xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

f7c9101c72ad1ded9a9c2e62600c587a0e6679a4

109

py

Python

codiag/flib/__init__.py

jimustafa/codiag

3b275da7f00551d5af5e26ce0432a6d91710fb15

[ "BSD-3-Clause" ]

null

codiag/flib/__init__.py

jimustafa/codiag

3b275da7f00551d5af5e26ce0432a6d91710fb15

[ "BSD-3-Clause" ]

null

codiag/flib/__init__.py

jimustafa/codiag

3b275da7f00551d5af5e26ce0432a6d91710fb15

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import, division, print_function from .codiag import * from . import givens

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f7fa41af81c49e6f6e970ef316571ea89fcdd869

240

py

Python

pacote-download/d012 - valor do produto de dar 5% desconto.py

Carlos-DOliveira/cursoemvideo-python3

4546c8a7360155243e2f7ecbbb80c57868f770a2

[ "MIT" ]

null

pacote-download/d012 - valor do produto de dar 5% desconto.py

Carlos-DOliveira/cursoemvideo-python3

4546c8a7360155243e2f7ecbbb80c57868f770a2

[ "MIT" ]

null

pacote-download/d012 - valor do produto de dar 5% desconto.py

Carlos-DOliveira/cursoemvideo-python3

4546c8a7360155243e2f7ecbbb80c57868f770a2

[ "MIT" ]

null

''' 012 Faça um algoritmo que leia o preço de um produto e mostre seu novo preço, com 5% de desconto''' valor = float(input('Digite o valor do protudo: R$ ')) print(f'O Valor do produto com 5% de desconto é {valor - (valor * 5)/100:.2f}')

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7930719bb140601d6f74af8523ec00b5538c6d64

75

py

Python

satispy/io/__init__.py

NickGardi/satispy

ff0485921fc9fa290446837a10432929c4e04d90

[ "BSD-3-Clause" ]

54

2015-04-01T06:17:06.000Z

2021-08-19T23:04:17.000Z

satispy/io/__init__.py

NickGardi/satispy

ff0485921fc9fa290446837a10432929c4e04d90

[ "BSD-3-Clause" ]

11

2015-04-25T15:05:29.000Z

2019-04-12T18:34:41.000Z

satispy/io/__init__.py

NickGardi/satispy

ff0485921fc9fa290446837a10432929c4e04d90

[ "BSD-3-Clause" ]

21

2015-03-09T20:41:36.000Z

2019-06-15T17:26:57.000Z

from __future__ import absolute_import from satispy.io.dimacs_cnf import *

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5

f7188a5d6697e456a89894a7330aa0af3fad00a2

146

py

Python

tests/filetwo.py

alexandrevicenzi/lazyconfig

a03aa0b92cf8f810a8652728d80dd0d792dd66ed

[ "MIT" ]

null

tests/filetwo.py

alexandrevicenzi/lazyconfig

a03aa0b92cf8f810a8652728d80dd0d792dd66ed

[ "MIT" ]

null

tests/filetwo.py

alexandrevicenzi/lazyconfig

a03aa0b92cf8f810a8652728d80dd0d792dd66ed

[ "MIT" ]

null

# -*- coding: utf-8 -*- import sys sys.path.append('./') from lazyconfig import lazyconfig def get_name(): return lazyconfig.config.name

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5

f71dc8aeb93f7835f2caaf5b59252fc6ba16d798

135

py

Python

tests/fixtures/unused_import_comment_6.py

cdce8p/python-typing-update

2ad78b9ce4b5e3d8e8ff5dd35474c8e214d69983

[ "MIT" ]

5

2021-03-17T16:12:09.000Z

2021-09-12T22:19:51.000Z

tests/fixtures/unused_import_comment_6.py

cdce8p/python-typing-update

2ad78b9ce4b5e3d8e8ff5dd35474c8e214d69983

[ "MIT" ]

10

2021-03-23T18:14:24.000Z

2022-03-28T03:05:18.000Z

tests/fixtures/unused_import_comment_6.py

cdce8p/python-typing-update

2ad78b9ce4b5e3d8e8ff5dd35474c8e214d69983

[ "MIT" ]

2

2021-03-20T08:47:52.000Z

2021-06-07T04:02:02.000Z

"""Test unused import retention.""" from logging import DEBUG # unused-import from typing import Any, List var1: List[str] var2: Any

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f72394108b2b48963e86a1dfb5530319995e885c

4,375

py

Python

tests/test_graph.py

Nikolay-Lysenko/gpn

a59f43e90536f85f8b0051c5ce6d0497081a5a8f

[ "MIT" ]

null

tests/test_graph.py

Nikolay-Lysenko/gpn

a59f43e90536f85f8b0051c5ce6d0497081a5a8f

[ "MIT" ]

null

tests/test_graph.py

Nikolay-Lysenko/gpn

a59f43e90536f85f8b0051c5ce6d0497081a5a8f

[ "MIT" ]

null

""" Test `graph.py` module. Author: Nikolay Lysenko """ from typing import List, Tuple import pytest import tensorflow as tf import numpy as np from gpn.graph import sample_multiple_fragments @pytest.mark.parametrize( "images, corners, fragment_size, frame_size, n_channels, expected", [ ( # `images` np.array([ [ [[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], [[1, 1, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1]] ], [ [[1, 1, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 1], [0, 0, 1, 1]] ] ]).swapaxes(1, 3), # `corners` [(1, 1), (0, 2)], # `fragment_size` 4, # `frame_size` 1, # `n_channels` 3, # `expected` np.array([ [ [[1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1]], [[1, 1, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 1, 1]] ], [ [[1, 1, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 1], [0, 0, 1, 1]] ], [ [[0, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 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, 1, 1, 1], [0, 0, 0, 0]] ], [ [[0, 1, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 1, 1], [0, 0, 1, 1], [0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 0]] ], ]).swapaxes(1, 3) ) ] ) def test_sample_multiple_fragments( images: np.ndarray, corners: List[Tuple[int, int]], fragment_size: int, frame_size: int, n_channels: int, expected: np.ndarray ) -> None: """Test `sample_multiple_fragments` function.""" graph = tf.Graph() with graph.as_default(): tensor_images = tf.placeholder(tf.float32, images.shape) tensor_corners = [ tf.placeholder(tf.int32, (2,), name=f'corner_{i}') for i, _ in enumerate(corners) ] tensor_fragments = sample_multiple_fragments( tensor_images, tensor_corners, fragment_size, frame_size, n_channels ) with tf.Session(graph=graph) as sess: feed_dict = { tensor_images: images, **{k: v for k, v in zip(tensor_corners, corners)} } fragments = tensor_fragments.eval(feed_dict, sess) np.testing.assert_array_equal(fragments, expected)

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5

f732e9b2a6ebaaa5570c84740a20877a5638855d

162

py

Python

tests/test_models/test_backbones/__init__.py

mrzhuzhe/mmdetection

c04ca2c2a65500bc248a5d2ab6ace5b15f00064d

[ "Apache-2.0" ]

null

tests/test_models/test_backbones/__init__.py

mrzhuzhe/mmdetection

c04ca2c2a65500bc248a5d2ab6ace5b15f00064d

[ "Apache-2.0" ]

null

tests/test_models/test_backbones/__init__.py

mrzhuzhe/mmdetection

c04ca2c2a65500bc248a5d2ab6ace5b15f00064d

[ "Apache-2.0" ]

null

32.4

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f7850ce08a9f9ce42c7a3f146540abc49e1c1f12

41,598

py

Python

stable_baselines/td3/policies.py

Ow-woo/stable-baselines

ece376f62b0eaa3b58e90593b7db5fb9de3d82c5

[ "MIT" ]

1

2021-03-03T14:59:11.000Z

stable_baselines/td3/policies.py

Ow-woo/stable-baselines

ece376f62b0eaa3b58e90593b7db5fb9de3d82c5

[ "MIT" ]

null

stable_baselines/td3/policies.py

Ow-woo/stable-baselines

ece376f62b0eaa3b58e90593b7db5fb9de3d82c5

[ "MIT" ]

4

2019-10-07T23:11:26.000Z

2021-08-24T13:00:40.000Z

import tensorflow as tf import numpy as np from gym.spaces import Box import copy from stable_baselines.common.policies import BasePolicy, nature_cnn, register_policy, cnn_1d_extractor from stable_baselines.sac.policies import mlp from stable_baselines.a2c.utils import lstm, batch_to_seq, seq_to_batch class TD3Policy(BasePolicy): """ Policy object that implements a TD3-like actor critic :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param scale: (bool) whether or not to scale the input """ def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, scale=False, add_action_ph=False): super(TD3Policy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=reuse, scale=scale, add_action_ph=add_action_ph) assert isinstance(ac_space, Box), "Error: the action space must be of type gym.spaces.Box" self.qf1 = None self.qf2 = None self.q_discrepancy = None self.policy = None def make_actor(self, obs=None, reuse=False, scope="pi"): """ Creates an actor object :param obs: (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) :param reuse: (bool) whether or not to reuse parameters :param scope: (str) the scope name of the actor :return: (TensorFlow Tensor) the output tensor """ raise NotImplementedError def make_critics(self, obs=None, action=None, reuse=False, scope="qvalues_fn"): """ Creates the two Q-Values approximator :param obs: (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) :param action: (TensorFlow Tensor) The action placeholder :param reuse: (bool) whether or not to reuse parameters :param scope: (str) the scope name :return: ([tf.Tensor]) Mean, action and log probability """ raise NotImplementedError def step(self, obs, state=None, mask=None): """ Returns the policy for a single step :param obs: ([float] or [int]) The current observation of the environment :param state: ([float]) The last states (used in recurrent policies) :param mask: ([float]) The last masks (used in recurrent policies) :return: ([float]) actions """ raise NotImplementedError def proba_step(self, obs, state=None, mask=None): """ Returns the policy for a single step :param obs: ([float] or [int]) The current observation of the environment :param state: ([float]) The last states (used in recurrent policies) :param mask: ([float]) The last masks (used in recurrent policies) :return: ([float]) actions """ return self.step(obs, state, mask) class FeedForwardPolicy(TD3Policy): """ Policy object that implements a DDPG-like actor critic, using a feed forward neural network. :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param layers: ([int]) The size of the Neural network for the policy (if None, default to [64, 64]) :param cnn_extractor: (function (TensorFlow Tensor, ``**kwargs``): (TensorFlow Tensor)) the CNN feature extraction :param feature_extraction: (str) The feature extraction type ("cnn" or "mlp") :param layer_norm: (bool) enable layer normalisation :param act_fun: (tf.func) the activation function to use in the neural network. :param kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, layers=None, cnn_extractor=nature_cnn, feature_extraction="cnn", layer_norm=False, act_fun=tf.nn.relu, obs_module_indices=None, **kwargs): super(FeedForwardPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=reuse, scale=(feature_extraction == "cnn" and cnn_extractor == nature_cnn)) self._kwargs_check(feature_extraction, kwargs) self.layer_norm = layer_norm self.feature_extraction = feature_extraction self.cnn_kwargs = kwargs self.cnn_extractor = cnn_extractor self.cnn_vf = self.cnn_kwargs.pop("cnn_vf", True) self.reuse = reuse if layers is None: layers = [64, 64] self.layers = layers self.obs_module_indices = obs_module_indices self.policy_pre_activation = None assert len(layers) >= 1, "Error: must have at least one hidden layer for the policy." self.activ_fn = act_fun def make_actor(self, obs=None, reuse=False, scope="pi"): if obs is None: obs = self.processed_obs if self.obs_module_indices is not None: obs = tf.gather(obs, self.obs_module_indices["pi"], axis=-1) with tf.variable_scope(scope, reuse=reuse): if self.feature_extraction == "cnn": pi_h = self.cnn_extractor(obs, name="pi_c1", act_fun=self.activ_fn, **self.cnn_kwargs) else: pi_h = tf.layers.flatten(obs) pi_h = mlp(pi_h, self.layers, self.activ_fn, layer_norm=self.layer_norm) self.policy_pre_activation = tf.layers.dense(pi_h, self.ac_space.shape[0]) self.policy = policy = tf.tanh(self.policy_pre_activation) return policy def make_critics(self, obs=None, action=None, reuse=False, scope="values_fn", extracted_callback=None): if obs is None: obs = self.processed_obs if self.obs_module_indices is not None: obs = tf.gather(obs, self.obs_module_indices["vf"], axis=-1) with tf.variable_scope(scope, reuse=reuse): if self.feature_extraction == "cnn" and self.cnn_vf: critics_h = self.cnn_extractor(obs, name="vf_c1", act_fun=self.activ_fn, **self.cnn_kwargs) else: critics_h = tf.layers.flatten(obs) if extracted_callback is not None: critics_h = extracted_callback(critics_h) # Concatenate preprocessed state and action qf_h = tf.concat([critics_h, action], axis=-1) # Double Q values to reduce overestimation with tf.variable_scope('qf1', reuse=reuse): qf1_h = mlp(qf_h, self.layers, self.activ_fn, layer_norm=self.layer_norm) qf1 = tf.layers.dense(qf1_h, 1, name="qf1") with tf.variable_scope('qf2', reuse=reuse): qf2_h = mlp(qf_h, self.layers, self.activ_fn, layer_norm=self.layer_norm) qf2 = tf.layers.dense(qf2_h, 1, name="qf2") self.qf1 = qf1 self.qf2 = qf2 # TODO: assumes that all qf1 and qf2 can never have opposite signs #self.q_discrepancy = tf.square(self.qf1 - self.qf2) / tf.square(tf.maximum(self.qf1, self.qf2)) #self.q_discrepancy = tf.abs(self.qf1 - self.qf2) return self.qf1, self.qf2 def step(self, obs, state=None, mask=None): return self.sess.run(self.policy, {self.obs_ph: obs}) def get_q_discrepancy(self, obs): if isinstance(obs, np.ndarray) and len(obs.shape) == 1: # TODO: check for MLP or CNN policy here obs = np.expand_dims(obs, axis=0) return self.sess.run(self.q_discrepancy, {self.obs_ph: obs}) class RecurrentPolicy(TD3Policy): """ Policy object that implements a DDPG-like actor critic, using a feed forward neural network. :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param layers: ([int]) The size of the Neural network for the policy (if None, default to [64, 64]) :param cnn_extractor: (function (TensorFlow Tensor, ``**kwargs``): (TensorFlow Tensor)) the CNN feature extraction :param feature_extraction: (str) The feature extraction type ("cnn" or "mlp") :param layer_norm: (bool) enable layer normalisation :param act_fun: (tf.func) the activation function to use in the neural network. :param kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ recurrent = True def __init__(self, sess, ob_space, ac_space, layers, n_env=1, n_steps=1, n_batch=None, reuse=False, cnn_extractor=nature_cnn, feature_extraction="mlp", n_lstm=128, share_lstm=False, save_state=False, save_target_state=False, layer_norm=False, act_fun=tf.nn.relu, obs_module_indices=None, **kwargs): super(RecurrentPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=reuse, add_action_ph=True, scale=(feature_extraction == "cnn" and cnn_extractor == nature_cnn)) self._kwargs_check(feature_extraction, kwargs) self.layer_norm = layer_norm self.feature_extraction = feature_extraction self.cnn_kwargs = kwargs self.cnn_extractor = cnn_extractor self.cnn_vf = self.cnn_kwargs.pop("cnn_vf", True) self.reuse = reuse self.layers = layers self.obs_module_indices = obs_module_indices self.activ_fn = act_fun self.n_lstm = n_lstm self.share_lstm = share_lstm self._obs_ph = self.processed_obs # Base class has self.obs_ph as property getting self._obs_ph self.obs_tp1_ph = self.processed_obs assert self.n_batch % self.n_steps == 0, "The batch size must be a multiple of sequence length (n_steps)" self._lstm_n_batch = self.n_batch // self.n_steps self.action_prev = np.zeros((1, *self.ac_space.shape)) self._initial_state = np.zeros((self._lstm_n_batch, self.n_lstm * 2), dtype=np.float32) if self.share_lstm: self.state = None else: self.pi_state = None self.qf1_state = None self.qf2_state = None with tf.variable_scope("input", reuse=False): self.dones_ph = tf.placeholder_with_default(np.zeros((self.n_batch,), dtype=np.float32), (self.n_batch,), name="dones_ph") # (done t-1) if self.share_lstm: self.state_ph = tf.placeholder_with_default(self.initial_state, (self._lstm_n_batch, self.n_lstm * 2), name="state_ph") else: self.pi_state_ph = tf.placeholder_with_default(self.initial_state, (self._lstm_n_batch, self.n_lstm * 2), name="pi_state_ph") self.qf1_state_ph = tf.placeholder_with_default(self.initial_state, (self._lstm_n_batch, self.n_lstm * 2), name="qf1_state_ph") self.qf2_state_ph = tf.placeholder_with_default(self.initial_state, (self._lstm_n_batch, self.n_lstm * 2), name="qf2_state_ph") self.action_prev_ph = tf.placeholder(np.float32, (self.n_batch, *self.ac_space.shape), name="action_prev_ph") self.save_state = save_state self.save_target_state = save_target_state self.extra_phs = ["action_prev"] self.rnn_inputs = ["obs", "action_prev"] self.extra_data_names = ["action_prev"] if self.save_target_state: self.extra_data_names = sorted(self.extra_data_names + ["target_action_prev"]) self.rnn_inputs = sorted(self.rnn_inputs + ["obs_tp1"]) self.extra_phs = sorted(self.extra_phs + ["target_action_prev"]) if self.save_state: state_names = ["state"] if self.share_lstm else ["pi_state", "qf1_state", "qf2_state"] if self.save_target_state: state_names.extend(["target_" + state_name for state_name in state_names]) if self.share_lstm: self.extra_data_names = sorted(self.extra_data_names + state_names) self.extra_phs = sorted(self.extra_phs + state_names) else: self.extra_data_names = sorted(self.extra_data_names + state_names) self.extra_phs = sorted(self.extra_phs + state_names) def _process_phs(self, **phs): for ph_name, ph_val in phs.items(): if ph_val is None: phs[ph_name] = getattr(self, ph_name + "_ph") else: try: setattr(self, ph_name + "_ph", ph_val) except AttributeError: setattr(self, "_" + ph_name + "_ph", ph_val) return phs.values() def _make_branch(self, branch_name, input_tensor, dones=None, state_ph=None): if branch_name == "lstm": for i, fc_layer_units in enumerate(self.layers["lstm"]): input_tensor = self.activ_fn(tf.layers.dense(input_tensor, fc_layer_units, name="lstm_fc{}".format(i))) input_tensor = batch_to_seq(input_tensor, self._lstm_n_batch, self.n_steps) masks = batch_to_seq(dones, self._lstm_n_batch, self.n_steps) input_tensor, state = lstm(input_tensor, masks, state_ph, "lstm", n_hidden=self.n_lstm, layer_norm=self.layer_norm) input_tensor = seq_to_batch(input_tensor) return input_tensor, state else: for i, fc_layer_units in enumerate(self.layers[branch_name]): input_tensor = self.activ_fn(tf.layers.dense(input_tensor, fc_layer_units, name="{}_fc{}".format(branch_name, i))) return input_tensor def make_actor(self, ff_phs=None, rnn_phs=None, dones=None, reuse=False, scope="pi"): lstm_branch = tf.concat([tf.layers.flatten(ph) for ph in rnn_phs], axis=-1) if ff_phs is not None: ff_branch = tf.concat([tf.layers.flatten(ph) for ph in ff_phs], axis=-1) if dones is None: dones = self.dones_ph if self.share_lstm: with tf.variable_scope("shared", reuse=tf.AUTO_REUSE): lstm_branch, self.state = self._make_branch("lstm", lstm_branch, dones, self.state_ph) with tf.variable_scope(scope, reuse=reuse): if self.layers["ff"] is not None: ff_branch = self._make_branch("ff", ff_branch) if not self.share_lstm: lstm_branch, self.pi_state = self._make_branch("lstm", lstm_branch, dones, self.pi_state_ph) if ff_phs is not None: head = tf.concat([ff_branch, lstm_branch], axis=-1) else: head = lstm_branch head = self._make_branch("head", head) self.policy_pre_activation = tf.layers.dense(head, self.ac_space.shape[0]) self.policy = policy = tf.tanh(self.policy_pre_activation) return policy def make_critics(self, ff_phs=None, rnn_phs=None, dones=None, reuse=False, scope="values_fn"): lstm_branch_in = tf.concat([tf.layers.flatten(ph) for ph in rnn_phs], axis=-1) if ff_phs is not None: ff_branch_in = tf.concat([tf.layers.flatten(ph) for ph in ff_phs], axis=-1) if dones is None: dones = self.dones_ph self.qf1, self.qf2 = None, None self.qf1_state, self.qf2_state = None, None if self.share_lstm: with tf.variable_scope("shared", reuse=tf.AUTO_REUSE): lstm_branch_s, self.state = self._make_branch("lstm", lstm_branch_in, dones, self.state_ph) with tf.variable_scope(scope, reuse=reuse): # Double Q values to reduce overestimation for qf_i in range(1, 3): with tf.variable_scope('qf{}'.format(qf_i), reuse=reuse): lstm_branch = lstm_branch_in if self.layers["ff"] is not None: ff_branch = self._make_branch("ff", ff_branch_in) elif ff_phs is not None: ff_branch = ff_branch_in if not self.share_lstm: lstm_branch, state = self._make_branch("lstm", lstm_branch, dones, getattr(self, "qf{}_state_ph".format(qf_i))) setattr(self, "qf{}_state".format(qf_i), state) else: lstm_branch = lstm_branch_s if ff_phs is not None: head = tf.concat([ff_branch, lstm_branch], axis=-1) else: head = lstm_branch head = self._make_branch("head", head) setattr(self, "qf{}".format(qf_i), tf.layers.dense(head, 1, name="qf{}".format(qf_i))) return self.qf1, self.qf2 def step(self, obs, action_prev=None, state=None, mask=None, feed_dict=None, **kwargs): if feed_dict is None: feed_dict = {} if state is None: state = self.initial_state if mask is None: mask = np.array([False]) if action_prev is None: assert obs.shape[0] == 1 if mask[0]: self.action_prev = np.zeros((1, *self.ac_space.shape)) action_prev = self.action_prev rnn_node = self.state if self.share_lstm else self.pi_state state_ph = self.state_ph if self.share_lstm else self.pi_state_ph feed_dict.update({self.obs_ph: obs, state_ph: state, self.dones_ph: mask, self.action_prev_ph: action_prev}) action, out_state = self.sess.run([self.policy, rnn_node], feed_dict) self.action_prev = action return action, out_state @property def initial_state(self): return self._initial_state def collect_data(self, _locals, _globals): data = {} if self.save_state: if self.share_lstm: data["state"] = _locals["prev_policy_state"][0, :] else: data["pi_state"] = _locals["prev_policy_state"][0, :] if len(_locals["episode_data"]) == 0: qf1_state, qf2_state = self.initial_state, self.initial_state else: qf_feed_dict = { self.qf1_state_ph: _locals["episode_data"][-1]["qf1_state"][None], self.qf2_state_ph: _locals["episode_data"][-1]["qf2_state"][None], } qf_feed_dict.update({getattr(self, data_name + "_ph"): _locals["episode_data"][-1][data_name][None] for data_name in self.rnn_inputs}) qf1_state, qf2_state = self.sess.run([self.qf1_state, self.qf2_state], feed_dict=qf_feed_dict) data["qf1_state"] = qf1_state[0, :] data["qf2_state"] = qf2_state[0, :] if len(_locals["episode_data"]) == 0: data["action_prev"] = np.zeros(*self.ac_space.shape, dtype=np.float32) else: data["action_prev"] = _locals["episode_data"][-1]["action"] if self.save_target_state: data["target_action_prev_rnn"] = _locals["action"] return data class DRPolicy(RecurrentPolicy): """ Policy object that implements a DDPG-like actor critic, using a feed forward neural network. :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param layers: ([int]) The size of the Neural network for the policy (if None, default to [64, 64]) :param cnn_extractor: (function (TensorFlow Tensor, ``**kwargs``): (TensorFlow Tensor)) the CNN feature extraction :param feature_extraction: (str) The feature extraction type ("cnn" or "mlp") :param layer_norm: (bool) enable layer normalisation :param act_fun: (tf.func) the activation function to use in the neural network. :param kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ recurrent = True def __init__(self, sess, ob_space, ac_space, goal_size, my_size, n_env=1, n_steps=1, n_batch=None, reuse=False, layers=None, cnn_extractor=nature_cnn, feature_extraction="mlp", n_lstm=128, share_lstm=False, layer_norm=False, act_fun=tf.nn.relu, obs_module_indices=None, **kwargs): if layers is None: layers = {"ff": [128], "lstm": [128], "head": [128, 128]} super().__init__(sess, ob_space, ac_space, layers, n_env, n_steps, n_batch, reuse=reuse, cnn_extractor=cnn_extractor, feature_extraction=feature_extraction, n_lstm=n_lstm, share_lstm=share_lstm, layer_norm=layer_norm, act_fun=act_fun, obs_module_indices=obs_module_indices, **kwargs) with tf.variable_scope("input", reuse=False): self.my_ph = tf.placeholder(tf.float32, (None, my_size), name="my_ph") # the dynamics of the environment self.goal_size = goal_size self.extra_phs = sorted(self.extra_phs + ["my"]) self.extra_data_names = sorted(self.extra_data_names + ["my"]) def make_actor(self, obs_ff=None, obs_rnn=None, action_prev=None, dones=None, reuse=False, scope="pi"): if obs_ff is None: obs_ff = self.processed_obs if obs_rnn is None: obs_rnn = self.processed_obs if action_prev is None: action_prev = self.action_prev_ph obs_ff, goal = obs_ff[:, :-self.goal_size], obs_ff[:, -self.goal_size:] goal = tf.subtract(goal, obs_ff[:, -self.goal_size:], name="goal_relative") obs_rnn = obs_rnn[:, :-self.goal_size] ff_phs = [obs_ff, goal] rnn_phs = [obs_rnn, action_prev] return super().make_actor(ff_phs=ff_phs, rnn_phs=rnn_phs, dones=dones, reuse=reuse, scope=scope) def make_critics(self, obs_ff=None, action_ff=None, my=None, obs_rnn=None, action_prev=None, dones=None, reuse=False, scope="values_fn"): if obs_ff is None: obs_ff = self.processed_obs if action_ff is None: action_ff = self.action_ph if my is None: my = self.my_ph if obs_rnn is None: obs_rnn = self.processed_obs if action_prev is None: action_prev = self.action_prev_ph obs_ff, goal = obs_ff[:, :-self.goal_size], obs_ff[:, -self.goal_size:] goal = tf.subtract(goal, obs_ff[:, -self.goal_size:], name="goal_relative") obs_rnn = obs_rnn[:, :-self.goal_size] ff_phs = [obs_ff, goal, my, action_ff] rnn_phs = [obs_rnn, action_prev] return super().make_critics(ff_phs=ff_phs, rnn_phs=rnn_phs, dones=dones, reuse=reuse, scope=scope) def collect_data(self, _locals, _globals, **kwargs): data = super().collect_data(_locals, _globals) if "my" not in _locals or _locals["episode_data"]: data["my"] = _locals["self"].env.get_env_parameters() return data class LstmMlpPolicy(RecurrentPolicy): recurrent = True def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, layers=None, cnn_extractor=nature_cnn, feature_extraction="mlp", n_lstm=128, share_lstm=False, layer_norm=False, act_fun=tf.nn.relu, obs_module_indices=None, **kwargs): if layers is None: layers = {"ff": None, "lstm": [64, 64], "head": []} else: assert layers["ff"] is None super().__init__(sess, ob_space, ac_space, layers, n_env, n_steps, n_batch, reuse=reuse, cnn_extractor=cnn_extractor, feature_extraction=feature_extraction, n_lstm=n_lstm, share_lstm=share_lstm, layer_norm=layer_norm, act_fun=act_fun, obs_module_indices=obs_module_indices, **kwargs) def make_actor(self, obs=None, action_prev=None, dones=None, reuse=False, scope="pi"): obs, action_prev, dones = self._process_phs(obs=obs, action_prev=action_prev, dones=dones) ff_phs = None rnn_phs = [obs, action_prev] return super().make_actor(ff_phs=ff_phs, rnn_phs=rnn_phs, dones=dones, reuse=reuse, scope=scope) def make_critics(self, obs=None, action=None, action_prev=None, dones=None, reuse=False, scope="values_fn"): obs, action, action_prev, dones = self._process_phs(obs=obs, action=action, action_prev=action_prev, dones=dones) ff_phs = [action] rnn_phs = [obs, action_prev] return super().make_critics(ff_phs=ff_phs, rnn_phs=rnn_phs, dones=dones, reuse=reuse, scope=scope) class LstmFFMlpPolicy(RecurrentPolicy): recurrent = True def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, layers=None, cnn_extractor=nature_cnn, feature_extraction="mlp", n_lstm=128, share_lstm=False, layer_norm=False, act_fun=tf.nn.relu, obs_module_indices=None, **kwargs): if layers is None: layers = {"ff": [64], "lstm": [64, 64], "head": []} super().__init__(sess, ob_space, ac_space, layers, n_env, n_steps, n_batch, reuse=reuse, cnn_extractor=cnn_extractor, feature_extraction=feature_extraction, n_lstm=n_lstm, share_lstm=share_lstm, layer_norm=layer_norm, act_fun=act_fun, obs_module_indices=obs_module_indices, **kwargs) def make_actor(self, obs=None, action_prev=None, dones=None, reuse=False, scope="pi"): obs, action_prev, dones = self._process_phs(obs=obs, action_prev=action_prev, dones=dones) ff_phs = [obs] rnn_phs = [obs, action_prev] return super().make_actor(ff_phs=ff_phs, rnn_phs=rnn_phs, dones=dones, reuse=reuse, scope=scope) def make_critics(self, obs=None, action=None, action_prev=None, dones=None, reuse=False, scope="values_fn"): obs, action, action_prev, dones = self._process_phs(obs=obs, action=action, action_prev=action_prev, dones=dones) ff_phs = [obs, action] rnn_phs = [obs, action_prev] return super().make_critics(ff_phs=ff_phs, rnn_phs=rnn_phs, dones=dones, reuse=reuse, scope=scope) class CnnPolicy(FeedForwardPolicy): """ Policy object that implements actor critic, using a CNN (the nature CNN) :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs): super(CnnPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, feature_extraction="cnn", **_kwargs) class CnnMlpPolicy(FeedForwardPolicy): """ Policy object that implements actor critic, using a CNN (the nature CNN) :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs): super(CnnMlpPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, cnn_extractor=cnn_1d_extractor, feature_extraction="cnn", **_kwargs) class DRCnnMlpPolicy(FeedForwardPolicy): """ Policy object that implements actor critic, using a CNN (the nature CNN) :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ def __init__(self, sess, ob_space, ac_space, my_size, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs): super(DRCnnMlpPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, cnn_extractor=cnn_1d_extractor, feature_extraction="cnn", **_kwargs) with tf.variable_scope("input", reuse=False): self.my_ph = tf.placeholder(tf.float32, (self.n_batch, *my_size), name="my_ph") # (done t-1) self.extra_phs = ["my", "target_my"] self.extra_data_names = ["my", "target_my"] def make_critics(self, obs=None, action=None, my=None, reuse=False, scope="values_fn"): if my is None: my = self.my_ph return super().make_critics(obs, action, reuse, scope, extracted_callback=lambda x: tf.concat([x, my], axis=-1)) def collect_data(self, _locals, _globals): data = [] for env_i in range(_locals["self"].n_envs): d = {} if len(_locals["episode_data"][env_i]) == 0 or "my" not in _locals["episode_data"][env_i]: if _locals["self"].n_envs == 1: d["my"] = _locals["self"].env.get_env_parameters() else: d["my"] = _locals["self"].env.env_method("get_env_parameters", indices=env_i)[0] else: d["my"] = _locals["episode_data"][env_i][-1]["my"] d["target_my"] = d["my"] data.append(d) return data class DRMyEstPolicy(FeedForwardPolicy): """ Policy object that implements actor critic, using a CNN (the nature CNN) :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ def __init__(self, sess, ob_space, ac_space, my_size, n_env=1, n_steps=1, n_batch=None, reuse=False, loss_weight=1e-3, **_kwargs): super().__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, cnn_extractor=cnn_1d_extractor, feature_extraction="mlp", **_kwargs) self._obs_ph = self.processed_obs # Base class has self.obs_ph as property getting self._obs_ph with tf.variable_scope("input", reuse=False): self.my_ph = tf.placeholder(tf.float32, (self.n_batch, *my_size), name="my_ph") # (done t-1) self.action_prev_ph = tf.placeholder(tf.float32, (self.n_batch, *self.ac_space.shape), name="action_prev_ph") self.obs_prev_ph = tf.placeholder(tf.float32, (self.n_batch, *self.ob_space.shape), name="obs_prev_ph") self.loss_weight = loss_weight self.obs_prev = np.zeros((1, *self.ob_space.shape)) self.action_prev = np.zeros((1, *self.ac_space.shape)) self.my_est_loss_op = None self.my_est_op = None self.policy_loss = None self.my_est = None self.extra_phs = ["my", "action_prev", "obs_prev", "target_my", "target_action_prev", "target_obs_prev"] self.extra_data_names = ["my", "action_prev", "obs_prev", "target_my", "target_action_prev", "target_obs_prev"] def _process_phs(self, **phs): for ph_name, ph_val in phs.items(): if ph_val is None: phs[ph_name] = getattr(self, ph_name + "_ph") else: try: setattr(self, ph_name + "_ph", ph_val) except AttributeError: setattr(self, "_" + ph_name + "_ph", ph_val) return phs.values() def make_actor(self, obs=None, obs_prev=None, action_prev=None, my_gt=None, reuse=False, scope="pi"): obs, obs_prev, action_prev, my_gt = self._process_phs(obs=obs, obs_prev=obs_prev, action_prev=action_prev, my=my_gt) if self.obs_module_indices is not None: obs = tf.gather(obs, self.obs_module_indices["pi"], axis=-1) obs_prev = tf.gather(obs_prev, self.obs_module_indices["pi"], axis=-1) with tf.variable_scope(scope + "/my", reuse=reuse): my_h = tf.concat([obs, obs_prev, action_prev], axis=-1) my_h = mlp(my_h, [64, 64], self.activ_fn, layer_norm=self.layer_norm) self.my_est_op = tf.layers.dense(my_h, self.my_ph.shape[-1]) self.my_est_loss_op = tf.reduce_mean((self.my_est_op - my_gt) ** 2) self.policy_loss = self.loss_weight * self.my_est_loss_op obs = tf.concat([obs, self.my_est_op], axis=-1) with tf.variable_scope(scope, reuse=reuse): if self.feature_extraction == "cnn": pi_h = self.cnn_extractor(obs, name="pi_c1", act_fun=self.activ_fn, **self.cnn_kwargs) else: pi_h = tf.layers.flatten(obs) pi_h = mlp(pi_h, self.layers, self.activ_fn, layer_norm=self.layer_norm) self.policy_pre_activation = tf.layers.dense(pi_h, self.ac_space.shape[0]) self.policy = policy = tf.tanh(self.policy_pre_activation) return policy def make_critics(self, obs=None, action=None, my=None, reuse=False, scope="values_fn"): obs, action, my = self._process_phs(obs=obs, action=action, my=my) return super().make_critics(obs, action, reuse, scope, extracted_callback=lambda x: tf.concat([x, my], axis=-1)) def collect_data(self, _locals, _globals): data = {} if "my" not in _locals or _locals["episode_data"]: data["my"] = _locals["self"].env.get_env_parameters() data["target_my"] = data["my"] if len(_locals["episode_data"]) == 0: data["obs_prev"] = _locals["obs"] data["action_prev"] = _locals["action"] else: data["obs_prev"] = _locals["episode_data"][-1]["obs"] data["action_prev"] = _locals["episode_data"][-1]["action"] data["target_obs_prev"] = data["obs_prev"] data["target_action_prev"] = data["action_prev"] return data def step(self, obs, obs_prev=None, action_prev=None, mask=None): if action_prev is None: assert obs.shape[0] == 1 if mask is not None and mask[0]: self.action_prev = np.zeros((1, *self.ac_space.shape)) action_prev = self.action_prev if obs_prev is None: if mask is not None and mask[0]: self.obs_prev = np.zeros((1, *self.ob_space.shape)) obs_prev = self.obs_prev action, my_est = self.sess.run([self.policy, self.my_est_op], {self.obs_ph: obs, self.action_prev_ph: action_prev, self.obs_prev_ph: obs_prev}) self.action_prev = action self.obs_prev = obs self.my_est = my_est #return action, my_est return action class LnCnnPolicy(FeedForwardPolicy): """ Policy object that implements actor critic, using a CNN (the nature CNN), with layer normalisation :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs): super(LnCnnPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, feature_extraction="cnn", layer_norm=True, **_kwargs) class MlpPolicy(FeedForwardPolicy): """ Policy object that implements actor critic, using a MLP (2 layers of 64) :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs): super(MlpPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, feature_extraction="mlp", **_kwargs) class LnMlpPolicy(FeedForwardPolicy): """ Policy object that implements actor critic, using a MLP (2 layers of 64), with layer normalisation :param sess: (TensorFlow session) The current TensorFlow session :param ob_space: (Gym Space) The observation space of the environment :param ac_space: (Gym Space) The action space of the environment :param n_env: (int) The number of environments to run :param n_steps: (int) The number of steps to run for each environment :param n_batch: (int) The number of batch to run (n_envs * n_steps) :param reuse: (bool) If the policy is reusable or not :param _kwargs: (dict) Extra keyword arguments for the nature CNN feature extraction """ def __init__(self, sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs): super(LnMlpPolicy, self).__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse, feature_extraction="mlp", layer_norm=True, **_kwargs) register_policy("LstmFFMlpPolicy", LstmFFMlpPolicy) register_policy("LstmMlpPolicy", LstmMlpPolicy) register_policy("DRPolicy", DRPolicy) register_policy("CnnPolicy", CnnPolicy) register_policy("LnCnnPolicy", LnCnnPolicy) register_policy("MlpPolicy", MlpPolicy) register_policy("LnMlpPolicy", LnMlpPolicy) register_policy("CnnMlpPolicy", CnnMlpPolicy) register_policy("DRCnnMlpPolicy", DRCnnMlpPolicy) register_policy("DRMyEstPolicy", DRMyEstPolicy)

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Python

students/k3342/practical_works/Demin_Danil/django_project_demin/project_first_app/admin.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

10

2020-03-20T09:06:12.000Z

2021-07-27T13:06:02.000Z

students/k3342/practical_works/Demin_Danil/django_project_demin/project_first_app/admin.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

134

2020-03-23T09:47:48.000Z

2022-03-12T01:05:19.000Z

students/k3342/practical_works/Demin_Danil/django_project_demin/project_first_app/admin.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

71

2020-03-20T12:45:56.000Z

2021-10-31T19:22:25.000Z

from django.contrib import admin from .models import Owner admin.site.register(Owner) from .models import Car admin.site.register(Car) from .models import Owning admin.site.register(Owning) from .models import License admin.site.register(License)

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Python

expressly/__init__.py

expressly/expressly-plugin-sdk-python3-core

97b28e78b69a30bb2bd087e9df48da1f30ef757c

[ "MIT" ]

null

expressly/__init__.py

expressly/expressly-plugin-sdk-python3-core

97b28e78b69a30bb2bd087e9df48da1f30ef757c

[ "MIT" ]

null

expressly/__init__.py

expressly/expressly-plugin-sdk-python3-core

97b28e78b69a30bb2bd087e9df48da1f30ef757c

[ "MIT" ]

null

from expressly.api import Api from expressly.routes import routes api_url = 'prod.expresslyapp.com'

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e3907a7a3ed197b3e8c41adfbfed302ddb7fb9d7

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py

Python

casepro/msgs/context_processors.py

rapidpro/ureport-partners

16e5b95eae36ecbbe8ab2a59f34a2f5fd32ceacd

[ "BSD-3-Clause" ]

21

2015-07-21T15:57:49.000Z

2021-11-04T18:26:35.000Z

casepro/msgs/context_processors.py

rapidpro/ureport-partners

16e5b95eae36ecbbe8ab2a59f34a2f5fd32ceacd

[ "BSD-3-Clause" ]

357

2015-05-22T07:26:45.000Z

2022-03-12T01:08:28.000Z

casepro/msgs/context_processors.py

rapidpro/ureport-partners

16e5b95eae36ecbbe8ab2a59f34a2f5fd32ceacd

[ "BSD-3-Clause" ]

24

2015-05-28T12:30:25.000Z

2021-11-19T01:57:38.000Z

from django.conf import settings def messages(request): """ Context processor for information relating to messages """ return {"max_msg_chars": settings.SITE_MAX_MESSAGE_CHARS}

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Python

flextensor/testing/others/test_conv2d_cuda_different_schedule.py

imxian/FlexTensor

311af3362856ea1b0073404fffad42c54585c205

[ "MIT" ]

135

2020-03-15T11:28:48.000Z

2022-03-26T00:54:32.000Z

flextensor/testing/others/test_conv2d_cuda_different_schedule.py

imxian/FlexTensor

311af3362856ea1b0073404fffad42c54585c205

[ "MIT" ]

11

2020-03-23T11:06:38.000Z

2022-01-24T06:25:41.000Z

flextensor/testing/others/test_conv2d_cuda_different_schedule.py

imxian/FlexTensor

311af3362856ea1b0073404fffad42c54585c205

[ "MIT" ]

32

2020-03-17T05:12:59.000Z

2022-03-26T00:54:33.000Z

""" Test different schedule on conv2d_nchw Target NVIDIA GPU ==================================== **Author**: `Size Zheng` """ import tvm import json from flextensor.measure import _evaluate from flextensor.nn import conv2d_nchw from flextensor.configs.conv2d_config import yolo_shapes_b8 from flextensor.utils import any_factor_split class Parameter(object): def __init__(self): self.b_factors = [2, 4, 1, 1] self.k_factors = [8, 4, 8, 2] self.p_factors = [7, 1, 2, 1] self.q_factors = [1, 1, 14, 1] self.rc_factors = [1, 32, 32] self.ry_factors = [1, 1, 1] self.rx_factors = [1, 1, 1] def __str__(self): ret = "" ret += str(self.b_factors) + "\n" ret += str(self.k_factors) + "\n" ret += str(self.p_factors) + "\n" ret += str(self.q_factors) + "\n" ret += str(self.rc_factors) + "\n" ret += str(self.ry_factors) + "\n" ret += str(self.rx_factors) + "\n" return ret def schedule_yolo_conv_cuda_1(s, outputs, inputs, weight, parameter): # inline the padding operation padded = outputs.op.input_tensors[0] # create cache write_cache = s.cache_write(outputs, "local") read_share_weight = s.cache_read(weight, "shared", [write_cache]) # read_local_weight = s.cache_read(read_share_weight, "local", [write_cache]) read_share_inputs = s.cache_read(padded, "shared", [write_cache]) # read_local_inputs = s.cache_read(read_share_inputs, "local", [write_cache]) b_factors = parameter.b_factors k_factors = parameter.k_factors p_factors = parameter.p_factors q_factors = parameter.q_factors rc_factors = parameter.rc_factors ry_factors = parameter.ry_factors rx_factors = parameter.rx_factors # prepare thread_axis bx = tvm.te.thread_axis("blockIdx.x") by = tvm.te.thread_axis("blockIdx.y") bz = tvm.te.thread_axis("blockIdx.z") vx = tvm.te.thread_axis("vthread") vy = tvm.te.thread_axis("vthread") vz = tvm.te.thread_axis("vthread") tx = tvm.te.thread_axis("threadIdx.x") ty = tvm.te.thread_axis("threadIdx.y") tz = tvm.te.thread_axis("threadIdx.z") # split the spatial axes b, k, p, q = s[outputs].op.axis kernel_scope, b = s[outputs].split(b, nparts=1) bo, bi = s[outputs].split(b, nparts=b_factors[0]) ko, ki = s[outputs].split(k, nparts=k_factors[0]) po, pi = s[outputs].split(p, nparts=p_factors[0]) qo, qi = s[outputs].split(q, nparts=q_factors[0]) vbo, bi = s[outputs].split(bi, nparts=b_factors[1]) vko, ki = s[outputs].split(ki, nparts=k_factors[1]) vpo, pi = s[outputs].split(pi, nparts=p_factors[1]) vqo, qi = s[outputs].split(qi, nparts=q_factors[1]) tbo, bi = s[outputs].split(bi, nparts=b_factors[2]) tko, ki = s[outputs].split(ki, nparts=k_factors[2]) tpo, pi = s[outputs].split(pi, nparts=p_factors[2]) tqo, qi = s[outputs].split(qi, nparts=q_factors[2]) # reorder s[outputs].reorder(bo, ko, po, qo, vbo, vko, vpo, vqo, tbo, tko, tpo, tqo, bi, ki, pi, qi) # fuse bko = s[outputs].fuse(bo, ko) vbko = s[outputs].fuse(vbo, vko) tbko = s[outputs].fuse(tbo, tko) bki = s[outputs].fuse(bi, ki) # bind s[outputs].bind(bko, bz) s[outputs].bind(po, by) s[outputs].bind(qo, bx) s[outputs].bind(vbko, vz) s[outputs].bind(vpo, vy) s[outputs].bind(vqo, vx) s[outputs].bind(tbko, tz) s[outputs].bind(tpo, ty) s[outputs].bind(tqo, tx) # compute at write cache s[write_cache].compute_at(s[outputs], tqo) rc, ry, rx = s[write_cache].op.reduce_axis rco, rci = s[write_cache].split(rc, nparts=rc_factors[0]) rcm, rci = s[write_cache].split(rci, nparts=rc_factors[1]) ryo, ryi = s[write_cache].split(ry, nparts=ry_factors[0]) rym, ryi = s[write_cache].split(ryi, nparts=ry_factors[1]) rxo, rxi = s[write_cache].split(rx, nparts=rx_factors[0]) rxm, rxi = s[write_cache].split(rxi, nparts=rx_factors[1]) a, b, c, d = s[write_cache].op.axis s[write_cache].reorder(rco, ryo, rxo, rcm, rym, rxm, rci, ryi, rxi, a, b, c, d) # compute at read cache s[read_share_weight].compute_at(s[write_cache], rxm) # s[read_local_weight].compute_at(s[write_cache], rxi) s[read_share_inputs].compute_at(s[write_cache], rxm) # s[read_local_inputs].compute_at(s[write_cache], rxi) # cooperative fetching for cache in [read_share_inputs, read_share_weight]: cb, ck, ch, cw = s[cache].op.axis fused = s[cache].fuse(cb, ck, ch, cw) fused, bindx = s[cache].split(fused, factor=q_factors[2]) fused, bindy = s[cache].split(fused, factor=p_factors[2]) fused, bindz = s[cache].split(fused, factor=b_factors[2] * k_factors[2]) s[cache].bind(bindx, tx) s[cache].bind(bindy, ty) s[cache].bind(bindz, tz) s[outputs].pragma(kernel_scope, 'auto_unroll_max_step', 1500) s[outputs].pragma(kernel_scope, 'unroll_explicit', 1) s[padded].compute_inline() def schedule_yolo_conv_cuda_2(s, outputs, inputs, weight, parameter): # inline the padding operation padded = outputs.op.input_tensors[0] # create cache write_cache = s.cache_write(outputs, "local") read_share_weight = s.cache_read(weight, "shared", [write_cache]) # read_local_weight = s.cache_read(read_share_weight, "local", [write_cache]) read_share_inputs = s.cache_read(padded, "shared", [write_cache]) # read_local_inputs = s.cache_read(read_share_inputs, "local", [write_cache]) b_factors = parameter.b_factors k_factors = parameter.k_factors p_factors = parameter.p_factors q_factors = parameter.q_factors rc_factors = parameter.rc_factors ry_factors = parameter.ry_factors rx_factors = parameter.rx_factors # prepare thread_axis bx = tvm.te.thread_axis("blockIdx.x") by = tvm.te.thread_axis("blockIdx.y") bz = tvm.te.thread_axis("blockIdx.z") vx = tvm.te.thread_axis("vthread") vy = tvm.te.thread_axis("vthread") vz = tvm.te.thread_axis("vthread") tx = tvm.te.thread_axis("threadIdx.x") ty = tvm.te.thread_axis("threadIdx.y") tz = tvm.te.thread_axis("threadIdx.z") # split the spatial axes b, k, p, q = s[outputs].op.axis kernel_scope, b = s[outputs].split(b, nparts=1) ko, ki = s[outputs].split(k, nparts=k_factors[0]) po, pi = s[outputs].split(p, nparts=p_factors[0]) qo, qi = s[outputs].split(q, nparts=q_factors[0]) vko, ki = s[outputs].split(ki, nparts=k_factors[1]) vpo, pi = s[outputs].split(pi, nparts=p_factors[1]) vqo, qi = s[outputs].split(qi, nparts=q_factors[1]) tko, ki = s[outputs].split(ki, nparts=k_factors[2]) tpo, pi = s[outputs].split(pi, nparts=p_factors[2]) tqo, qi = s[outputs].split(qi, nparts=q_factors[2]) # reorder s[outputs].reorder(ko, po, qo, vko, vpo, vqo, tko, tpo, tqo, ki, pi, qi) # s[outputs].reorder(po, bko, qo, vqo, vbko, vpo, tbko, tpo, tqo, bki, pi, qi) # fuse bko = s[outputs].fuse(b, ko) # bind s[outputs].bind(bko, bz) s[outputs].bind(po, by) s[outputs].bind(qo, bx) s[outputs].bind(vko, vz) s[outputs].bind(vpo, vy) s[outputs].bind(vqo, vx) s[outputs].bind(tko, tz) s[outputs].bind(tpo, ty) s[outputs].bind(tqo, tx) # compute at write cache s[write_cache].compute_at(s[outputs], tqo) rc, ry, rx = s[write_cache].op.reduce_axis rco, rci = s[write_cache].split(rc, nparts=rc_factors[0]) rcm, rci = s[write_cache].split(rci, nparts=rc_factors[1]) ryo, ryi = s[write_cache].split(ry, nparts=ry_factors[0]) rym, ryi = s[write_cache].split(ryi, nparts=ry_factors[1]) rxo, rxi = s[write_cache].split(rx, nparts=rx_factors[0]) rxm, rxi = s[write_cache].split(rxi, nparts=rx_factors[1]) a, b, c, d = s[write_cache].op.axis s[write_cache].reorder(rco, ryo, rxo, rcm, rym, rxm, rci, ryi, rxi, a, b, c, d) # compute at read cache s[read_share_weight].compute_at(s[write_cache], rxm) # s[read_local_weight].compute_at(s[write_cache], rxi) s[read_share_inputs].compute_at(s[write_cache], rxm) # s[read_local_inputs].compute_at(s[write_cache], rxi) # cooperative fetching for cache in [read_share_inputs, read_share_weight]: cb, ck, ch, cw = s[cache].op.axis fused = s[cache].fuse(cb, ck, ch, cw) fused, bindx = s[cache].split(fused, factor=q_factors[2]) fused, bindy = s[cache].split(fused, factor=p_factors[2]) fused, bindz = s[cache].split(fused, factor=k_factors[2]) s[cache].bind(bindx, tx) s[cache].bind(bindy, ty) s[cache].bind(bindz, tz) s[outputs].pragma(kernel_scope, 'auto_unroll_max_step', 1500) s[outputs].pragma(kernel_scope, 'unroll_explicit', 1) s[padded].compute_inline() def schedule_yolo_conv_cuda_3(s, outputs, inputs, weight, parameter): # inline the padding operation padded = outputs.op.input_tensors[0] # create cache write_cache = s.cache_write(outputs, "local") read_share_weight = s.cache_read(weight, "shared", [write_cache]) # read_local_weight = s.cache_read(read_share_weight, "local", [write_cache]) read_share_inputs = s.cache_read(padded, "shared", [write_cache]) # read_local_inputs = s.cache_read(read_share_inputs, "local", [write_cache]) b_factors = parameter.b_factors k_factors = parameter.k_factors p_factors = parameter.p_factors q_factors = parameter.q_factors rc_factors = parameter.rc_factors ry_factors = parameter.ry_factors rx_factors = parameter.rx_factors # prepare thread_axis bx = tvm.te.thread_axis("blockIdx.x") by = tvm.te.thread_axis("blockIdx.y") bz = tvm.te.thread_axis("blockIdx.z") vx = tvm.te.thread_axis("vthread") vy = tvm.te.thread_axis("vthread") vz = tvm.te.thread_axis("vthread") tx = tvm.te.thread_axis("threadIdx.x") ty = tvm.te.thread_axis("threadIdx.y") tz = tvm.te.thread_axis("threadIdx.z") # split the spatial axes b, k, p, q = s[outputs].op.axis kernel_scope, b = s[outputs].split(b, nparts=1) bo, bi = s[outputs].split(b, nparts=b_factors[0]) ko, ki = s[outputs].split(k, nparts=k_factors[0]) po, pi = s[outputs].split(p, nparts=p_factors[0]) qo, qi = s[outputs].split(q, nparts=q_factors[0]) vbo, bi = s[outputs].split(bi, nparts=b_factors[1]) vko, ki = s[outputs].split(ki, nparts=k_factors[1]) vpo, pi = s[outputs].split(pi, nparts=p_factors[1]) vqo, qi = s[outputs].split(qi, nparts=q_factors[1]) tbo, bi = s[outputs].split(bi, nparts=b_factors[2]) tko, ki = s[outputs].split(ki, nparts=k_factors[2]) tpo, pi = s[outputs].split(pi, nparts=p_factors[2]) tqo, qi = s[outputs].split(qi, nparts=q_factors[2]) # reorder s[outputs].reorder(bo, ko, po, qo, vbo, vko, vpo, vqo, tbo, tko, tpo, tqo, bi, ki, pi, qi) # fuse outer = s[outputs].fuse(bo, ko, po, qo) middle = s[outputs].fuse(vbo, vko, vpo, vqo) inner = s[outputs].fuse(tbo, tko, tpo, tqo) # bind s[outputs].bind(outer, bx) s[outputs].bind(inner, tx) # compute at write cache s[write_cache].compute_at(s[outputs], inner) rc, ry, rx = s[write_cache].op.reduce_axis rco, rci = s[write_cache].split(rc, nparts=rc_factors[0]) rcm, rci = s[write_cache].split(rci, nparts=rc_factors[1]) ryo, ryi = s[write_cache].split(ry, nparts=ry_factors[0]) rym, ryi = s[write_cache].split(ryi, nparts=ry_factors[1]) rxo, rxi = s[write_cache].split(rx, nparts=rx_factors[0]) rxm, rxi = s[write_cache].split(rxi, nparts=rx_factors[1]) a, b, c, d = s[write_cache].op.axis s[write_cache].reorder(rco, ryo, rxo, rcm, rym, rxm, rci, ryi, rxi, a, b, c, d) # compute at read cache s[read_share_weight].compute_at(s[write_cache], rxm) # s[read_local_weight].compute_at(s[write_cache], rxi) s[read_share_inputs].compute_at(s[write_cache], rxm) # s[read_local_inputs].compute_at(s[write_cache], rxi) # cooperative fetching for cache in [read_share_inputs, read_share_weight]: cb, ck, ch, cw = s[cache].op.axis fused = s[cache].fuse(cb, ck, ch, cw) fused, bindx = s[cache].split(fused, factor=b_factors[2] * k_factors[2] * p_factors[2] * q_factors[2]) s[cache].bind(bindx, tx) s[outputs].pragma(kernel_scope, 'auto_unroll_max_step', 1500) s[outputs].pragma(kernel_scope, 'unroll_explicit', 1) s[padded].compute_inline() def schedule_yolo_conv_opencl(s, outputs, inputs, weight): # inline the padding operation padded = outputs.op.input_tensors[0] # prepare thread_axis bx = tvm.te.thread_axis("blockIdx.x") # split the spatial axes b, k, p, q = s[outputs].op.axis bo, bi = s[outputs].split(b, nparts=1) s[outputs].bind(bo, bx) s[padded].compute_inline() def try_yolo_conv(config, parameter, fsch): # get the compute # (1, 3, 448, 448, 64, 3, 7, 7, 1, 2, 3, 1, 1) batch, CI, H, W, CO, _, kh, kw, _, st, pad, dilation, group = config inputs = tvm.te.placeholder((batch, CI, H, W), dtype="float32") weight = tvm.te.placeholder((CO, CI, kh, kw), dtype="float32") outputs = conv2d_nchw(inputs, weight, stride=st, padding=pad, dilation=dilation, groups=group) s = tvm.te.create_schedule(outputs.op) fsch(s, outputs, inputs, weight, parameter) arg_bufs = [inputs, weight, outputs] stmt = tvm.lower(s, arg_bufs, simple_mode=True) # print(stmt) dev_id = 2 ctx = tvm.nd.context("cuda", dev_id) max_dims = ctx.max_thread_dimensions kwargs = { "max_shared_memory_per_block": ctx.max_shared_memory_per_block, "max_threads_per_block": ctx.max_threads_per_block, "max_thread_x": max_dims[0], "max_thread_y": max_dims[1], "max_thread_z": max_dims[2] } verify = tvm.tir.ir_pass.VerifyGPUCode(stmt, kwargs) # print("config is:\n %s" % (str(config))) if verify: print("Valid kernel") time_cost = _evaluate(s, arg_bufs, "cuda", dev_id, 10) print("Yolo conv use", time_cost, "ms\n") else: print("Invalid kernel") time_cost = float("inf") return time_cost if __name__ == "__main__": res = [] parameters = [] with open("yolo_conv_b8_parameters.txt", "r") as fin: for line in fin: _, content = line.split(":", 1) obj = json.loads(content) op_parameters = obj[0] conv_parameters = op_parameters[1] parameter = Parameter() parameter.b_factors = conv_parameters["spatial"][0] parameter.k_factors = conv_parameters["spatial"][1] parameter.p_factors = conv_parameters["spatial"][2] parameter.q_factors = conv_parameters["spatial"][3] parameter.rc_factors = conv_parameters["reduce"][0] parameter.ry_factors = conv_parameters["reduce"][1] parameter.rx_factors = conv_parameters["reduce"][2] parameters.append(parameter) for config, parameter in list(zip(yolo_shapes_b8, parameters))[:]: cost = try_yolo_conv(config, parameter, schedule_yolo_conv_cuda_3) res.append(cost) for ele in res: print(ele)

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localstack/__init__.py

supaflysnooka/localstack

078d477a42244d58bd0c8606a0fe80a048f06cb7

[ "Apache-2.0" ]

null

localstack/__init__.py

supaflysnooka/localstack

078d477a42244d58bd0c8606a0fe80a048f06cb7

[ "Apache-2.0" ]

null

localstack/__init__.py

supaflysnooka/localstack

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[ "Apache-2.0" ]

null

__version__ = "0.12.20"

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Python

models/__init__.py

mpiannucci/crosswynds-promo

66ab0250b5c587374d4f538335b0994f70cf739b

[ "MIT" ]

null

models/__init__.py

mpiannucci/crosswynds-promo

66ab0250b5c587374d4f538335b0994f70cf739b

[ "MIT" ]

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2015-03-06T18:46:28.000Z

2015-03-11T16:42:00.000Z

models/__init__.py

mpiannucci/crosswynds-promo

66ab0250b5c587374d4f538335b0994f70cf739b

[ "MIT" ]

null

# Add the models to the parent namespace from ipaddress import * from user import *

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py

Python

test/sqlalchemy_filterparams_tests/models/__init__.py

cbrand/python-sqlalchemy-filterparams

6e555cfe9e2f0f2c5f6d6606485de50bc76aaf73

[ "MIT" ]

2

2016-02-24T03:07:26.000Z

2016-05-22T22:00:40.000Z

test/sqlalchemy_filterparams_tests/models/__init__.py

cbrand/python-sqlalchemy-filterparams

6e555cfe9e2f0f2c5f6d6606485de50bc76aaf73

[ "MIT" ]

null

test/sqlalchemy_filterparams_tests/models/__init__.py

cbrand/python-sqlalchemy-filterparams

6e555cfe9e2f0f2c5f6d6606485de50bc76aaf73

[ "MIT" ]

null

# -*- encoding: utf-8 -*- from .base import Base from .domain import Domain from .email import EMail from .user import User

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Python

federatedscope/core/regularizer/__init__.py

alibaba/FederatedScope

fcf6d237624769ea094cfd68803901622f14fc23

[ "Apache-2.0" ]

9

2022-03-24T07:59:37.000Z

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federatedscope/core/regularizer/__init__.py

alibaba/FederatedScope

fcf6d237624769ea094cfd68803901622f14fc23

[ "Apache-2.0" ]

1

2022-03-28T13:52:17.000Z

federatedscope/core/regularizer/__init__.py

alibaba/FederatedScope

fcf6d237624769ea094cfd68803901622f14fc23

[ "Apache-2.0" ]

null

from federatedscope.core.regularizer.proximal_regularizer import *

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Python

test/graph/parse/test_cdg_java.py

acheshkov/program_slicing

124d2dcf6c9c2cd8e505b96f4f47f3ea98f0a260

[ "MIT" ]

5

2021-11-06T04:35:17.000Z

2022-03-21T09:11:54.000Z

test/graph/parse/test_cdg_java.py

acheshkov/program_slicing

124d2dcf6c9c2cd8e505b96f4f47f3ea98f0a260

[ "MIT" ]

19

2021-11-15T14:42:56.000Z

2022-02-01T14:30:34.000Z

test/graph/parse/test_cdg_java.py

acheshkov/program_slicing

124d2dcf6c9c2cd8e505b96f4f47f3ea98f0a260

[ "MIT" ]

null

__licence__ = 'MIT' __author__ = 'kuyaki' __credits__ = ['kuyaki'] __maintainer__ = 'kuyaki' __date__ = '2021/03/30' from typing import List, Dict from unittest import TestCase from program_slicing.graph.parse import cdg_java from program_slicing.graph.statement import Statement, StatementType class CDGJavaTestCase(TestCase): def __check_cdg_children(self, children: List[Statement], statement_type_map: Dict[int, StatementType]) -> None: for i, child in enumerate(children): statement_type = statement_type_map.get(i, StatementType.UNKNOWN) self.assertEqual(statement_type, child.statement_type) def test_switch(self) -> None: source_code = """ { switch(a) { default: a = 1; case 10: myFoo(); case 5: break; case 4: a = -1; } """ cdg = cdg_java.parse(source_code) self.assertEqual(23, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(6, len(function_children)) self.__check_cdg_children(function_children, { 0: StatementType.SCOPE, 3: StatementType.SCOPE, 4: StatementType.BRANCH, 5: StatementType.EXIT }) branch_children = [child for child in cdg.successors(function_children[4])] self.assertEqual(16, len(branch_children)) self.__check_cdg_children(branch_children, { 0: StatementType.SCOPE, 4: StatementType.ASSIGNMENT, 5: StatementType.SCOPE, 7: StatementType.CALL, 8: StatementType.SCOPE, 9: StatementType.GOTO, 10: StatementType.SCOPE, 15: StatementType.ASSIGNMENT }) def test_while(self) -> None: source_code = """ { while (1) { } } """ cdg = cdg_java.parse(source_code) self.assertEqual(7, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(5, len(function_children)) self.__check_cdg_children(function_children, { 0: StatementType.SCOPE, 3: StatementType.LOOP, 4: StatementType.EXIT }) loop_children = [child for child in cdg.successors(function_children[3])] self.assertEqual(1, len(loop_children)) self.__check_cdg_children(loop_children, { 0: StatementType.SCOPE }) def test_for_each(self) -> None: source_code = """ class A { int main(String word) { for (char a : word) { foo(a); } } } """ cdg = cdg_java.parse(source_code) self.assertEqual(16, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(9, len(function_children)) self.__check_cdg_children(function_children, { 1: StatementType.VARIABLE, 2: StatementType.SCOPE, 3: StatementType.VARIABLE, 7: StatementType.LOOP, 8: StatementType.EXIT }) loop_children = [child for child in cdg.successors(function_children[7])] self.assertEqual(3, len(loop_children)) self.__check_cdg_children(loop_children, { 0: StatementType.SCOPE, 2: StatementType.CALL }) self.assertEqual({"a"}, loop_children[2].affected_by) def test_for_each_modifiers(self) -> None: source_code = """ class A { int main(String word) { for (final char a : word) { } } } """ cdg = cdg_java.parse(source_code) self.assertEqual(15, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(10, len(function_children)) self.__check_cdg_children(function_children, { 1: StatementType.VARIABLE, 2: StatementType.SCOPE, 3: StatementType.VARIABLE, 8: StatementType.LOOP, 9: StatementType.EXIT }) loop_children = [child for child in cdg.successors(function_children[8])] self.assertEqual(1, len(loop_children)) self.__check_cdg_children(loop_children, { 0: StatementType.SCOPE }) def test_try_catch(self) -> None: source_code = """ class A { int main(String args) { try { a = args[10]; } catch (Exception e) { e.printStackTrace(); } finally { System.out.println("The 'try catch' is finished."); } } } """ cdg = cdg_java.parse(source_code) self.assertEqual(25, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(9, len(function_children)) self.__check_cdg_children(function_children, { 1: StatementType.VARIABLE, 2: StatementType.SCOPE, 3: StatementType.BRANCH, 5: StatementType.SCOPE, 7: StatementType.CALL, 8: StatementType.EXIT }) try_children = [child for child in cdg.successors(function_children[3])] self.assertEqual(9, len(try_children)) self.__check_cdg_children(try_children, { 0: StatementType.SCOPE, 6: StatementType.ASSIGNMENT, 7: StatementType.VARIABLE, 8: StatementType.BRANCH }) catch_children = [child for child in cdg.successors(try_children[8])] self.assertEqual(3, len(catch_children)) self.__check_cdg_children(catch_children, { 0: StatementType.SCOPE, 2: StatementType.CALL }) def test_resourced_try_multi_catch(self) -> None: source_code = """ class A { int main(String args) { try (int i = 10) { a = args[i]; } catch (MyException1 e) { e.printStackTrace(); } catch (MyException2 e) { } } } """ cdg = cdg_java.parse(source_code) self.assertEqual(28, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(9, len(function_children)) self.__check_cdg_children(function_children, { 1: StatementType.VARIABLE, 2: StatementType.SCOPE, 7: StatementType.BRANCH, 8: StatementType.EXIT }) try_children = [child for child in cdg.successors(function_children[7])] self.assertEqual(9, len(try_children)) self.__check_cdg_children(try_children, { 0: StatementType.SCOPE, 6: StatementType.ASSIGNMENT, 7: StatementType.VARIABLE, 8: StatementType.BRANCH }) catch_1_children = [child for child in cdg.successors(try_children[8])] self.assertEqual(5, len(catch_1_children)) self.__check_cdg_children(catch_1_children, { 0: StatementType.SCOPE, 2: StatementType.CALL, 3: StatementType.VARIABLE, 4: StatementType.BRANCH }) catch_2_children = [child for child in cdg.successors(catch_1_children[4])] self.assertEqual(1, len(catch_2_children)) self.__check_cdg_children(catch_2_children, { 0: StatementType.SCOPE }) def test_update(self) -> None: source_code = """ { int n = 0; for (int i = 0; i < 10; i++) { ++n; } } """ cdg = cdg_java.parse(source_code) self.assertEqual(19, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(12, len(function_children)) self.__check_cdg_children(function_children, { 0: StatementType.SCOPE, 3: StatementType.VARIABLE, 6: StatementType.VARIABLE, 10: StatementType.LOOP, 11: StatementType.EXIT }) loop_children = [child for child in cdg.successors(function_children[10])] self.assertEqual(6, len(loop_children)) self.__check_cdg_children(loop_children, { 0: StatementType.SCOPE, 3: StatementType.ASSIGNMENT, 5: StatementType.ASSIGNMENT }) def test_multiple_returns(self) -> None: source_code = """ { int n = 0; int a = 10; if (n < a) { return n; } return a; } """ cdg = cdg_java.parse(source_code) self.assertEqual(19, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(15, len(function_children)) self.__check_cdg_children(function_children, { 0: StatementType.SCOPE, 3: StatementType.VARIABLE, 6: StatementType.VARIABLE, 11: StatementType.BRANCH, 13: StatementType.GOTO, 14: StatementType.EXIT }) self.assertEqual({"a", "n"}, function_children[11].affected_by) branch_children = [child for child in cdg.successors(function_children[11])] self.assertEqual(3, len(branch_children)) self.__check_cdg_children(branch_children, { 0: StatementType.SCOPE, 2: StatementType.GOTO }) def test_synchronized(self) -> None: source_code = """ { synchronized(a) { a = -1; } } """ cdg = cdg_java.parse(source_code) self.assertEqual(12, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(5, len(function_children)) self.__check_cdg_children(function_children, { 0: StatementType.SCOPE, 3: StatementType.LOOP, 4: StatementType.EXIT }) loop_children = [child for child in cdg.successors(function_children[3])] self.assertEqual(6, len(loop_children)) self.__check_cdg_children(loop_children, { 0: StatementType.SCOPE, 5: StatementType.ASSIGNMENT }) def test_parse(self) -> None: source_code = """ class A { public static int main() { int n = 10; for(int i = 0; i < n; i += 1) { if (i < 4) { System.out.println("lol"); continue; } if (i > 6) { System.out.println("che bu rek"); break; } else System.out.println("kek"); } return n; } } """ cdg = cdg_java.parse(source_code) self.assertEqual(44, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(15, len(function_children)) self.__check_cdg_children(function_children, { 1: StatementType.SCOPE, 4: StatementType.VARIABLE, 7: StatementType.VARIABLE, 11: StatementType.LOOP, 13: StatementType.GOTO, 14: StatementType.EXIT }) loop_children = [child for child in cdg.successors(function_children[11])] self.assertEqual(14, len(loop_children)) self.__check_cdg_children(loop_children, { 0: StatementType.SCOPE, 5: StatementType.BRANCH, 10: StatementType.BRANCH, 13: StatementType.ASSIGNMENT }) branch_1_children = [child for child in cdg.successors(loop_children[5])] self.assertEqual(4, len(branch_1_children)) self.__check_cdg_children(branch_1_children, { 0: StatementType.SCOPE, 2: StatementType.CALL, 3: StatementType.GOTO }) branch_2_children = [child for child in cdg.successors(loop_children[10])] self.assertEqual(7, len(branch_2_children)) self.__check_cdg_children(branch_1_children, { 0: StatementType.SCOPE, 2: StatementType.CALL, 3: StatementType.GOTO, 4: StatementType.GOTO, 6: StatementType.CALL }) def test_parse_without_class(self) -> None: source_code = """ public static int main(int arg) { int n = 10 + arg; return n; } """ cdg = cdg_java.parse(source_code) self.assertEqual(10, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(9, len(function_children)) self.__check_cdg_children(function_children, { 1: StatementType.VARIABLE, 2: StatementType.SCOPE, 5: StatementType.VARIABLE, 7: StatementType.GOTO, 8: StatementType.EXIT }) def test_parse_without_function(self) -> None: source_code = """ int n = 10 + arg; return n; """ cdg = cdg_java.parse(source_code) self.assertEqual(7, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(6, len(function_children)) self.__check_cdg_children(function_children, { 2: StatementType.VARIABLE, 4: StatementType.GOTO, 5: StatementType.EXIT }) def test_parse_with_inner_functions(self) -> None: source_code = """ class A { int main() { int n = 0; class B { int gain() { int k = 0; } } } } """ cdg = cdg_java.parse(source_code) self.assertEqual(19, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(2, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION, 1: StatementType.FUNCTION }) for entry_point in entry_points: function_children = [child for child in cdg.successors(entry_point)] if len(function_children) == 6: self.__check_cdg_children(function_children, { 1: StatementType.SCOPE, 4: StatementType.VARIABLE, 5: StatementType.EXIT }) elif len(function_children) == 8: self.__check_cdg_children(function_children, { 1: StatementType.SCOPE, 4: StatementType.VARIABLE, 6: StatementType.SCOPE, 7: StatementType.EXIT }) else: self.assertFalse(True) def test_parse_constructor(self) -> None: source_code = """ class MyClass { int a; MyClass() { this.a = 0; } } """ cdg = cdg_java.parse(source_code) self.assertEqual(16, len(cdg.nodes)) entry_points = [entry_point for entry_point in cdg.entry_points] self.assertEqual(1, len(entry_points)) self.__check_cdg_children(entry_points, { 0: StatementType.FUNCTION }) function_children = [child for child in cdg.successors(entry_points[0])] self.assertEqual(9, len(function_children)) self.__check_cdg_children(function_children, { 1: StatementType.SCOPE, 7: StatementType.ASSIGNMENT, 8: StatementType.EXIT })

36.423372

116

0.564771

1,970

19,013

5.195431

0.074619

0.075232

0.070347

0.085979

0.794822

0.77616

0.750171

0.739521

0.712262

0.697509

0

0.024262

0.340977

19,013

521

117

36.493282

0.792578

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0.007968

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0.049801

0.011952

0

null

0

1

0

null

0

5

5840454d831c1e7ac8805dae83f87938318dd1d6

4,078

py

Python

gators/feature_generation_dt/tests/test_cyclic_day_of_week.py

Aditya-Kapadiya/gators

d7c9967e3a8e304a601b6a92ad834d03d3e36338

[ "Apache-2.0" ]

4

2021-10-29T18:20:52.000Z

2022-03-31T22:53:03.000Z

gators/feature_generation_dt/tests/test_cyclic_day_of_week.py

Aditya-Kapadiya/gators

d7c9967e3a8e304a601b6a92ad834d03d3e36338

[ "Apache-2.0" ]

1

2022-02-21T20:02:16.000Z

gators/feature_generation_dt/tests/test_cyclic_day_of_week.py

Aditya-Kapadiya/gators

d7c9967e3a8e304a601b6a92ad834d03d3e36338

[ "Apache-2.0" ]

5

2021-11-17T20:16:54.000Z

2022-02-21T18:21:02.000Z

# License: Apache-2.0 import databricks.koalas as ks import numpy as np import pandas as pd import pytest from pandas.testing import assert_frame_equal from gators.feature_generation_dt import CyclicDayOfWeek ks.set_option("compute.default_index_type", "distributed-sequence") @pytest.fixture def data(): X = pd.DataFrame( { "A": ["2020-05-04T00", None, np.nan], "B": ["2020-05-06T06", None, np.nan], "C": ["2020-05-08T23", None, np.nan], "D": ["2020-05-09T06", None, np.nan], "E": ["2020-05-10T06", None, np.nan], "X": ["x", None, np.nan], } ) columns = ["A", "B", "C", "D", "E"] X["A"] = X["A"].astype("datetime64[ns]") X["B"] = X["B"].astype("datetime64[ms]") X["C"] = X["C"].astype("datetime64[s]") X["D"] = X["D"].astype("datetime64[m]") X["E"] = X["E"].astype("datetime64[h]") X_expected = pd.DataFrame( { "A__day_of_week_cos": [1.0, np.nan, np.nan], "A__day_of_week_sin": [0.0, np.nan, np.nan], "B__day_of_week_cos": [-0.4999999999999998, np.nan, np.nan], "B__day_of_week_sin": [0.8660254037844388, np.nan, np.nan], "C__day_of_week_cos": [-0.5000000000000004, np.nan, np.nan], "C__day_of_week_sin": [-0.8660254037844384, np.nan, np.nan], "D__day_of_week_cos": [0.4999999999999993, np.nan, np.nan], "D__day_of_week_sin": [-0.866025403784439, np.nan, np.nan], "E__day_of_week_cos": [1.0, None, np.nan], "E__day_of_week_sin": [-2.4492935982947064e-16, None, np.nan], } ) X_expected = pd.concat([X.copy(), X_expected], axis=1) obj = CyclicDayOfWeek(columns=columns).fit(X) return obj, X, X_expected @pytest.fixture def data_ks(): X = ks.DataFrame( { "A": ["2020-05-04T00", None, np.nan], "B": ["2020-05-06T06", None, np.nan], "C": ["2020-05-08T23", None, np.nan], "D": ["2020-05-09T06", None, np.nan], "E": ["2020-05-10T06", None, np.nan], "X": ["x", None, np.nan], } ) columns = ["A", "B", "C", "D", "E"] X[columns] = X[columns].astype("datetime64[ns]") X_expected = pd.DataFrame( { "A__day_of_week_cos": [1.0, np.nan, np.nan], "A__day_of_week_sin": [0.0, np.nan, np.nan], "B__day_of_week_cos": [-0.4999999999999998, np.nan, np.nan], "B__day_of_week_sin": [0.8660254037844388, np.nan, np.nan], "C__day_of_week_cos": [-0.5000000000000004, np.nan, np.nan], "C__day_of_week_sin": [-0.8660254037844384, np.nan, np.nan], "D__day_of_week_cos": [0.4999999999999993, np.nan, np.nan], "D__day_of_week_sin": [-0.866025403784439, np.nan, np.nan], "E__day_of_week_cos": [1.0, None, np.nan], "E__day_of_week_sin": [-2.4492935982947064e-16, None, np.nan], } ) X_expected = pd.concat([X.to_pandas().copy(), X_expected], axis=1) obj = CyclicDayOfWeek(columns=columns).fit(X) return obj, X, X_expected def test_pd(data): obj, X, X_expected = data X_new = obj.transform(X) assert_frame_equal(X_new, X_expected) @pytest.mark.koalas def test_ks(data_ks): obj, X, X_expected = data_ks X_new = obj.transform(X) assert_frame_equal(X_new.to_pandas(), X_expected) def test_pd_np(data): obj, X, X_expected = data X_numpy_new = obj.transform_numpy(X.to_numpy()) X_new = pd.DataFrame(X_numpy_new) X_expected = pd.DataFrame(X_expected.values) assert_frame_equal(X_new, X_expected) @pytest.mark.koalas def test_ks_np(data_ks): obj, X, X_expected = data_ks X_numpy_new = obj.transform_numpy(X.to_numpy()) X_new = pd.DataFrame(X_numpy_new) X_expected = pd.DataFrame(X_expected.values) assert_frame_equal(X_new, X_expected) def test_init(): with pytest.raises(TypeError): _ = CyclicDayOfWeek(columns=0) with pytest.raises(ValueError): _ = CyclicDayOfWeek(columns=[])

34.559322

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0.592202

611

4,078

3.672668

0.148936

0.106952

0.080214

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117

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0.15

0

null

0

1

0

null

0

5

58816e377b7292b3b4b138724d8702d6205b486f

170

py

Python

inventory4h4h/logs/admin.py

Yashub/InventoryForHabit

6b4811bc6e48dcfbde54160311f043afff626e4f

[ "MIT" ]

null

inventory4h4h/logs/admin.py

Yashub/InventoryForHabit

6b4811bc6e48dcfbde54160311f043afff626e4f

[ "MIT" ]

null

inventory4h4h/logs/admin.py

Yashub/InventoryForHabit

6b4811bc6e48dcfbde54160311f043afff626e4f

[ "MIT" ]

null

from django.contrib import admin from .models import DonationData, Address admin.site.register(DonationData) admin.site.register(Address) # Register your models here.

18.888889

41

0.811765

22

170

6.272727

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0.130435

0.246377

0

0.111765

170

8

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0.5

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1

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null

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1

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5

58991d9ea4f4e39bd45def89c88764d3497c0f5c

49

py

Python

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/server/__init__.py

Maximilien-R/cookiecutter-tartiflette-aiohttp

66e7e0897b315df6a1908c6c31ec58b74e0b3a6f

[ "MIT" ]

3

2020-06-01T14:16:19.000Z

2021-11-07T19:54:08.000Z

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/server/__init__.py

Maximilien-R/cookiecutter-tartiflette-aiohttp

66e7e0897b315df6a1908c6c31ec58b74e0b3a6f

[ "MIT" ]

88

2019-11-15T17:35:54.000Z

2021-08-02T04:50:51.000Z

{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/server/__init__.py

Maximilien-R/cookiecutter-tartiflette-aiohttp

66e7e0897b315df6a1908c6c31ec58b74e0b3a6f

[ "MIT" ]

2

2020-05-04T08:35:34.000Z

2020-10-22T17:47:26.000Z

from .app import run_app __all__ = ("run_app",)

12.25

24

0.693878

8

49

3.5

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0.428571

0

0.163265

49

3

25

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0.682927

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0.142857

0

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false

0

0.5

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0.5

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null

1

0

1

0

null

0

1

0

5

58a59d58b3c391b86e3ac22ed5480efa7463ff0e

46

py

Python

test/login.py

shuxiang-python/second_git

24245972b96dadebd3068a7942cb5333538b3a0f

[ "MIT" ]

null

test/login.py

shuxiang-python/second_git

24245972b96dadebd3068a7942cb5333538b3a0f

[ "MIT" ]

null

test/login.py

shuxiang-python/second_git

24245972b96dadebd3068a7942cb5333538b3a0f

[ "MIT" ]

null

num1 =100 num2 = 200 num3 = 300 num5 =500

5.111111

10

0.608696

8

46

3.5

1

0

0.5

0.304348

46

8

11

5.75

0.375

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

543978d21fb8085d99daafd5a10cff343031d949

44

py

Python

tests/components/panel_custom/__init__.py

domwillcode/home-assistant

f170c80bea70c939c098b5c88320a1c789858958

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/panel_custom/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/panel_custom/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Tests for the panel_custom component."""

22

43

0.727273

6

44

5.166667

1

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0.113636

44

1

44

0.794872

0.840909

0

null

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null

0

null

0

null

1

null

true

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null

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null

0

1

0

1

0

null

0

1

0

5

544e71351ee9ff4c4c0b27e6decf6603806e2633

16,657

py

Python

cogs/karaoke.py

Focus1019/Discord-Karaoke-Handler

af9bc211338e1e782fa0632fcc611d762a8ee751

[ "MIT" ]

1

2021-11-23T07:41:14.000Z

cogs/karaoke.py

Focus1019/Discord-Karaoke-Handler

af9bc211338e1e782fa0632fcc611d762a8ee751

[ "MIT" ]

null

cogs/karaoke.py

Focus1019/Discord-Karaoke-Handler

af9bc211338e1e782fa0632fcc611d762a8ee751

[ "MIT" ]

null

import discord from discord.ext import commands import checks class Karaoke(commands.Cog): def __init__(self, bot): self.bot = bot self.current_users = {} self.locked = False @commands.command(name='help') @checks.is_karaoke_channel() @commands.guild_only() async def _help(self, ctx): embed = discord.Embed(colour=discord.Colour.dark_teal()) embed.title = 'Karaoke Help' embed.description = '{prefix}queue **-** Show the current karaoke queue.\n' \ '{prefix}join **-** Join a karaoke queue.\n' \ '{prefix}leave **-** Leave a karaoke queue.\n' \ '{next}next **-** Select the next user in queue.\n' \ \ 'Admin Commands.\n' \ \ '{prefix}channel add|remove **-** add or remove karaoke queue channel.\n' \ '{prefix}clear **-** Clear the current queue.\n' \ '{prefix}remove [user] **-** Removes the mentioned user from the queue.\n' \ '{prefix}add [user] **-** Add the mentioned user from the queue.\n' \ '{prefix}swap [user1] [user2] **-** Swaps the Mentioned users on the queue.\n' \ '{prefix}lock **-** lock or unlock the command for the queue.\n' \ 'need help? kindly DM Focus™#0001' embed.set_footer(text='set your footer here') await ctx.send(embed=embed) @commands.command(name='queue') @checks.is_karaoke_channel() @commands.guild_only() async def _queue(self, ctx): embed = discord.Embed() embed.title = 'Karaoke Queue' embed.set_footer(text='karaoke queue') if ctx.channel not in self.current_users: embed.colour = discord.Colour.gold() embed.description = 'The queue is currently empty.' return await ctx.send(embed=embed) members = self.current_users[ctx.channel] formatted_members = [] for i, member in enumerate(members): member: discord.Member f = lambda x: f'{member}{f"({member.nick})" if member.nick else ""}' if i == 0: formatted_members.append(f'**Current turn:** {f(member)}') continue formatted_members.append(f'**{i}** | {f(member)}') embed.colour = discord.Colour.dark_teal() embed.description = '\n\n'.join(formatted_members) return await ctx.send(embed=embed) @commands.command(name='join') @checks.is_karaoke_channel() @commands.guild_only() async def _join(self, ctx): if self.locked: embed = discord.Embed(colour=discord.Colour.red()) embed.description = 'The queue is currently locked.' embed.set_footer(text='karaoke queue') return await ctx.send(embed=embed) if ctx.channel not in self.current_users: self.current_users[ctx.channel] = [] if ctx.author in self.current_users[ctx.channel]: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'You are already in the queue.' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) self.current_users[ctx.channel].append(ctx.author) embed = discord.Embed(colour=discord.Colour.green()) embed.description = 'You got successfully added to the queue.' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) @commands.command(name='leave') @checks.is_karaoke_channel() @commands.guild_only() async def _leave(self, ctx): if self.locked: embed = discord.Embed(colour=discord.Colour.red()) embed.description = 'The queue is currently locked.' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) if ctx.channel not in self.current_users: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The queue is currently empty' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) if ctx.author not in self.current_users[ctx.channel]: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'You are not in the queue.' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) if len(self.current_users[ctx.channel]) == 1: del self.current_users[ctx.channel] else: self.current_users[ctx.channel].remove(ctx.author) embed = discord.Embed(colour=discord.Colour.green()) embed.description = 'You got successfully got removed from the queue.' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) @commands.command(name='next') @checks.is_karaoke_channel() @commands.guild_only() async def _next(self, ctx): if ctx.channel not in self.current_users: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The queue is currently empty' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) if ctx.author is not self.current_users[ctx.channel][0]: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'Only the current member can execute this command.' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) finished_member = self.current_users[ctx.channel].pop(0) if len(self.current_users[ctx.channel]) > 0: current_member = self.current_users[ctx.channel][0] embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'{finished_member.name} finished.\n\n' \ f'**{current_member.name} now it\'s your turn.**' embed.set_footer(text='set your footer here') return await ctx.send(content=current_member.mention, embed=embed) else: del self.current_users[ctx.channel] embed = discord.Embed(colour=discord.Colour.gold()) embed.description = f'{finished_member.name} finished.\n\n' \ f'**The queue is now empty.**' embed.set_footer(text='set your footer here') return await ctx.send(embed=embed) # Admin Commands @commands.command(name='clear') @commands.has_permissions(administrator=True) @checks.is_karaoke_channel() async def _clear(self, ctx): if ctx.channel not in self.current_users: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The queue is already empty.' return await ctx.send(embed=embed) del self.current_users[ctx.channel] embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully cleared the queue.' return await ctx.send(embed=embed) @commands.command(name='remove') @commands.has_permissions(administrator=True) @checks.is_karaoke_channel() async def _remove(self, ctx, member: discord.Member): if ctx.channel not in self.current_users: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The queue is empty.' return await ctx.send(embed=embed) if member not in self.current_users[ctx.channel]: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The member is not in the queue.' return await ctx.send(embed=embed) self.current_users[ctx.channel].remove(member) embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully removed {member.name} from the queue.' return await ctx.send(embed=embed) @_remove.error async def _remove_error(self, ctx: commands.Context, error): if isinstance(error, commands.MissingRequiredArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = f'Please use `{ctx.prefix}remove <Member>`.' return await ctx.send(embed=embed) elif isinstance(error, commands.BadArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The given argument is no member.' return await ctx.send(embed=embed) @commands.command(name='add') @commands.has_permissions(administrator=True) @checks.is_karaoke_channel() async def _add(self, ctx, member: discord.Member): if ctx.channel not in self.current_users: self.current_users[ctx.channel] = [] if member in self.current_users[ctx.channel]: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The member is already in the queue.' return await ctx.send(embed=embed) self.current_users[ctx.channel].append(member) embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully added {member.name} to the queue.' return await ctx.send(embed=embed) @_add.error async def _add_error(self, ctx: commands.Context, error): if isinstance(error, commands.MissingRequiredArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = f'Please use `{ctx.prefix}add <Member>`.' return await ctx.send(embed=embed) elif isinstance(error, commands.BadArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The given argument is no member.' return await ctx.send(embed=embed) @commands.command(name='swap') @commands.has_permissions(administrator=True) @checks.is_karaoke_channel() async def _swap(self, ctx, member1: discord.Member, member2: discord.Member): if ctx.channel not in self.current_users: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The queue is empty.' return await ctx.send(embed=embed) for member in [member1, member2]: if member not in self.current_users[ctx.channel]: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The member is not in the queue.' return await ctx.send(embed=embed) pos1 = self.current_users[ctx.channel].index(member1) pos2 = self.current_users[ctx.channel].index(member2) self.current_users[ctx.channel].remove(member1) self.current_users[ctx.channel].remove(member2) self.current_users[ctx.channel].insert(pos2, member1) self.current_users[ctx.channel].insert(pos1, member2) embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully switched {member1.name} and {member2.name} position.' return await ctx.send(embed=embed) @_swap.error async def _swap_error(self, ctx: commands.Context, error): if isinstance(error, commands.MissingRequiredArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = f'Please use `{ctx.prefix}swap <Member1> <Member2>`.' return await ctx.send(embed=embed) elif isinstance(error, commands.BadArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The given argument is no member.' return await ctx.send(embed=embed) @commands.command(name='prefix') @commands.has_permissions(administrator=True) async def _prefix(self, ctx, prefix: str): ctx.bot.cfg.set('core.token', prefix) embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully set prefix.\n' \ f'> Prefix: `{prefix}`' return await ctx.send(embed=embed) @_prefix.error async def _prefix_error(self, ctx: commands.Context, error): if isinstance(error, commands.MissingRequiredArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = f'Please use `{ctx.prefix}prefix <Prefix>`.' return await ctx.send(embed=embed) @commands.command(name='lock') @commands.command(administrator=True) async def _lock(self, ctx): if self.locked: self.locked = False embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully unlocked commands.' return await ctx.send(embed=embed) else: self.locked = True embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully locked commands.' return await ctx.send(embed=embed) @commands.group(name='channel', invoke_without_command=True) @commands.has_permissions(administrator=True) async def _channel(self, ctx): channel_ids = ctx.bot.cfg.get('karaoke.channels') channels = [ctx.bot.get_channel(cid) for cid in channel_ids] formatted_channels = [c.mention if c else 'Not Found' for c in channels] embed = discord.Embed(colour=discord.Colour.green()) embed.title = 'Karaoke Channels' embed.description = ', '.join(formatted_channels) embed.add_field(name='**Help**', value=f'`{ctx.prefix}channel add <Channel>` - Add a karaoke channel\n' f'`{ctx.prefix}channel remove <Channel>` - Remove a karaoke channel') return await ctx.send(embed=embed) @_channel.command(name='add') async def _channel_add(self, ctx, channel: discord.TextChannel): channel_ids = ctx.bot.cfg.get('karaoke.channels') if channel.id in channel_ids: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = f'The channel is already a karaoke channel.' return await ctx.send(embed=embed) channel_ids.append(channel.id) ctx.bot.cfg.set('karaoke.channels', channel_ids) embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully added {channel.mention}.' return await ctx.send(embed=embed) @_channel_add.error async def _channel_add_error(self, ctx: commands.Context, error): if isinstance(error, commands.MissingRequiredArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = f'Please use `{ctx.prefix}channel add <Channel>`.' return await ctx.send(embed=embed) elif isinstance(error, commands.BadArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The given argument is no channel.' return await ctx.send(embed=embed) @_channel.command(name='remove') async def _channel_remove(self, ctx, channel: discord.TextChannel): channel_ids = ctx.bot.cfg.get('karaoke.channels') if channel.id not in channel_ids: embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = f'The channel is no karaoke channel.' return await ctx.send(embed=embed) channel_ids.remove(channel.id) ctx.bot.cfg.set('karaoke.channels', channel_ids) embed = discord.Embed(colour=discord.Colour.green()) embed.description = f'Successfully removed {channel.mention}.' return await ctx.send(embed=embed) @_channel_remove.error async def _channel_remove_error(self, ctx: commands.Context, error): if isinstance(error, commands.MissingRequiredArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = f'Please use `{ctx.prefix}channel remove <Channel>`.' return await ctx.send(embed=embed) elif isinstance(error, commands.BadArgument): embed = discord.Embed(colour=discord.Colour.dark_red()) embed.description = 'The given argument is no channel.' return await ctx.send(embed=embed) def setup(bot): bot.add_cog(Karaoke(bot))

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null

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545cf5f598dc74b4c13da6876364a1b882db8d47

191

py

Python

shirt.py

BeniJan/palletization_team

0bae088c3d2b7629eef339f1af42ff192eeb6c47

[ "MIT" ]

null

shirt.py

BeniJan/palletization_team

0bae088c3d2b7629eef339f1af42ff192eeb6c47

[ "MIT" ]

null

shirt.py

BeniJan/palletization_team

0bae088c3d2b7629eef339f1af42ff192eeb6c47

[ "MIT" ]

null

class Shirt: def __init__(self, size, color): self.size = size.lower() self.color = color.lower() def __repr__(self): return str(self.size + "&" + self.color)

27.285714

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0.581152

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0.272251

191

7

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0.333333

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0

1

0

null

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0

5

546d168d4d6d5568624a5b842abb2841c19cb914

91

py

Python

pythran/tests/user_defined_import/global_init_alias_main.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

1,647

2015-01-13T01:45:38.000Z

2022-03-28T01:23:41.000Z

pythran/tests/user_defined_import/global_init_alias_main.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

1,116

2015-01-01T09:52:05.000Z

2022-03-18T21:06:40.000Z

pythran/tests/user_defined_import/global_init_alias_main.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

180

2015-02-12T02:47:28.000Z

2022-03-14T10:28:18.000Z

import global_init as gi XX = [gi.aa(), 3] #pythran export bb() def bb(): return XX

10.111111

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0.014493

0.241758

91

8

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0

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null

0

1

0

1

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5

5471c2fb9bcaf084d221ea30c73362ed0ec94385

83

py

Python

segnet/__init__.py

Abdiel-EMT/segnet

474a68079000a85d1e62ad9723d316074bb1eb8d

[ "MIT" ]

null

segnet/__init__.py

Abdiel-EMT/segnet

474a68079000a85d1e62ad9723d316074bb1eb8d

[ "MIT" ]

null

segnet/__init__.py

Abdiel-EMT/segnet

474a68079000a85d1e62ad9723d316074bb1eb8d

[ "MIT" ]

null

name = "segnet" from .metrics import * from .models import * from .utils import *

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0

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0

null

0

1

0

1

0

5

547a1b1e370ec4d3064c444c33912f861fcb8191

203

py

Python

testapp/custom_runner.py

Smyle/django-nose

0590bfcb4024987035623664eea94e01c0bb67a7

[ "BSD-3-Clause" ]

null

testapp/custom_runner.py

Smyle/django-nose

0590bfcb4024987035623664eea94e01c0bb67a7

[ "BSD-3-Clause" ]

null

testapp/custom_runner.py

Smyle/django-nose

0590bfcb4024987035623664eea94e01c0bb67a7

[ "BSD-3-Clause" ]

null

"""Custom runner to test overriding runner.""" from django_nose import NoseTestSuiteRunner class CustomNoseTestSuiteRunner(NoseTestSuiteRunner): """Custom test runner, to test overring runner."""

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5

54809f788453a5e208c9a4de3924fdd65904fea9

200

py

Python

applications/alkaid/alkaid/strategy_base.py

FrederichRiver/neutrino

e91db53486e56ddeb83ae9714311d606b33fb165

[ "BSD-3-Clause" ]

2

2019-02-10T15:14:23.000Z

2019-02-12T13:59:52.000Z

applications/alkaid/alkaid/strategy_base.py

FrederichRiver/neutrino

e91db53486e56ddeb83ae9714311d606b33fb165

[ "BSD-3-Clause" ]

null

applications/alkaid/alkaid/strategy_base.py

FrederichRiver/neutrino

e91db53486e56ddeb83ae9714311d606b33fb165

[ "BSD-3-Clause" ]

null

#!/usr/bin/python3 class strategyBase(object): def __init__(self): pass def _get_data(self): pass def _settle(self): pass if __name__ == "__main__": pass

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null

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null

0

1

0

1

0

5

5490e61394ee15f2a555a5910ca6937e6c850945

167

py

Python

general/recursion/string/3. binary_2_decimal.py

SaPhyoThuHtet/problem-solving

f73dd0f14942574f4cb91fbfc86a554be274796f

[ "MIT" ]

null

general/recursion/string/3. binary_2_decimal.py

SaPhyoThuHtet/problem-solving

f73dd0f14942574f4cb91fbfc86a554be274796f

[ "MIT" ]

null

general/recursion/string/3. binary_2_decimal.py

SaPhyoThuHtet/problem-solving

f73dd0f14942574f4cb91fbfc86a554be274796f

[ "MIT" ]

null

def decimal_to_binary(num): if (num == 0): return 0 return num%2+10*decimal_to_binary(num//2) print(decimal_to_binary(2))

15.181818

45

0.550898

24

167

3.583333

0.458333

0.313953

0.523256

0.418605

0

0.063063

0.335329

167

10

46

16.7

0.711712

0

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false

0

0.6

0.2

1

0

null

1

0

null

0

1

0

5

54b1a86a5d7e48afe4dbb2a290188e8fd2c4a47b

141

py

Python

tests/module/__init__.py

MD-Studio/MDStudio_SMARTCyp

92ebd48af891188d509c23e297437218c00ec136

[ "Apache-2.0" ]

3

2019-10-17T01:10:27.000Z

2022-01-19T23:11:49.000Z

tests/module/__init__.py

MD-Studio/MDStudio_SMARTCyp

92ebd48af891188d509c23e297437218c00ec136

[ "Apache-2.0" ]

1

2019-10-18T22:07:16.000Z

2019-10-21T11:19:48.000Z

tests/module/__init__.py

MD-Studio/MDStudio_SMARTCyp

92ebd48af891188d509c23e297437218c00ec136

[ "Apache-2.0" ]

1

2019-10-17T01:14:04.000Z

import sys version = sys.version_info MAJOR_PY_VERSION = sys.version_info.major PY_VERSION = '{0}.{1}'.format(version.major, version.minor)

23.5

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0.77305

22

141

4.727273

0.454545

0.288462

0.326923

0.403846

0.605769

0

0.015625

0.092199

141

5

60

28.2

0.796875

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0.049645

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1

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false

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0

1

0

null

1

0

1

0

null

0

5

54bb35806e71240058384e4ede8654dc9c80aae4

49

py

Python

blueprint_example/run.py

gtsofa/mnseriesflask

58a84b698527ff3e790f0f7179193335bd440e3c

[ "MIT" ]

null

blueprint_example/run.py

gtsofa/mnseriesflask

58a84b698527ff3e790f0f7179193335bd440e3c

[ "MIT" ]

null

blueprint_example/run.py

gtsofa/mnseriesflask

58a84b698527ff3e790f0f7179193335bd440e3c

[ "MIT" ]

null

# run.py from blue import app app.run(debug=True)

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null

0

1

0

1

0

5

49d59637ab6bdb39099fd750090f7c99375854cd

4,702

py

Python

model-optimizer/extensions/ops/activation_test.py

JOCh1958/openvino

070201feeec5550b7cf8ec5a0ffd72dc879750be

[ "Apache-2.0" ]

1

2021-04-06T03:32:12.000Z

model-optimizer/extensions/ops/activation_test.py

JOCh1958/openvino

070201feeec5550b7cf8ec5a0ffd72dc879750be

[ "Apache-2.0" ]

28

2021-09-24T09:29:02.000Z

2022-03-28T13:20:46.000Z

model-optimizer/extensions/ops/activation_test.py

JOCh1958/openvino

070201feeec5550b7cf8ec5a0ffd72dc879750be

[ "Apache-2.0" ]

1

2020-08-30T11:48:03.000Z

# Copyright (C) 2018-2021 Intel Corporation # SPDX-License-Identifier: Apache-2.0 import unittest import numpy as np from extensions.ops.activation_ops import Elu, SoftPlus, Mish from mo.graph.graph import Node from mo.utils.unittest.graph import build_graph class TestActivationOp(unittest.TestCase): nodes_attributes = { 'node_1': { 'shape': np.array([4]), 'value': None }, 'activation_node': { 'op': 'Activation', 'kind': 'op', 'operation': None }, 'node_3': { 'shape': None } } def test_activation_elu_infer(self): graph = build_graph(self.nodes_attributes, [ ('node_1', 'activation_node'), ('activation_node', 'node_3') ], { 'node_1': { 'value': np.array([6, -4, -2, -1]) }, 'activation_node': { 'operation': 'elu', 'alpha': 1.0, }, 'node_3': { 'value': None } }) graph.graph['layout'] = 'NCHW' activation_node = Node(graph, 'activation_node') Elu.infer(activation_node) exp_shape = np.array([4]) res_shape = graph.node['node_3']['shape'] res_value = graph.node['node_3']['value'] exp_value = np.array([6., -0.98168436, -0.86466472, -0.63212056]) for i, value in enumerate(exp_shape): self.assertEqual(res_shape[i], value) for i, value in enumerate(exp_value): self.assertAlmostEqual(res_value[i], value) def test_activation_softplus_infer(self): graph = build_graph(self.nodes_attributes, [ ('node_1', 'activation_node'), ('activation_node', 'node_3') ], { 'node_1': { 'value': np.array([-1.0, 0.0, 1.0, 20.0]) }, 'activation_node': { 'op': 'SoftPlus', 'operation': SoftPlus.operation, }, 'node_3': { 'value': None } }) graph.graph['layout'] = 'NCHW' activation_node = Node(graph, 'activation_node') SoftPlus.infer(activation_node) exp_shape = np.array([4]) res_shape = graph.node['node_3']['shape'] res_value = graph.node['node_3']['value'] exp_value = np.array([0.3132617, 0.6931472, 1.3132617, 20.0]) for i, value in enumerate(exp_shape): self.assertEqual(res_shape[i], value) for i, value in enumerate(exp_value): self.assertAlmostEqual(res_value[i], value) def test_activation_mish_infer(self): graph = build_graph(self.nodes_attributes, [ ('node_1', 'activation_node'), ('activation_node', 'node_3') ], { 'node_1': { 'value': np.array([-1.0, 0.0, 1.0, 20.0]) }, 'activation_node': { 'op': 'Mish', 'operation': Mish.operation, }, 'node_3': { 'value': None } }) graph.graph['layout'] = 'NCHW' activation_node = Node(graph, 'activation_node') Mish.infer(activation_node) exp_shape = np.array([4]) res_shape = graph.node['node_3']['shape'] res_value = graph.node['node_3']['value'] exp_value = np.array([-0.30340146, 0.0, 0.8650984, 20.0]) for i, value in enumerate(exp_shape): self.assertEqual(res_shape[i], value) for i, value in enumerate(exp_value): self.assertAlmostEqual(res_value[i], value)

39.847458

77

0.406635

407

4,702

4.498772

0.167076

0.145276

0.044238

0.045877

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0

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117

78

40.188034

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false

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0

0.093458

0

null

0

1

0

null

0

5

49f31a36f9f75998ad7a5713b883c956808b310e

291

py

Python

atari/experiment_1_atari/random_agent.py

arcosin/Task_Detector

318f61aa45c17059941b8ad5417208bb891ed64e

[ "MIT" ]

null

atari/experiment_1_atari/random_agent.py

arcosin/Task_Detector

318f61aa45c17059941b8ad5417208bb891ed64e

[ "MIT" ]

null

atari/experiment_1_atari/random_agent.py

arcosin/Task_Detector

318f61aa45c17059941b8ad5417208bb891ed64e

[ "MIT" ]

null

import random class RandomAgent: def __init__(self, actSize): super().__init__() self.actSize = actSize def act(self, state): return random.randint(0, self.actSize - 1) #===============================================================================

16.166667

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5

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0

0.008696

0.209622

291

17

81

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0.271478

0

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false

0

0.142857

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0

null

1

0

null

0

1

0

1

0

5

49f44766855124ba8a658773e50cf7c70e6967a7

71

py

Python

strong_glm/utils/__init__.py

strongio/strong-glm

db05cb8a297858e46961e5d91105a515531dfdbb

[ "MIT" ]

2

2021-04-20T17:00:03.000Z

2022-03-03T16:33:01.000Z

strong_glm/utils/__init__.py

strongio/strong-glm

db05cb8a297858e46961e5d91105a515531dfdbb

[ "MIT" ]

1

2020-02-26T16:48:56.000Z

strong_glm/utils/__init__.py

strongio/strong-glm

db05cb8a297858e46961e5d91105a515531dfdbb

[ "MIT" ]

null

from .tensor import to_tensor from .simulate_data import simulate_data

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b70a094b3c9510fb785270947cb699b2b7adb112

192

py

Python

app/dept/__init__.py

xasos/IlliniGuide

a2695decde1479843503e52fb48677c9d75d559a

[ "MIT" ]

null

app/dept/__init__.py

xasos/IlliniGuide

a2695decde1479843503e52fb48677c9d75d559a

[ "MIT" ]

null

app/dept/__init__.py

xasos/IlliniGuide

a2695decde1479843503e52fb48677c9d75d559a

[ "MIT" ]

null

from flask import Blueprint dept = Blueprint('dept', __name__, url_prefix="/dept", template_folder="templates", static_folder = "static", static_url_path='/static/dept') from . import views

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b70ed27f13c6996dc61afb06a40031934add3791

143

py

Python

solutions/python3/1238.py

sm2774us/amazon_interview_prep_2021

f580080e4a6b712b0b295bb429bf676eb15668de

[ "MIT" ]

42

2020-08-02T07:03:49.000Z

2022-03-26T07:50:15.000Z

solutions/python3/1238.py

ajayv13/leetcode

de02576a9503be6054816b7444ccadcc0c31c59d

[ "MIT" ]

null

solutions/python3/1238.py

ajayv13/leetcode

de02576a9503be6054816b7444ccadcc0c31c59d

[ "MIT" ]

40

2020-02-08T02:50:24.000Z

2022-03-26T15:38:10.000Z

class Solution: def circularPermutation(self, n: int, start: int) -> List[int]: return [start ^ i ^ i >> 1 for i in range(1 << n)]

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null

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0

5

b713f41c30fa90add9409d8aebc099674049b09d

197

py

Python

CodeForces/SpecificTastesOfAndre/SpecificTastesOfAndre.py

GeorgianBadita/algorithmic-problems

6b260050b7a1768b5e47a1d7d4ef7138a52db210

[ "MIT" ]

1

2021-07-05T16:32:14.000Z

CodeForces/SpecificTastesOfAndre/SpecificTastesOfAndre.py

GeorgianBadita/algorithmic-problems

6b260050b7a1768b5e47a1d7d4ef7138a52db210

[ "MIT" ]

null

CodeForces/SpecificTastesOfAndre/SpecificTastesOfAndre.py

GeorgianBadita/algorithmic-problems

6b260050b7a1768b5e47a1d7d4ef7138a52db210

[ "MIT" ]

1

2021-05-14T15:40:09.000Z

def perfect_array(length): return ' '.join(['1']*length) def main(): t = int(input()) for _ in range(t): length = int(input()) print(perfect_array(length)) main()

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5

b7278a6d4cc1b8ed4600ae2fcb4f2567be411146

99

py

Python

app/core/admin.py

akagrv/StockMonitor

af0175b9ef0a9678fd358f9ddfab8f167b58b0aa

[ "MIT" ]

null

app/core/admin.py

akagrv/StockMonitor

af0175b9ef0a9678fd358f9ddfab8f167b58b0aa

[ "MIT" ]

1

2021-05-11T16:29:16.000Z

app/core/admin.py

akagrv/StockMonitor

af0175b9ef0a9678fd358f9ddfab8f167b58b0aa

[ "MIT" ]

1

2021-09-25T06:29:30.000Z

from django.contrib import admin from core.models import WatchList admin.site.register(WatchList)

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5

b72f3c2622c3b8d60304e8a0161a837176afde31

38

py

Python

errors.py

jackbicknell14/spotify-apps

a44a76b541bd27880c3aa088e34e89fba441314c

[ "Apache-2.0" ]

null

errors.py

jackbicknell14/spotify-apps

a44a76b541bd27880c3aa088e34e89fba441314c

[ "Apache-2.0" ]

null

errors.py

jackbicknell14/spotify-apps

a44a76b541bd27880c3aa088e34e89fba441314c

[ "Apache-2.0" ]

null

class TrackError(Exception): pass

12.666667

28

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38

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1

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38

2

29

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0.903226

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true

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null

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1

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null

0

1

0

5

3f97dc6d5e675ae86e5b3b1abbb20fb22825f9ab

197

py

Python

extract/extract_location_and_date.py

emmacunningham/court-reminder

0494e7f864c3922d1ac0bc41c6e255cd88e021a8

[ "MIT" ]

2

2019-10-02T05:31:37.000Z

2021-07-31T16:24:24.000Z

extract/extract_location_and_date.py

emmacunningham/court-reminder

0494e7f864c3922d1ac0bc41c6e255cd88e021a8

[ "MIT" ]

10

2017-02-11T05:35:31.000Z

2018-12-31T19:58:51.000Z

extract/extract_location_and_date.py

emmacunningham/court-reminder

0494e7f864c3922d1ac0bc41c6e255cd88e021a8

[ "MIT" ]

2

2017-02-12T19:11:49.000Z

2019-09-30T22:44:13.000Z

class Extractor(object): def __init__(self, transcript): self.transcript = transcript def get_location(self): return "TBD" def get_date(self): return "TBD"

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197

5.227273

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0

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0

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null

1

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null

0

1

0

1

0

5

3fb31f38b96e905ee893b0165c0942adaf415fd6

65

py

Python

tests/test_client.py

josephschorr/Python-Wrapper

2b0cb60049ddf621a44f329b1b374bca2063eb20

[ "MIT" ]

45

2016-04-07T04:38:34.000Z

2021-12-19T02:10:38.000Z

tests/test_client.py

josephschorr/Python-Wrapper

2b0cb60049ddf621a44f329b1b374bca2063eb20

[ "MIT" ]

30

2016-08-05T22:50:10.000Z

2021-05-18T08:51:00.000Z

tests/test_client.py

josephschorr/Python-Wrapper

2b0cb60049ddf621a44f329b1b374bca2063eb20

[ "MIT" ]

19

2016-08-05T15:18:23.000Z

2020-05-07T23:00:09.000Z

# Still need to write these... def test_client(): assert 1 == 1

21.666667

30

0.661538

11

65

3.818182

0.909091

0

0.038462

0.2

65

3

31

21.666667

0.769231

0.430769

0

0.5

1

0.5

true

0

0.5

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null

0

1

0

null

0

1

0

1

0

5

3fbca29903ceeed10a2e4538e65476233e8a4e85

76

py

Python

vary/information_bottlekneck/__init__.py

joshloyal/Vary

cd79a941336c7b335dead8ca718a2d0d949d44bb

[ "MIT" ]

1

2017-05-14T11:54:09.000Z

vary/information_bottlekneck/__init__.py

joshloyal/Vary

cd79a941336c7b335dead8ca718a2d0d949d44bb

[ "MIT" ]

null

vary/information_bottlekneck/__init__.py

joshloyal/Vary

cd79a941336c7b335dead8ca718a2d0d949d44bb

[ "MIT" ]

1

2020-11-17T11:44:56.000Z

from vary.information_bottlekneck.bottlekneck import InformationBottlekneck

38

75

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7

76

9.857143

0.857143

0

0.052632

76

1

76

0.958333

0

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true

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1

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1

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1

0

null

0

1

0

1

0

1

0

5

3fe7a88e56218951474549a1771d6e7ca0654c52

103

py

Python

src/affe/io/__init__.py

eliavw/affe

0e57d7f40cb67f9a300292e03e3f83b4b591d1e3

[ "MIT" ]

1

2020-12-02T06:16:00.000Z

src/affe/io/__init__.py

eliavw/affe

0e57d7f40cb67f9a300292e03e3f83b4b591d1e3

[ "MIT" ]

null

src/affe/io/__init__.py

eliavw/affe

0e57d7f40cb67f9a300292e03e3f83b4b591d1e3

[ "MIT" ]

null

from .dirs import * from .file import * from .tree import * from .dump import dump_object, load_object

20.6

42

0.757282

16

103

4.75

0.5

0.394737

0

0.165049

103

4

43

25.75

0.883721

0

1

0

true

0

1

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1

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1

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null

1

0

1

0

null

0

1

0

1

0

5

b20aa3c0ea92adc16044211540a9711b673aad30

52

py

Python

django_command_cron/models/__init__.py

andrewp-as-is/django-command-cron.py

6b2b20cb1a9ccd80e2377b316316d6af68c8f9c6

[ "Unlicense" ]

1

2021-09-23T18:16:56.000Z

django_command_cron/models/__init__.py

andrewp-as-is/django-command-cron.py

6b2b20cb1a9ccd80e2377b316316d6af68c8f9c6

[ "Unlicense" ]

null

django_command_cron/models/__init__.py

andrewp-as-is/django-command-cron.py

6b2b20cb1a9ccd80e2377b316316d6af68c8f9c6

[ "Unlicense" ]

null

from .call import Call from .command import Command

17.333333

28

0.807692

8

52

5.25

0.5

0

0.153846

52

2

29

26

0.954545

0

1

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true

0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

5

b7476f18a3132a504732c99123ed32a15248e962

31

py

Python

tianyancha/__init__.py

iamsk/tianyancha

388302f20ea199a775f83b903bd6915bf21afd3a

[ "MIT" ]

2

2020-12-11T05:26:09.000Z

2020-12-11T12:25:10.000Z

tianyancha/__init__.py

iamsk/tianyancha

388302f20ea199a775f83b903bd6915bf21afd3a

[ "MIT" ]

null

tianyancha/__init__.py

iamsk/tianyancha

388302f20ea199a775f83b903bd6915bf21afd3a

[ "MIT" ]

null

from .client import Tianyancha

15.5

30

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31

6.5

1

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0.129032

31

1

31

0.962963

0

1

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true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

5

b74dc0ec22e8846c08502fe4ab1e1cea7c0f1f4e

301

py

Python

payload/Payload.py

avegao/pybatrium

9f24d84f0b57888afb841121b308527c6b7365e4

[ "MIT" ]

null

payload/Payload.py

avegao/pybatrium

9f24d84f0b57888afb841121b308527c6b7365e4

[ "MIT" ]

null

payload/Payload.py

avegao/pybatrium

9f24d84f0b57888afb841121b308527c6b7365e4

[ "MIT" ]

null

from __future__ import annotations from abc import abstractmethod from typing import Dict class Payload: @staticmethod @abstractmethod def parse(data: bytes) -> Dict: pass # @staticmethod # @abstractmethod # def __from_struct(data: Dict) -> Payload: # pass

17.705882

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0.671096

31

301

6.290323

0.548387

0.266667

0.297436

0

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301

16

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0.874439

0.265781

0

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1

0

null

0

1

0

1

0

5

b768f50334c5a6644de17381036d6aa8d89342a5

6,239

py

Python

tests/test_menu.py

ivddorrka/OP_nutriotionproject

20aa3c707fa1141a7425a158e47ef1f12744b1c7

[ "FSFAP" ]

1

2021-04-08T20:34:14.000Z

tests/test_menu.py

ivddorrka/OP_nutriotionproject

20aa3c707fa1141a7425a158e47ef1f12744b1c7

[ "FSFAP" ]

3

2021-04-03T22:20:36.000Z

2021-05-17T16:32:26.000Z

tests/test_menu.py

ivddorrka/OP_nutriotionproject

20aa3c707fa1141a7425a158e47ef1f12744b1c7

[ "FSFAP" ]

null

""" Module for testing class Menu. """ import unittest from unittest import TestCase from modules.menu import Menu class TestMenu(TestCase): """ Class for testing menu.py. """ def setUp(self) -> None: """ Set up menus for tests. """ self.first_menu = Menu(2000, 70, 60, 300, ['tomato']) self.second_menu = Menu(1900, 60, 65, 290, []) def tearDown(self) -> None: """ Refreshes menus (makes them empty). """ self.first_menu = Menu(2000, 70, 60, 300, ['tomato']) self.second_menu = Menu(1900, 60, 65, 290, []) def test_init(self): """ Testing init (it also checks method choose_dishes()). """ self.assertEqual(self.first_menu.calories, 2000) self.assertEqual(self.first_menu.proteins, 70) self.assertEqual(self.first_menu.fats, 60) self.assertEqual(self.first_menu.carbohydrates, 300) self.assertEqual(self.first_menu.daily_calories, 0) self.assertEqual(self.first_menu.daily_fats, 0) self.assertEqual(self.first_menu.daily_proteins, 0) self.assertEqual(self.first_menu.daily_carbohydrates, 0) self.assertEqual(len(self.first_menu.all_dishes), 6580) self.assertEqual(len(self.second_menu.all_dishes), 7344) def test_menu(self): """ Testing methods __str__(), generate_menu(), accept_dish(), generate_dish() and delete_dish(). """ self.assertEqual(self.first_menu.__str__(), '') self.first_menu.generate_menu() self.assertNotEqual(self.first_menu.__str__(), '') self.assertEqual(len(self.first_menu.menu), 3) self.assertTrue(0.85*self.first_menu.calories <= self.first_menu.menu[0].calories + self.first_menu.menu[1].calories + self.first_menu.menu[2].calories <= 1.15*self.first_menu.calories) self.assertTrue(0.85*self.first_menu.proteins <= self.first_menu.menu[0].proteins + self.first_menu.menu[1].proteins + self.first_menu.menu[2].proteins <= 1.15*self.first_menu.proteins) self.assertTrue(0.85*self.first_menu.fats <= self.first_menu.menu[0].fats + self.first_menu.menu[1].fats + self.first_menu.menu[2].fats <= 1.15*self.first_menu.fats) self.assertTrue(0.85*self.first_menu.carbohydrates <= self.first_menu.menu[0].carbohydrates + self.first_menu.menu[1].carbohydrates + self.first_menu.menu[2].carbohydrates <= 1.15*self.first_menu.carbohydrates) self.first_menu.accept_dish(self.first_menu.menu[0]) self.assertTrue(self.first_menu.daily_calories <= 0.4*self.first_menu.calories) self.assertTrue(self.first_menu.daily_proteins <= 0.4*self.first_menu.proteins) self.assertTrue(self.first_menu.daily_fats <= 0.4*self.first_menu.calories) self.assertTrue(self.first_menu.daily_fats <= 0.4*self.first_menu.carbohydrates) self.first_menu.accept_dish(self.first_menu.menu[1]) self.assertTrue(0.5*self.first_menu.calories <= self.first_menu.daily_calories <= 0.9*self.first_menu.calories) self.assertTrue(0.5*self.first_menu.proteins <= self.first_menu.daily_proteins <= 0.9*self.first_menu.proteins) self.assertTrue(0.5*self.first_menu.fats <= self.first_menu.daily_fats <= 0.9*self.first_menu.fats) self.assertTrue(0.5*self.first_menu.carbohydrates <= self.first_menu.daily_carbohydrates <= 0.9*self.first_menu.carbohydrates) self.first_menu.accept_dish(self.first_menu.menu[2]) self.assertTrue(0.85*self.first_menu.calories <= self.first_menu.daily_calories <= 1.15*self.first_menu.calories) self.assertTrue(0.85*self.first_menu.proteins <= self.first_menu.daily_proteins <= 1.15*self.first_menu.proteins) self.assertTrue(0.85*self.first_menu.fats <= self.first_menu.daily_fats <= 1.15*self.first_menu.fats) self.assertTrue(0.85*self.first_menu.carbohydrates <= self.first_menu.daily_carbohydrates <= 1.15*self.first_menu.carbohydrates) self.second_menu.generate_menu() self.second_menu.delete_dish(self.second_menu.menu[2]) self.second_menu.accept_dish(self.second_menu.menu[0]) self.second_menu.accept_dish(self.second_menu.menu[1]) self.second_menu.accept_dish(self.second_menu.menu[2]) self.assertTrue(0.85*self.first_menu.calories <= self.first_menu.daily_calories <= 1.15*self.first_menu.calories) self.assertTrue(0.85*self.first_menu.proteins <= self.first_menu.daily_proteins <= 1.15*self.first_menu.proteins) self.assertTrue(0.85*self.first_menu.fats <= self.first_menu.daily_fats <= 1.15*self.first_menu.fats) self.assertTrue(0.85*self.first_menu.carbohydrates <= self.first_menu.daily_carbohydrates <= 1.15*self.first_menu.carbohydrates) def test_product(self): """ Testing methods search_product(), choose_product(). """ self.assertEqual(len(self.first_menu.search_product('tomato')), 50) self.assertEqual( len(self.second_menu.search_product('mango juice')), 50) self.first_menu.choose_product('tomatoes, raw', 100) self.assertAlmostEqual(self.first_menu.daily_calories, 18.0) self.assertAlmostEqual(self.first_menu.daily_proteins, 0.88) self.assertAlmostEqual(self.first_menu.daily_fats, 0.2) self.assertAlmostEqual(self.first_menu.daily_carbohydrates, 3.89) self.first_menu.choose_product('mango nectar', 150) self.assertAlmostEqual(self.first_menu.daily_calories, 94.5) self.assertAlmostEqual(self.first_menu.daily_proteins, 1.045) self.assertAlmostEqual(self.first_menu.daily_fats, 0.29) self.assertAlmostEqual(self.first_menu.daily_carbohydrates, 23.54) if __name__ == '__main__': unittest.main()

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null

0

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5

b7992b33c3ef1bcd3e499a57e61569e7d14b6c0d

124

py

Python

tools/__init__.py

santiagoRuizSchiphol/squeezenext-tensorflow

e514f3d173fac098116ac28ade2862fb5acb498f

[ "MIT" ]

58

2018-07-19T11:52:57.000Z

2021-04-22T07:36:24.000Z

tools/__init__.py

santiagoRuizSchiphol/squeezenext-tensorflow

e514f3d173fac098116ac28ade2862fb5acb498f

[ "MIT" ]

6

2018-09-28T19:04:36.000Z

2020-10-18T10:27:28.000Z

tools/__init__.py

santiagoRuizSchiphol/squeezenext-tensorflow

e514f3d173fac098116ac28ade2862fb5acb498f

[ "MIT" ]

21

2018-09-11T02:00:42.000Z

2021-01-20T22:31:11.000Z

from tools import define_first_dim, get_checkpoint_step,get_or_create_global_step,warmup_phase import stats import fine_tune

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0

1

0

5

b7ea540cf8dffbaef60c32b8d6d6d6babbf12fc8

110

py

Python

cwApp/admin.py

cs-fullstack-2019-spring/django-validation-cw-Litterial

8b1fb8f4cd3feca57210fe4a48c8938bbc6d50d6

[ "Apache-2.0" ]

null

cwApp/admin.py

cs-fullstack-2019-spring/django-validation-cw-Litterial

8b1fb8f4cd3feca57210fe4a48c8938bbc6d50d6

[ "Apache-2.0" ]

null

cwApp/admin.py

cs-fullstack-2019-spring/django-validation-cw-Litterial

8b1fb8f4cd3feca57210fe4a48c8938bbc6d50d6

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Car # Register your models here. admin.site.register(Car)

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null

0

1

0

1

0

1

0

5

b7f3c638a8bc5d4246744618385437f1ff42a19c

322

py

Python

asteroid/data/__init__.py

mhu-coder/AsSteroid

56dd2b81bb16c1f081b0b91e3bbb8b29dd587dbd

[ "MIT" ]

null

asteroid/data/__init__.py

mhu-coder/AsSteroid

56dd2b81bb16c1f081b0b91e3bbb8b29dd587dbd

[ "MIT" ]

null

asteroid/data/__init__.py

mhu-coder/AsSteroid

56dd2b81bb16c1f081b0b91e3bbb8b29dd587dbd

[ "MIT" ]

null

from .wham_dataset import WhamDataset from .whamr_dataset import WhamRDataset from .dns_dataset import DNSDataset from .librimix_dataset import LibriMix from .wsj0_mix import Wsj0mixDataset from .musdb18_dataset import MUSDB18Dataset from .sms_wsj_dataset import SmsWsjDataset from .kinect_wsj import KinectWsjMixDataset

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null

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1

0

1

0

1

0

5

4d14584056aecc5a90dcaeb0ac6374a0209c7655

115

py

Python

utils/__init__.py

Justin900429/vision-transformer

e149092efbb83c166449944137db0ee5200f9325

[ "MIT" ]

1

2021-09-01T03:29:03.000Z

utils/__init__.py

Justin900429/vision-transformer

e149092efbb83c166449944137db0ee5200f9325

[ "MIT" ]

null

utils/__init__.py

Justin900429/vision-transformer

e149092efbb83c166449944137db0ee5200f9325

[ "MIT" ]

null

from .EMA import EMA from .loss import LabelSmoothing from .stochastic import StochasticDepth from .utils import *

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null

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1

0

1

0

5

4d277c5eae28a56d4edf0781f3ca5b2093b4f60f

44

py

Python

sphinx/cmd/__init__.py

daobook/sphinx

ef8daca1f9a82ede9b4b0b5cde93f3414cee3dfe

[ "BSD-2-Clause" ]

null

sphinx/cmd/__init__.py

daobook/sphinx

ef8daca1f9a82ede9b4b0b5cde93f3414cee3dfe

[ "BSD-2-Clause" ]

1,662

2015-01-02T11:45:27.000Z

2015-01-03T12:21:29.000Z

sphinx/cmd/__init__.py

daobook/sphinx

ef8daca1f9a82ede9b4b0b5cde93f3414cee3dfe

[ "BSD-2-Clause" ]

null

"""Modules for command line executables."""

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43

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44

6.4

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0

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44

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null

0

1

0

1

0

null

0

1

0

5

4d3674fd0e3f53c047ce044d830a93509b3affa1

66

py

Python

test/data/70.py

suliveevil/vista.vim

a0469c645dcbe4033b857da27d35491f39e2f776

[ "MIT" ]

1,764

2019-02-16T04:36:30.000Z

2022-03-29T07:00:42.000Z

test/data/70.py

suliveevil/vista.vim

a0469c645dcbe4033b857da27d35491f39e2f776

[ "MIT" ]

358

2019-02-16T09:33:47.000Z

2022-03-25T03:51:38.000Z

test/data/70.py

suliveevil/vista.vim

a0469c645dcbe4033b857da27d35491f39e2f776

[ "MIT" ]

108

2019-02-16T06:55:59.000Z

2022-02-15T13:38:19.000Z

class Foo: class Bar: def baz(self): pass

13.2

22

0.454545

8

66

3.75

0.875

0

0.469697

66

4

23

16.5

0.857143

0

1

0.25

false

0.25

0

0.75

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null

0

1

0

null

0

1

0

1

0

1

0

5

4d47c43ffa02b032c2aa6029613079eadfacf4e4

729

py

Python

pavucina.py

filipjenis/Python

fb5aa05caf175b46a4fb6e9830191218b8c4404b

[ "CNRI-Python" ]

null

pavucina.py

filipjenis/Python

fb5aa05caf175b46a4fb6e9830191218b8c4404b

[ "CNRI-Python" ]

null

pavucina.py

filipjenis/Python

fb5aa05caf175b46a4fb6e9830191218b8c4404b

[ "CNRI-Python" ]

2

2020-05-07T13:16:53.000Z

2020-06-01T16:53:57.000Z

from random import * import tkinter canvas=tkinter.Canvas(bg='black',width=1000,height=800) canvas.pack() x=10 y=10 for i in range(1,26): x=x+20 y=y+20 canvas.create_line(x,10,510,y,fill='white',width=2) canvas.update() canvas.after(100) x=x+20 y=y+20 for i in range(1,26): x=x-20 y=y-20 canvas.create_line(x,510,30,y,fill='white',width=2) canvas.update() canvas.after(100) x=x-20 y=510 for i in range(1,26): x=x+20 y=y-20 canvas.create_line(x,10,30,y,fill='white',width=2) canvas.update() canvas.after(100) x=x+20 y=y-20 for i in range(1,26): x=x-20 y=y+20 canvas.create_line(x,510,510,y,fill='white',width=2) canvas.update() canvas.after(100)

17.780488

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729

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729

40

57

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0.055556

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null

0

1

0

null

0

5

4d61e64a3bae8fae68693acb229013ab9bcd213d

67

py

Python

python_lessons/MtMk_Test_Files/Install_modules.py

1986MMartin/coding-sections-markus

e13be32e5d83e69250ecfb3c76a04ee48a320607

[ "Apache-2.0" ]

null

python_lessons/MtMk_Test_Files/Install_modules.py

1986MMartin/coding-sections-markus

e13be32e5d83e69250ecfb3c76a04ee48a320607

[ "Apache-2.0" ]

null

python_lessons/MtMk_Test_Files/Install_modules.py

1986MMartin/coding-sections-markus

e13be32e5d83e69250ecfb3c76a04ee48a320607

[ "Apache-2.0" ]

null

import pandas_datareader as pdr print(pdr.get_data_fred('GS10'))

22.333333

33

0.791045

11

67

4.545455

0.909091

0

0.033333

0.104478

67

2

34

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0

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null

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1

0

null

0

1

0

1

0

1

0

5

4d9063821f7e37387458404241c009b7a34fe815

6,504

py

Python

Thirdparty/libwebp/build.py

stinvi/dava.engine

2b396ca49cdf10cdc98ad8a9ffcf7768a05e285e

[ "BSD-3-Clause" ]

26

2018-09-03T08:48:22.000Z

2022-02-14T05:14:50.000Z

Thirdparty/libwebp/build.py

ANHELL-blitz/dava.engine

ed83624326f000866e29166c7f4cccfed1bb41d4

[ "BSD-3-Clause" ]

null

Thirdparty/libwebp/build.py

ANHELL-blitz/dava.engine

ed83624326f000866e29166c7f4cccfed1bb41d4

[ "BSD-3-Clause" ]

45

2018-05-11T06:47:17.000Z

2022-02-03T11:30:55.000Z

import os import shutil import build_utils def get_supported_targets(platform): if platform == 'win32': return ['win32', 'win10', 'android'] elif platform == 'darwin': return ['macos', 'ios', 'android'] elif platform == 'linux': return ['android', 'linux'] else: return [] def get_dependencies_for_target(target): return [] def build_for_target(target, working_directory_path, root_project_path): if target == 'win32': _build_win32(working_directory_path, root_project_path) elif target == 'win10': _build_win10(working_directory_path, root_project_path) elif target == 'macos': _build_macos(working_directory_path, root_project_path) elif target == 'ios': _build_ios(working_directory_path, root_project_path) elif target == 'android': _build_android(working_directory_path, root_project_path) elif target == 'linux': _build_linux(working_directory_path, root_project_path) def get_download_info(): return 'https://github.com/webmproject/libwebp/archive/v0.4.3.tar.gz' def _download_and_extract(working_directory_path): source_folder_path = os.path.join(working_directory_path, 'libwebp_source') url = get_download_info() build_utils.download_and_extract( url, working_directory_path, source_folder_path, 'libwebp-0.4.3') return source_folder_path @build_utils.run_once def _patch_sources(source_folder_path, working_directory_path): build_utils.apply_patch( os.path.abspath('patch_win.diff'), working_directory_path) def _build_win32(working_directory_path, root_project_path): source_folder_path = _download_and_extract(working_directory_path) _patch_sources(source_folder_path, working_directory_path) # x86 x86_env = build_utils.get_win32_vs_x86_env() build_utils.run_process( ['nmake', '-f', 'Makefile.vc', 'CFG=release-static', 'RTLIBCFG=dynamic', 'OBJDIR=output'], process_cwd=source_folder_path, environment=x86_env, shell=True) build_utils.run_process( ['nmake', '-f', 'Makefile.vc', 'CFG=debug-static', 'RTLIBCFG=dynamic', 'OBJDIR=output'], process_cwd=source_folder_path, environment=x86_env, shell=True) # x64 x64_env = build_utils.get_win32_vs_x64_env() build_utils.run_process( ['nmake', '-f', 'Makefile.vc', 'CFG=release-static', 'RTLIBCFG=dynamic', 'OBJDIR=output'], process_cwd=source_folder_path, environment=x64_env, shell=True) build_utils.run_process( ['nmake', '-f', 'Makefile.vc', 'CFG=debug-static', 'RTLIBCFG=dynamic', 'OBJDIR=output'], process_cwd=source_folder_path, environment=x64_env, shell=True) libs_win_root = os.path.join(root_project_path, 'Libs/lib_CMake/win') shutil.copyfile( os.path.join(source_folder_path, 'output/debug-static/x86/lib/libwebp_debug.lib'), os.path.join(libs_win_root, 'x86/Debug/libwebp.lib')) shutil.copyfile( os.path.join(source_folder_path, 'output/release-static/x86/lib/libwebp.lib'), os.path.join(libs_win_root, 'x86/Release/libwebp.lib')) shutil.copyfile( os.path.join(source_folder_path, 'output/debug-static/x64/lib/libwebp_debug.lib'), os.path.join(libs_win_root, 'x64/Debug/libwebp.lib')) shutil.copyfile( os.path.join(source_folder_path, 'output/release-static/x64/lib/libwebp.lib'), os.path.join(libs_win_root, 'x64/Release/libwebp.lib')) _copy_headers(source_folder_path, root_project_path) def _build_win10(working_directory_path, root_project_path): source_folder_path = _download_and_extract(working_directory_path) _patch_sources(source_folder_path, working_directory_path) build_utils.build_and_copy_libraries_win10_cmake( os.path.join(working_directory_path, 'gen'), source_folder_path, root_project_path, 'libwebp.sln', 'webp', 'webp.lib', 'webp.lib', 'libwebp.lib', 'libwebp.lib', 'libwebp.lib', 'libwebp.lib', 'libwebp.lib', 'libwebp.lib', ['-DCMAKE_SYSTEM_PROCESSOR=arm']) _copy_headers(source_folder_path, root_project_path) def _build_macos(working_directory_path, root_project_path): source_folder_path = _download_and_extract(working_directory_path) _patch_sources(source_folder_path, working_directory_path) build_utils.build_and_copy_libraries_macos_cmake( os.path.join(working_directory_path, 'gen'), source_folder_path, root_project_path, 'libwebp.xcodeproj', 'webp', 'libwebp.a', 'libwebp.a') _copy_headers(source_folder_path, root_project_path) def _build_ios(working_directory_path, root_project_path): source_folder_path = _download_and_extract(working_directory_path) _patch_sources(source_folder_path, working_directory_path) build_utils.build_and_copy_libraries_ios_cmake( os.path.join(working_directory_path, 'gen'), source_folder_path, root_project_path, 'libwebp.xcodeproj', 'webp', 'libwebp.a', 'libwebp.a') _copy_headers(source_folder_path, root_project_path) def _build_android(working_directory_path, root_project_path): source_folder_path = _download_and_extract(working_directory_path) _patch_sources(source_folder_path, working_directory_path) build_utils.build_and_copy_libraries_android_cmake( os.path.join(working_directory_path, 'gen'), source_folder_path, root_project_path, 'libwebp.a', 'libwebp.a', arm_abi='armeabi-v7a') _copy_headers(source_folder_path, root_project_path) def _build_linux(working_directory_path, root_project_path): source_folder_path = _download_and_extract(working_directory_path) _patch_sources(source_folder_path, working_directory_path) build_utils.build_and_copy_libraries_linux_cmake( gen_folder_path=os.path.join(working_directory_path, 'gen'), source_folder_path=source_folder_path, root_project_path=root_project_path, target="all", lib_name='libwebp.a') _copy_headers(source_folder_path, root_project_path) def _copy_headers(source_folder_path, root_project_path): include_path = os.path.join(root_project_path, 'Libs/include/webp') build_utils.copy_files( os.path.join(source_folder_path, 'src/webp'), include_path, '*.h')

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1

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null

0

5

4d96e335fcfbb51e0f4654cdcde1b1f9004ee887

23,433

py

Python

tests/finance/test_slippage.py

nathanwolfe/zipline-minute-bars

bcc6532731503c4521c6f7c4f9ee5e7ee545c013

[ "Apache-2.0" ]

null

tests/finance/test_slippage.py

nathanwolfe/zipline-minute-bars

bcc6532731503c4521c6f7c4f9ee5e7ee545c013

[ "Apache-2.0" ]

null

tests/finance/test_slippage.py

nathanwolfe/zipline-minute-bars

bcc6532731503c4521c6f7c4f9ee5e7ee545c013

[ "Apache-2.0" ]

null

# # Copyright 2013 Quantopian, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an 'AS IS' BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ''' Unit tests for finance.slippage ''' import datetime import pytz from nose_parameterized import parameterized import pandas as pd from pandas.tslib import normalize_date from zipline.finance.slippage import VolumeShareSlippage from zipline.protocol import DATASOURCE_TYPE from zipline.finance.blotter import Order from zipline.data.data_portal import DataPortal from zipline.protocol import BarData from zipline.testing import tmp_bcolz_equity_minute_bar_reader from zipline.testing.fixtures import ( WithDataPortal, WithSimParams, ZiplineTestCase, ) class SlippageTestCase(WithSimParams, WithDataPortal, ZiplineTestCase): START_DATE = pd.Timestamp('2006-01-05 14:31', tz='utc') END_DATE = pd.Timestamp('2006-01-05 14:36', tz='utc') SIM_PARAMS_CAPITAL_BASE = 1.0e5 SIM_PARAMS_DATA_FREQUENCY = 'minute' SIM_PARAMS_EMISSION_RATE = 'daily' ASSET_FINDER_EQUITY_SIDS = (133,) ASSET_FINDER_EQUITY_START_DATE = pd.Timestamp('2006-01-05', tz='utc') ASSET_FINDER_EQUITY_END_DATE = pd.Timestamp('2006-01-07', tz='utc') minutes = pd.DatetimeIndex( start=START_DATE, end=END_DATE - pd.Timedelta('1 minute'), freq='1min' ) @classmethod def make_equity_minute_bar_data(cls): yield 133, pd.DataFrame( { 'open': [3.0, 3.0, 3.5, 4.0, 3.5], 'high': [3.15, 3.15, 3.15, 3.15, 3.15], 'low': [2.85, 2.85, 2.85, 2.85, 2.85], 'close': [3.0, 3.5, 4.0, 3.5, 3.0], 'volume': [2000, 2000, 2000, 2000, 2000], }, index=cls.minutes, ) @classmethod def init_class_fixtures(cls): super(SlippageTestCase, cls).init_class_fixtures() cls.ASSET133 = cls.env.asset_finder.retrieve_asset(133) def test_volume_share_slippage(self): assets = ( (133, pd.DataFrame( { 'open': [3.00], 'high': [3.15], 'low': [2.85], 'close': [3.00], 'volume': [200], }, index=[self.minutes[0]], )), ) days = pd.date_range( start=normalize_date(self.minutes[0]), end=normalize_date(self.minutes[-1]) ) with tmp_bcolz_equity_minute_bar_reader(self.trading_calendar, days, assets) \ as reader: data_portal = DataPortal( self.env.asset_finder, self.trading_calendar, first_trading_day=reader.first_trading_day, equity_minute_reader=reader, ) slippage_model = VolumeShareSlippage() open_orders = [ Order( dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), amount=100, filled=0, sid=self.ASSET133 ) ] bar_data = BarData(data_portal, lambda: self.minutes[0], 'minute') orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 1) _, txn = orders_txns[0] expected_txn = { 'price': float(3.0001875), 'dt': datetime.datetime( 2006, 1, 5, 14, 31, tzinfo=pytz.utc), 'amount': int(5), 'sid': int(133), 'commission': None, 'type': DATASOURCE_TYPE.TRANSACTION, 'order_id': open_orders[0].id } self.assertIsNotNone(txn) # TODO: Make expected_txn an Transaction object and ensure there # is a __eq__ for that class. self.assertEquals(expected_txn, txn.__dict__) open_orders = [ Order( dt=datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), amount=100, filled=0, sid=self.ASSET133 ) ] # Set bar_data to be a minute ahead of last trade. # Volume share slippage should not execute when there is no trade. bar_data = BarData(data_portal, lambda: self.minutes[1], 'minute') orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) def test_orders_limit(self): slippage_model = VolumeShareSlippage() slippage_model.data_portal = self.data_portal # long, does not trade open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': 100, 'filled': 0, 'sid': self.ASSET133, 'limit': 3.5}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[3], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) # long, does not trade - impacted price worse than limit price open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': 100, 'filled': 0, 'sid': self.ASSET133, 'limit': 3.5}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[3], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) # long, does trade open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': 100, 'filled': 0, 'sid': self.ASSET133, 'limit': 3.6}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[3], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 1) txn = orders_txns[0][1] expected_txn = { 'price': float(3.50021875), 'dt': datetime.datetime( 2006, 1, 5, 14, 34, tzinfo=pytz.utc), # we ordered 100 shares, but default volume slippage only allows # for 2.5% of the volume. 2.5% * 2000 = 50 shares 'amount': int(50), 'sid': int(133), 'order_id': open_orders[0].id } self.assertIsNotNone(txn) for key, value in expected_txn.items(): self.assertEquals(value, txn[key]) # short, does not trade open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': -100, 'filled': 0, 'sid': self.ASSET133, 'limit': 3.5}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[0], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) # short, does not trade - impacted price worse than limit price open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': -100, 'filled': 0, 'sid': self.ASSET133, 'limit': 3.5}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[0], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) # short, does trade open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': -100, 'filled': 0, 'sid': self.ASSET133, 'limit': 3.4}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[1], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 1) _, txn = orders_txns[0] expected_txn = { 'price': float(3.49978125), 'dt': datetime.datetime( 2006, 1, 5, 14, 32, tzinfo=pytz.utc), 'amount': int(-50), 'sid': int(133) } self.assertIsNotNone(txn) for key, value in expected_txn.items(): self.assertEquals(value, txn[key]) STOP_ORDER_CASES = { # Stop orders can be long/short and have their price greater or # less than the stop. # # A stop being reached is conditional on the order direction. # Long orders reach the stop when the price is greater than the stop. # Short orders reach the stop when the price is less than the stop. # # Which leads to the following 4 cases: # # | long | short | # | price > stop | | | # | price < stop | | | # # Currently the slippage module acts according to the following table, # where 'X' represents triggering a transaction # | long | short | # | price > stop | | X | # | price < stop | X | | # # However, the following behavior *should* be followed. # # | long | short | # | price > stop | X | | # | price < stop | | X | 'long | price gt stop': { 'order': { 'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'), 'amount': 100, 'filled': 0, 'sid': 133, 'stop': 3.5 }, 'event': { 'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'), 'volume': 2000, 'price': 4.0, 'high': 3.15, 'low': 2.85, 'sid': 133, 'close': 4.0, 'open': 3.5 }, 'expected': { 'transaction': { 'price': 4.00025, 'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'), 'amount': 50, 'sid': 133, } } }, 'long | price lt stop': { 'order': { 'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'), 'amount': 100, 'filled': 0, 'sid': 133, 'stop': 3.6 }, 'event': { 'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'), 'volume': 2000, 'price': 3.5, 'high': 3.15, 'low': 2.85, 'sid': 133, 'close': 3.5, 'open': 4.0 }, 'expected': { 'transaction': None } }, 'short | price gt stop': { 'order': { 'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'), 'amount': -100, 'filled': 0, 'sid': 133, 'stop': 3.4 }, 'event': { 'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'), 'volume': 2000, 'price': 3.5, 'high': 3.15, 'low': 2.85, 'sid': 133, 'close': 3.5, 'open': 3.0 }, 'expected': { 'transaction': None } }, 'short | price lt stop': { 'order': { 'dt': pd.Timestamp('2006-01-05 14:30', tz='UTC'), 'amount': -100, 'filled': 0, 'sid': 133, 'stop': 3.5 }, 'event': { 'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'), 'volume': 2000, 'price': 3.0, 'high': 3.15, 'low': 2.85, 'sid': 133, 'close': 3.0, 'open': 3.0 }, 'expected': { 'transaction': { 'price': 2.9998125, 'dt': pd.Timestamp('2006-01-05 14:31', tz='UTC'), 'amount': -50, 'sid': 133, } } }, } @parameterized.expand([ (name, case['order'], case['event'], case['expected']) for name, case in STOP_ORDER_CASES.items() ]) def test_orders_stop(self, name, order_data, event_data, expected): data = order_data data['sid'] = self.ASSET133 order = Order(**data) assets = ( (133, pd.DataFrame( { 'open': [event_data['open']], 'high': [event_data['high']], 'low': [event_data['low']], 'close': [event_data['close']], 'volume': [event_data['volume']], }, index=[pd.Timestamp('2006-01-05 14:31', tz='UTC')], )), ) days = pd.date_range( start=normalize_date(self.minutes[0]), end=normalize_date(self.minutes[-1]) ) with tmp_bcolz_equity_minute_bar_reader(self.trading_calendar, days, assets) \ as reader: data_portal = DataPortal( self.env.asset_finder, self.trading_calendar, first_trading_day=reader.first_trading_day, equity_minute_reader=reader, ) slippage_model = VolumeShareSlippage() try: dt = pd.Timestamp('2006-01-05 14:31', tz='UTC') bar_data = BarData(data_portal, lambda: dt, 'minute') _, txn = next(slippage_model.simulate( bar_data, self.ASSET133, [order], )) except StopIteration: txn = None if expected['transaction'] is None: self.assertIsNone(txn) else: self.assertIsNotNone(txn) for key, value in expected['transaction'].items(): self.assertEquals(value, txn[key]) def test_orders_stop_limit(self): slippage_model = VolumeShareSlippage() slippage_model.data_portal = self.data_portal # long, does not trade open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': 100, 'filled': 0, 'sid': self.ASSET133, 'stop': 4.0, 'limit': 3.0}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[2], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) bar_data = BarData(self.data_portal, lambda: self.minutes[3], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) # long, does not trade - impacted price worse than limit price open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': 100, 'filled': 0, 'sid': self.ASSET133, 'stop': 4.0, 'limit': 3.5}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[2], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) bar_data = BarData(self.data_portal, lambda: self.minutes[3], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) # long, does trade open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': 100, 'filled': 0, 'sid': self.ASSET133, 'stop': 4.0, 'limit': 3.6}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[2], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) bar_data = BarData(self.data_portal, lambda: self.minutes[3], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 1) _, txn = orders_txns[0] expected_txn = { 'price': float(3.50021875), 'dt': datetime.datetime( 2006, 1, 5, 14, 34, tzinfo=pytz.utc), 'amount': int(50), 'sid': int(133) } for key, value in expected_txn.items(): self.assertEquals(value, txn[key]) # short, does not trade open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': -100, 'filled': 0, 'sid': self.ASSET133, 'stop': 3.0, 'limit': 4.0}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[0], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) bar_data = BarData(self.data_portal, lambda: self.minutes[1], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) # short, does not trade - impacted price worse than limit price open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': -100, 'filled': 0, 'sid': self.ASSET133, 'stop': 3.0, 'limit': 3.5}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[0], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) bar_data = BarData(self.data_portal, lambda: self.minutes[1], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) # short, does trade open_orders = [ Order(**{ 'dt': datetime.datetime(2006, 1, 5, 14, 30, tzinfo=pytz.utc), 'amount': -100, 'filled': 0, 'sid': self.ASSET133, 'stop': 3.0, 'limit': 3.4}) ] bar_data = BarData(self.data_portal, lambda: self.minutes[0], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 0) bar_data = BarData(self.data_portal, lambda: self.minutes[1], self.sim_params.data_frequency) orders_txns = list(slippage_model.simulate( bar_data, self.ASSET133, open_orders, )) self.assertEquals(len(orders_txns), 1) _, txn = orders_txns[0] expected_txn = { 'price': float(3.49978125), 'dt': datetime.datetime( 2006, 1, 5, 14, 32, tzinfo=pytz.utc), 'amount': int(-50), 'sid': int(133) } for key, value in expected_txn.items(): self.assertEquals(value, txn[key])

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1

0

1

0

1

0

null

0

5

4da96ab0a9cc14afad4ace395ccd5246ac5b2156

59

py

Python

call.py

hearteam/Linebot_project

532b81d3c8bd1a658e0ec8f1bf473ee3fa4d232d

[ "MIT" ]

null

call.py

hearteam/Linebot_project

532b81d3c8bd1a658e0ec8f1bf473ee3fa4d232d

[ "MIT" ]

null

call.py

hearteam/Linebot_project

532b81d3c8bd1a658e0ec8f1bf473ee3fa4d232d

[ "MIT" ]

2

2021-08-24T13:21:24.000Z

2021-08-25T02:18:51.000Z

from ECdic import ECdic print(ECdic().EtoC("tofu_skin"))

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py

Python

exceptions.py

AmitaiF/Dlu-Bot

fb5e5a04f550b951f6299e0be302a9295653c58d

[ "MIT" ]

null

exceptions.py

AmitaiF/Dlu-Bot

fb5e5a04f550b951f6299e0be302a9295653c58d

[ "MIT" ]

null

exceptions.py

AmitaiF/Dlu-Bot

fb5e5a04f550b951f6299e0be302a9295653c58d

[ "MIT" ]

null

class NoLastBookException (Exception): pass class OpenLastBookFileFailed (Exception): pass

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py

Python

helper.py

mollysall/cs3240-labdemo

c08212df44433daf99e143d8a07a8a5a67c9f018

[ "MIT" ]

null

helper.py

mollysall/cs3240-labdemo

c08212df44433daf99e143d8a07a8a5a67c9f018

[ "MIT" ]

null

helper.py

mollysall/cs3240-labdemo

c08212df44433daf99e143d8a07a8a5a67c9f018

[ "MIT" ]

null

def greeting(message): print(message)

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4ddb0a4290fd4d8c2b24e222d292bc86af6fdb3c

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py

Python

alphazero/__init__.py

zhiyiYo/Alpha-Gobang-Zero

b0e90ae456b02754956be83a0d6495391390e666

[ "MIT" ]

14

2021-04-01T14:19:10.000Z

2022-03-17T06:29:35.000Z

alphazero/__init__.py

zhiyiYo/Alpha-Gobang-Zero

b0e90ae456b02754956be83a0d6495391390e666

[ "MIT" ]

1

2021-06-20T13:21:52.000Z

2021-06-22T12:41:06.000Z

alphazero/__init__.py

zhiyiYo/Alpha-Gobang-Zero

b0e90ae456b02754956be83a0d6495391390e666

[ "MIT" ]

4

2021-06-24T13:18:19.000Z

2021-12-26T06:00:54.000Z

from .alpha_zero_mcts import AlphaZeroMCTS from .chess_board import ChessBoard, ColorError from .policy_value_net import PolicyValueNet from .rollout_mcts import RolloutMCTS

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py

Python

mpa/modules/datasets/pipelines/__init__.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

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mpa/modules/datasets/pipelines/__init__.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

[ "Apache-2.0" ]

null

mpa/modules/datasets/pipelines/__init__.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

[ "Apache-2.0" ]

null

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4dfca92764519d80dcb93e207e757fbd87a987ee

177

py

Python

put_together.py

Mattjez914/Blackjack_Microchallenge

c4f60b62a3ada14663eb30ce72563af994e1eda4

[ "Apache-2.0" ]

null

put_together.py

Mattjez914/Blackjack_Microchallenge

c4f60b62a3ada14663eb30ce72563af994e1eda4

[ "Apache-2.0" ]

null

put_together.py

Mattjez914/Blackjack_Microchallenge

c4f60b62a3ada14663eb30ce72563af994e1eda4

[ "Apache-2.0" ]

1

2019-04-17T06:12:23.000Z

from learntools.core import binder; binder.bind(globals()) from learntools.python.ex3 import q7 as blackjack from should_hit import should_hit blackjack.simulate_one_game()

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py

Python

panelserverextension.py

bjrnfrdnnd/panel-test

4609a259e749825b2a2012d8a7e48ed8e8a78deb

[ "MIT" ]

null

panelserverextension.py

bjrnfrdnnd/panel-test

4609a259e749825b2a2012d8a7e48ed8e8a78deb

[ "MIT" ]

1

2019-07-26T22:12:19.000Z

2019-10-31T17:48:51.000Z

panelserverextension.py

bjrnfrdnnd/panel-test

4609a259e749825b2a2012d8a7e48ed8e8a78deb

[ "MIT" ]

1

2019-09-19T11:54:45.000Z

from subprocess import Popen def load_jupyter_server_extension(nbapp): """serve the dnmr_ab.ipynb directory with bokeh server""" Popen(["panel", "serve", "dnmr_ab.ipynb", "--allow-websocket-origin=*"])

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5

128804fe67b902d3aaa44bde8604d4a0af8e6a6c

121

py

Python

pyDataProcesser/IO/__init__.py

Psicowired87/pyProcesser

8c45f98869cddd833442908a0616a329ce4a2085

[ "MIT" ]

null

pyDataProcesser/IO/__init__.py

Psicowired87/pyProcesser

8c45f98869cddd833442908a0616a329ce4a2085

[ "MIT" ]

null

pyDataProcesser/IO/__init__.py

Psicowired87/pyProcesser

8c45f98869cddd833442908a0616a329ce4a2085

[ "MIT" ]

null

from aux_functions import get_extension_file from parse_dataframe import parse_manual_csv, parse_xlsx, parse_dataframe

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1

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1

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5

12b4a84f93b547a8346793bc37e9f589bde6a3e7

242

py

Python

django18/views.py

dresl/django18-bootstrap

6e17572f8fbb0cacd2ca1e56c3a3fb5f276d4de9

[ "Apache-2.0" ]

null

django18/views.py

dresl/django18-bootstrap

6e17572f8fbb0cacd2ca1e56c3a3fb5f276d4de9

[ "Apache-2.0" ]

null

django18/views.py

dresl/django18-bootstrap

6e17572f8fbb0cacd2ca1e56c3a3fb5f276d4de9

[ "Apache-2.0" ]

null

from django.shortcuts import render def home(request): return render(request, 'apps/about.html', {}) def about(request): return render(request, 'apps/about.html', {}) def contact(request): return render(request, 'apps/contact.html', {})

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24.2

0.813953

0

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0.428571

false

0

0.142857

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null

1

0

null

0

1

0

1

0

5

12c7b5d233df00c8d230d020d16216b5d702ed04

1,235

py

Python

tests/unit/bokeh/model/test_data_model.py

g-parki/bokeh

664ead5306bba64609e734d4105c8aa8cfb76d81

[ "BSD-3-Clause" ]

null

tests/unit/bokeh/model/test_data_model.py

g-parki/bokeh

664ead5306bba64609e734d4105c8aa8cfb76d81

[ "BSD-3-Clause" ]

null

tests/unit/bokeh/model/test_data_model.py

g-parki/bokeh

664ead5306bba64609e734d4105c8aa8cfb76d81

[ "BSD-3-Clause" ]

null

#----------------------------------------------------------------------------- # Copyright (c) 2012 - 2022, Anaconda, Inc., and Bokeh Contributors. # All rights reserved. # # The full license is in the file LICENSE.txt, distributed with this software. #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Boilerplate #----------------------------------------------------------------------------- from __future__ import annotations # isort:skip import pytest ; pytest #----------------------------------------------------------------------------- # Imports #----------------------------------------------------------------------------- # Module under test import bokeh.model.data_model as bmd # isort:skip #----------------------------------------------------------------------------- # Setup #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # General API #----------------------------------------------------------------------------- def test_DataModel() -> None: assert bmd.DataModel.__data_model__ is True

38.59375

78

0.269636

62

1,235

5.193548

0.758065

0.055901

0

0.006969

0.070445

1,235

31

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39.83871

0.273519

0.820243

0

0.2

1

0.2

true

0

0.6

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0.8

0

null

0

1

0

null

0

1

0

1

0

1

0

5

12e414fc3bf00e6152f953b989914f034edfe9e1

45

py

Python

crabageprediction/venv/Lib/site-packages/fontTools/otlLib/__init__.py

13rianlucero/CrabAgePrediction

92bc7fbe1040f49e820473e33cc3902a5a7177c7

[ "MIT" ]

38,667

2015-01-01T00:15:34.000Z

2022-03-31T22:57:03.000Z

crabageprediction/venv/Lib/site-packages/fontTools/otlLib/__init__.py

13rianlucero/CrabAgePrediction

92bc7fbe1040f49e820473e33cc3902a5a7177c7

[ "MIT" ]

1,599

2016-09-27T09:07:36.000Z

2022-03-31T23:04:51.000Z

crabageprediction/venv/Lib/site-packages/fontTools/otlLib/__init__.py

13rianlucero/CrabAgePrediction

92bc7fbe1040f49e820473e33cc3902a5a7177c7

[ "MIT" ]

11,269

2015-01-01T08:41:17.000Z

2022-03-31T16:12:52.000Z

"""OpenType Layout-related functionality."""

22.5

44

0.755556

4

45

8.5

1

0

0.066667

45

1

45

0.809524

0.844444

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

12e6c2e1c26f603e0570b41f2a7cf244db2c1b53

11,601

py

Python

src/unittest/python/install_utils_tests.py

klr8/pybuilder

2812021c18ce850009ce5ec7f7c18195eff73b10

[ "Apache-2.0" ]

1,419

2015-01-02T20:51:04.000Z

2022-03-23T21:26:00.000Z

src/unittest/python/install_utils_tests.py

klr8/pybuilder

2812021c18ce850009ce5ec7f7c18195eff73b10

[ "Apache-2.0" ]

670

2015-01-01T10:26:03.000Z

2022-02-23T16:33:13.000Z

src/unittest/python/install_utils_tests.py

klr8/pybuilder

2812021c18ce850009ce5ec7f7c18195eff73b10

[ "Apache-2.0" ]

270

2015-01-02T05:01:53.000Z

2022-01-20T10:22:59.000Z

# -*- coding: utf-8 -*- # # This file is part of PyBuilder # # Copyright 2011-2020 PyBuilder Team # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from os.path import normcase as nc, join as jp from pybuilder.core import (Project, Logger, Dependency, RequirementsFile) from pybuilder.install_utils import install_dependencies from pybuilder.pip_utils import PIP_MODULE_STANZA from pybuilder.plugins.python.install_dependencies_plugin import initialize_install_dependencies_plugin from test_utils import Mock, ANY, patch __author__ = "Arcadiy Ivanov" class InstallDependencyTest(unittest.TestCase): def setUp(self): self.project = Project("unittest", ".") self.project.set_property("dir_install_logs", "any_directory") self.project.set_property("dir_target", "/any_target_directory") self.logger = Mock(Logger) self.pyb_env = Mock() self.pyb_env.executable = ["exec"] self.pyb_env.site_paths = [] self.pyb_env.env_dir = "a" self.pyb_env.execute_command.return_value = 0 initialize_install_dependencies_plugin(self.project) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_requirements_file_dependency(self, *_): dependency = RequirementsFile("requirements.txt") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "-r", "requirements.txt"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_without_version(self, *_): dependency = Dependency("spam") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch", constraints_file_name="constraint_file") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "-c", nc(jp(self.pyb_env.env_dir, "constraint_file")), "spam"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_without_version_on_windows_derivate(self, *_): dependency = Dependency("spam") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "spam"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_insecurely_when_property_is_set(self, *_): dependency = Dependency("spam") self.project.set_property("install_dependencies_insecure_installation", ["spam"]) install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "--allow-unverified", "spam", "--allow-external", "spam", "spam"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_securely_when_property_is_not_set_to_dependency(self, *_): dependency = Dependency("spam") self.project.set_property("install_dependencies_insecure_installation", ["some-other-dependency"]) install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch", constraints_file_name="constraint_file") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "-c", ANY, "--allow-unverified", "some-other-dependency", "--allow-external", "some-other-dependency", "spam"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) # some-other-dependency might be a dependency of "spam" # so we always have to put the insecure dependencies in the command line :-( @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_using_custom_index_url(self, *_): self.project.set_property("install_dependencies_index_url", "some_index_url") dependency = Dependency("spam") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "--index-url", "some_index_url", "spam"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_use_extra_index_url_when_index_url_is_not_set(self, *_): self.project.set_property("install_dependencies_extra_index_url", "some_extra_index_url") dependency = Dependency("spam") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "--extra-index-url", "some_extra_index_url", "spam"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_use_index_and_extra_index_url_when_index_and_extra_index_url_are_set(self, *_): self.project.set_property("install_dependencies_index_url", "some_index_url") self.project.set_property("install_dependencies_extra_index_url", "some_extra_index_url") dependency = Dependency("spam") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "--index-url", "some_index_url", "--extra-index-url", "some_extra_index_url", "spam"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_with_version(self, *_): dependency = Dependency("spam", "0.1.2") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "spam>=0.1.2"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_with_version_and_operator(self, *_): dependency = Dependency("spam", "==0.1.2") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "spam==0.1.2"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) def test_should_install_dependency_with_wrong_version_and_operator(self): self.assertRaises(ValueError, Dependency, "spam", "~=1") @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_with_url(self, *_): dependency = Dependency("spam", url="some_url") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "--force-reinstall", "some_url"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True) @patch("pybuilder.install_utils.tail_log") @patch("pybuilder.install_utils.open") @patch("pybuilder.install_utils.create_constraint_file") @patch("pybuilder.install_utils.get_packages_info", return_value={}) def test_should_install_dependency_with_url_even_if_version_is_given(self, *_): dependency = Dependency("spam", version="0.1.2", url="some_url") install_dependencies(self.logger, self.project, dependency, self.pyb_env, "install_batch") self.pyb_env.execute_command.assert_called_with( self.pyb_env.executable + PIP_MODULE_STANZA + ["install", "--force-reinstall", "some_url"], cwd=ANY, env=ANY, error_file_name=ANY, outfile_name=ANY, shell=False, no_path_search=True)

50.659389

110

0.717697

1,469

11,601

5.319946

0.124575

0.10032

0.13167

0.159693

0.782598

0.760333

0.755982

0.754703

0

0.003101

0.16602

11,601

228

111

50.881579

0.804651

0.065684

0

0.664634

0

0.269021

0.190811

0

0.079268

1

0.085366

false

0

0.042683

0

0.134146

0

null

0

1

0

null

0

5

4219cd53dd0257d94d5d48f540651b8297643d47

9,302

py

Python

pycotcp/pycotcp/adapter.py

matteobarato/pyvdetelweb

a49da458536aca4f0efa407b6db55c1455c3f75c

[ "MIT" ]

1

2018-09-19T11:28:05.000Z

pycotcp/pycotcp/adapter.py

matteobarato/pyvdetelweb

a49da458536aca4f0efa407b6db55c1455c3f75c

[ "MIT" ]

null

pycotcp/pycotcp/adapter.py

matteobarato/pyvdetelweb

a49da458536aca4f0efa407b6db55c1455c3f75c

[ "MIT" ]

null

#!/usr/bin/env python class Adapter: NOT_IMPLEMENTED = "Not yet implemented" def __init__(self): print "initing adapter" pass def testfunc(self): print "I'm an adapter" def deleteLink4(self): raise NotImplementedError(NOT_IMPLEMENTED) def deleteLink6(self): raise NotImplementedError(NOT_IMPLEMENTED) def deleteSocketBox(self): raise NotImplementedError(NOT_IMPLEMENTED) def createDevice(self): raise NotImplementedError(NOT_IMPLEMENTED) def createMreq(self): raise NotImplementedError(NOT_IMPLEMENTED) def deleteMreq(self): raise NotImplementedError(NOT_IMPLEMENTED) def createMreqSource(self): raise NotImplementedError(NOT_IMPLEMENTED) def deleteMreqSource(self): raise NotImplementedError(NOT_IMPLEMENTED) def createKvVector(self): raise NotImplementedError(NOT_IMPLEMENTED) def deleteKvVector(self): raise NotImplementedError(NOT_IMPLEMENTED) def createRTree(self): raise NotImplementedError(NOT_IMPLEMENTED) def stackTick(self): raise NotImplementedError(NOT_IMPLEMENTED) def idle(self): raise NotImplementedError(NOT_IMPLEMENTED) def getError(self): #was getPicoError raise NotImplementedError(NOT_IMPLEMENTED) def isNetmaskIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def isUnicastIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def findSourceIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def natEnableIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def natDisableIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def linkFindIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def linkGetIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def linkDelIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def routeAddIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def routeDelIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def portForwardIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def routeGetGatewayIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def pingStartIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def pingAbortIp4(self): raise NotImplementedError(NOT_IMPLEMENTED) def isMulticastIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def isUnicastIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def isGlobalIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def isUniqueLocalIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def isSiteLocalIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def isLocalHostIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def isUnspecifiedIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def findSourceIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def linkFindIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def linkAddIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def linkAddIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def routeAddIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def routeDelIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def routingEnableIpv6(self): raise NotImplementedError(NOT_IMPLEMENTED) def routingDisableIpv6(self): raise NotImplementedError(NOT_IMPLEMENTED) def routeGetGatewayIp6(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketOpen(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketBind(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketConnect(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketSend(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketRecv(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketRecvFrom(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketRecvFromExt(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketWrite(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketRead(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketClose(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketShutdown(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketListen(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketAccept(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketSendTo(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketSendToExt(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketgetName(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketGetPeerName(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketSetOption(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketSetOptionMreq(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketSetOptionMreqSource(self): raise NotImplementedError(NOT_IMPLEMENTED) def socketGetOption(self): raise NotImplementedError(NOT_IMPLEMENTED) def dhcpClientInitiate(self): raise NotImplementedError(NOT_IMPLEMENTED) def dhcpClientAbort(self): raise NotImplementedError(NOT_IMPLEMENTED) def dhcpServerInitiate(self): raise NotImplementedError(NOT_IMPLEMENTED) def dhcpServerDestroy(self): raise NotImplementedError(NOT_IMPLEMENTED) def sntpSync(self): raise NotImplementedError(NOT_IMPLEMENTED) def sntpGetTimeOfTheDay(self): raise NotImplementedError(NOT_IMPLEMENTED) def slaacv4UnregisterIP(self): raise NotImplementedError(NOT_IMPLEMENTED) def dnsNameServer(self): raise NotImplementedError(NOT_IMPLEMENTED) def dnsGetAddr(self): raise NotImplementedError(NOT_IMPLEMENTED) def dnsGetName(self): raise NotImplementedError(NOT_IMPLEMENTED) def filterIpv4Add(self): raise NotImplementedError(NOT_IMPLEMENTED) def filterIpv4Del(self): raise NotImplementedError(NOT_IMPLEMENTED) def olsrAdd(self): raise NotImplementedError(NOT_IMPLEMENTED) def aodvAdd(self): raise NotImplementedError(NOT_IMPLEMENTED) def pppSetSerialread(self): raise NotImplementedError(NOT_IMPLEMENTED) def pppSetSerialWrite(self): raise NotImplementedError(NOT_IMPLEMENTED) def pppSetSerialSpeed(self): raise NotImplementedError(NOT_IMPLEMENTED) def pppSetAPN(self): raise NotImplementedError(NOT_IMPLEMENTED) def pppSetUsername(self): raise NotImplementedError(NOT_IMPLEMENTED) def pppSetPassword(self): raise NotImplementedError(NOT_IMPLEMENTED) def pppConnect(self): raise NotImplementedError(NOT_IMPLEMENTED) def pppDisconnect(self): raise NotImplementedError(NOT_IMPLEMENTED) def dnssdInit(self): raise NotImplementedError(NOT_IMPLEMENTED) def dnssdRegisterService(self): raise NotImplementedError(NOT_IMPLEMENTED) def dnssdKVVectorAdd(self): raise NotImplementedError(NOT_IMPLEMENTED) def mdnsInit(self): raise NotImplementedError(NOT_IMPLEMENTED) def mdnsGetHostname(self): raise NotImplementedError(NOT_IMPLEMENTED) def mdnsSetHostname(self): raise NotImplementedError(NOT_IMPLEMENTED) def mdnsClaim(self): raise NotImplementedError(NOT_IMPLEMENTED) def mdnsGetRecord(self): raise NotImplementedError(NOT_IMPLEMENTED) def mdnsRecordCreate(self): raise NotImplementedError(NOT_IMPLEMENTED) def mdnsIsHostnameRecord(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpListen(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpRejectRequest(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpSessionSetup(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpSetOption(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpGetOption(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpParseRequestArgs(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpSend(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpCloseServer(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpAppSetup(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpAppStartRx(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpAppStartTx(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpGet(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpPut(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpStartTx(self): raise NotImplementedError(NOT_IMPLEMENTED) def tftpStartRx(self): raise NotImplementedError(NOT_IMPLEMENTED)

26.653295

50

0.72608

812

9,302

8.173645

0.165025

0.238361

0.455628

0.641254

0.761338

0.748832

0.023354

0

0.004772

0.21146

9,302

348

51

26.729885

0.900068

0.00387

0

0.493506

0

0.005181

0

null

0.008658

0

null

0.008658

0

null

1

0

1

0

null

0

1

0

5

4268dedc4f196ce3c22a737e9a8814bb72fec166

140

py

Python

pyaz/netappfiles/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/netappfiles/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/netappfiles/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

1

2022-02-03T09:12:01.000Z

''' Manage Azure NetApp Files (ANF) Resources. ''' from .. pyaz_utils import _call_az from . import account, pool, snapshot, vault, volume

20

52

0.728571

19

140

5.210526

0.894737

0

0.157143

140

6

53

23.333333

0.838983

0.3

0

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null

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1

0

1

0

1

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5

35fe41c3bbfa008331efcfd72dcee99276f0c045

1,430

py

Python

specHdl/rawdata/PacketProcess.py

huhub/prototypeTester

3ebb1af5afef26c678fad8d36f945ca2fd804b7d

[ "Apache-2.0" ]

null

specHdl/rawdata/PacketProcess.py

huhub/prototypeTester

3ebb1af5afef26c678fad8d36f945ca2fd804b7d

[ "Apache-2.0" ]

null

specHdl/rawdata/PacketProcess.py

huhub/prototypeTester

3ebb1af5afef26c678fad8d36f945ca2fd804b7d

[ "Apache-2.0" ]

null

PacketProcess = {'CtlPpGapConfig': ['gapValue'], 'DsMemRouteTsn': ['isTsn', 'tsnHandle'], 'DsMemRoute': ['destMap', 'nexthopIdx', 'flowPolicePtr', 'flowPoliceValid', 'flowStatsValid', 'flowStatsPtr', 'mirrorEn', 'discard', 'copyToCpu', 'nat'], 'DsMemMac': ['pending', 'flowPolicePtr', 'flowPoliceValid', 'flowStatsValid', 'flowStatsPtr', 'destMap', 'isMcast', 'mirrorEn', 'dstDiscard', 'copyToCpu'], 'DsMemMacTsn': ['isTsn', 'tsnHandle'], 'DsMemCustomFdb': ['isTsn', 'tsnHandle', 'flowPolicePtr', 'flowPoliceValid', 'flowStatsValid', 'flowStatsPtr', 'destMap', 'isMcast', 'mirrorEn'], 'CtlStormCntl': ['enable', 'stormCurPtr', 'stormInterval', 'stormMaxPtr', 'stormMinPtr', 'stormFinalDelay', 'stormCurRound', 'stormUpdRound'], 'DsMemStormCtrl': ['stormCtrlEn', 'threshold', 'usePktCount'], 'DsMemStorm': ['runningCounter'], 'DsRegPortLearnCtrl': ['lock', 'violationToCpu', 'maxMacNum', 'macNumLimitEn', 'lrnNumExceedDiscard'], 'DsRegPortLearnNum': ['learntMacNum'], 'CtlPktProcLog': ['cpuFifoFullNum', 'hwFifoFullNum', 'aclQosLogEn', 'aclDiscard', 'routeDiscard', 'routeExcpDiscard', 'routeProcess', 'bridgeProcess', 'destMacKnown', 'l2UcastSrcMatchDiscard', 'macDaDiscard', 'igrStpCheckDiscard', 'stormDrop', 'lrnPortLockDiscard', 'lrnNumExceedDiscard', 'isTsn', 'tsnHandle', 'igrFlowSpan', 'entryPend'], 'CtlMacLearn': ['cpuLearnEn', 'cpuLearnNum', 'cpuFifoDepth', 'cpuLrnNumThrd', 'hwLearnNum', 'hwFifoDepth', 'hwLrnNumThrd']}

1,430

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null

0

1

0

1

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null

0

5

c444238b992aa2e729ccd8592544ab07c94b5c39

86

py

Python

accounts/admin.py

mirsazzathossain/SPMS-Project

eb2b9144b6ddb8d18c146a4c4d6f79b9f7a7eeb5

[ "MIT" ]

190

2021-02-06T10:47:54.000Z

2022-02-15T23:45:07.000Z

accounts/admin.py

mirsazzathossain/SPMS-Project

eb2b9144b6ddb8d18c146a4c4d6f79b9f7a7eeb5

[ "MIT" ]

105

2020-06-17T19:40:51.000Z

2022-03-01T20:23:04.000Z

accounts/admin.py

mirsazzathossain/SPMS-Project

eb2b9144b6ddb8d18c146a4c4d6f79b9f7a7eeb5

[ "MIT" ]

52

2018-03-08T11:18:12.000Z

2021-08-02T16:07:04.000Z

from django.contrib import admin from .models import User admin.site.register(User)

14.333333

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0.802326

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0.127907

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null

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1

0

1

0

1

0

5

c4567fd2bde78f229a843af7d5b833c0f0b99b70

215

py

Python

apps/public/apps.py

aeasringnar/-django-RESTfulAPI

3065f7617dc3534005ab94cd08324c2b51526634

[ "MIT" ]

null

apps/public/apps.py

aeasringnar/-django-RESTfulAPI

3065f7617dc3534005ab94cd08324c2b51526634

[ "MIT" ]

null

apps/public/apps.py

aeasringnar/-django-RESTfulAPI

3065f7617dc3534005ab94cd08324c2b51526634

[ "MIT" ]

null

from django.apps import AppConfig class PublicConfig(AppConfig): name = 'apps.public' # todo 修改app名称，使应用可以在apps目录中存在，并且可以正常的导入到settings # 激活signals def ready(self): import apps.public.signals

21.5

74

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6.826087

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215

9

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null

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1

0

1

0

1

0

5

c4667abf783360e90a5080d3edac2a45dd910901

219

py

Python

backend/app/tests/test_tasks.py

huideyeren/odaikun

31ebacdce3398e442891c93fc0877416ed902c27

[ "MIT" ]

null

backend/app/tests/test_tasks.py

huideyeren/odaikun

31ebacdce3398e442891c93fc0877416ed902c27

[ "MIT" ]

20

2020-11-12T03:21:24.000Z

2020-11-24T00:10:40.000Z

backend/app/tests/test_tasks.py

huideyeren/odaikun

31ebacdce3398e442891c93fc0877416ed902c27

[ "MIT" ]

null

from app import tasks def test_example_task(): """ test_example_task サンプルのタスクを受信できているかのテスト """ task_output = tasks.example_task("Hello World") assert task_output == "test task returns Hello World"

21.9

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219

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0.518519

0.221477

0.201342

0

0.200913

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9

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24.333333

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false

0

0.25

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0.5

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null

1

0

1

0

null

0

1

0

5

c473d6491a0eb0339c728bd0ce69fb79c1114e95

2,744

py

Python

tests/test_logger.py

gosion/pyPvm

d7326799c907b660db11b02fd16843fdb4733eb7

[ "MIT" ]

null

tests/test_logger.py

gosion/pyPvm

d7326799c907b660db11b02fd16843fdb4733eb7

[ "MIT" ]

null

tests/test_logger.py

gosion/pyPvm

d7326799c907b660db11b02fd16843fdb4733eb7

[ "MIT" ]

null

import os import pytest import sys import time from pvm.features.logging import LogFeature, StreamWriter, FileWriter from tests.processes import pause_and_continute @pytest.fixture def init(): file_name = ( "abc." + time.strftime("%Y%m%d", time.localtime(time.time())) + ".log" ) if os.path.exists(file_name): os.remove(file_name) yield file_name def test_log_to_console(capsys): process = pause_and_continute(StreamWriter()) prices = [26, 32, 15] initData1 = { "price1": prices[0], "price2": prices[1], } process.start(initData1) waiting_ids = process.process_context.scope.get("waiting_ids", []) assert waiting_ids is not None assert len(waiting_ids) == 1 assert process.process_context.scope.get("total") == ( prices[0] + prices[1] ) initData2 = {"user_input": 0} process.proceed(waiting_ids[0], initData2) assert process.process_context.scope.get("total", prices[0] + prices[1]) initData2["user_input"] = prices[2] process.proceed(waiting_ids[0], initData2) assert process.process_context.scope.get("total") == ( prices[0] + prices[1] + prices[2] ) expected = [ "occurs.", "is ready to execute.", "inished ths executions.", "passed.", "is ready to execute.", "I am waiting.", ] out, err = capsys.readouterr() lines = err.split(os.linesep) for i, e in enumerate(expected): assert lines[i].split("-")[-1].strip().endswith(e) def test_log_to_file(init): process = pause_and_continute(FileWriter("abc.log")) prices = [26, 32, 15] initData1 = { "price1": prices[0], "price2": prices[1], } process.start(initData1) waiting_ids = process.process_context.scope.get("waiting_ids", []) assert waiting_ids is not None assert len(waiting_ids) == 1 assert process.process_context.scope.get("total") == ( prices[0] + prices[1] ) file_name = init initData2 = {"user_input": 0} process.proceed(waiting_ids[0], initData2) assert process.process_context.scope.get("total", prices[0] + prices[1]) initData2["user_input"] = prices[2] process.proceed(waiting_ids[0], initData2) assert process.process_context.scope.get("total") == ( prices[0] + prices[1] + prices[2] ) expected = [ "occurs.", "is ready to execute.", "inished ths executions.", "passed.", "is ready to execute.", "I am waiting.", ] with open(file_name, mode="r") as f: lines = f.readlines() for i, e in enumerate(expected): assert lines[i].split("-")[-1].strip().endswith(e)

24.5

78

0.612609

342

2,744

4.792398

0.274854

0.073215

0.102502

0.126907

0.708969

0

0.027831

0.240525

2,744

111

79

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0.758637

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0.626506

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0

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0.036145

false

0.024096

0.072289

0

0.108434

0

null

0

1

0

null

0

5

6709ef99dbc39e6aafe1755a80e82b6351387391

55

py

Python

opy/testdata/hello_py2_print.py

bb010g/oil

660f6ad283d53e3b9c4b1088b39ff1002e6a8d55

[ "Apache-2.0" ]

1

2018-10-15T10:09:32.000Z

opy/testdata/hello_py2_print.py

bb010g/oil

660f6ad283d53e3b9c4b1088b39ff1002e6a8d55

[ "Apache-2.0" ]

1

2018-05-28T21:30:28.000Z

opy/testdata/hello_py2_print.py

bb010g/oil

660f6ad283d53e3b9c4b1088b39ff1002e6a8d55

[ "Apache-2.0" ]

null

#!/usr/bin/python import sys print >>sys.stderr, 'hi'

11

24

0.672727

9

55

4.111111

0.888889

0

0.127273

55

4

25

13.75

0.770833

0.290909

0

0.052632

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6738fdf7fa1dbd32d627d0c964d9456d21566f86

2,185

py

Python

checkov/common/checks_infra/solvers/attribute_solvers/__init__.py

Devocean8-Official/checkov

8ce61421fa838a97981ab3bd0ae2a12e541666b2

[ "Apache-2.0" ]

1

2022-02-15T20:46:07.000Z

checkov/common/checks_infra/solvers/attribute_solvers/__init__.py

Devocean8-Official/checkov

8ce61421fa838a97981ab3bd0ae2a12e541666b2

[ "Apache-2.0" ]

3

2022-03-07T20:37:31.000Z

2022-03-21T20:20:14.000Z

checkov/common/checks_infra/solvers/attribute_solvers/__init__.py

Devocean8-Official/checkov

8ce61421fa838a97981ab3bd0ae2a12e541666b2

[ "Apache-2.0" ]

null

from checkov.common.checks_infra.solvers.attribute_solvers.any_attribute_solver import AnyResourceSolver from checkov.common.checks_infra.solvers.attribute_solvers.contains_attribute_solver import ContainsAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.not_contains_attribute_solver import NotContainsAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.ending_with_attribute_solver import EndingWithAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.equals_attribute_solver import EqualsAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.regex_match_attribute_solver import RegexMatchAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.exists_attribute_solver import ExistsAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.not_ending_with_attribute_solver import NotEndingWithAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.not_equals_attribute_solver import NotEqualsAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.not_regex_match_attribute_solver import NotRegexMatchAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.not_exists_attribute_solver import NotExistsAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.not_starting_with_attribute_solver import NotStartingWithAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.starting_with_attribute_solver import StartingWithAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.within_attribute_solver import WithinAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.greater_than_attribute_solver import GreaterThanAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.greater_than_or_equal_attribute_solver import GreaterThanOrEqualAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.less_than_attribute_solver import LessThanAttributeSolver from checkov.common.checks_infra.solvers.attribute_solvers.less_than_or_equal_attribute_solver import LessThanOrEqualAttributeSolver

115

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2,185

7.602362

0.169291

0.102538

0.158467

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0.631797

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0.293112

0.125324

0

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2,185

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true

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0

null

0

1

0

1

0

1

0

5

675235c0ada6906064d1f7efdbb6ab2363adb9be

239

py

Python

PYTHON/ex074.py

george-git-dev/CursoemVideo

307933ef91f1ad3a0be11bfb326fe86211f2f156

[ "MIT" ]

null

PYTHON/ex074.py

george-git-dev/CursoemVideo

307933ef91f1ad3a0be11bfb326fe86211f2f156

[ "MIT" ]

null

PYTHON/ex074.py

george-git-dev/CursoemVideo

307933ef91f1ad3a0be11bfb326fe86211f2f156

[ "MIT" ]

1

2021-04-01T22:31:19.000Z

from random import randint n = (randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10)) print(f"Eu sorteei os valores {n}") print(f"O maior valor sorteado foi {max(n)}") print(f"O menor valor sorteado foi {min(n)}")

47.8

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3.622222

0.466667

0.245399

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0.417178

0.306748

0

0.072464

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239

5

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false

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null

1

0

1

0

null

0

1

0

5

67606b53b22673f3a87f2a297ad0ea1434e5893b

2,656

py

Python

demo/plot_wavelets.py

SalvoCas/pywt

75b3b7b37102aad27780153b4b0fdaf184b205a4

[ "MIT" ]

1,435

2015-07-29T18:28:27.000Z

2022-03-31T10:16:46.000Z

demo/plot_wavelets.py

SalvoCas/pywt

75b3b7b37102aad27780153b4b0fdaf184b205a4

[ "MIT" ]

547

2015-07-29T18:10:15.000Z

2022-03-24T18:42:57.000Z

demo/plot_wavelets.py

SalvoCas/pywt

75b3b7b37102aad27780153b4b0fdaf184b205a4

[ "MIT" ]

421

2015-07-30T13:08:25.000Z

2022-03-24T11:10:07.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Plot scaling and wavelet functions for db, sym, coif, bior and rbio families import itertools import matplotlib.pyplot as plt import pywt plot_data = [('db', (4, 3)), ('sym', (4, 3)), ('coif', (3, 2))] for family, (rows, cols) in plot_data: fig = plt.figure() fig.subplots_adjust(hspace=0.2, wspace=0.2, bottom=.02, left=.06, right=.97, top=.94) colors = itertools.cycle('bgrcmyk') wnames = pywt.wavelist(family) i = iter(wnames) for col in range(cols): for row in range(rows): try: wavelet = pywt.Wavelet(next(i)) except StopIteration: break phi, psi, x = wavelet.wavefun(level=5) color = next(colors) ax = fig.add_subplot(rows, 2 * cols, 1 + 2 * (col + row * cols)) ax.set_title(wavelet.name + " phi") ax.plot(x, phi, color) ax.set_xlim(min(x), max(x)) ax = fig.add_subplot(rows, 2*cols, 1 + 2*(col + row*cols) + 1) ax.set_title(wavelet.name + " psi") ax.plot(x, psi, color) ax.set_xlim(min(x), max(x)) for family, (rows, cols) in [('bior', (4, 3)), ('rbio', (4, 3))]: fig = plt.figure() fig.subplots_adjust(hspace=0.5, wspace=0.2, bottom=.02, left=.06, right=.97, top=.94) colors = itertools.cycle('bgrcmyk') wnames = pywt.wavelist(family) i = iter(wnames) for col in range(cols): for row in range(rows): try: wavelet = pywt.Wavelet(next(i)) except StopIteration: break phi, psi, phi_r, psi_r, x = wavelet.wavefun(level=5) row *= 2 color = next(colors) ax = fig.add_subplot(2*rows, 2*cols, 1 + 2*(col + row*cols)) ax.set_title(wavelet.name + " phi") ax.plot(x, phi, color) ax.set_xlim(min(x), max(x)) ax = fig.add_subplot(2*rows, 2*cols, 2*(1 + col + row*cols)) ax.set_title(wavelet.name + " psi") ax.plot(x, psi, color) ax.set_xlim(min(x), max(x)) row += 1 ax = fig.add_subplot(2*rows, 2*cols, 1 + 2*(col + row*cols)) ax.set_title(wavelet.name + " phi_r") ax.plot(x, phi_r, color) ax.set_xlim(min(x), max(x)) ax = fig.add_subplot(2*rows, 2*cols, 1 + 2*(col + row*cols) + 1) ax.set_title(wavelet.name + " psi_r") ax.plot(x, psi_r, color) ax.set_xlim(min(x), max(x)) plt.show()

31.247059

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0.508283

378

2,656

3.497355

0.219577

0.045386

0.036309

0.068079

0.826021

0.765507

0.742814

0.680787

0.662632

0

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0.336596

2,656

84

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31.619048

0.714529

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0

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0

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false

0

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0

0.047619

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null

0

1

0

1

0

null

0

5

6782ba7147a1da69b9c83481218f3f3b394e9bfb

7,547

py

Python

tests/trees/test_hoeffding_adaptive_tree.py

jiahy0825/scikit-multiflow

910fa62605de49dea3e4599bb233c3d9c6f4527b

[ "BSD-3-Clause" ]

null

tests/trees/test_hoeffding_adaptive_tree.py

jiahy0825/scikit-multiflow

910fa62605de49dea3e4599bb233c3d9c6f4527b

[ "BSD-3-Clause" ]

null

tests/trees/test_hoeffding_adaptive_tree.py

jiahy0825/scikit-multiflow

910fa62605de49dea3e4599bb233c3d9c6f4527b

[ "BSD-3-Clause" ]

null

import numpy as np from array import array import os from skmultiflow.data import ConceptDriftStream, SEAGenerator, HyperplaneGenerator from skmultiflow.trees import HAT def test_hat_mc(test_path): stream = ConceptDriftStream(stream=SEAGenerator(random_state=1, noise_percentage=0.05), drift_stream=SEAGenerator(random_state=2, classification_function=2, noise_percentage=0.05), random_state=1, position=250, width=10) stream.prepare_for_use() learner = HAT(leaf_prediction='mc') cnt = 0 max_samples = 1000 y_pred = array('i') y_proba = [] wait_samples = 20 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred.append(learner.predict(X)[0]) y_proba.append(learner.predict_proba(X)[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('i', [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) assert np.alltrue(y_pred == expected_predictions) test_file = os.path.join(test_path, 'test_hoeffding_adaptive_tree_mc.npy') data = np.load(test_file) assert np.allclose(y_proba, data) expected_info = "HAT(binary_split=False, bootstrap_sampling=True, grace_period=200,\n" \ " leaf_prediction='mc', max_byte_size=33554432,\n" \ " memory_estimate_period=1000000, nb_threshold=0, no_preprune=False,\n" \ " nominal_attributes=None, remove_poor_atts=False, split_confidence=1e-07,\n" \ " split_criterion='info_gain', stop_mem_management=False, tie_threshold=0.05)" assert learner.get_info() == expected_info expected_model_1 = 'Leaf = Class 1.0 | {0.0: 398.0, 1.0: 1000.0}\n' assert (learner.get_model_description() == expected_model_1) assert type(learner.predict(X)) == np.ndarray assert type(learner.predict_proba(X)) == np.ndarray stream.restart() X, y = stream.next_sample(5000) learner = HAT(max_byte_size=30, leaf_prediction='mc', grace_period=10) learner.partial_fit(X, y) def test_hat_nb(test_path): stream = ConceptDriftStream(stream=SEAGenerator(random_state=1, noise_percentage=0.05), drift_stream=SEAGenerator(random_state=2, classification_function=2, noise_percentage=0.05), random_state=1, position=250, width=10) stream.prepare_for_use() learner = HAT(leaf_prediction='nb') cnt = 0 max_samples = 1000 y_pred = array('i') y_proba = [] wait_samples = 20 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred.append(learner.predict(X)[0]) y_proba.append(learner.predict_proba(X)[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('i', [1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1]) assert np.alltrue(y_pred == expected_predictions) test_file = os.path.join(test_path, 'test_hoeffding_adaptive_tree_nb.npy') data = np.load(test_file) assert np.allclose(y_proba, data) expected_info = "HAT(binary_split=False, bootstrap_sampling=True, grace_period=200,\n" \ " leaf_prediction='nb', max_byte_size=33554432,\n" \ " memory_estimate_period=1000000, nb_threshold=0, no_preprune=False,\n" \ " nominal_attributes=None, remove_poor_atts=False, split_confidence=1e-07,\n" \ " split_criterion='info_gain', stop_mem_management=False, tie_threshold=0.05)" assert learner.get_info() == expected_info assert type(learner.predict(X)) == np.ndarray assert type(learner.predict_proba(X)) == np.ndarray def test_hat_nba(test_path): stream = HyperplaneGenerator(mag_change=0.001, noise_percentage=0.1, random_state=2) stream.prepare_for_use() learner = HAT(leaf_prediction='nba') cnt = 0 max_samples = 5000 y_pred = array('i') y_proba = [] wait_samples = 100 while cnt < max_samples: X, y = stream.next_sample() # Test every n samples if (cnt % wait_samples == 0) and (cnt != 0): y_pred.append(learner.predict(X)[0]) y_proba.append(learner.predict_proba(X)[0]) learner.partial_fit(X, y) cnt += 1 expected_predictions = array('i', [1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0]) assert np.alltrue(y_pred == expected_predictions) test_file = os.path.join(test_path, 'test_hoeffding_adaptive_tree_nba.npy') data = np.load(test_file) assert np.allclose(y_proba, data) expected_info = "HAT(binary_split=False, bootstrap_sampling=True, grace_period=200,\n" \ " leaf_prediction='nba', max_byte_size=33554432,\n" \ " memory_estimate_period=1000000, nb_threshold=0, no_preprune=False,\n" \ " nominal_attributes=None, remove_poor_atts=False, split_confidence=1e-07,\n" \ " split_criterion='info_gain', stop_mem_management=False, tie_threshold=0.05)" assert learner.get_info() == expected_info assert type(learner.predict(X)) == np.ndarray assert type(learner.predict_proba(X)) == np.ndarray def test_hoeffding_adaptive_tree_categorical_features(test_path): data_path = os.path.join(test_path, 'ht_categorical_features_testcase.npy') stream = np.load(data_path) # Removes the last two columns (regression targets) stream = stream[:, :-2] X, y = stream[:, :-1], stream[:, -1] nominal_attr_idx = np.arange(7).tolist() learner = HAT(nominal_attributes=nominal_attr_idx) learner.partial_fit(X, y, classes=np.unique(y)) expected_description = "if Attribute 0 = -15.0:\n" \ " Leaf = Class 2 | {2: 475.0}\n" \ "if Attribute 0 = 0.0:\n" \ " Leaf = Class 0 | {0: 560.0, 1: 345.0}\n" \ "if Attribute 0 = 1.0:\n" \ " Leaf = Class 1 | {0: 416.0, 1: 464.0}\n" \ "if Attribute 0 = 2.0:\n" \ " Leaf = Class 1 | {0: 335.0, 1: 504.0}\n" \ "if Attribute 0 = 3.0:\n" \ " Leaf = Class 1 | {0: 244.0, 1: 644.0}\n" \ "if Attribute 0 = -30.0:\n" \ " Leaf = Class 3.0 | {3.0: 65.0, 4.0: 55.0}\n" assert learner.get_model_description() == expected_description

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0.551875

1,015

7,547

3.902463

0.15468

0.043928

0.053774

0.060591

0.797526

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0.745014

0.744761

0.702095

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0

0.079632

0.322777

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42.162011

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0

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0

null

0

1

0

null

0

5

678a715802279b3ffa2b1e247706b3f4c90568b3

88

py

Python

finance/__init__.py

codingwithchad/finance

4202692024ec0137670ff02a13b2f92f17da0cb2

[ "MIT" ]

null

finance/__init__.py

codingwithchad/finance

4202692024ec0137670ff02a13b2f92f17da0cb2

[ "MIT" ]

null

finance/__init__.py

codingwithchad/finance

4202692024ec0137670ff02a13b2f92f17da0cb2

[ "MIT" ]

null

from categorize import categorize from financeutil import toFloat from category import *

29.333333

33

0.863636

11

88

6.909091

0.545455

0

0.125

88

3

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29.333333

0.987013

0

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0

true

0

1

0

1

0

1

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null

0

1

0

null

0

1

0

1

0

1

0

5

6795af2659c58f77f302d69349e684baeaff81a1

137

py

Python

app/loader.py

ganggas95/E-Wisata

fb66fc7d3d4cc5a45ad9acea42fb306140a6449f

[ "Apache-2.0" ]

null

app/loader.py

ganggas95/E-Wisata

fb66fc7d3d4cc5a45ad9acea42fb306140a6449f

[ "Apache-2.0" ]

null

app/loader.py

ganggas95/E-Wisata

fb66fc7d3d4cc5a45ad9acea42fb306140a6449f

[ "Apache-2.0" ]

1

2020-02-12T09:21:15.000Z

from .create_app import login from .user_app import User @login.user_loader def load_user(user_id): return User.get_by_id(user_id)

17.125

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0.788321

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137

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137

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false

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0.4

0.2

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0

1

0

null

0

1

0

null

0

1

0

5

67d105d412565b0a5337b25419ff11939265dc1b

10,095

py

Python

senlin/tests/unit/api/openstack/v1/test_policy_types.py

openstack/senlin

390779ca1e08f819683e79993696f945f1c0393e

[ "Apache-2.0" ]

45

2015-10-18T02:56:50.000Z

2022-03-01T15:28:02.000Z

senlin/tests/unit/api/openstack/v1/test_policy_types.py

openstack/senlin

390779ca1e08f819683e79993696f945f1c0393e

[ "Apache-2.0" ]

2

2019-04-26T10:44:47.000Z

2020-12-16T19:45:34.000Z

senlin/tests/unit/api/openstack/v1/test_policy_types.py

openstack/senlin

390779ca1e08f819683e79993696f945f1c0393e

[ "Apache-2.0" ]

45

2015-10-19T02:35:57.000Z

2021-09-28T09:01:42.000Z

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from unittest import mock from webob import exc from senlin.api.common import util from senlin.api.middleware import fault from senlin.api.openstack.v1 import policy_types from senlin.common import exception as senlin_exc from senlin.common import policy from senlin.rpc import client as rpc_client from senlin.tests.unit.api import shared from senlin.tests.unit.common import base @mock.patch.object(policy, 'enforce') class PolicyTypeControllerTest(shared.ControllerTest, base.SenlinTestCase): def setUp(self): super(PolicyTypeControllerTest, self).setUp() class DummyConfig(object): bind_port = 8777 cfgopts = DummyConfig() self.controller = policy_types.PolicyTypeController(options=cfgopts) @mock.patch.object(util, 'parse_request') @mock.patch.object(rpc_client.EngineClient, 'call') def test_list(self, mock_call, mock_parse, mock_enforce): self._mock_enforce_setup(mock_enforce, 'index', True) req = self._get('/policy_types') engine_response = [ {'name': 'senlin.policy.p1', 'version': '1.0', 'attr': 'v1'}, {'name': 'senlin.policy.p2', 'version': '1.0', 'attr': 'v2'} ] mock_call.return_value = engine_response obj = mock.Mock() mock_parse.return_value = obj response = self.controller.index(req) self.assertEqual( [ {'name': 'senlin.policy.p1-1.0'}, {'name': 'senlin.policy.p2-1.0'}, ], response['policy_types'] ) mock_parse.assert_called_once_with( 'PolicyTypeListRequest', req, {}) mock_call.assert_called_once_with( req.context, 'policy_type_list', mock.ANY) @mock.patch.object(util, 'parse_request') @mock.patch.object(rpc_client.EngineClient, 'call') def test_list_old_version(self, mock_call, mock_parse, mock_enforce): self._mock_enforce_setup(mock_enforce, 'index', True) req = self._get('/policy_types', version='1.3') engine_response = [ {'name': 'senlin.policy.p1', 'version': '1.0'}, {'name': 'senlin.policy.p2', 'version': '1.1'} ] mock_call.return_value = engine_response obj = mock.Mock() mock_parse.return_value = obj response = self.controller.index(req) self.assertEqual( [ {'name': 'senlin.policy.p1-1.0'}, {'name': 'senlin.policy.p2-1.1'} ], response['policy_types'] ) mock_parse.assert_called_once_with( 'PolicyTypeListRequest', req, {}) mock_call.assert_called_once_with( req.context, 'policy_type_list', mock.ANY) @mock.patch.object(util, 'parse_request') @mock.patch.object(rpc_client.EngineClient, 'call') def test_list_new_version(self, mock_call, mock_parse, mock_enforce): self._mock_enforce_setup(mock_enforce, 'index', True) req = self._get('/policy_types', version='1.5') engine_response = [ {'name': 'senlin.policy.p1', 'version': '1.0', 'a1': 'v1'}, {'name': 'senlin.policy.p2', 'version': '1.1', 'a2': 'v2'} ] mock_call.return_value = engine_response obj = mock.Mock() mock_parse.return_value = obj response = self.controller.index(req) self.assertEqual(engine_response, response['policy_types']) mock_parse.assert_called_once_with( 'PolicyTypeListRequest', req, {}) mock_call.assert_called_once_with( req.context, 'policy_type_list', mock.ANY) def test_list_err_denied_policy(self, mock_enforce): self._mock_enforce_setup(mock_enforce, 'index', False) req = self._get('/policy_types') resp = shared.request_with_middleware(fault.FaultWrapper, self.controller.index, req) self.assertEqual(403, resp.status_int) self.assertIn('403 Forbidden', str(resp)) @mock.patch.object(util, 'parse_request') @mock.patch.object(rpc_client.EngineClient, 'call') def test_get_old_version(self, mock_call, mock_parse, mock_enforce): self._mock_enforce_setup(mock_enforce, 'get', True) type_name = 'SimplePolicy' req = self._get('/policy_types/%(type)s' % {'type': type_name}, version='1.3') engine_response = { 'name': type_name, 'schema': { 'Foo': {'type': 'String', 'required': False}, 'Bar': {'type': 'Integer', 'required': False}, }, } mock_call.return_value = engine_response obj = mock.Mock() mock_parse.return_value = obj response = self.controller.get(req, type_name=type_name) self.assertEqual(engine_response, response['policy_type']) mock_parse.assert_called_once_with( 'PolicyTypeGetRequest', req, {'type_name': type_name}) mock_call.assert_called_once_with( req.context, 'policy_type_get', mock.ANY) @mock.patch.object(util, 'parse_request') @mock.patch.object(rpc_client.EngineClient, 'call') def test_get_new_version(self, mock_call, mock_parse, mock_enforce): self._mock_enforce_setup(mock_enforce, 'get', True) type_name = 'SimplePolicy' req = self._get('/policy_types/%(type)s' % {'type': type_name}, version='1.5') engine_response = { 'name': type_name, 'schema': { 'Foo': {'type': 'String', 'required': False}, 'Bar': {'type': 'Integer', 'required': False}, }, 'support_status': 'faked_status' } mock_call.return_value = engine_response obj = mock.Mock() mock_parse.return_value = obj response = self.controller.get(req, type_name=type_name) self.assertEqual(engine_response, response['policy_type']) mock_parse.assert_called_once_with( 'PolicyTypeGetRequest', req, {'type_name': type_name}) mock_call.assert_called_once_with( req.context, 'policy_type_get', mock.ANY) @mock.patch.object(util, 'parse_request') @mock.patch.object(rpc_client.EngineClient, 'call') def test_policy_type_get(self, mock_call, mock_parse, mock_enforce): self._mock_enforce_setup(mock_enforce, 'get', True) type_name = 'SimplePolicy' req = self._get('/policy_types/%(type)s' % {'type': type_name}) engine_response = { 'name': type_name, 'schema': { 'Foo': {'type': 'String', 'required': False}, 'Bar': {'type': 'Integer', 'required': False}, }, } mock_call.return_value = engine_response obj = mock.Mock() mock_parse.return_value = obj response = self.controller.get(req, type_name=type_name) self.assertEqual(engine_response, response['policy_type']) mock_parse.assert_called_once_with( 'PolicyTypeGetRequest', req, {'type_name': type_name}) mock_call.assert_called_once_with( req.context, 'policy_type_get', mock.ANY) @mock.patch.object(util, 'parse_request') @mock.patch.object(rpc_client.EngineClient, 'call') def test_policy_type_get_not_found(self, mock_call, mock_parse, mock_enforce): self._mock_enforce_setup(mock_enforce, 'get', True) type_name = 'BogusPolicyType' req = self._get('/policy_types/%(type)s' % {'type': type_name}) error = senlin_exc.ResourceNotFound(type='policy_type', id=type_name) mock_call.side_effect = shared.to_remote_error(error) resp = shared.request_with_middleware(fault.FaultWrapper, self.controller.get, req, type_name=type_name) self.assertEqual(404, resp.json['code']) self.assertEqual('ResourceNotFound', resp.json['error']['type']) @mock.patch.object(util, 'parse_request') @mock.patch.object(rpc_client.EngineClient, 'call') def test_policy_type_get_bad_param(self, mock_call, mock_parse, mock_enforce): self._mock_enforce_setup(mock_enforce, 'get', True) type_name = 11 req = self._get('/policy_types/%(type)s' % {'type': type_name}) mock_parse.side_effect = exc.HTTPBadRequest("bad param") ex = self.assertRaises(exc.HTTPBadRequest, self.controller.get, req, type_name=type_name) self.assertEqual("bad param", str(ex)) mock_parse.assert_called_once_with( 'PolicyTypeGetRequest', req, {'type_name': type_name}) self.assertEqual(0, mock_call.call_count) def test_policy_type_schema_err_denied_policy(self, mock_enforce): self._mock_enforce_setup(mock_enforce, 'get', False) type_name = 'FakePolicyType' req = self._get('/policy_types/%(type)s' % {'type': type_name}) resp = shared.request_with_middleware(fault.FaultWrapper, self.controller.get, req, type_name=type_name) self.assertEqual(403, resp.status_int) self.assertIn('403 Forbidden', str(resp))

39.280156

77

0.613967

1,171

10,095

5.029035

0.149445

0.048905

0.043301

0.04415

0.762608

0.759042

0.754627

0.737816

0.705043

0

0.009573

0.26528

10,095

256

78

39.433594

0.784414

0.052006

0

0.648241

0

0.143545

0.020402

0

0.140704

1

0.055276

false

0

0.050251

0

0.115578

0

null

0

1

0

null

0

5

db145e992bf04f84c24bb5ff99b6daeae33b35b1

1,184

py

Python

src/players/tests/test_parse_spectators/test_spectators.py

codacy-badger/hbscorez

215e4d2617ac9be91bb9d561bbfc552349cd4781

[ "MIT" ]

12

2018-03-20T21:38:53.000Z

2021-10-31T10:00:12.000Z

src/players/tests/test_parse_spectators/test_spectators.py

codacy-badger/hbscorez

215e4d2617ac9be91bb9d561bbfc552349cd4781

[ "MIT" ]

79

2018-03-18T14:26:47.000Z

2022-03-01T15:51:40.000Z

src/players/tests/test_parse_spectators/test_spectators.py

codacy-badger/hbscorez

215e4d2617ac9be91bb9d561bbfc552349cd4781

[ "MIT" ]

4

2018-05-18T15:39:56.000Z

2020-10-29T09:28:41.000Z

import os import tabula from django.test import TestCase from players.management.commands.parse_report import parse_spectators class ParseSpectators(TestCase): def test_value(self): base = os.path.dirname(os.path.abspath(__file__)) path = os.path.join(base, 'report-with-spectators.pdf') table = tabula.read_pdf(path, output_format='json', **{'pages': 1, 'lattice': True})[0] spectators = parse_spectators(table) self.assertEqual(spectators, 60) def test_unknown(self): base = os.path.dirname(os.path.abspath(__file__)) path = os.path.join(base, 'report-with-unknown-spectators.pdf') table = tabula.read_pdf(path, output_format='json', **{'pages': 1, 'lattice': True})[0] spectators = parse_spectators(table) self.assertEqual(spectators, None) def test_invalid(self): base = os.path.dirname(os.path.abspath(__file__)) path = os.path.join(base, 'report-with-invalid-spectators.pdf') table = tabula.read_pdf(path, output_format='json', **{'pages': 1, 'lattice': True})[0] spectators = parse_spectators(table) self.assertEqual(spectators, None)

34.823529

95

0.673142

149

1,184

5.174497

0.275168

0.070039

0.038911

0.054475

0.749676

0

0.008316

0.1875

1,184

33

96

35.878788

0.793139

0

0.478261

0

0.119932

0.079392

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1

0.130435

false

0

0.173913

0

0.347826

0

null

0

1

0

null

0

5

db1b947d4cd98708096ef8e54e90ee5cade83492

26

py

Python

stop/__init__.py

iboraham/senior-project

766fa2c9dd8b4beaa85d48ef71e3c70b525beef2

[ "MIT" ]

1

2021-01-28T07:55:26.000Z

stop/__init__.py

iboraham/senior-project

766fa2c9dd8b4beaa85d48ef71e3c70b525beef2

[ "MIT" ]

null

stop/__init__.py

iboraham/senior-project

766fa2c9dd8b4beaa85d48ef71e3c70b525beef2

[ "MIT" ]

2

2020-02-03T11:30:44.000Z

2020-02-03T11:58:06.000Z

import stop stop.main()

5.2

11

0.692308

4

26

4.5

0.75

0

0.192308

26

4

12

6.5

0.857143

0

1

0

true

0

0.5

0

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0

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null

0

1

0

null

0

1

0

1

0

5

db3897bc53520c5189403c48c14a796fc7c94191

16

py

Python

num/test1.py

ziyecen/redis_test

705b539b62eb613d9a2b528c02028ff299d85483

[ "MIT" ]

null

num/test1.py

ziyecen/redis_test

705b539b62eb613d9a2b528c02028ff299d85483

[ "MIT" ]

null

num/test1.py

ziyecen/redis_test

705b539b62eb613d9a2b528c02028ff299d85483

[ "MIT" ]

null

nu1 = 1 num2 = 2

8

0.5625

4

16

2.25

1

0

0.363636

0.3125

16

2

8

0.454545

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

e1f0df7570fd2cded81150bc2a7f43a8fefd308c

223

py

Python

qtools3/errors.py

jkpr/qtools3

f3e97619177a71db091ee04eb904479810978025

[ "MIT" ]

null

qtools3/errors.py

jkpr/qtools3

f3e97619177a71db091ee04eb904479810978025

[ "MIT" ]

8

2019-08-06T07:59:46.000Z

2019-10-07T18:55:07.000Z

qtools3/errors.py

jkpr/qtools3

f3e97619177a71db091ee04eb904479810978025

[ "MIT" ]

3

2019-07-18T18:34:14.000Z

2020-07-31T20:26:30.000Z

"""A module with errors used in the qtools3 package.""" class XlsformError(Exception): pass class ConvertError(Exception): pass class XformError(Exception): pass class QxmleditError(Exception): pass

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13.117647

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null

1

0

null

0

1

0

5

c01ce96afeb76e2c7ea71c31197c10b631d9c72e

146

py

Python

client/src/grafana_launcher.py

estcube/telemetry-forwarding-client

be659c8dd8e4bd26d1d1974d63f90acffd150e34

[ "MIT" ]

3

2020-06-11T12:34:25.000Z

2020-09-16T12:06:32.000Z

client/src/grafana_launcher.py

estcube/telemetry-forwarding-client

be659c8dd8e4bd26d1d1974d63f90acffd150e34

[ "MIT" ]

57

2020-09-16T09:11:04.000Z

2022-02-28T01:32:13.000Z

client/src/grafana_launcher.py

estcube/Telemetry-Forwarding-Client

be659c8dd8e4bd26d1d1974d63f90acffd150e34

[ "MIT" ]

null

""" Hook to start Grafana server """ import subprocess subprocess.Popen([r"../grafana/bin/grafana-server.exe", "--homepath=grafana"], cwd="..")

24.333333

88

0.684932

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146

5.555556

0.722222

0.26

0

0.09589

146

5

89

29.2

0.757576

0.191781

0

0.481818

0.3

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true

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0.5

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null

1

0

1

0

null

0

1

0

1

0

5

c021586bf901ce06c374aa8ba599d3129aca7020

38

py

Python

pandas2tensorboard/test/__init__.py

Anselmoo/pandas2tensorboard

ec5e16135416d23b83daa6bc618d701cf6feb30d

[ "MIT" ]

null

pandas2tensorboard/test/__init__.py

Anselmoo/pandas2tensorboard

ec5e16135416d23b83daa6bc618d701cf6feb30d

[ "MIT" ]

16

2022-02-06T18:50:39.000Z

2022-03-28T16:30:27.000Z

pandas2tensorboard/test/__init__.py

Anselmoo/pandas2tensorboard

ec5e16135416d23b83daa6bc618d701cf6feb30d

[ "MIT" ]

null

"""Test of the Pandas2Tensorboard."""

19

37

0.710526

4

38

6.75

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0.105263

38

1

38

0.764706

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null

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null

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0

1

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1

0

null

0

1

0

5

c02afe1eb2695be8d6342fb95a78446b6e09d5c3

229

py

Python

walle/core/__init__.py

kaixin-bai/walle

031e48c080fe439418d017c689ea7e6350ebbbb1

[ "MIT" ]

null

walle/core/__init__.py

kaixin-bai/walle

031e48c080fe439418d017c689ea7e6350ebbbb1

[ "MIT" ]

null

walle/core/__init__.py

kaixin-bai/walle

031e48c080fe439418d017c689ea7e6350ebbbb1

[ "MIT" ]

null

"""Module importing all core classes. """ from walle.core.matrix import RotationMatrix from walle.core.orientation import Orientation from walle.core.pose import Pose from walle.core.quaternion import UnitQuaternion, Quaternion

28.625

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0.825328

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229

6.3

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0.190476

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0.104803

229

7

61

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null

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1

0

1

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1

0

5

c02e9582e259a2a723cd5f593585f282cc44b380

321

py

Python

pysigfox/exceptions.py

optimdata/pysigfox

9998a3fb4813d80b44aa0974fbd4f5936de54fa6

[ "MIT" ]

1

2021-03-12T10:22:07.000Z

pysigfox/exceptions.py

optimdata/pysigfox

9998a3fb4813d80b44aa0974fbd4f5936de54fa6

[ "MIT" ]

1

2021-04-30T13:31:03.000Z

pysigfox/exceptions.py

optimdata/pysigfox

9998a3fb4813d80b44aa0974fbd4f5936de54fa6

[ "MIT" ]

null

# -*- coding: utf-8 -*- class SigfoxBaseException(BaseException): pass class SigfoxConnectionError(SigfoxBaseException): pass class SigfoxBadStatusError(SigfoxBaseException): pass class SigfoxResponseError(SigfoxBaseException): pass class SigfoxTooManyRequestsError(SigfoxBaseException): pass

16.05

54

0.775701

23

321

10.826087

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0.144578

0.337349

0

0.003663

0.149533

321

19

55

16.894737

0.908425

0.065421

0

0.5

0

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true

0.5

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0.5

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1

0

1

null

0

1

0

null

0

1

0

5

c0428808ab2e951ae05bf84c69ce853b1cf337d3

159

py

Python

scripts/taxes_Baybay.py

mgbaybay/Data-Science

4c077b5bd6f693f1d5f0a5fa1996b2ebb4260caf

[ "MIT" ]

null

scripts/taxes_Baybay.py

mgbaybay/Data-Science

4c077b5bd6f693f1d5f0a5fa1996b2ebb4260caf

[ "MIT" ]

null

scripts/taxes_Baybay.py

mgbaybay/Data-Science

4c077b5bd6f693f1d5f0a5fa1996b2ebb4260caf

[ "MIT" ]

null

income = float(input()) gross_pay = income taxes_owed = income * .12 net_pay = gross_pay - taxes_owed print(gross_pay) print(taxes_owed) print(net_pay)

19.875

33

0.72956

25

159

4.32

0.4

0.222222

0.259259

0

0.015038

0.163522

159

8

34

19.875

0.796992

0

1

0

false

0

0.428571

1

0

null

1

0

1

0

null

0

1

0

5

c051f98e0d65bdd8119a2205f764e931bce686fa

3,791

py

Python

dolphindb/vector.py

ShenHongFei/dolphindb-python

36f6cc0ded6d9b4b3f25d5eadd83dc3f3314fd8c

[ "Apache-2.0" ]

1

2020-12-29T11:23:07.000Z

dolphindb/vector.py

ShenHongFei/dolphindb-python

36f6cc0ded6d9b4b3f25d5eadd83dc3f3314fd8c

[ "Apache-2.0" ]

null

dolphindb/vector.py

ShenHongFei/dolphindb-python

36f6cc0ded6d9b4b3f25d5eadd83dc3f3314fd8c

[ "Apache-2.0" ]

null

from pandas import Series class Vector(object): def __init__(self, name=None, data=None, s=None, tableName=None): self.__name = name self.__tableName = tableName self.__session = s # type : session if isinstance(data, list): self.__vec = Series(data) elif isinstance(data, Series): self.__vec = data else: self.__vec = None def name(self): return self.__name def tableName(self): return self.__tableName def as_series(self, useCache=False): if useCache is True and self.__vec is not None: return self.__vec self.__vec = Series(self.__session.run('.'.join((self.__tableName, self.__name)))) return self.__vec def __str__(self): return self.__name def __lt__(self, other): return FilterCond(self.__name, '<', str(other)) def __le__(self, other): return FilterCond(self.__name, '<=', str(other)) def __gt__(self, other): return FilterCond(self.__name, '>', str(other)) def __ge__(self, other): return FilterCond(self.__name, '>=', str(other)) def __eq__(self, other): return FilterCond(self.__name, '==', str(other)) def __ne__(self, other): return FilterCond(self.__name, '!=', str(other)) def __add__(self, other): return FilterCond(self.__name, '+', str(other)) def __sub__(self, other): return FilterCond(self.__name, '-', str(other)) def __mul__(self, other): return FilterCond(self.__name, '*', str(other)) def __div__(self, other): return FilterCond(self.__name, '/', str(other)) def __mod__(self, other): return FilterCond(self.__name, '%', str(other)) def __lshift__(self, other): return FilterCond(self.__name, '<<', str(other)) def __rshift__(self, other): return FilterCond(self.__name, '>>', str(other)) def __floordiv__(self, other): return FilterCond('int(', str(self), ')') class FilterCond(object): def __init__(self, lhs, op, rhs): self.__lhs = lhs self.__op = op self.__rhs = rhs def __str__(self): return '(' + str(self.__lhs) + ' ' + str(self.__op) + ' ' + str(self.__rhs) + ')' def __or__(self, other): return FilterCond(str(self), 'or', str(other)) def __and__(self, other): return FilterCond(str(self), 'and', str(other)) def __lt__(self, other): return FilterCond(str(self), '<', str(other)) def __le__(self, other): return FilterCond(str(self), '<=', str(other)) def __gt__(self, other): return FilterCond(str(self), '>', str(other)) def __ge__(self, other): return FilterCond(str(self), '>=', str(other)) def __eq__(self, other): return FilterCond(str(self), '==', str(other)) def __ne__(self, other): return FilterCond(str(self), '!=', str(other)) def __add__(self, other): return FilterCond(str(self), '+', str(other)) def __sub__(self, other): return FilterCond(str(self), '-', str(other)) def __mul__(self, other): return FilterCond(str(self), '*', str(other)) def __div__(self, other): return FilterCond(str(self), '/', str(other)) def __mod__(self, other): return FilterCond(str(self), '%', str(other)) def __lshift__(self, other): return FilterCond(str(self), '<<', str(other)) def __rshift__(self, other): return FilterCond(str(self), '>>', str(other)) def __floordiv__(self, other): return FilterCond('int(', str(self), ')')

29.161538

91

0.572408

430

3,791

4.565116

0.127907

0.137545

0.229241

0.382068

0.715232

0.695364

0.657667

0.619969

0.055018

0

0.276972

3,791

129

92

29.387597

0.716162

0.003693

0

0.4

0

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1

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false

0

0.011111

0.377778

0.844444

0

null

0

1

0

null

0

1

0

1

0

5

fbeb96861175ca463a76b8d5d17b8e8149caad30

181

py

Python

dsalgos/tests/test_linear_search.py

psd314/dsalgos

c8f40c99ee00009c2b32317f85aa11fdff6693ff

[ "MIT" ]

null

dsalgos/tests/test_linear_search.py

psd314/dsalgos

c8f40c99ee00009c2b32317f85aa11fdff6693ff

[ "MIT" ]

null

dsalgos/tests/test_linear_search.py

psd314/dsalgos

c8f40c99ee00009c2b32317f85aa11fdff6693ff

[ "MIT" ]

null

from src.algos.search.linear_search import linear_search def test_linear_search(): a = [1, 2, 3] assert linear_search(a, 1) == True assert linear_search(a, 0) == False

25.857143

56

0.696133

29

181

4.137931

0.551724

0.5

0.325

0.233333

0

0.034014

0.187845

181

6

57

30.166667

0.782313

0

0.4

1

0.2

false

0

0.2

0

0.4

0

1

0

null

1

0

1

0

null

0

5

224fc41a4e713cb22b93b5f81c752a3eeeb5cf69

43

py

Python

Scripts/dk/timer.py

hhg128/DKGL

c61bc6546ac5655da97462cc532a9034ba08516d

[ "PSF-2.0", "BSD-3-Clause" ]

14

2015-09-12T01:32:05.000Z

2021-10-13T02:52:53.000Z

Scripts/dk/timer.py

hhg128/DKGL

c61bc6546ac5655da97462cc532a9034ba08516d

[ "PSF-2.0", "BSD-3-Clause" ]

null

Scripts/dk/timer.py

hhg128/DKGL

c61bc6546ac5655da97462cc532a9034ba08516d

[ "PSF-2.0", "BSD-3-Clause" ]

3

2015-11-10T03:12:49.000Z

2018-10-15T15:38:31.000Z

import _dk_core as core Timer = core.Timer

14.333333

23

0.790698

8

43

4

0.625

0.5625

0

0.162791

43

3

24

14.333333

0.888889

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

226be7067891ded045daeeaa4ca3a2d4ff543c87

3,624

py

Python

LogReg.py

cedorman/footballmodel

7300e631d3d460b04b69e2769f4f5ac784f6ceb1

[ "Apache-2.0" ]

null

LogReg.py

cedorman/footballmodel

7300e631d3d460b04b69e2769f4f5ac784f6ceb1

[ "Apache-2.0" ]

null

LogReg.py

cedorman/footballmodel

7300e631d3d460b04b69e2769f4f5ac784f6ceb1

[ "Apache-2.0" ]

null

# # Simple wrapper for Logistic Regression # from sklearn.linear_model import LogisticRegressionCV from sklearn.metrics import roc_auc_score import logger class LogReg: def __init__(self, X_train, X_test, y_train, y_test): """Run a 'standard' LogReg process, use CV to optimize, then print results on train and test.""" self.log = logger.getLogger() self.X_train = X_train self.X_test = X_test self.y_train = y_train self.y_test = y_test # without cross validation # logistic_regression = LogisticRegression(random_state=0, max_iter=2000).fit(X_train, y_train) # With cross validation. self.logistic_regression = LogisticRegressionCV(random_state=0, max_iter=2000).fit(X_train, y_train) def score(self): """ Score, where the y_train / y_test is binary. """ x_shape = self.X_train.shape y_shape = self.y_train.shape if x_shape[0] != y_shape[0]: self.log.warning(f"Problem with shape of x/y {x_shape} {y_shape}") if len(y_shape) != 1: self.log.warning(f"Problem with shape of x/y {x_shape} {y_shape}") # ---------------------------------- # Training data # Score on training data. # Note that this uses the 'natural' scoring for this sort of classifier, # which is accuracy_score from _classification.py, which is simply # the % that match. score = self.logistic_regression.score(self.X_train, self.y_train) self.log.info(f"Train: {score}") # AUC score prediction = self.logistic_regression.predict_proba(self.X_train)[:, 1] auc_score = roc_auc_score(self.y_train, prediction, multi_class='ovr') self.log.info(f"Train: {auc_score}") # ---------------------------------- # Test data score = self.logistic_regression.score(self.X_test, self.y_test) self.log.info(f"Test: {score}") # AUC score prediction = self.logistic_regression.predict_proba(self.X_test)[:, 1] auc_score = roc_auc_score(self.y_test, prediction, multi_class='ovr') self.log.info(f"Test: {auc_score}") def score_multi_class(self): """ Score, where the y_train / y_test is multiclass. """ x_shape = self.X_train.shape y_shape = self.y_train.shape if x_shape[0] != y_shape[0]: self.log.warning(f"Problem with shape of x/y {x_shape} {y_shape}") if len(y_shape) > 1: self.log.warning(f"Problem with shape of x/y {x_shape} {y_shape}") # ---------------------------------- # Training data # Score on training data. # Note that this uses the 'natural' scoring for this sort of classifier, # which is accuracy_score from _classification.py, which is simply # the % that match. score = self.logistic_regression.score(self.X_train, self.y_train) self.log.info(f"Train: {score}") # AUC score prediction = self.logistic_regression.predict_proba(self.X_train) auc_score = roc_auc_score(self.y_train, prediction, multi_class='ovr') self.log.info(f"Train: {auc_score}") # ---------------------------------- # Test data score = self.logistic_regression.score(self.X_test, self.y_test) self.log.info(f"Test: {score}") # AUC score prediction = self.logistic_regression.predict_proba(self.X_test) auc_score = roc_auc_score(self.y_test, prediction, multi_class='ovr') self.log.info(f"Test: {auc_score}")

36.979592

108

0.612859

494

3,624

4.275304

0.176113

0.064394

0.09375

0.045455

0.769886

0.768939

0.74053

0

0.006593

0.246689

3,624

97

109

37.360825

0.767033

0.263245

0

0.577778

0

0.122184

0

1

0.066667

false

0

0.066667

0

0.155556

0

null

0

1

0

null

0

5

2274abf6a22cee96952d2bf90ddaa9d8d4c86f7e

126

py

Python

odin/utilities/__init__.py

gsamarakoon/Odin

e2e9d638c68947d24f1260d35a3527dd84c2523f

[ "MIT" ]

103

2017-01-14T19:38:14.000Z

2022-03-10T12:52:09.000Z

odin/utilities/__init__.py

gsamarakoon/Odin

e2e9d638c68947d24f1260d35a3527dd84c2523f

[ "MIT" ]

6

2017-01-19T01:38:53.000Z

2020-03-09T19:03:18.000Z

odin/utilities/__init__.py

JamesBrofos/Odin

e2e9d638c68947d24f1260d35a3527dd84c2523f

[ "MIT" ]

33

2017-02-05T21:51:17.000Z

2021-12-22T20:38:30.000Z

from .odin_init import odin_init from .compute_days_elapsed import compute_days_elapsed from .fund_actions import period_dict

31.5

54

0.880952

20

126

5.15

0.55

0.15534

0.349515

0

0.095238

126

3

55

42

0.903509

0

1

0

true

0

1

0

1

0

1

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null

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1

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1

0

null

0

1

0

1

0

5

97e97c98e0747a68f726e7e83f4d6d9c5a7729a7

40

py

Python

classes/Loss/__init__.py

coopersigrist/DnDML

782b8908147fc9d90c6fb1dbb25a394ca4022b14

[ "MIT" ]

2

2021-05-31T22:44:50.000Z

2021-09-12T03:19:21.000Z

classes/Loss/__init__.py

coopersigrist/DnDML

782b8908147fc9d90c6fb1dbb25a394ca4022b14

[ "MIT" ]

null

classes/Loss/__init__.py

coopersigrist/DnDML

782b8908147fc9d90c6fb1dbb25a394ca4022b14

[ "MIT" ]

1

2021-07-22T12:54:47.000Z

from .wrapper import create_loss_wrapper

40

0.9

6

40

5.666667

0.833333

0

0.075

40

1

40

0.918919

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

97f19c3836dff71534af3b763817774ff1d5b3f6

151

py

Python

academy/www/index.py

frappe/academy

052090fd714542e35997eb3a1285e6ccd7cebeaa

[ "MIT" ]

3

2019-06-18T04:57:58.000Z

2020-03-24T09:56:05.000Z

academy/www/index.py

frappe/academy

052090fd714542e35997eb3a1285e6ccd7cebeaa

[ "MIT" ]

1

2019-06-22T14:38:16.000Z

academy/www/index.py

frappe/academy

052090fd714542e35997eb3a1285e6ccd7cebeaa

[ "MIT" ]

10

2019-12-04T07:47:34.000Z

2022-03-15T07:23:27.000Z

from __future__ import unicode_literals import frappe no_cache = 1 def get_context(context): context.academy = frappe.get_single("Academy Settings")

21.571429

56

0.81457

21

151

5.47619

0.714286

0.243478

0

0.007463

0.112583

151

7

56

21.571429

0.850746

0

0.105263

0

1

0.2

false

0

0.4

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0.6

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null

1

0

1

0

null

0

1

0

1

0

5