/parladata/models/question.py", "/parladata/models/memberships.py", "/parladata/models/legislation.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/common.py", "/parladata/models/public_question.py", "/parlacards/models.py", "/parlacards/serializers/media.py", "/parlacards/serializers/person.py", "/parlacards/serializers/organization.py", "/parlacards/serializers/legislation.py", "/parlacards/serializers/ballot.py", "/parlacards/serializers/voting_distance.py", "/parlacards/serializers/membership.py", "/parlacards/serializers/style_scores.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/tfidf.py", "/parlacards/serializers/facets.py", "/parlacards/serializers/question.py", "/parlacards/serializers/agenda_item.py", "/parlacards/serializers/common.py", "/parlacards/serializers/public_question.py", "/parlacards/solr.py", "/parlacards/pagination.py", "/parlacards/recaptcha.py"], "/parladata/utils.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/agenda_item.py"], "/tests/scores/test_voting_distance.py": ["/parlacards/scores/voting_distance.py", "/tests/fixtures/common.py"], "/parladata/models/versionable_properties.py": ["/parladata/behaviors/models.py", "/parladata/models/common.py"], "/parladata/models/legislation.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/set_tfidf.py": ["/parladata/models/__init__.py", "/parlacards/scores/tfidf.py", "/parlacards/utils.py"], "/export/resources/group.py": ["/export/resources/common.py", "/parlacards/models.py", "/parladata/models/__init__.py", "/parladata/models/common.py"], "/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,365
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/migrations/0017_auto_20210511_1240.py
|
# Generated by Django 3.2 on 2021-05-11 12:40
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('parladata', '0016_auto_20210511_1223'),
]
operations = [
migrations.RenameField(
model_name='motion',
old_name='recap',
new_name='summary',
),
migrations.RemoveField(
model_name='motion',
name='agenda_item',
),
migrations.RemoveField(
model_name='motion',
name='date',
),
migrations.RemoveField(
model_name='motion',
name='doc_title',
),
migrations.RemoveField(
model_name='motion',
name='organization',
),
migrations.RemoveField(
model_name='motion',
name='party',
),
migrations.RemoveField(
model_name='motion',
name='person',
),
migrations.AddField(
model_name='motion',
name='agenda_items',
field=models.ManyToManyField(blank=True, help_text='Agenda items', related_name='motions', to='parladata.AgendaItem'),
),
migrations.AddField(
model_name='motion',
name='champions',
field=models.ManyToManyField(help_text='The people who proposed the motion.', to='parladata.Person'),
),
migrations.AddField(
model_name='motion',
name='datetime',
field=models.DateTimeField(blank=True, help_text='The date and time when the motion was proposed', null=True),
),
migrations.AlterField(
model_name='motion',
name='classification',
field=models.TextField(blank=True, help_text='Motion classification', null=True),
),
migrations.AlterField(
model_name='motion',
name='requirement',
field=models.TextField(blank=True, help_text='The requirement for the motion to pass', null=True),
),
migrations.AlterField(
model_name='motion',
name='result',
field=models.TextField(blank=True, help_text='Did the motion pass?', null=True),
),
migrations.AlterField(
model_name='motion',
name='title',
field=models.TextField(blank=True, help_text='Title of the motion', null=True),
),
migrations.AlterField(
model_name='motion',
name='uid',
field=models.TextField(blank=True, help_text='motions uid from DZ page', null=True),
),
]
|
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"/parlacards/utils.py", "/parladata/models/__init__.py"], "/parlacards/serializers/cards/cards.py": ["/parladata/models/agenda_item.py", "/parladata/models/ballot.py", "/parladata/models/media.py", "/parladata/models/vote.py", "/parladata/models/question.py", "/parladata/models/memberships.py", "/parladata/models/legislation.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/common.py", "/parladata/models/public_question.py", "/parlacards/models.py", "/parlacards/serializers/media.py", "/parlacards/serializers/person.py", "/parlacards/serializers/organization.py", "/parlacards/serializers/legislation.py", "/parlacards/serializers/ballot.py", "/parlacards/serializers/voting_distance.py", "/parlacards/serializers/membership.py", "/parlacards/serializers/style_scores.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/tfidf.py", "/parlacards/serializers/facets.py", "/parlacards/serializers/question.py", "/parlacards/serializers/agenda_item.py", "/parlacards/serializers/common.py", "/parlacards/serializers/public_question.py", "/parlacards/solr.py", "/parlacards/pagination.py", "/parlacards/recaptcha.py"], "/parladata/utils.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/agenda_item.py"], "/tests/scores/test_voting_distance.py": ["/parlacards/scores/voting_distance.py", "/tests/fixtures/common.py"], "/parladata/models/versionable_properties.py": ["/parladata/behaviors/models.py", "/parladata/models/common.py"], "/parladata/models/legislation.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/set_tfidf.py": ["/parladata/models/__init__.py", "/parlacards/scores/tfidf.py", "/parlacards/utils.py"], "/export/resources/group.py": ["/export/resources/common.py", "/parlacards/models.py", "/parladata/models/__init__.py", "/parladata/models/common.py"], "/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,366
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/management/commands/startparlameter.py
|
from django.core.management.base import BaseCommand, CommandError
from parladata.models import Organization
class Command(BaseCommand):
help = 'Merges people together'
def add_arguments(self, parser):
parser.add_argument('people', nargs='+')
def handle(self, *args, **options):
self.stdout.write('I am about to start new data nedded for parlameter')
self.stdout.write('\n')
dz = Organization(name='DZ')
dz.save()
coalition = Organization(name='Coalition', classification='Coalition', parent=dz)
coalition.save()
opposition = Organization(name='Opposition', classification='Opposition', parent=dz)
opposition.save()
self.stdout.write('\n')
self.stdout.write('DONE')
|
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"/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": 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"/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,367
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/migrations/0051_rename_authors_question_person_authors.py
|
# Generated by Django 3.2.9 on 2021-11-29 17:47
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('parladata', '0050_mediareport_uri'),
]
operations = [
migrations.RenameField(
model_name='question',
old_name='authors',
new_name='person_authors',
),
]
|
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["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,368
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/management/commands/run_tasks.py
|
from django.core.management.base import BaseCommand, CommandError
from django.contrib.auth.models import Group
from django.conf import settings
from django.utils.translation import gettext as _
from importlib import import_module
from parladata.models.task import Task
from datetime import datetime
from parladata.update_utils import send_email
class Command(BaseCommand):
help = 'Run '
def handle(self, *args, **options):
tasks = Task.objects.filter(started_at__isnull=True).order_by('created_at')
msgs = []
for task in tasks:
try:
# skip task if it's started in another runner
check_task = Task.objects.get(id=task.id)
if check_task.started_at:
continue
tasks_module = import_module(task.module_name)
task_method = getattr(tasks_module, task.name)
task.started_at = datetime.now()
task.save()
task_method(**task.payload)
task.finished_at = datetime.now()
task.save()
if task.email_msg:
msgs.append(task.email_msg)
except Exception as e:
self.stdout.write(str(e))
task.errored_at = datetime.now()
task.save()
if msgs:
editor_permission_group = Group.objects.filter(name__icontains="editor").first()
for editor in editor_permission_group.user_set.all():
send_email(
_('Completed tasks at the Parlameter ') + settings.INSTALATION_NAME,
editor.email,
'email_on_tasks_compeleted.html',
{
'base_url': settings.BASE_URL,
'msgs': msgs,
'instalation_name': settings.INSTALATION_NAME
}
)
|
{"/parladata_project/urls.py": ["/parladata/views.py", "/parlacards/admin_views.py"], "/sandbox/uk_imports.py": ["/parladata/models/__init__.py"], "/parladata/migrations/0002_organizationmembership.py": ["/parladata/models/__init__.py"], "/parladata/admin.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/setVotesResult.py": ["/parladata/models/__init__.py"], "/parladata/api_views.py": ["/parladata/models/__init__.py", "/parladata/serializers.py", "/parladata/models/versionable_properties.py"], "/parladata/serializers.py": ["/parladata/models/__init__.py"], "/parladata/forms.py": ["/parladata/models/__init__.py"], "/parladata/urls_v2.py": ["/parladata/views.py", "/parladata/utils.py"], "/sandbox/utils.py": ["/parladata/models/__init__.py"], "/sandbox/urls.py": ["/sandbox/counters.py"], "/parlacards/serializers/cards/person/recent_activity.py": ["/parlacards/pagination.py", "/parlacards/serializers/common.py", "/parlacards/serializers/recent_activity.py", "/parladata/models/ballot.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/common.py"], "/parlacards/management/commands/upload_votes_to_solr.py": ["/parladata/models/vote.py", "/parlacards/serializers/vote.py", "/parlacards/solr.py"], "/parladata/management/commands/send_daily_notifications.py": ["/parladata/update_utils.py"], "/tests/views/test_card_views.py": ["/tests/fixtures/common.py", "/parlacards/views.py"], "/parladata/models/question.py": ["/parladata/behaviors/models.py"], "/parladata/management/commands/delete_all_sessions.py": ["/parladata/models/__init__.py"], "/tests/scores/test_number_of_questions.py": ["/parlacards/scores/number_of_questions.py", "/tests/fixtures/common.py"], "/parladata/management/commands/startparlameter.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/run_tasks.py": ["/parladata/models/task.py", "/parladata/update_utils.py"], "/parladata/admin/motion.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/versionable_properties.py", "/parladata/models/common.py", "/parladata/admin/link.py", "/parladata/admin/filters.py"], "/parladata/admin/session.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parlacards/admin/number_of_spoken_words.py": ["/export/resources/person.py", "/parlacards/models.py", "/parlacards/admin/common.py"], "/parladata/migrations/0002_question_date_of_answer.py": ["/parladata/models/__init__.py"], "/tests/scores/test_tfidf.py": ["/parlacards/scores/common.py", "/parlacards/scores/tfidf.py", "/tests/fixtures/common.py"], "/test/test_votes.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/create_absent_ballots_for_voters.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/set_legislation_results.py": ["/parladata/update_helpers/methods.py"], "/parladata/management/commands/public_questions_notifications.py": ["/parladata/models/public_question.py", "/parladata/update_utils.py"], "/parlacards/serializers/voting_distance.py": ["/parlacards/serializers/common.py"], "/parladata/management/commands/mergepeople.py": ["/parladata/models/__init__.py"], "/parlacards/lemmatizers/ua/lemmatizer.py": ["/parlacards/lemmatizers/ua/stop_words.py"], "/parladata/apps.py": ["/parladata/signals.py"], "/parlacards/management/commands/upload_legislation_to_solr.py": ["/parladata/models/legislation.py", "/parlacards/solr.py"], "/parlacards/scores/tfidf.py": ["/parladata/models/session.py", "/parladata/models/common.py", "/parladata/models/speech.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/utils.py", "/parlacards/scores/common.py"], "/tests/resources/test_misc_resources.py": ["/tests/fixtures/common.py", "/export/resources/misc.py"], "/parladata/admin/link.py": ["/parladata/models/link.py", "/parladata/admin/filters.py"], "/parlacards/serializers/cards/misc/groups.py": ["/parlacards/serializers/common.py", "/parlacards/models.py", "/parladata/models/memberships.py"], "/parladata/models/task.py": ["/parladata/behaviors/models.py"], "/parladata_project/settings/local.py": ["/parladata_project/settings/defaults.py"], "/parlacards/serializers/question.py": ["/parlacards/serializers/common.py"], "/parlacards/scores/deviation_from_group.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/update_helpers/sl/legislation.py": ["/parladata/models/__init__.py"], "/parladata/admin/filters.py": ["/parladata/models/memberships.py", "/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/legislation.py"], "/parlacards/management/commands/upload_speeches_to_solr.py": ["/parladata/models/speech.py", "/parlacards/solr.py"], "/tests/conftest.py": ["/parladata/models/organization.py", "/parladata/models/person.py"], "/parlacards/serializers/agenda_item.py": ["/parladata/models/agenda_item.py", "/parladata/models/link.py", "/parladata/models/vote.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parlacards/serializers/session.py"], "/parladata/admin/speech.py": ["/parladata/models/speech.py", "/parladata/models/motion.py", "/parladata/admin/filters.py"], "/tests/scores/test_attendance.py": ["/parlacards/scores/attendance.py", "/tests/fixtures/common.py"], "/parladata/models/__init__.py": ["/parladata/models/versionable_properties.py", "/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parladata/models/area.py", "/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/motion.py", "/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/legislation.py", "/parladata/models/agenda_item.py", "/parladata/models/question.py", "/parladata/models/public_question.py", "/parladata/models/link.py", "/parladata/models/document.py", "/parladata/models/media.py"], "/parladata/models/media.py": ["/parladata/behaviors/models.py"], "/parladata/admin/__init__.py": ["/parladata/admin/admin.py", "/parladata/admin/person.py", "/parladata/admin/organization.py", "/parladata/admin/session.py", "/parladata/admin/speech.py", "/parladata/admin/agenda_item.py", "/parladata/admin/vote.py", "/parladata/admin/motion.py", "/parladata/admin/memberships.py", "/parladata/admin/media.py", "/parladata/admin/legislation.py", "/parladata/admin/question.py", "/parladata/admin/public_question.py"], "/export/resources/misc.py": ["/export/resources/common.py", "/parladata/models/__init__.py", "/parlacards/models.py", "/parladata/models/person.py"], "/parladata/views.py": ["/parladata/forms.py", "/parladata/utils.py", "/parladata/models/person.py", "/parladata/models/ballot.py", "/parladata/models/vote.py", "/parladata/models/task.py", "/parladata/models/common.py"], "/parladata/models/link.py": ["/parladata/behaviors/models.py"], "/parladata/management/commands/create_editor_group.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/models/common.py", "/parlacards/models.py"], "/parlacards/scores/monthly_attendance.py": ["/parladata/models/ballot.py", "/parladata/models/vote.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/urls.py": ["/export/views/person.py", "/export/views/misc.py", "/export/views/group.py", "/export/views/session.py"], "/parladata/management/commands/min_seed.py": ["/parladata/models/versionable_properties.py", "/parladata/models/__init__.py"], "/parlacards/serializers/cards/misc/sessions.py": ["/parlacards/pagination.py", "/parlacards/serializers/common.py", "/parlacards/serializers/session.py", "/parladata/models/organization.py", "/parladata/models/session.py", "/parladata/models/versionable_properties.py"], "/parlacards/serializers/group_attendance.py": ["/parlacards/serializers/common.py", "/parlacards/models.py"], "/parladata/admin/media.py": ["/parladata/models/__init__.py"], "/parlacards/scores/session_attendance.py": ["/parladata/models/ballot.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/urls.py": ["/parladata/api_views.py", "/parladata/views.py"], "/parlacards/serializers/recent_activity.py": ["/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/question.py", "/parlacards/serializers/ballot.py", "/parlacards/serializers/question.py", "/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/parladata/admin/person.py": ["/export/resources/misc.py", "/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/management/commands/set_votes_result.py": ["/parladata/update_utils.py"], "/tests/scores/test_vocabulary_size.py": ["/parladata/models/speech.py", "/parlacards/scores/vocabulary_size.py", "/tests/fixtures/common.py"], "/parlacards/urls.py": ["/parlacards/views.py"], "/parlacards/models.py": ["/parladata/behaviors/models.py"], "/parladata/models/session.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/cards/group/questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/question.py", "/parlacards/pagination.py"], "/parlacards/serializers/public_question.py": ["/parladata/models/public_question.py"], "/parlacards/serializers/ballot.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py"], "/parlacards/solr.py": ["/parladata/models/speech.py", "/parladata/models/vote.py", "/parladata/models/legislation.py", "/parladata/models/agenda_item.py", "/parladata/models/common.py"], "/parlacards/serializers/media.py": ["/parlacards/serializers/common.py"], "/parlacards/scores/seed.py": ["/parladata/models/organization.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/attendance.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/style.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/tfidf.py", "/parlacards/scores/discord.py", "/parlacards/scores/session_attendance.py"], "/parlacards/serializers/cards/search/usage_through_time.py": ["/parlacards/serializers/common.py", "/parlacards/solr.py"], "/parladata/admin/memberships.py": ["/parladata/admin/link.py", "/parladata/models/__init__.py", "/parladata/admin/filters.py", "/export/resources/misc.py"], "/parlacards/serializers/cards/search/votes.py": ["/parladata/models/__init__.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/solr.py", "/parlacards/pagination.py"], "/parlacards/serializers/cards/session/tfidf.py": ["/parlacards/models.py", "/parlacards/serializers/tfidf.py", "/parlacards/serializers/common.py"], "/parlacards/serializers/tfidf.py": ["/parlacards/serializers/common.py"], "/parladata/management/commands/deletepeople.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/reset_order_with_agenda_items.py": ["/parladata/update_utils.py"], "/parlacards/serializers/cards/misc/last_session.py": ["/parladata/models/__init__.py", "/parlacards/models.py", "/parlacards/serializers/common.py", "/parlacards/serializers/group_attendance.py", "/parlacards/serializers/session.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/cards/session/tfidf.py", "/parlacards/pagination.py"], "/parlacards/lemmatizers/sl/lemmatizer.py": ["/tagger/lemmatiser.py"], "/parladata/models/area.py": ["/parladata/behaviors/models.py"], "/parladata/management/commands/fetch_members_articles.py": ["/parladata/models/media.py", "/parladata/models/common.py"], "/parladata/models/public_question.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/speech.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parladata/models/vote.py"], "/parladata/management/commands/import_sentio_data.py": ["/parladata/models/__init__.py", "/parladata/models/common.py"], "/parladata/models/document.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/flush_solr.py": ["/parlacards/solr.py"], "/parladata/admin/question.py": ["/parladata/admin/filters.py", "/parladata/models/question.py", "/parladata/admin/link.py"], "/parladata/models/memberships.py": ["/parladata/behaviors/models.py"], "/parlacards/pagination.py": ["/parlacards/solr.py"], "/parlacards/serializers/common.py": ["/parladata/models/person.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/utils.py", "/parladata/exceptions.py"], "/parladata/management/commands/set_session_to_votes_ua.py": ["/parladata/update_utils.py"], "/parladata/migrations/0001_initial.py": ["/parladata/models/__init__.py"], "/parlacards/admin/admin.py": ["/parlacards/models.py", "/parladata/models/memberships.py", "/parladata/admin/filters.py", "/parlacards/admin/filters.py"], "/parlacards/utils.py": ["/parladata/models/memberships.py"], "/parlacards/management/commands/seed_scores.py": ["/parladata/models/organization.py", "/parlacards/scores/seed.py"], "/tests/scores/test_monthly_attendance.py": ["/parlacards/scores/monthly_attendance.py", "/tests/fixtures/common.py"], "/parlacards/serializers/cards/misc/legislation.py": ["/parlacards/pagination.py", "/parlacards/serializers/common.py", "/parlacards/serializers/legislation.py", "/parladata/models/legislation.py"], "/parlacards/serializers/cards/person/questions.py": ["/parlacards/pagination.py", "/parlacards/serializers/common.py", "/parlacards/serializers/question.py", "/parladata/models/question.py", "/parladata/models/common.py"], "/export/views/misc.py": ["/export/resources/misc.py", "/export/views/common.py"], "/export/resources/session.py": ["/export/resources/common.py", "/parladata/models/__init__.py"], "/parlacards/scores/voting_distance.py": ["/parladata/models/ballot.py", "/parladata/models/vote.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/tests/fixtures/common.py": ["/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/common.py"], "/parlacards/management/commands/upload_agenda_items_to_solr.py": ["/parladata/models/agenda_item.py", "/parlacards/solr.py"], "/parladata/signals.py": ["/parladata/models/__init__.py"], "/parlacards/serializers/style_scores.py": ["/parladata/models/person.py", "/parladata/models/organization.py", "/parlacards/serializers/common.py", "/parlacards/models.py"], "/parlacards/admin_views.py": ["/parlacards/utils.py", "/parladata/models/task.py", "/parladata/models/session.py"], "/tests/scores/test_number_of_spoken_words.py": ["/parladata/models/speech.py", "/parlacards/scores/number_of_spoken_words.py", "/tests/fixtures/common.py"], "/parlacards/scores/vocabulary_size.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/tests/resources/test_person_resources.py": ["/tests/fixtures/common.py", "/export/resources/person.py"], "/parlacards/serializers/cards/session/legislation.py": ["/parlacards/pagination.py", "/parlacards/serializers/cards/misc/legislation.py", "/parlacards/serializers/common.py", "/parlacards/serializers/legislation.py"], "/parlacards/management/commands/lemmatize_speeches.py": ["/parladata/models/speech.py"], "/export/views/person.py": ["/export/resources/person.py", "/export/views/common.py"], "/export/views/session.py": ["/export/views/common.py", "/export/resources/session.py"], "/parladata/models/motion.py": ["/parladata/behaviors/models.py"], "/parlacards/admin/discord.py": ["/export/resources/group.py", "/parlacards/models.py", "/parlacards/admin/common.py"], "/parladata/tasks.py": ["/parladata/models/__init__.py", "/parlacards/solr.py"], "/parladata/management/commands/prepare_test_database.py": ["/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/vote.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py"], "/tests/scores/test_deviation_from_group.py": ["/parlacards/scores/deviation_from_group.py", "/tests/fixtures/common.py"], "/parladata/models/speech.py": ["/parladata/behaviors/models.py", "/parlacards/scores/common.py"], "/parladata/admin/public_question.py": ["/parladata/models/public_question.py"], "/parlacards/views.py": ["/parladata/models/person.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/vote.py", "/parladata/models/legislation.py", "/parladata/models/agenda_item.py", "/parlacards/models.py", "/parlacards/serializers/cards/__init__.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/group_attendance.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/public_question.py"], "/parladata/admin/agenda_item.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/admin/admin.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/versionable_properties.py", "/parladata/models/common.py", "/parladata/admin/filters.py", "/parladata/admin/link.py"], "/parlacards/serializers/facets.py": ["/parlacards/serializers/common.py"], "/export/resources/person.py": ["/parladata/models/__init__.py", "/parlacards/models.py", "/export/resources/common.py"], "/parlacards/management/commands/set_tfidf_for_sessions.py": ["/parlacards/scores/tfidf.py", "/parlacards/utils.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/speech.py"], "/parladata/models/common.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py"], "/export/views/group.py": ["/export/resources/group.py", "/export/views/common.py"], "/tests/scores/test_style.py": ["/parladata/models/speech.py", "/parlacards/scores/style.py", "/tests/fixtures/common.py"], "/parlacards/serializers/area.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/person.py": ["/parladata/models/link.py", "/parlacards/serializers/common.py", "/parlacards/serializers/area.py"], "/parladata/models/agenda_item.py": ["/parladata/behaviors/models.py"], "/parlacards/serializers/link.py": ["/parladata/models/link.py"], "/parlacards/serializers/session.py": ["/parlacards/serializers/common.py", "/parladata/models/agenda_item.py", "/parladata/models/vote.py"], "/parlacards/admin/__init__.py": ["/parlacards/admin/number_of_spoken_words.py", "/parlacards/admin/discord.py", "/parlacards/admin/admin.py"], "/parlacards/serializers/motion.py": ["/parlacards/serializers/common.py"], "/parlacards/management/commands/cache_votes.py": ["/parladata/models/vote.py", "/parlacards/serializers/vote.py"], "/tests/scores/test_group_attenadance_on_session.py": ["/parlacards/scores/session_attendance.py", "/tests/fixtures/common.py"], "/parladata/models/person.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parladata/exceptions.py"], "/parlacards/serializers/cards/__init__.py": ["/parlacards/serializers/cards/misc/last_session.py", "/parlacards/serializers/cards/misc/members.py", "/parlacards/serializers/cards/misc/groups.py", "/parlacards/serializers/cards/misc/legislation.py", "/parlacards/serializers/cards/misc/sessions.py", "/parlacards/serializers/cards/person/questions.py", "/parlacards/serializers/cards/person/recent_activity.py", "/parlacards/serializers/cards/group/questions.py", "/parlacards/serializers/cards/group/votes.py", "/parlacards/serializers/cards/session/single.py", "/parlacards/serializers/cards/session/tfidf.py", "/parlacards/serializers/cards/session/legislation.py", "/parlacards/serializers/cards/search/votes.py", "/parlacards/serializers/cards/search/usage_through_time.py", "/parlacards/serializers/cards/cards.py"], "/parladata/management/commands/create_sl_education_levels.py": ["/parladata/models/common.py"], "/test/data_test.py": ["/parladata/models/__init__.py"], "/parlacards/serializers/organization.py": ["/parladata/models/link.py", "/parlacards/serializers/common.py"], "/parladata/models/organization.py": ["/parladata/models/link.py", "/parladata/models/person.py", "/parladata/models/memberships.py", "/parladata/models/ballot.py", "/parladata/behaviors/models.py", "/parladata/exceptions.py", "/parladata/models/versionable_properties.py"], "/parlacards/serializers/vote.py": ["/parladata/models/ballot.py", "/parlacards/serializers/session.py", "/parlacards/serializers/link.py", "/parlacards/serializers/common.py"], "/scripts/pl_parser.py": ["/sandbox/import_pl.py"], "/parladata/management/commands/pair_votes_and_speeches.py": ["/parladata/update_utils.py"], "/parladata/models/vote.py": ["/parladata/behaviors/models.py", "/parladata/models/memberships.py", "/parladata/models/ballot.py"], "/parladata_project/settings/k8s.py": ["/parladata_project/settings/defaults.py"], "/parlacards/tasks.py": ["/parlacards/scores/tfidf.py", "/parlacards/utils.py", "/parladata/models/__init__.py"], "/parlacards/serializers/cards/cards.py": ["/parladata/models/agenda_item.py", "/parladata/models/ballot.py", "/parladata/models/media.py", "/parladata/models/vote.py", "/parladata/models/question.py", "/parladata/models/memberships.py", "/parladata/models/legislation.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/person.py", "/parladata/models/common.py", "/parladata/models/public_question.py", "/parlacards/models.py", "/parlacards/serializers/media.py", "/parlacards/serializers/person.py", "/parlacards/serializers/organization.py", "/parlacards/serializers/legislation.py", "/parlacards/serializers/ballot.py", "/parlacards/serializers/voting_distance.py", "/parlacards/serializers/membership.py", "/parlacards/serializers/style_scores.py", "/parlacards/serializers/speech.py", "/parlacards/serializers/quote.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/tfidf.py", "/parlacards/serializers/facets.py", "/parlacards/serializers/question.py", "/parlacards/serializers/agenda_item.py", "/parlacards/serializers/common.py", "/parlacards/serializers/public_question.py", "/parlacards/solr.py", "/parlacards/pagination.py", "/parlacards/recaptcha.py"], "/parladata/utils.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/agenda_item.py"], "/tests/scores/test_voting_distance.py": ["/parlacards/scores/voting_distance.py", "/tests/fixtures/common.py"], "/parladata/models/versionable_properties.py": ["/parladata/behaviors/models.py", "/parladata/models/common.py"], "/parladata/models/legislation.py": ["/parladata/behaviors/models.py"], "/parlacards/management/commands/set_tfidf.py": ["/parladata/models/__init__.py", "/parlacards/scores/tfidf.py", "/parlacards/utils.py"], "/export/resources/group.py": ["/export/resources/common.py", "/parlacards/models.py", "/parladata/models/__init__.py", "/parladata/models/common.py"], "/tests/resources/test_session_resources.py": ["/tests/fixtures/common.py", "/export/resources/session.py", "/export/resources/misc.py"], "/parlacards/scores/discord.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/admin/filters.py": ["/parladata/models/common.py"], "/tests/scores/test_discord.py": ["/parlacards/scores/discord.py", "/tests/fixtures/common.py"], "/parladata/admin/legislation.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parladata/management/commands/run_data_validation.py": ["/parladata/data_tests.py"], "/parlacards/management/commands/run_people_analyses.py": ["/parlacards/scores/update.py"], "/parlacards/scores/attendance.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/export/resources/common.py": ["/parladata/models/__init__.py"], "/tests/scores/test_average_number_of_speeches_per_session.py": ["/parlacards/scores/avg_number_of_speeches_per_session.py", "/tests/fixtures/common.py"], "/parladata/admin/organization.py": ["/parladata/models/__init__.py", "/parladata/models/versionable_properties.py", "/parladata/admin/link.py", "/parladata/forms.py"], "/parladata/models/ballot.py": ["/parladata/behaviors/models.py"], "/parlacards/scores/update.py": ["/parlacards/scores/attendance.py", "/parlacards/scores/avg_number_of_speeches_per_session.py", "/parlacards/scores/deviation_from_group.py", "/parlacards/scores/discord.py", "/parlacards/scores/monthly_attendance.py", "/parlacards/scores/number_of_questions.py", "/parlacards/scores/number_of_spoken_words.py", "/parlacards/scores/session_attendance.py", "/parlacards/scores/vocabulary_size.py", "/parlacards/scores/voting_distance.py", "/parlacards/utils.py", "/parladata/models/motion.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/organization.py", "/parladata/models/memberships.py"], "/parlacards/scores/avg_number_of_speeches_per_session.py": ["/parladata/models/session.py", "/parladata/models/speech.py", "/parladata/models/ballot.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/style.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_questions.py": ["/parladata/models/question.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parlacards/scores/number_of_spoken_words.py": ["/parladata/models/speech.py", "/parladata/models/common.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/admin/vote.py": ["/parladata/models/__init__.py", "/export/resources/session.py"], "/parlacards/serializers/cards/misc/members.py": ["/parladata/models/area.py", "/parladata/models/organization.py", "/parladata/models/memberships.py", "/parladata/models/versionable_properties.py", "/parlacards/models.py", "/parlacards/serializers/area.py", "/parlacards/serializers/common.py", "/parlacards/pagination.py", "/parlacards/utils.py"], "/parlacards/serializers/cards/session/single.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/session.py"], "/tests/resources/test_group_resources.py": ["/tests/fixtures/common.py", "/export/resources/group.py"], "/parlacards/serializers/legislation.py": ["/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/link.py", "/parladata/models/vote.py", "/parladata/models/link.py"], "/parladata/data_tests.py": ["/parladata/models/__init__.py", "/parladata/update_utils.py"], "/parlacards/serializers/cards/group/votes.py": ["/parladata/models/vote.py", "/parladata/models/ballot.py", "/parladata/models/memberships.py", "/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,369
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/admin/motion.py
|
from django.contrib import admin
from django.utils.safestring import mark_safe
from django.conf import settings
from django.db.models import Q
from django.urls import reverse
from parladata.models import *
from parladata.models.task import Task
from parladata.models.versionable_properties import *
from parladata.models.common import *
from parladata.admin.link import LinkMotionInline
from parladata.admin.filters import SessionListFilter
from collections import Counter
class MotionAdmin(admin.ModelAdmin):
#form = MotionForm
list_display = (
'id',
'title',
'session_name',
'result',
'requirement',
'get_for',
'get_against',
'get_abstain',
'get_not',
'link_to_vote',
'datetime',
'created_at',
'tag_list',
)
autocomplete_fields = ('session', 'champions', 'agenda_items', 'law')
list_editable = ('result',)
list_filter = (SessionListFilter, 'result', 'datetime',)
search_fields = ['text', 'title']
inlines = [
LinkMotionInline,
]
list_per_page = 25
readonly_fields = ['created_at', 'updated_at']
# set order of fields in the dashboard
fields = ['title', 'session', 'datetime', 'result', 'agenda_items', 'law', 'champions', 'summary', 'text', 'classification', 'requirement', 'parser_names', 'tags']
def get_queryset(self, request):
return Motion.objects.all().prefetch_related('session', 'vote').order_by('-id')
def get_for(self, obj):
results = dict(Counter(Ballot.objects.filter(vote__motion=obj).values_list("option", flat=True))).get("for", 0)
return results
def get_against(self, obj):
results = dict(Counter(Ballot.objects.filter(vote__motion=obj).values_list("option", flat=True))).get("against", 0)
return results
def get_abstain(self, obj):
results = dict(Counter(Ballot.objects.filter(vote__motion=obj).values_list("option", flat=True))).get("abstain", 0)
return results
def get_not(self, obj):
results = dict(Counter(Ballot.objects.filter(vote__motion=obj).values_list("option", flat=True))).get("absent", 0)
return results
def link_to_vote(self, obj):
try:
link = reverse("admin:parladata_vote_change", args=[obj.vote.first().id])
except:
return ''
return mark_safe(f'<a href="{link}">Vote</a>')
def session_name(self, obj):
return obj.session.name if obj.session else ''
def tag_list(self, obj):
return u", ".join(o.name for o in obj.tags.all())
link_to_vote.allow_tags = True
get_for.short_description = 'for'
get_against.short_description = 'against'
get_abstain.short_description = 'abstain'
get_not.short_description = 'absent'
def get_search_results(self, request, queryset, search_term):
url = request.META.get('HTTP_REFERER', '')
# if autocompelte calls from speech admin then filter motions by speech session
if '/admin/parladata/speech/' in url:
speech_id = url.split('/')[-3]
session = Speech.objects.get(id=speech_id).session
queryset = queryset.filter(session=session)
results = super().get_search_results(
request,
queryset,
search_term
)
return results
admin.site.register(Motion, MotionAdmin)
|
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"/parladata/models/ballot.py", "/parladata/models/question.py", "/parladata/models/speech.py", "/parladata/models/common.py"], "/parlacards/management/commands/upload_votes_to_solr.py": ["/parladata/models/vote.py", "/parlacards/serializers/vote.py", "/parlacards/solr.py"], "/parladata/management/commands/send_daily_notifications.py": ["/parladata/update_utils.py"], "/tests/views/test_card_views.py": ["/tests/fixtures/common.py", "/parlacards/views.py"], "/parladata/models/question.py": ["/parladata/behaviors/models.py"], "/parladata/management/commands/delete_all_sessions.py": ["/parladata/models/__init__.py"], "/tests/scores/test_number_of_questions.py": ["/parlacards/scores/number_of_questions.py", "/tests/fixtures/common.py"], "/parladata/management/commands/startparlameter.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/run_tasks.py": ["/parladata/models/task.py", "/parladata/update_utils.py"], "/parladata/admin/motion.py": ["/parladata/models/__init__.py", "/parladata/models/task.py", "/parladata/models/versionable_properties.py", "/parladata/models/common.py", "/parladata/admin/link.py", "/parladata/admin/filters.py"], "/parladata/admin/session.py": ["/parladata/models/__init__.py", "/parladata/admin/filters.py"], "/parlacards/admin/number_of_spoken_words.py": ["/export/resources/person.py", "/parlacards/models.py", "/parlacards/admin/common.py"], "/parladata/migrations/0002_question_date_of_answer.py": ["/parladata/models/__init__.py"], "/tests/scores/test_tfidf.py": ["/parlacards/scores/common.py", "/parlacards/scores/tfidf.py", "/tests/fixtures/common.py"], "/test/test_votes.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/create_absent_ballots_for_voters.py": ["/parladata/models/__init__.py"], "/parladata/management/commands/set_legislation_results.py": ["/parladata/update_helpers/methods.py"], "/parladata/management/commands/public_questions_notifications.py": ["/parladata/models/public_question.py", "/parladata/update_utils.py"], "/parlacards/serializers/voting_distance.py": ["/parlacards/serializers/common.py"], "/parladata/management/commands/mergepeople.py": ["/parladata/models/__init__.py"], "/parlacards/lemmatizers/ua/lemmatizer.py": ["/parlacards/lemmatizers/ua/stop_words.py"], "/parladata/apps.py": ["/parladata/signals.py"], "/parlacards/management/commands/upload_legislation_to_solr.py": ["/parladata/models/legislation.py", "/parlacards/solr.py"], "/parlacards/scores/tfidf.py": ["/parladata/models/session.py", "/parladata/models/common.py", "/parladata/models/speech.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/utils.py", "/parlacards/scores/common.py"], "/tests/resources/test_misc_resources.py": ["/tests/fixtures/common.py", "/export/resources/misc.py"], "/parladata/admin/link.py": ["/parladata/models/link.py", "/parladata/admin/filters.py"], "/parlacards/serializers/cards/misc/groups.py": ["/parlacards/serializers/common.py", "/parlacards/models.py", "/parladata/models/memberships.py"], "/parladata/models/task.py": ["/parladata/behaviors/models.py"], "/parladata_project/settings/local.py": ["/parladata_project/settings/defaults.py"], "/parlacards/serializers/question.py": ["/parlacards/serializers/common.py"], "/parlacards/scores/deviation_from_group.py": ["/parladata/models/ballot.py", "/parladata/models/common.py", "/parladata/models/memberships.py", "/parlacards/models.py", "/parlacards/scores/common.py"], "/parladata/update_helpers/sl/legislation.py": ["/parladata/models/__init__.py"], "/parladata/admin/filters.py": ["/parladata/models/memberships.py", "/parladata/models/session.py", "/parladata/models/organization.py", "/parladata/models/common.py", "/parladata/models/legislation.py"], "/parlacards/management/commands/upload_speeches_to_solr.py": ["/parladata/models/speech.py", "/parlacards/solr.py"], "/tests/conftest.py": ["/parladata/models/organization.py", "/parladata/models/person.py"], 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"/parladata/models/common.py", "/parlacards/serializers/common.py", "/parlacards/serializers/vote.py", "/parlacards/serializers/ballot.py", "/parlacards/pagination.py"], "/parladata/update_utils.py": ["/parladata/models/vote.py", "/parladata/models/session.py", "/parladata/models/motion.py", "/parladata/models/speech.py", "/parladata/models/person.py", "/parladata/models/public_question.py", "/parlacards/models.py"], "/parlacards/serializers/membership.py": ["/parlacards/serializers/common.py"], "/parlacards/serializers/quote.py": ["/parlacards/serializers/session.py", "/parlacards/serializers/common.py", "/parlacards/serializers/speech.py", "/parlacards/models.py"]}
|
36,183,370
|
danesjenovdan/parladata
|
refs/heads/dev
|
/parladata/migrations/0023_alter_personmembership_role.py
|
# Generated by Django 3.2 on 2021-05-12 19:34
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('parladata', '0022_alter_law_passed'),
]
operations = [
migrations.AlterField(
model_name='personmembership',
name='role',
field=models.TextField(default='member', help_text='The role that the person fulfills in the organization', verbose_name='role'),
),
]
|
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