content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
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| token_count
int64 1
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|
|---|---|---|---|
import sys
import numpy as np
def safe_isinstance(obj, class_path_str):
# this function is copy-paste from the code of the SHAP Python library
# Copyright (c) 2018 Scott Lundberg
"""
Acts as a safe version of isinstance without having to explicitly
import packages which may not exist in the users environment.
Checks if obj is an instance of type specified by class_path_str.
Parameters
----------
obj: Any
Some object you want to test against
class_path_str: str or list
A string or list of strings specifying full class paths
Example: `sklearn.ensemble.RandomForestRegressor`
Returns
--------
bool: True if isinstance is true and the package exists, False otherwise
"""
if isinstance(class_path_str, str):
class_path_strs = [class_path_str]
elif isinstance(class_path_str, list) or isinstance(class_path_str, tuple):
class_path_strs = class_path_str
else:
class_path_strs = ['']
# try each module path in order
for class_path_str in class_path_strs:
if "." not in class_path_str:
raise ValueError("class_path_str must be a string or list of strings specifying a full \
module path to a class. Eg, 'sklearn.ensemble.RandomForestRegressor'")
# Splits on last occurence of "."
module_name, class_name = class_path_str.rsplit(".", 1)
# here we don't check further if the model is not imported, since we shouldn't have
# an object of that types passed to us if the model the type is from has never been
# imported. (and we don't want to import lots of new modules for no reason)
if module_name not in sys.modules:
continue
module = sys.modules[module_name]
#Get class
_class = getattr(module, class_name, None)
if _class is None:
continue
if isinstance(obj, _class):
return True
return False
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] | 2.696477 | 738 |
# this file imports custom routes into the experiment server
from flask import Blueprint, render_template, request, jsonify, Response, abort, current_app
from jinja2 import TemplateNotFound
from functools import wraps
from sqlalchemy import or_
from psiturk.psiturk_config import PsiturkConfig
from psiturk.experiment_errors import ExperimentError, InvalidUsage
from psiturk.user_utils import PsiTurkAuthorization, nocache
# # Database setup
from psiturk.db import db_session, init_db
from psiturk.models import Participant
from json import dumps, loads
# load the configuration options
config = PsiturkConfig()
config.load_config()
# if you want to add a password protect route use this
myauth = PsiTurkAuthorization(config)
# explore the Blueprint
custom_code = Blueprint('custom_code', __name__,
template_folder='templates', static_folder='static')
###########################################################
# serving warm, fresh, & sweet custom, user-provided routes
# add them here
###########################################################
# ----------------------------------------------
# example custom route
# ----------------------------------------------
@custom_code.route('/my_custom_view')
# ----------------------------------------------
# example using HTTP authentication
# ----------------------------------------------
@custom_code.route('/my_password_protected_route')
@myauth.requires_auth
# ----------------------------------------------
# example accessing data
# ----------------------------------------------
@custom_code.route('/view_data')
@myauth.requires_auth
# ----------------------------------------------
# example computing bonus
# ----------------------------------------------
@custom_code.route('/compute_bonus', methods=['GET'])
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] | 4.149083 | 436 |
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import cv2
import os
import time
import warnings
import torchvision.transforms as transforms
import torchvision.models as models
import copy
# content loss
# style loss
# gram matrix
# for normalizing the input image
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] | 3.238806 | 134 |
import pandas as pd
import numpy as np
from rdtools import energy_from_power
import pytest
@pytest.fixture
@pytest.fixture
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import copy
import unittest
from pathlib import Path
import ignite
import ignite.metrics as metrics
import torch.nn as nn
import torch.optim as optim
from ignite.metrics import Precision, Recall, MetricsLambda
from transfer_nlp.plugins.config import ExperimentConfig
from transfer_nlp.plugins.regularizers import L1
from .trainer_utils import *
PLUGINS = {
'CrossEntropyLoss': nn.CrossEntropyLoss,
'BCEWithLogitsLoss': nn.BCEWithLogitsLoss,
"Adam": optim.Adam,
"SGD": optim.SGD,
"AdaDelta": optim.Adadelta,
"AdaGrad": optim.Adagrad,
"SparseAdam": optim.SparseAdam,
"AdaMax": optim.Adamax,
"ASGD": optim.ASGD,
"LBFGS": optim.LBFGS,
"RMSPROP": optim.RMSprop,
"Rprop": optim.Rprop,
"ReduceLROnPlateau": optim.lr_scheduler.ReduceLROnPlateau,
"MultiStepLR": optim.lr_scheduler.MultiStepLR,
"ExponentialLR": optim.lr_scheduler.ExponentialLR,
"CosineAnnealingLR": optim.lr_scheduler.CosineAnnealingLR,
"LambdaLR": optim.lr_scheduler.LambdaLR,
"ReLU": nn.functional.relu,
"LeakyReLU": nn.functional.leaky_relu,
"Tanh": nn.functional.tanh,
"Softsign": nn.functional.softsign,
"Softshrink": nn.functional.softshrink,
"Softplus": nn.functional.softplus,
"Sigmoid": nn.Sigmoid,
"CELU": nn.CELU,
"SELU": nn.functional.selu,
"RReLU": nn.functional.rrelu,
"ReLU6": nn.functional.relu6,
"PReLU": nn.functional.prelu,
"LogSigmoid": nn.functional.logsigmoid,
"Hardtanh": nn.functional.hardtanh,
"Hardshrink": nn.functional.hardshrink,
"ELU": nn.functional.elu,
"Softmin": nn.functional.softmin,
"Softmax": nn.functional.softmax,
"LogSoftmax": nn.functional.log_softmax,
"GLU": nn.functional.glu,
"TanhShrink": nn.functional.tanhshrink,
"Accuracy": metrics.Accuracy,
}
for plugin_name, plugin in PLUGINS.items():
register_plugin(registrable=plugin, alias=plugin_name)
@register_plugin
EXPERIMENT = {
"my_dataset_splits": {
"_name": "TestDataset",
"data_file": Path(__file__).parent.resolve() / "sample_data.csv",
"batch_size": 128,
"vectorizer": {
"_name": "TestVectorizer",
"data_file": Path(__file__).parent.resolve() / "sample_data.csv"
}
},
"model": {
"_name": "TestModel",
"hidden_dim": 100,
"data": "$my_dataset_splits"
},
"optimizer": {
"_name": "Adam",
"lr": 0.01,
"params": {
"_name": "TrainableParameters",
"model": "$model"
}
},
"scheduler": {
"_name": "ReduceLROnPlateau",
"patience": 1,
"mode": "min",
"factor": 0.5,
"optimizer": "$optimizer"
},
"trainer": {
"_name": "SingleTaskTrainer",
"model": "$model",
"dataset_splits": "$my_dataset_splits",
"loss": {
"_name": "CrossEntropyLoss"
},
"optimizer": "$optimizer",
"gradient_clipping": 0.25,
"num_epochs": 5,
"seed": 1337,
"scheduler": "$scheduler",
"regularizer": {
"_name": "L1"
},
"metrics": {
"accuracy": {
"_name": "Accuracy"
},
"fbeta": {
"_name": "create_fbeta"
},
"loss": {
"_name": "LossMetric",
"loss_fn": {
"_name": "CrossEntropyLoss"
}
}
}
}
}
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] | 1.999429 | 1,752 |
#!/usr/bin/env python
# coding: utf-8
import sys
sys.path.append('../isoclique')
import isoclique as ic
import networkx as nx
import random
import time
if __name__ == '__main__':
E = nx.karate_club_graph().edges()
'''
encode_method=None
E = [[x,y] for x,y in E]
'''
'''
# generate a graph with node id offset.
# edge must be a list for irregular node IDs.
# (e.g. (x,y)->[x+5,y+5])
E = [[x+5,y+5] for x,y in E]
encode_method = 'array'
'''
#'''
# for string node labels
E = [[e1,e2] for e1,e2 in E]
for i in range(len(E)):
if E[i][0]==1:
E[i][0] = 'one'
elif E[i][1]==1:
E[i][1] = 'one'
encode_method = 'hash'
#'''
#print(E)
print("WITHOUT_SORT")
start = time.time()
print("start to constust AdjacencyList.")
sorted_graph = ic.AdjacencyList(E,do_sort=False,encode_method=encode_method)
elapsed_time = time.time()-start
print("%.5f sec. elapsed for graph sorting."%elapsed_time)
nodes = sorted_graph.nodes()
neighbors = sorted_graph.edges()
for v,neigh in zip(nodes,neighbors):
print(v,": ", neigh)
print("WITH_SORT")
start = time.time()
sorted_graph = ic.AdjacencyList(E,do_sort=True,encode_method=encode_method)
elapsed_time = time.time()-start
print("%.5f sec. elapsed for graph sorting."%elapsed_time)
nodes = sorted_graph.nodes()
neighbors = sorted_graph.edges()
for v,neigh in zip(nodes,neighbors):
print(v,": ", neigh)
Gs = sorted_graph.subgraph([4,5,6,7,8])
print(Gs.edges(edge_list_format='list'))
Gs.complement()
print(Gs.edges(edge_list_format='list'))
sys.exit()
| [
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] | 2.145704 | 803 |
"""
module to run with the -m flag
python -m async_sched.client.stop_schedule "Task 1" --list_schedules 1
"""
import argparse
from async_sched.client.client import stop_schedule
from async_sched.utils import DEFAULT_HOST, DEFAULT_PORT
__all__ = ['NAME', 'get_argparse', 'main']
NAME = 'stop_schedule'
if __name__ == '__main__':
P = get_argparse()
ARGS = P.parse_args()
KWARGS = {n: getattr(ARGS, n) for n in dir(ARGS) if not n.startswith('_') and getattr(ARGS, n, None) is not None}
main(**KWARGS)
| [
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7,
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11,
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8,
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299,
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7,
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8,
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299,
13,
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2032,
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62,
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7,
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198,
220,
220,
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1388,
7,
1174,
42,
16279,
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8,
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] | 2.536232 | 207 |
import os
if __name__ == "__main__":
main()
| [
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] | 2.380952 | 21 |
import numpy as np
# ========================================================
#
# Environment-specific cost functions:
#
# ========================================================
#
# Cost function for a whole trajectory:
#
| [
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] | 4.833333 | 48 |
from .. import *
import math
| [
6738,
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198
] | 3.625 | 8 |
from titan.dodo_pkg.layer import LayerConfig
| [
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] | 3.538462 | 13 |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""asgd"""
from mindspore.ops import functional as F, operations as P
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
import mindspore.common.dtype as mstype
import mindspore
from mindspore._checkparam import Validator as validator
from .optimizer import Optimizer
from .optimizer import opt_init_args_register
class ASGD(Optimizer):
r"""
Implements Average Stochastic Gradient Descent.
Introduction to ASGD can be found at `Acceleration of stochastic approximation by average
<http://dl.acm.org/citation.cfm?id=131098>`_.
The updating formulas are as follows:
.. math::
\begin{gather*}
w_{t} = w_{t-1} * (1 - \lambda * \eta_{t-1}) - \eta_{t-1} * g_{t} \\
ax_{t} = (w_t - ax_{t-1}) * \mu_{t-1} \\
\eta_{t} = \frac{1.}{(1 + \lambda * lr * t)^\alpha} \\
\mu_{t} = \frac{1}{\max(1, t - t0)}
\end{gather*}
:math:`\lambda` represents the decay term, :math:`\mu` and :math:`\eta` are tracked to
update :math:`ax` and :math:`w`, :math:`t0` represents the point of starting averaging,
:math:`\alpha` represents the power for eta update, :math:`ax` represents the averaged
parameter value, :math:`t` represents the current step, :math:`g` represents `gradients`,
:math:`w` represents `params`.
Note:
If parameters are not grouped, the `weight_decay` in optimizer will be applied on the parameters without 'beta'
or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When parameters
are grouped, each group can set `weight_decay`, if not, the `weight_decay` in optimizer will be applied.
Args:
params (Union[list[Parameter], list[dict]]): Must be list of `Parameter` or list of `dict`. When the
`parameters` is a list of `dict`, the "params", "lr", "weight_decay", "grad_centralization" and
"order_params" are the keys can be parsed.
- params: Required. Parameters in current group. The value must be a list of `Parameter`.
- lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used.
If not, the `learning_rate` in optimizer will be used. Fixed and dynamic learning rate are supported.
- weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay
will be used. If not, the `weight_decay` in the optimizer will be used.
- grad_centralization: Optional. Must be Boolean. If "grad_centralization" is in the keys, the set value
will be used. If not, the `grad_centralization` is False by default. This configuration only works on the
convolution layer.
- order_params: Optional. When parameters is grouped, this usually is used to maintain the order of
parameters that appeared in the network to improve performance. The value should be parameters whose
order will be followed in optimizer.
If `order_params` in the keys, other keys will be ignored and the element of 'order_params' must be in
one group of `params`.
learning_rate (Union[float, int, Tensor, Iterable, LearningRateSchedule]):
- float: The fixed learning rate value. Must be equal to or greater than 0.
- int: The fixed learning rate value. Must be equal to or greater than 0. It will be converted to float.
- Tensor: Its value should be a scalar or a 1-D vector. For scalar, fixed learning rate will be applied.
For vector, learning rate is dynamic, then the i-th step will take the i-th value as the learning rate.
- Iterable: Learning rate is dynamic. The i-th step will take the i-th value as the learning rate.
- LearningRateSchedule: Learning rate is dynamic. During training, the optimizer calls the instance of
LearningRateSchedule with step as the input to get the learning rate of current step.
lambd (float): The decay term. Default: 1e-4.
alpha (float): The power for eta update. Default: 0.75.
t0 (float): The point of starting averaging. Default: 1e6.
weight_decay (int, float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0.
Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`.
Outputs:
Tensor[bool], the value is True.
Raises:
TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule.
TypeError: If element of `parameters` is neither Parameter nor dict.
TypeError: If `lambd`, `alpha` or `t0` is not a float.
TypeError: If `weight_decay` is neither float nor int.
ValueError: If `weight_decay` is less than 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import nn, Model
>>>
>>> net = Net()
>>> #1) All parameters use the same learning rate and weight decay
>>> optim = nn.ASGD(params=net.trainable_params())
>>>
>>> #2) Use parameter groups and set different values
>>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
>>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params()))
>>> group_params = [{'params': conv_params,'grad_centralization':True},
... {'params': no_conv_params, 'lr': 0.01},
... {'order_params': net.trainable_params()}]
>>> optim = nn.ASGD(group_params, learning_rate=0.1, weight_decay=0.0)
>>> # The conv_params's parameters will use default learning rate of 0.1 default weight decay of 0.0 and grad
>>> # centralization of True.
>>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0 and grad
>>> # centralization of False.
>>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'.
>>>
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> model = Model(net, loss_fn=loss, optimizer=optim)
"""
@opt_init_args_register
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256,
15,
38165,
198,
220,
220,
220,
220,
220,
220,
220,
3467,
437,
90,
70,
1032,
9,
92,
628,
220,
220,
220,
1058,
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25,
63,
59,
50033,
63,
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262,
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3381,
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25,
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63,
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25,
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63,
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18283,
284,
198,
220,
220,
220,
4296,
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25,
63,
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25,
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86,
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25,
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32824,
11,
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4600,
6551,
62,
12501,
323,
63,
287,
6436,
7509,
481,
307,
5625,
319,
262,
10007,
1231,
705,
31361,
6,
198,
220,
220,
220,
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220,
220,
220,
393,
705,
28483,
2611,
6,
287,
511,
3891,
13,
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284,
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286,
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943,
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25,
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307,
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13,
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2494,
389,
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13,
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220,
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220,
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323,
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11,
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220,
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220,
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13,
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11,
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62,
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1634,
63,
318,
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416,
4277,
13,
770,
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691,
2499,
319,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3063,
2122,
7679,
13,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
1502,
62,
37266,
25,
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13,
1649,
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318,
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11,
428,
3221,
318,
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284,
5529,
262,
1502,
286,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10007,
326,
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287,
262,
3127,
284,
2987,
2854,
13,
383,
1988,
815,
307,
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3025,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1502,
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307,
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287,
6436,
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13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
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62,
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63,
287,
262,
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11,
584,
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307,
9514,
290,
262,
5002,
286,
705,
2875,
62,
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6,
1276,
307,
287,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
530,
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286,
4600,
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44646,
628,
220,
220,
220,
220,
220,
220,
220,
4673,
62,
4873,
357,
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58,
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11,
493,
11,
309,
22854,
11,
40806,
540,
11,
18252,
32184,
27054,
5950,
60,
2599,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
12178,
25,
383,
5969,
4673,
2494,
1988,
13,
12039,
307,
4961,
284,
393,
3744,
621,
657,
13,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
493,
25,
383,
5969,
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2494,
1988,
13,
12039,
307,
4961,
284,
393,
3744,
621,
657,
13,
632,
481,
307,
11513,
284,
12178,
13,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
309,
22854,
25,
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1988,
815,
307,
257,
16578,
283,
393,
257,
352,
12,
35,
15879,
13,
1114,
16578,
283,
11,
5969,
4673,
2494,
481,
307,
5625,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1114,
15879,
11,
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2494,
318,
8925,
11,
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262,
1312,
12,
400,
2239,
481,
1011,
262,
1312,
12,
400,
1988,
355,
262,
4673,
2494,
13,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
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25,
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318,
8925,
13,
383,
1312,
12,
400,
2239,
481,
1011,
262,
1312,
12,
400,
1988,
355,
262,
4673,
2494,
13,
628,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
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32184,
27054,
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25,
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2494,
318,
8925,
13,
5856,
3047,
11,
262,
6436,
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3848,
262,
4554,
286,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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27054,
5950,
351,
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355,
262,
5128,
284,
651,
262,
4673,
2494,
286,
1459,
2239,
13,
628,
220,
220,
220,
220,
220,
220,
220,
19343,
67,
357,
22468,
2599,
383,
22119,
3381,
13,
15161,
25,
352,
68,
12,
19,
13,
198,
220,
220,
220,
220,
220,
220,
220,
17130,
357,
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2599,
383,
1176,
329,
2123,
64,
4296,
13,
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25,
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13,
2425,
13,
198,
220,
220,
220,
220,
220,
220,
220,
256,
15,
357,
22468,
2599,
383,
966,
286,
3599,
20430,
13,
15161,
25,
352,
68,
21,
13,
198,
220,
220,
220,
220,
220,
220,
220,
3463,
62,
12501,
323,
357,
600,
11,
12178,
2599,
14331,
22119,
357,
43,
17,
7389,
737,
632,
1276,
307,
4961,
284,
393,
3744,
621,
657,
13,
15161,
25,
657,
13,
15,
13,
628,
220,
220,
220,
23412,
82,
25,
198,
220,
220,
220,
220,
220,
220,
220,
532,
12429,
9744,
2334,
1174,
357,
83,
29291,
58,
51,
22854,
12962,
532,
383,
3915,
2334,
286,
4600,
37266,
47671,
262,
5485,
318,
262,
976,
355,
4600,
37266,
44646,
628,
220,
220,
220,
25235,
82,
25,
198,
220,
220,
220,
220,
220,
220,
220,
309,
22854,
58,
30388,
4357,
262,
1988,
318,
6407,
13,
628,
220,
220,
220,
7567,
2696,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5994,
12331,
25,
1002,
4600,
40684,
62,
4873,
63,
318,
407,
530,
286,
493,
11,
12178,
11,
309,
22854,
11,
40806,
540,
11,
18252,
32184,
27054,
5950,
13,
198,
220,
220,
220,
220,
220,
220,
220,
5994,
12331,
25,
1002,
5002,
286,
4600,
17143,
7307,
63,
318,
6159,
25139,
2357,
4249,
8633,
13,
198,
220,
220,
220,
220,
220,
220,
220,
5994,
12331,
25,
1002,
4600,
2543,
17457,
47671,
4600,
26591,
63,
393,
4600,
83,
15,
63,
318,
407,
257,
12178,
13,
198,
220,
220,
220,
220,
220,
220,
220,
5994,
12331,
25,
1002,
4600,
6551,
62,
12501,
323,
63,
318,
6159,
12178,
4249,
493,
13,
198,
220,
220,
220,
220,
220,
220,
220,
11052,
12331,
25,
1002,
4600,
6551,
62,
12501,
323,
63,
318,
1342,
621,
657,
13,
628,
220,
220,
220,
36848,
19193,
82,
25,
198,
220,
220,
220,
220,
220,
220,
220,
7559,
32,
1416,
437,
15506,
7559,
33346,
15506,
7559,
36037,
15506,
628,
220,
220,
220,
21066,
25,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
422,
2000,
2777,
382,
1330,
299,
77,
11,
9104,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
2010,
796,
3433,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1303,
16,
8,
1439,
10007,
779,
262,
976,
4673,
2494,
290,
3463,
22119,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
6436,
796,
299,
77,
13,
1921,
45113,
7,
37266,
28,
3262,
13,
27432,
540,
62,
37266,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
1303,
17,
8,
5765,
11507,
2628,
290,
900,
1180,
3815,
198,
220,
220,
220,
220,
220,
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28,
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13,
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220,
220,
220,
220,
220,
220,
220,
13163,
2994,
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299,
77,
13,
18380,
9806,
21544,
14539,
28338,
3152,
11187,
896,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
2746,
796,
9104,
7,
3262,
11,
2994,
62,
22184,
28,
22462,
11,
6436,
7509,
28,
40085,
8,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
2488,
8738,
62,
15003,
62,
22046,
62,
30238,
198
] | 2.801037 | 2,508 |
import multiprocessing
print("cpu count is", multiprocessing.cpu_count())
| [
11748,
18540,
305,
919,
278,
198,
4798,
7203,
36166,
954,
318,
1600,
18540,
305,
919,
278,
13,
36166,
62,
9127,
28955,
198
] | 3.363636 | 22 |
from abc import ABCMeta, abstractmethod
| [
6738,
450,
66,
1330,
9738,
48526,
11,
12531,
24396,
628
] | 4.1 | 10 |
import json
import lzma
import struct
import logging
from typing import Tuple, Iterator, List
from lzma import LZMAError, LZMADecompressor
from toolkit.uf2.uf2 import UF2
log = logging.getLogger(__name__)
| [
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] | 2.888889 | 72 |
#!/usr/bin/env python3
import unittest
from collections import OrderedDict
from thrift.test.sample_structs.ttypes import DummyStruct, Struct
from thrift.util import struct_to_dict
| [
2,
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import os
from abc import abstractmethod
from collections.abc import Sequence
from ..options_merger import OptionsMerger
| [
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] | 4.241379 | 29 |
# Copyright © 2021 Ingram Micro Inc. All rights reserved.
import sys
from dj_cqrs.constants import NO_QUEUE
from dj_cqrs.management.commands.cqrs_sync import Command as SyncCommand
from dj_cqrs.registries import MasterRegistry
from django.core.management.base import BaseCommand, CommandError
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] | 3.348315 | 89 |
import torch
from .misc import config_to_string
def _load_pretraining_dict(model, state_dict):
"""Load state dictionary from a pre-training snapshot
This is an even less strict version of `model.load_state_dict(..., False)`, which also ignores parameters from
`state_dict` that don't have the same shapes as the corresponding ones in `model`. This is useful when loading
from pre-trained models that are trained on different datasets.
Parameters
----------
model : torch.nn.Model
Target model
state_dict : dict
Dictionary of model parameters
"""
model_sd = model.state_dict()
for k, v in model_sd.items():
if k in state_dict:
if v.shape != state_dict[k].shape:
del state_dict[k]
model.load_state_dict(state_dict, False)
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25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1619,
1181,
62,
11600,
58,
74,
60,
628,
220,
220,
220,
2746,
13,
2220,
62,
5219,
62,
11600,
7,
5219,
62,
11600,
11,
10352,
8,
198
] | 2.852234 | 291 |
# import tensorflow as tf
# def get_pretrained_classfier(path = '/home/jupyter-ivanljh123/Simulating-Brain-signal-to-control-Hand-Movement-using-GPT2/pretrained/09_0.92'):
# #load pretrained model
# model = tf.keras.models.load_model(path)
# model.trainable = False
# return model
import mne
import numpy as np
import os
import os.path as op
import matplotlib.pyplot as plt
import nibabel as nib
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse_epochs
from mne.datasets import fetch_fsaverage
import scipy.io
from scipy.io import loadmat
from scipy.spatial import Delaunay
import PIL
from PIL import Image
import datetime
import tensorflow as tf
import tfplot
from tensorflow.python.ops.numpy_ops import np_config
np_config.enable_numpy_behavior()
from tensorflow import keras
from keras.preprocessing.image import ImageDataGenerator
from keras import Sequential
from keras.layers import Conv2D, MaxPool2D, GlobalAveragePooling2D, Dense, Flatten, Concatenate, BatchNormalization, Dropout, Input
from keras.layers.merge import concatenate
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.model_selection import StratifiedKFold
import gc
DIRECTORY_PATH = os.getcwd()
@tfplot.autowrap() | [
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13,
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1330,
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198,
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13,
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1330,
9922,
62,
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11,
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62,
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11,
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62,
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11,
277,
16,
62,
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198,
6738,
1341,
35720,
13,
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62,
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1330,
29186,
1431,
42,
37,
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198,
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308,
66,
198,
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17931,
23988,
15513,
62,
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13,
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66,
16993,
3419,
198,
198,
31,
27110,
29487,
13,
2306,
322,
2416,
3419
] | 2.930586 | 461 |
import random
from selenium.webdriver.common.by import By
from ui_test.page.base_page import BasePage
| [
11748,
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] | 3.21875 | 32 |
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE_fast_rcnn for details]
# Written by Ross Girshick
# --------------------------------------------------------
import numpy as np
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
cmdclass = {}
ext_modules = [
Extension(
"cython_bbox",
["bbox.pyx"],
extra_compile_args=["-Wno-cpp", "-Wno-unused-function"],
)
]
cmdclass.update({'build_ext': build_ext})
setup(
name='fast_rcnn',
cmdclass=cmdclass,
ext_modules=ext_modules,
include_dirs=[np.get_include()]
)
| [
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] | 2.979253 | 241 |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
from subprocess import check_output
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn import model_selection
print(check_output(["ls", "../input"]).decode("utf8"))
from sklearn.linear_model import LinearRegression
# Any results you write to the current directory are saved as output.
# **Loading dataset into pandas dataframe**
# In[ ]:
shanghai = pd.read_csv('../input/shanghaiData.csv')
# Checking for datatypes of columns
# In[ ]:
print(shanghai.dtypes)
# **Splitting dataset into Training data and Test data**
# In[ ]:
X = shanghai[['alumni','award','hici','ns','pub','pcp']]
y = shanghai.total_score
X = X.fillna(method='ffill')
y = y.fillna(method='ffill')
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.25)
# * **Applying Linear Regression and fitting data on a straing line.**
# * Predicting the test data.
# In[ ]:
reg = LinearRegression()
reg.fit(X_train,y_train)
y_pred = reg.predict(X_test)
# **Printing the score and other variables on the straight line**
# In[ ]:
print('Scoring =',reg.score(X_train,y_train))
print('Coefficient :',reg.coef_)
print('Intercept :',reg.intercept_)
# **Printing scores of metrics**
# In[ ]:
print('Root Mean Square Error =',np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
print('Mean Absolute Error =',metrics.mean_absolute_error(y_test, y_pred))
print('Mean Square Error =',metrics.mean_squared_error(y_test, y_pred))
print('R^2 =',metrics.r2_score(y_test, y_pred))
# **Printing scores of metrics using K fold cross validation**
# In[ ]:
kfold = model_selection.KFold(n_splits=10, random_state=7)
scoring = 'neg_mean_absolute_error'
results = cross_val_score(reg, X, y, cv=kfold, scoring=scoring)
print("Mean Absolute Error: %.3f (%.3f)" % (results.mean(), results.std()))
# In[ ]:
scoring = 'neg_mean_squared_error'
results = model_selection.cross_val_score(reg, X, y, cv=kfold, scoring=scoring)
print("Mean Squared Error: %.3f (%.3f)" % (results.mean(), results.std()))
# In[ ]:
scoring = 'r2'
results = model_selection.cross_val_score(reg, X, y, cv=kfold, scoring=scoring)
print("R^2: %.3f (%.3f)" % (results.mean(), results.std()))
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] | 2.896928 | 1,009 |
import logging
import pathlib
import pkg_resources
from mopidy import config, ext
__version__ = pkg_resources.get_distribution("Mopidy-YTMusic").version
logger = logging.getLogger(__name__)
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from loaddata import TitanicData, HousePriceData, CervicalCancerData
from models import Classifier, Regressor
import json
import os
import argparse
if __name__ == '__main__':
# arguments
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, choices=['LR','DT','RF','XGB','DNN'],
help='LR: Logistic Regression or Linear Regression / DT: Decision Tree / RF: Random Forest / XGB: XGBoost / DNN: Deep Neural Network')
parser.add_argument('--dataset', type=str, choices=['titanic','house_price','bike_sharing','cervical_cancer','youtube_spam'],
help='dataset category')
parser.add_argument('--file_path',type=str, default='../dataset', help='dataset directory')
parser.add_argument('--save_path',type=str, default='../saved_models',help='save model directory')
parser.add_argument('--params_path',type=str, default='../params', help='model parameters directory')
args = parser.parse_args()
# file path
file_path = os.path.join(args.file_path, args.dataset)
# save path and name
save_path = os.path.join(args.save_path, f'{args.dataset}_{args.model}')
# load parameters
params_path = os.path.join(args.params_path, f'{args.model}_params.json')
params = json.load(open(params_path, 'r'))
# model training by dataset
model_train(file_path, args.dataset, args.model, save_path, **params)
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] | 2.637523 | 549 |
"Main"
import sys
from detection.detection import capture_video, run_detection
if __name__ == '__main__':
VIDEO = sys.argv[1]
XML = sys.argv[2]
CAP, CAR_CASCADE = capture_video(VIDEO, XML)
run_detection(CAP, CAR_CASCADE)
| [
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] | 2.438776 | 98 |
'''pyCis - Python Console Intelligence System'''
| [
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import os
import argparse
from pydub import AudioSegment
from pydub.utils import make_chunks
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="script to split audio files into chunks")
parser.add_argument('--seconds', type=int, default=None,
help='if set to None, then will record forever until keyboard interrupt')
parser.add_argument('--audio_file_name', type=str, default=None, required=True,
help='name of audio file')
parser.add_argument('--save_path', type=str, default=None, required=True,
help='full path to to save data. i.e. /to/path/saved_clips/')
args = parser.parse_args()
main(args)
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] | 2.584229 | 279 |
import abc
from sqlparse.sql import Token
from ..exceptions import SQLDecodeError
from .sql_tokens import AliasableToken, SQLToken
from . import query as query_module
from typing import Union as U
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] | 3.45 | 60 |
import time
from print_running_function import print_running_function
# Hackish method to import from another directory
# Useful while xendit-python isn't released yet to the public
import importlib.machinery
loader = importlib.machinery.SourceFileLoader("xendit", "../xendit/__init__.py")
xendit = loader.load_module("xendit")
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] | 3 | 117 |
##############################################################################
#
# Copyright (c) 2007 Zope Foundation and Contributors.
#
# This software is subject to the provisions of the Zope Public License,
# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.
# THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED
# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS
# FOR A PARTICULAR PURPOSE.
#
##############################################################################
"""ZopeLite layer
"""
from Testing.ZopeTestCase import utils
_deferred_setup = []
class ZopeLite(object):
'''The most base layer'''
@classmethod
def setUp(cls):
'''Brings up the ZopeLite environment.'''
for func, args, kw in _deferred_setup:
func(*args, **kw)
@classmethod
def tearDown(cls):
'''ZopeLite doesn't support tear down.
We don't raise NotImplementedError to avoid
triggering the testrunner's "resume layer"
mechanism.
See zope.testrunner-layers-ntd.txt
'''
ZopeLiteLayer = ZopeLite
def onsetup(func):
'''Defers a function call to layer setup.
Used as a decorator.
'''
return deferred_func
def appcall(func):
'''Defers a function call to layer setup.
Used as a decorator.
In addition, this decorator implements the appcall
protocol:
* The decorated function expects 'app' as first argument.
* If 'app' is provided by the caller, the function is
called immediately.
* If 'app' is omitted or None, the 'app' argument is
provided by the decorator, and the function call is
deferred to ZopeLite layer setup.
Also see utils.appcall.
'''
return appcalled_func
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62,
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] | 2.84101 | 673 |
# Copyright 2020 Google LLC
#
# 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.
# This file implements a recursive subset diff between two json/dict objects.
import sys
import json
def sdiff(actual, expected, path):
""" Performs a subset diff between two python dicts.
Actual is said to be a subset of expected iff:
1. Every key in Actual exists in expected, and its value is a subset
of the value in expected.
2. For base values (numbers, strings, etc), the values have to be equal.
3. For lists, the two lists must be equal in length, and every element in
actual must be a subset of the corresponding element in expected.
4. None values in expected are allowed to have a matching default value in
actual (e.g. "", False, 0, []).
Args:
actual: the subset dict.
expected: the super set dict.
path: a string specifying the current path in the recursive sdiff.
Returns:
A tuple (status: bool, path: str), where status is True if actual
is indeed a subset of expected, or False otherwise, and path is
the empty string if actual is a subset, or the path of an element
where actual disagreed with expected.
When the path looks like foo/bar it means that
"actual[foo][bar]" is an offending element that fails to meet
the subset criteria.
"""
try:
if expected is None and not actual: # equate default values to None
return (True, "")
if type(actual) != type(expected):
return (False, path)
if isinstance(actual, list):
if len(actual) != len(expected):
return (False, path)
for i in range(len(actual)):
status, rpath = sdiff(actual[i], expected[i], "%s/%d" % (path, i))
if not status:
return (status, rpath)
return (True, "")
if isinstance(actual, dict):
for k in actual.keys():
status, rpath = sdiff(actual[k],
expected.get(k, None),
"%s/%s" % (path, str(k)))
if not status:
return (status, rpath)
return (True, "")
return (actual == expected, path)
except Exception as exp:
return (False, "%s EXCEPTION %s" % (path, str(exp)))
if __name__ == "__main__":
expectedFilePath = sys.argv[1]
with open(expectedFilePath) as expectedFile:
expected = json.load(expectedFile)
actualFilePath = sys.argv[2]
with open(actualFilePath) as actualFile:
actual = json.load(actualFile)
status, path = sdiff(actual, expected, "")
if not status:
sys.exit("not subset diff! Error at path \"%s\"" % path)
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220,
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0,
13047,
379,
3108,
19990,
4,
82,
7879,
1,
4064,
3108,
8,
198
] | 2.959577 | 1,039 |
# Array
# Given an array A of integers, return true if and only if it is a valid mountain array.
#
# Recall that A is a mountain array if and only if:
#
# A.length >= 3
# There exists some i with 0 < i < A.length - 1 such that:
# A[0] < A[1] < ... A[i-1] < A[i]
# A[i] > A[i+1] > ... > A[B.length - 1]
#
#
# Example 1:
#
# Input: [2,1]
# Output: false
# Example 2:
#
# Input: [3,5,5]
# Output: false
# Example 3:
#
# Input: [0,3,2,1]
# Output: true
#
#
# Note:
#
# 0 <= A.length <= 10000
# 0 <= A[i] <= 10000
| [
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198,
2,
657,
19841,
317,
58,
72,
60,
19841,
33028,
198
] | 2.318182 | 220 |
from uuid import UUID
from ..exceptions import CheckError
| [
6738,
334,
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1330,
471,
27586,
198,
198,
6738,
11485,
1069,
11755,
1330,
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628,
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] | 3.5 | 18 |
import numpy as np
import librosa, librosa.display
import matplotlib.pyplot as plt
FIG_SIZE = (15,10)
file = "/home/jack/Documents/python/PycharmProjects/untitled2/show the wav/dog.wav"
signal, sample_rate = librosa.load(file, sr=22050)
plt.figure(figsize=FIG_SIZE)
librosa.display.waveplot(signal, sample_rate, alpha=0.4)
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.title("Waveform")
# MFCCs
hop_length = 512 # shifting
n_fft = 2048 # window in num
MFCCs = librosa.feature.mfcc(signal, sample_rate, n_fft=n_fft, hop_length=hop_length, n_mfcc=13)
plt.figure(figsize=FIG_SIZE)
librosa.display.specshow(MFCCs, sr=sample_rate, hop_length=hop_length)
plt.xlabel("Time")
plt.ylabel("MFCC coefficients")
plt.colorbar()
plt.title("MFCCs")
plt.show()
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import os
import functools
import tools
import tensorflow as tf
import numpy as np
import pickle
import pathlib
import off_policy
from dreamer import Dreamer, setup_dreamer, create_envs, count_steps, make_dataset, parse_dreamer_args
if __name__ == '__main__':
args, remaining = parse_dreamer_args()
main(args.logdir, remaining)
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from notebook_utils import synthesize
from utils import hparams as hp
import utils.text.cleaners as cleaners
from scipy.io import wavfile
import numpy as np
import string
import argparse
SAMPLE_RATE = 22050
parser = argparse.ArgumentParser()
parser.add_argument(
'-t','--text', help='Input text',
default='Đó là lần tôi ở Đà Lạt non một tháng làm nhiệm vụ nghiên cứu chính sách đất đai cho đồng bào dân tộc thiểu số Tây Nguyên cuối năm 2004'
)
parser.add_argument('-o', '--output', help='Directory to save output wave files', default='model_outputs')
args = parser.parse_args()
forward_model_path = 'pretrained/forward_300K.pyt'
voc_model_path = 'pretrained/model_loss0.028723_step860000_weights.pyt'
print('*** Configure hparams...')
synthesize.init_hparams('hparams.py')
print('*** Loading forward model')
forward_model = synthesize.get_forward_model(forward_model_path)
print('*** Loading VOC model')
voc_model = synthesize.get_wavernn_model(voc_model_path)
text = args.text
# Using WaveRNN vocoder
wav = synthesize.synthesize(text, forward_model, voc_model)
# Using Griffin-Lim vocoder
gl_wav = synthesize.synthesize(text, forward_model, 'griffinlim')
# Write the audio using WaveRNN vocoder
wavfile.write('{}/wavernn.wav'.format(args.output), SAMPLE_RATE, wav)
# Write the audio using Griffin-Lim vocoder
wavfile.write('{}/griffinlim.wav'.format(args.output), SAMPLE_RATE, gl_wav) | [
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68,
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66,
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1055,
952,
30,
1665,
8591,
1126,
40833,
30,
198,
73,
1228,
1228,
1228,
27792,
198
] | 3.089201 | 31,502 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
628
] | 3.888889 | 9 |
import numpy as np
import sys
| [
198,
11748,
299,
32152,
355,
45941,
198,
11748,
25064,
628
] | 3.2 | 10 |
# Copyright © 2018, D-Tech, LLC, All Rights Reserved.
# Disclaimer: This software is provided "AS IS" without warrantees.
# D-Tech, LLC has no oblication to provide any maintainence, update
# or support for this software. Under no circumstances shall D-Tech,
# LLC be liable to any parties for direct, indirect, special, incidential,
# or consequential damages, arising out of the use of this software
# and related data and documentation.
#
import cpecat.settings as settings
from pymongo import MongoClient
client = MongoClient(
settings.DATABASES['mongodb']["HOST"],
settings.DATABASES['mongodb']["PORT"]
)
db = client[settings.DATABASES['mongodb']['DATABASE']]
if settings.DATABASES['mongodb']["USERNAME"]:
db.authenticate(
settings.DATABASES['mongodb']["USERNAME"],
settings.DATABASES['mongodb']["PASSWORD"]
)
| [
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1,
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8973,
201,
198,
220,
220,
220,
1267,
201,
198,
201,
198,
201,
198
] | 2.812903 | 310 |
import wmi
_wmi_handle = None | [
11748,
266,
11632,
198,
198,
62,
86,
11632,
62,
28144,
796,
6045
] | 2.5 | 12 |
import datetime
import os
import uuid
import copy
import json
import shutil
import threading
import time
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Union, Optional, Tuple
from ludwig.api import LudwigModel
from ludwig.backend import RAY, initialize_backend
from ludwig.callbacks import Callback
from ludwig.constants import *
from ludwig.hyperopt.results import TrialResults, HyperoptResults, RayTuneResults
from ludwig.hyperopt.sampling import HyperoptSampler, RayTuneSampler, logger
from ludwig.hyperopt.utils import load_json_values
from ludwig.modules.metric_modules import get_best_function
from ludwig.utils.data_utils import NumpyEncoder
from ludwig.utils.defaults import default_random_seed
from ludwig.utils.misc_utils import get_from_registry, hash_dict
from ludwig.utils.fs_utils import has_remote_protocol
try:
import ray
from ray.util.queue import Queue as RayQueue
from ray import tune
from ray.tune import register_trainable
from ray.tune.suggest import BasicVariantGenerator, ConcurrencyLimiter
from ray.tune.syncer import get_cloud_sync_client
from ray.tune.utils import wait_for_gpu
from ray.tune.utils.placement_groups import PlacementGroupFactory
from ray.tune.sync_client import CommandBasedClient
from ludwig.backend.ray import RayBackend
except ImportError:
ray = None
get_horovod_kwargs = None
# TODO: refactor this into an interface
executor_registry = {
"serial": SerialExecutor,
"ray": RayTuneExecutor
}
def _run_experiment_unary(kwargs):
"""Unary function is needed by Fiber to map a list of args."""
return run_experiment(**kwargs)
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] | 3.103512 | 541 |
import sys
import cv2
import numpy as np
if __name__=='__main__':
# Create SIFT object
sift = cv2.xfeatures2d.SIFT_create()
# Create Flann Matcher Object
flann = cv2.FlannBasedMatcher(dict(algorithm = 0, trees = 5), dict(checks = 50))
# Gets list of image files from arguments
files = sys.argv[1:]
# Read all the images
images = []
for file in files:
image = cv2.imread(file, 1)
# image = cv2.resize(image, (0,0), None, 0.25, 0.25)
images.append(image)
# Initially, the panorama consists of the first image...
current = images[0]
# ... then, other images are progressively added.
for i in range(1,len(images)):
# Extract keypoints in the current panorama using SIFT
current_keypoints, current_descriptors = sift.detectAndCompute(current, None)
# Extract keypoints in the next image to be added using SIFT
next_keypoints, next_descriptors = sift.detectAndCompute(images[i], None)
# Finds matches between the current panorama and the next image using the
# FLANN Matcher
matches = flann.knnMatch(current_descriptors, next_descriptors, k=2)
# Filter out bad matches using Lowe's ratio test
good_matches = []
for m1,m2 in matches:
if m1.distance < 0.7*m2.distance:
good_matches.append(m1)
# Generates points to be used on finding the homography
current_points = np.float32([current_keypoints[good_match.queryIdx].pt for \
good_match in good_matches]).reshape(-1,1,2)
next_points = np.float32([next_keypoints[good_match.trainIdx].pt for \
good_match in good_matches]).reshape(-1,1,2)
# Find homography between the two images
M, mask = cv2.findHomography(next_points,current_points, cv2.RANSAC, 5.0)
# Finds the translation needed to apply over the orignal image using the
# homography matrix
rows_current, cols_current = current.shape[:2]
rows_next, cols_next = images[i].shape[:2]
list_of_points_current = np.float32([[0,0], [0,rows_current], \
[cols_current,rows_current],\
[cols_current,0]]).reshape(-1,1,2)
temp_points = np.float32([[0,0], [0,rows_next], [cols_next,rows_next], \
[cols_next,0]]).reshape(-1,1,2)
list_of_points_next = cv2.perspectiveTransform(temp_points, M)
list_of_points = np.concatenate((list_of_points_current, list_of_points_next), axis=0)
[x_min, y_min] = np.int32(list_of_points.min(axis=0).ravel() - 0.5)
[x_max, y_max] = np.int32(list_of_points.max(axis=0).ravel() + 0.5)
translation_dist = [-x_min, -y_min]
M_translation = np.array([[1, 0, translation_dist[0]], \
[0, 1, translation_dist[1]], [0,0,1]])
# Adds perspective to the next image
result = cv2.warpPerspective(images[i], M_translation.dot(M), \
(x_max-x_min, y_max-y_min))
# Place original image over the next image with perspective applied
for i in range(translation_dist[1],rows_current+translation_dist[1]):
for j in range(translation_dist[0],cols_current+translation_dist[0]):
if all(current[i-translation_dist[1],j-translation_dist[0]] != [0,0,0]):
result[i,j] = current[i-translation_dist[1],j-translation_dist[0]]
current = result
# When no more images are left to add...
# ... removes black background
current = cv2.cvtColor(current, cv2.COLOR_RGB2RGBA)
current[np.all(current == [0, 0, 0, 255], axis=2)] = [0, 0, 0, 0]
# ... export the final result
cv2.imwrite("panorama.png", current) | [
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11,
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11,
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60,
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220,
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220,
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269,
85,
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13,
320,
13564,
7203,
6839,
36161,
13,
11134,
1600,
1459,
8
] | 2.164719 | 1,797 |
# Copyright (c) 2017-2018 Wind River Systems, 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.
from django.conf.urls import include, url
from newton.registration.views import registration
from newton_base.openoapi import tenants
from newton.resource.views import capacity
from newton.resource.views import events
urlpatterns = [
url(r'^', include('newton.swagger.urls')),
url(r'^', include('newton.samples.urls')),
url(r'^api/multicloud-newton/v0/(?P<vimid>[0-9a-zA-Z_-]+)/registry$',
registration.Registry.as_view()),
url(r'^api/multicloud-newton/v0/(?P<vimid>[0-9a-zA-Z_-]+)$',
registration.Registry.as_view()),
url(r'^api/multicloud-newton/v0/(?P<vimid>[0-9a-zA-Z_-]+)/exten',
include('newton.extensions.urls')),
url(r'^api/multicloud-newton/v0/(?P<vimid>[0-9a-zA-Z_-]+)/',
include('newton.proxy.urls')),
url(r'^api/multicloud-newton/v0/(?P<vimid>[0-9a-zA-Z_-]+)/tenants$',
tenants.Tenants.as_view()),
url(r'^api/multicloud-newton/v0/(?P<vimid>[0-9a-zA-Z_-]+)/'
'(?P<tenantid>[0-9a-zA-Z_-]{20,})/', include('newton.requests.urls')),
# CapacityCheck
url(r'^api/multicloud-newton/v0/(?P<vimid>[0-9a-zA-Z_-]+)/capacity_check/?$',
capacity.CapacityCheck.as_view()),
# events
url(r'^api/multicloud-newton/v0/(?P<vimid>[0-9a-zA-Z_-]+)/events_check/?$',
events.EventsCheck.as_view()),
]
| [
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13,
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62,
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220,
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220,
220,
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7,
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6,
61,
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75,
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12,
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14,
85,
15,
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30,
47,
27,
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312,
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15,
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24,
64,
12,
89,
32,
12,
57,
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31534,
62,
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20924,
3,
3256,
198,
220,
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220,
220,
220,
220,
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2995,
13,
37103,
9787,
13,
292,
62,
1177,
3419,
828,
198,
60,
628,
198
] | 2.342298 | 818 |
import argparse
import os
import textwrap
import deployment_options
import utils
if __name__ == "__main__":
main()
| [
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] | 3.04878 | 41 |
# SPDX-FileCopyrightText: 2022 Geoffrey Lentner
# SPDX-License-Identifier: Apache-2.0
"""Automated tests for HyperShell."""
| [
2,
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] | 3.205128 | 39 |
import logging
from datetime import timedelta, date
import progressbar
import structlog
from collections import defaultdict
from django.core.management.base import BaseCommand
from django.utils import timezone, dateparse
from ...actions.accounts import create_invoices
from ...models import Account
logger = structlog.get_logger()
| [
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] | 3.885057 | 87 |
from collections import namedtuple
import concurrent
import io
import json
import os
import pathlib
import pickle
import tarfile
import boto3
import imageio
import numpy as np
import utils
ClassInfo = namedtuple('ClassInfo', ['wnid', 'cid', 'synset', 'wikipedia_pages', 'gloss'])
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] | 3.298851 | 87 |
""" Contains the Encoder-Decoder model for image captioning """
import torch.nn as nn
from cnn import CNN
from rnn import RNN
from transformer import Transformer
class CaptioningModel(nn.Module):
""" Image captioning model """
def forward(self, images, captions, predict=False):
""" Forward pass of the image captioning model """
features = self.cnn(images)
captions = self.decoder(features, captions, predict)
return captions
| [
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] | 3.105263 | 152 |
from pyspark import SparkContext
import json
import gzip
import marshal
''' Configuration Parameters '''
tweetsFile = "/tmp/ist169518/out_*/part*"
outFileProcessedDataset = "/tmp/ist169518/filtered-scored"
# Emotional Lexicons
# csv files with first column with the word, and second with the score separated with commas
word_list_info = [
{'key': 'en.hedo' , 'path': '/afs/.ist.utl.pt/users/1/8/ist169518/lexicons/es_anew_all.csv' , 'dimensions' : [{'name':'happiness', 'index':'1'}]},
{'key': 'es.anew' , 'path': '/afs/.ist.utl.pt/users/1/8/ist169518/lexicons/es_anew_all.csv', 'dimensions' : [{'name':'valence', 'index':1}, {'name':'arousal', 'index':2}, {'name':'dominance', 'index':3}]}
]
#The set of considered filters appliable to the lexicons. Note that every enty on this set must have an associated method whose name starts by "score_filter_", e.g., def score_filter_MYFILTER: ...
filters = ['more_than_7', 'delta_one_of_5', 'no_filter']
'''Auxiliar Functions Definiton'''
# functions that filters the words in the list accordingly to some parameter
#####################################
### DO NOT MODIFY BELOW THIS LINE ###
#####################################
states = ["alabama","arizona","arkansas","california","colorado","connecticut","delaware","florida","georgia","idaho","illinois","indiana","iowa","kansas","kentucky","louisiana","maine","maryland","massachusetts","michigan","minnesota","mississippi","missouri","montana","nebraska","nevada","new hampshire","new jersey","new mexico","new york","north carolina","north dakota","ohio","oklahoma","oregon","pennsylvania","rhode island","south carolina","south dakota","tennessee","texas","utah","vermont","virginia","washington","west virginia","wisconsin","wyoming"]
'''Loading Emotional Lexicons'''
word_lists = dict()
for filter_name in filters:
word_lists[filter_name] = list()
for info in word_list_info:
temporary_word_lists = dict()
for filter_name in filters:
temporary_word_lists[filter_name] = dict()
temporary_word_lists[filter_name] = dict()
for dimension in info['dimensions']:
temporary_word_lists[filter_name][dimension['name']] = dict()
with open(info['path'], "r") as fp:
for line in fp:
row = line.split(',')
word = row[0].lower().decode('utf-8')
for dimension in info['dimensions']:
score = float(row[int(dimension['index'])])
for filter_name in filters:
if globals()["score_filter_" + filter_name](score):
temporary_word_lists[filter_name][dimension['name']][word] = score
for filter_name in filters:
word_lists[filter_name].append({'key': info['key'], 'dimensions': temporary_word_lists[filter_name]})
'''Spark Code'''
sc = SparkContext()
#broadcast wordlists
word_lists_var = sc.broadcast(word_lists)
#not not line returns true if the line is not an empty string
thefile = sc.textFile(tweetsFile).filter(from_usa_and_required_fields).map(process_tweets).filter(lambda line: not not line).saveAsTextFile(outFileProcessedDataset)
sc.stop()
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] | 2.723183 | 1,156 |
# -*- coding: utf-8 -*-
import logging
from . import instrumentation
from .dotdict import dotdict
##################################################################
### defaults and constants
##################################################################
PRE = 'pre'
POST = 'post'
DEFAULT_MAX_HISTORY_SIZE = 2
INFINITE_HISTORY_SIZE = -1
NO_HISTORY = 1
DEBUG = logging.DEBUG
INFO = logging.INFO
WARNING = logging.WARNING
ERROR = logging.ERROR
CRITICAL = logging.CRITICAL
DEFAULT_ERROR_LEVEL = ERROR
##################################################################
### decorators
##################################################################
##################################################################
### info and data about specifications and monitors
##################################################################
##################################################################
### pre and post functions
##################################################################
@instrumentation.use_state(rv=True, inargs=True)
@instrumentation.use_state(rv=True, inargs=True, outargs=True)
##################################################################
### history functions
##################################################################
##################################################################
### plain data objects
##################################################################
##################################################################
### objects with logic
##################################################################
##################################################################
### configuration
##################################################################
DEFAULT_ERROR_HANDLER = RaiseExceptionErrorHandler()
_error_handler = DEFAULT_ERROR_HANDLER
_enable_copy_args = True
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] | 5.756923 | 325 |
import rumps
import requests
import json
API_URL = 'https://koinex.in/api/ticker'
UPDATE_INTERVAL = 60
CURRENCIES = {
'Bitcoin': 'BTC',
'Ethereum': 'ETH',
'Ripple': 'XRP',
'Litecoin': 'LTC',
'Bitcoin Cash': 'BCH',
}
if __name__ == "__main__":
KoinexStatusBarApp().run() | [
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# Generated by Django 2.1.7 on 2019-05-03 02:36
from django.db import migrations
| [
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# -*- coding: utf-8 -*-
"""
Created on Tue Apr 20 13:32:20 2021
#--- ag csv results to single file based on dashboard_dbs
#--- Murilo Vianna ([email protected])
#--- Jul, 2021.
#--- Dev-log in: https://github.com/Murilodsv/py-jules
@author: muril
"""
# DEBUG import os; os.chdir('C:/Murilo/py-jules')
#------------------------------#
#--- generate qsub-clusters ---#
#------------------------------#
dash_nm = 'dashboard_db.csv' # Filename of Dashboard CSV
wd_out = 'ginore/csv_res'
sim_id = 'future-crop'
dap_f = range(1,361)
var_nm = 'fsmc'
var_ag = 'pft'
f_sufix = '.day.time_pft_y_x.csv'
#--- Get scripts arguments
if __name__ == "__main__":
import sys
if len(sys.argv) > 1:
#--- use arguments
dash_nm = str(sys.argv[1]) # debug dash_nm = 'dashboard_db_future.csv'
wd_out = str(sys.argv[2])
sim_id = str(sys.argv[3])
dap_f = str(sys.argv[4])
var_nm = str(sys.argv[5])
var_ag = str(sys.argv[6])
f_sufix = str(sys.argv[7])
import numpy as np
dap_f = '1,360'
dap_f = np.array(dap_f.split(',')) # arg_run_id = np.array(str('SC3572').split(','))
if len(dap_f) == 1:
dap_f = range(0,int(dap_f))
else:
dap_f = range(int(dap_f[0]), int(dap_f[1])+1)
#----------------------#
#--- Load libraries ---#
#----------------------#
import os
import os.path
import util as u
from time import time
#--- Track progress
run_start = time()
#----------------------#
#--- Read dashboard ---#
#----------------------#
#--- get run wd
wd = os.getcwd().replace('\\','/')
#--- Open CSVs
dash = u.df_csv(wd+'/'+dash_nm)
#--- list of clusters
l_ids = dash['run_id'].unique()
ini_df = True
for i in l_ids:
#--- filename
fn = wd+'/jules_run/'+i+'/namelists/output/'+i+f_sufix
if os.path.exists(fn):
print('Reading '+i)
#--- read file
df_i = u.df_csv(fn)
#--- filter dap
df_i = df_i[:][df_i['dap'].isin(range(1,360))]
#--- aggregate the var_nm by mean and var_ag
df_i = df_i.groupby(var_ag, as_index=False)[var_nm].mean()
#--- flag with run_id
df_i['run_id'] = i
#--- bind to single df
if ini_df:
df_res = df_i
ini_df = False
else:
df_res = df_res.append(df_i)
#--- write results
df_res.to_csv(wd+'/'+wd_out+'/'+sim_id+f_sufix, index = None, header=True)
#--- track time
print("\nElapsed time of copying: --- %.3f seconds ---" % (time() - run_start))
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"""
With a physics component, you can fly.
Well, not for long.
You'll shortly fall to your death.
"""
import pymunk
import whoosh.maths
from whoosh.components.base import BaseComponent
class PhysicsComponent(BaseComponent):
"""
Useful for recreating Angry Birds.
GameWorld.add_blob will automatically add the physics body to the physics world as long as the component was added
to the blob before add_blob was called.
Boy that was a long sentence.
"""
def get_position(self):
"""
Returns the position of the body as a Vec2f.
"""
#print("PhysicsComponent.get_position returning {0}".format(self.body.position))
return whoosh.maths.Vec2f.from_pyvec(self.body.position)
def get_velocity(self):
"""
Returns the velocity of the body as a Vec2f.
"""
return whoosh.maths.Vec2f.from_pyvec(self.body.velocity)
def get_rotation(self):
"""
Returns the clockwise rotation of the body in degrees.
"""
return whoosh.maths.radians_to_degrees(self.body.angle)
def set_position(self, pos):
"""
Sets the position of the body.
The position won't actually be changed until the next physics world step, when
chipmunk calls our _set_velocity_func.
"""
self.body.position = pos
def is_touching_ceiling(self):
"""
Returns True/False whether the body is touching a ceiling
"""
return self._touching_ceiling
def is_touching_ground(self):
"""
Returns True/False whether the body is touching the ground
"""
return self._touching_ground
def is_touching_wall(self):
"""
Returns True/False whether the body is touching a wall
"""
return self._touching_wall
def is_in_air(self):
"""
Returns True/False whether the body is not touching the ground.
"""
return not self.is_touching_ground()
def add_circle(self, radius, **kwargs):
"""
Utility method which creates a pymunk Circle shape and adds it to the body.
"""
shape = pymunk.Circle(self.body, radius, **kwargs)
self.shapes.append(shape)
return shape
def add_poly(self, vertices, **kwargs):
"""
Utility method which creates a pymunk Poly shape and adds it to the body.
"""
shape = pymunk.Poly(self.body, vertices, **kwargs)
self.shapes.append(shape)
return shape
def add_rect(self, width, height):
"""
Utility method which creates a rectangular pymunk Poly shape and adds it to the body
"""
hw, hh = width/2.0, height/2.0
vertices = [(-hw, -hh), (hw, -hh), (hw, hh), (-hw, hh)]
return self.add_poly(vertices)
def add_segment(self, a, b, radius):
"""
Utility method which creates a pymunk Segment shape and adds it to the body.
"""
shape = pymunk.Segment(self.body, a, b, radius)
self.shapes.append(shape)
return shape
def add_equilateral(self, radius, sides):
"""
Utility method which creates a pymunk Poly shape and adds it to the body.
The vertices are calculated automatically using the given number of sides and radius.
"""
verts = whoosh.maths.get_ngon_vertices(radius, sides)
print("Verts are {0}".format(verts))
return self.add_poly(verts)
def _update_touching_info(self):
"""
Sets _touching_ground, _touching_wall and _touching_ceiling by checking all of the current collisions the body is in
with other static or kinematic bodies.
"""
f.touching_ceiling = False
f.touching_wall = False
f.touching_ground = False
self.body.each_arbiter(f)
self._touching_ceiling = f.touching_ceiling
self._touching_wall = f.touching_wall
self._touching_ground = f.touching_ground | [
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796,
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] | 2.444105 | 1,637 |
from Class.rqlite import rqlite
import subprocess, socket, psutil, json, time
| [
6738,
5016,
13,
81,
13976,
578,
1330,
374,
13976,
578,
198,
11748,
850,
14681,
11,
17802,
11,
26692,
22602,
11,
33918,
11,
640,
198
] | 3.25 | 24 |
"""
str - string
"""
print('aspas simples')
print("aspas duplas")
print(123456) # integer, conversao implicita para string
print("essa é uma 'string' (str).")
print('essa é uma "string" (str).')
print('essa é uma \'string\' (str).')
print("essa é uma \"string\" (str).") # '\' caracter de escape
print('essa é uma \n (str).')
print(r'essa é uma \n (str).') # o 'r' no inicio ignora oq tem dentro e gera tudo como string
| [
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] | 2.696203 | 158 |
#!/usr/bin/env python
###########################################################################
#
# Written in 2009 by Ian Katz <[email protected]>
# Terms: WTFPL (http://sam.zoy.org/wtfpl/)
# See COPYING and WARRANTY files included in this distribution
#
###########################################################################
# this program provides an audible alert for any change in the
# connected/disconnected state of a named host
#
# It takes 2 command line arguments:
# 1. the hostname/IP
# 2. the name to be spoken by the program as up/down
###
#
# note from Ian:
#
# these spoken phrases were crafted carefully to make good use of the
# digital voice (i.e., not sound too cheesy). each voice needs its own
# slight tweak of wording to minimize odd-sounding speech. this script
# was verbally optimized for "voice_nitech_us_slt_arctic_hts".
#
#
# i installed a bunch of voices as per
# http://ubuntuforums.org/showthread.php?t=751169
#
# a script called install-speech-software.sh is provided
# for your convenience
#
# this is my /etc/festival.scm
#
# (Parameter.set 'Audio_Method 'esdaudio)
# (set! voice_default 'voice_rab_diphone)
# (set! voice_default 'voice_nitech_us_rms_arctic_hts)
# (set! voice_default 'voice_nitech_us_clb_arctic_hts)
# (set! voice_default 'voice_nitech_us_slt_arctic_hts)
#
###
import sys
import os
import subprocess
import time
# simple ping
def ping(ip):
"""pings IP"""
return 0 == subprocess.call("ping -c 1 -W 1 %s" % ip,
shell=True,
stdout=open('/dev/null', 'w'),
stderr=subprocess.STDOUT)
# use the big boy words... text to speech!
######################################################################3
#run it
if __name__ == "__main__":
if len(sys.argv) != 3:
print "Usage: " + sys.argv[0] + "<IP> <name>"
exit(1)
monitor = PingMonitor()
monitor.main(sys.argv[1], sys.argv[2])
print "\n\n\nexited cleanly!\n\n"
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] | 2.672302 | 769 |
import numpy as np
import matplotlib
from matplotlib import colors
from matplotlib import cm
import matplotlib.pyplot as plt
import folium
from folium.plugins import HeatMap
from folium.plugins import MarkerCluster
from loci.analytics import bbox, kwds_freq
from wordcloud import WordCloud
from pysal.viz.mapclassify import Natural_Breaks
from pandas import DataFrame, concat
from geopandas import GeoDataFrame
import osmnx
def map_cluster_diff(clusters_a, clusters_b, intersection_color='#00ff00', diff_ab_color='#0000ff',
diff_ba_color='#ff0000', tiles='OpenStreetMap', width='100%', height='100%'):
"""Returns a Folium Map displaying the differences between two sets of clusters. Map center and zoom level
are set automatically.
Args:
clusters_a (GeoDataFrame): The first set of clusters.
clusters_b (GeoDataFrame): The second set of clusters.
intersection_color (color code): The color to use for A & B.
diff_ab_color (color code): The color to use for A - B.
diff_ba_color (color code): The color to use for B - A.
tiles (string): The tiles to use for the map (default: `OpenStreetMap`).
width (integer or percentage): Width of the map in pixels or percentage (default: 100%).
height (integer or percentage): Height of the map in pixels or percentage (default: 100%).
Returns:
A Folium Map object displaying cluster intersections and differences.
"""
if clusters_a.crs['init'] != '4326':
clusters_a = clusters_a.to_crs({'init': 'epsg:4326'})
if clusters_b.crs['init'] != '4326':
clusters_b = clusters_b.to_crs({'init': 'epsg:4326'})
spatial_index_b = clusters_b.sindex
prev_size_list = []
prev_cid_list = []
after_cid_list = []
after_size_list = []
intersect_area_percentage_list = []
intersection_polygons = []
diff_ab_polygs = []
diff_ba_polygs = []
new_polygs = []
intersection_polygons_attr = []
diff_ab_polygs_attr = []
diff_ba_polygs_attr = []
new_polygs_attr = []
for index_1, row_1 in clusters_a.iterrows():
size = row_1['size']
poly = row_1['geometry']
cid = row_1['cluster_id']
prev_area = poly.area
prev_cid_list.append(cid)
prev_size_list.append(size)
possible_matches_index = list(spatial_index_b.intersection(poly.bounds))
possible_matches = clusters_b.iloc[possible_matches_index]
max_area = 0.0
max_cid_intersect = -1
max_size = 0
max_polyg = None
max_intersect_polyg = None
for index_2, row_2 in possible_matches.iterrows():
size_2 = row_2['size']
poly_2 = row_2['geometry']
cid_2 = row_2['cluster_id']
intersect_polyg = poly.intersection(poly_2)
area = intersect_polyg.area
if area > max_area:
max_area = area
max_cid_intersect = cid_2
max_size = size_2
max_polyg = poly_2
max_intersect_polyg = intersect_polyg
if max_cid_intersect == -1:
after_cid_list.append(np.nan)
after_size_list.append(np.nan)
intersect_area_percentage_list.append(0.0)
new_polygs.append(poly)
new_polygs_attr.append('A (' + str(cid) + ')')
else:
after_cid_list.append(max_cid_intersect)
after_size_list.append(max_size)
intersect_area_percentage_list.append(max_area / prev_area)
ab_diff_poly = poly.difference(max_polyg)
ba_diff_poly = max_polyg.difference(poly)
intersection_polygons.append(max_intersect_polyg)
diff_ab_polygs.append(ab_diff_poly)
diff_ba_polygs.append(ba_diff_poly)
intersection_polygons_attr.append('A(' + str(cid) + ') & B(' + str(max_cid_intersect) + ')')
diff_ab_polygs_attr.append('A(' + str(cid) + ') - B(' + str(max_cid_intersect) + ')')
diff_ba_polygs_attr.append('B(' + str(max_cid_intersect) + ') - A(' + str(cid) + ')')
spatial_index_a = clusters_a.sindex
old_polys = []
old_poly_attr = []
for index, row in clusters_b.iterrows():
poly = row['geometry']
cid = row['cluster_id']
possible_matches_index = list(spatial_index_a.intersection(poly.bounds))
possible_matches = clusters_a.iloc[possible_matches_index]
max_area = 0.0
for index_2, row_2 in possible_matches.iterrows():
poly_2 = row_2['geometry']
intersect_polyg = poly.intersection(poly_2)
area = intersect_polyg.area
if area > max_area:
max_area = area
if max_area == 0.0:
old_polys.append(poly)
old_poly_attr.append('B (' + str(cid) + ')')
intersection_gdf = GeoDataFrame(list(zip(intersection_polygons, intersection_polygons_attr)),
columns=['geometry', 'diff'], crs=clusters_a.crs)
diff_ab_gdf = GeoDataFrame(list(zip(diff_ab_polygs + new_polygs, diff_ab_polygs_attr + new_polygs_attr)),
columns=['geometry', 'diff'], crs=clusters_a.crs)
diff_ba_gdf = GeoDataFrame(list(zip(diff_ba_polygs + old_polys, diff_ba_polygs_attr + old_poly_attr)),
columns=['geometry', 'diff'], crs=clusters_a.crs)
# Filter out erroneous rows
intersection_gdf = intersection_gdf[(intersection_gdf.geometry.area > 0.0)]
diff_ab_gdf = diff_ab_gdf[(diff_ab_gdf.geometry.area > 0.0)]
diff_ba_gdf = diff_ba_gdf[(diff_ba_gdf.geometry.area > 0.0)]
# Add colors
intersection_style = {'fillColor': intersection_color, 'weight': 2, 'color': 'black', 'fillOpacity': 0.8}
diff_ab_style = {'fillColor': diff_ab_color, 'weight': 2, 'color': 'black', 'fillOpacity': 0.8}
diff_ba_style = {'fillColor': diff_ba_color, 'weight': 2, 'color': 'black', 'fillOpacity': 0.8}
intersection_gdf['style'] = intersection_gdf.apply(lambda x: intersection_style, axis=1)
diff_ab_gdf['style'] = diff_ab_gdf.apply(lambda x: diff_ab_style, axis=1)
diff_ba_gdf['style'] = diff_ba_gdf.apply(lambda x: diff_ba_style, axis=1)
# Concatenate results
return concat([diff_ab_gdf, diff_ba_gdf, intersection_gdf], ignore_index=True, sort=False)
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220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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62,
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11,
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13,
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62,
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7635,
20520,
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62,
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62,
70,
7568,
13,
39014,
7,
50033,
2124,
25,
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62,
397,
62,
7635,
11,
16488,
28,
16,
8,
198,
220,
220,
220,
814,
62,
7012,
62,
70,
7568,
17816,
7635,
20520,
796,
814,
62,
7012,
62,
70,
7568,
13,
39014,
7,
50033,
2124,
25,
814,
62,
7012,
62,
7635,
11,
16488,
28,
16,
8,
628,
220,
220,
220,
1303,
1482,
9246,
268,
378,
2482,
198,
220,
220,
220,
1441,
1673,
265,
26933,
26069,
62,
397,
62,
70,
7568,
11,
814,
62,
7012,
62,
70,
7568,
11,
16246,
62,
70,
7568,
4357,
8856,
62,
9630,
28,
17821,
11,
3297,
28,
25101,
8,
198
] | 2.214953 | 2,889 |
from flask import Blueprint, request, jsonify
import pymongo
client = pymongo.MongoClient('localhost',27017)
db = client.TCGA_Slide_DB
dsa = Blueprint('dsa', __name__)
@dsa.route('/annotations', methods=['POST','OPTIONS'])
@dsa.route('/annotations', methods=['GET','OPTIONS'])
| [
6738,
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51,
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6,
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198
] | 2.772277 | 101 |
import urllib
# make response
| [
11748,
2956,
297,
571,
198,
198,
2,
787,
2882,
198,
220,
220,
220,
220,
198
] | 2.4 | 15 |
from unittest import mock
from django.urls import reverse
from parameterized import parameterized
from rest_framework import status
from api.applications.models import PartyOnApplication
from lite_content.lite_api.strings import PartyErrors
from api.parties.enums import PartyType, SubType
from api.parties.models import PartyDocument
from api.staticdata.countries.helpers import get_country
from test_helpers.clients import DataTestClient
| [
6738,
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62,
16794,
364,
13,
565,
2334,
1330,
6060,
14402,
11792,
628
] | 3.818966 | 116 |
from datetime import datetime
theTime = '2019-12-20 10:22:00'
theTime = datetime.strptime(theTime, '%Y-%m-%d %H:%M:%S')
print(theTime) | [
6738,
4818,
8079,
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198,
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705,
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] | 2.213115 | 61 |
import importlib
import sys
| [
11748,
1330,
8019,
198,
11748,
25064,
198
] | 4 | 7 |
#!C:\Python27\python.exe -u
#!/usr/bin/env python
import MySQLdb
import hashlib
import cgi
import uuid
print "Content-type: text/html"
print
print """
<html>
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Ultra Motivator Sign Up</title>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css">
<link rel="stylesheet" href="style.css" />
</head>
<body>
<h1> Ultra Motivator Sign Up </h1>
"""
form = cgi.FieldStorage()
if "username" not in form or "password" not in form or "confirm_password" not in form:
print """<h3>Please Sign Up</h3>"""
show_signup_ui()
else:
input_username = form.getvalue("username", "")
input_password = form.getvalue("password", "")
confirm_password = form.getvalue("confirm_password", "")
if input_password != confirm_password:
print """<h3>Password Fields Did Not Match!</h3>"""
show_signup_ui()
else:
try:
conn = MySQLdb.connect (
host = "my_host",
user = "my_user",
passwd = "my_password",
db = "my_db")
cur = conn.cursor()
command = "INSERT INTO User(username, password) VALUES(%s,%s)"
cur.execute(command, (input_username, hash_password(input_password)))
print """<h1>Signed Up As %s</h1>""" % input_username
print """<p>"If you want to build a ship, don't drum up the people to gather wood, and don't assign them tasks and work. Instead, teach them to yearn for the vast and endless sea." - Antoine de Saint-Exupery</p>"""
except MySQLdb.Error, e:
print """<h1>Error %d: %s</h1>""" % (e.args[0], e.args[1])
finally:
if cur:
cur.close()
if conn:
conn.close()
print """</body></html>"""
| [
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7,
4,
82,
11,
4,
82,
16725,
198,
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7,
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62,
29460,
11,
12234,
62,
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7,
15414,
62,
28712,
22305,
198,
197,
197,
197,
4798,
37227,
27,
71,
16,
29,
50,
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4064,
82,
3556,
71,
16,
29,
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4064,
5128,
62,
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198,
197,
197,
197,
4798,
37227,
27,
79,
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345,
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257,
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11,
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470,
13026,
510,
262,
661,
284,
6431,
4898,
11,
290,
836,
470,
8333,
606,
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290,
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614,
77,
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290,
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5417,
526,
532,
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42722,
390,
9281,
12,
3109,
48568,
88,
3556,
79,
29,
37811,
198,
197,
198,
197,
197,
16341,
33476,
9945,
13,
12331,
11,
304,
25,
198,
197,
197,
197,
4798,
37227,
27,
71,
16,
29,
12331,
4064,
67,
25,
4064,
82,
3556,
71,
16,
29,
37811,
4064,
357,
68,
13,
22046,
58,
15,
4357,
304,
13,
22046,
58,
16,
12962,
198,
197,
197,
69,
3289,
25,
198,
197,
197,
197,
361,
1090,
25,
198,
197,
197,
197,
197,
22019,
13,
19836,
3419,
198,
197,
197,
197,
361,
48260,
25,
198,
197,
197,
197,
197,
37043,
13,
19836,
3419,
198,
197,
4798,
37227,
3556,
2618,
12240,
6494,
29,
37811,
198
] | 2.577941 | 680 |
from .reminders import Reminders
Default = Reminders
| [
6738,
764,
2787,
29700,
1330,
3982,
29700,
198,
19463,
796,
3982,
29700,
198
] | 4.076923 | 13 |
#
# Copyright (c) 2013, Prometheus Research, LLC
# Released under MIT license, see `LICENSE` for details.
#
from .check import maybe, oneof, choiceof, listof, tupleof, dictof, raises
from .core import Test, Field, Record
from .ctl import Control
from .load import locate, Location
from .run import run, main
from .std import BaseCase, MatchCase
from .ui import UI, ConsoleUI, SilentUI
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import torch
import torch.nn as nn
class EvonormPrimitive(nn.Module):
"""Abstract base class for the evonorm primitive layers
"""
class PrimitiveAdd(EvonormPrimitive):
""" Primitive Add Operation
"""
class PrimitiveMul(EvonormPrimitive):
"""Primitive Multiply Operation
"""
class PrimitiveDiv(EvonormPrimitive):
"""Primitive Division Operation
"""
class PrimitiveMax(EvonormPrimitive):
"""Primitive Max Operation
"""
class PrimitiveSigmoid(EvonormPrimitive):
"""Primitive sigmoid Operation
"""
class PrimitiveNeg(EvonormPrimitive):
"""Primitive negation operation
"""
class PrimitiveTanh(EvonormPrimitive):
"""Primitive tanh operation
"""
class PrimitiveExp(EvonormPrimitive):
"""Primitive exp operation
"""
class PrimitiveLog(EvonormPrimitive):
"""Primitive log operation
"""
class PrimitiveAbs(EvonormPrimitive):
"""Primitive abs operation
"""
class PrimitiveSquare(EvonormPrimitive):
"""Primitive square operation
"""
class PrimitiveSqrt(EvonormPrimitive):
"""Primitive sqrt operation
"""
################### Primitive mean
class PrimitiveMeanBWH(EvonormPrimitive):
"""Primitive mean operation along b, w, h dimensions
"""
class PrimitiveMeanWHC(EvonormPrimitive):
"""Primitive mean operation along w, h, c dimensions
"""
class PrimitiveMeanWH(EvonormPrimitive):
"""Primitive mean operation along w, h dimensions
"""
class PrimitiveMeanWHCGrouped(EvonormPrimitive):
"""Primitive mean operation along w, h, cgrouped dimensions
"""
################### Primitive STD
class PrimitiveStdBWH(EvonormPrimitive):
"""Primitive std operation along b, w, h dimensions
"""
class PrimitiveStdWHC(EvonormPrimitive):
"""Primitive Std operation along w, h, c dimensions
"""
class PrimitiveStdWH(EvonormPrimitive):
"""Primitive Std operation along w, h dimensions
"""
class PrimitiveStdWHCGrouped(EvonormPrimitive):
"""Primitive Std operation along w, h, cgrouped dimensions
"""
################### Primitive STD Centered
class PrimitiveStdcenteredBWH(EvonormPrimitive):
"""Primitive std centered operation along b, w, h dimensions
"""
class PrimitivestdcenteredWHC(EvonormPrimitive):
"""Primitive stdcentered operation along w, h, c dimensions
"""
class PrimitivestdcenteredWH(EvonormPrimitive):
"""Primitive stdcentered operation along w, h dimensions
"""
class PrimitivestdcenteredWHCGrouped(EvonormPrimitive):
"""Primitive stdcentered operation along w, h, cgrouped dimensions
"""
PRIMITIVES = [
PrimitiveAdd,
PrimitiveMul,
PrimitiveDiv,
PrimitiveMax,
PrimitiveNeg,
PrimitiveSigmoid,
PrimitiveTanh,
PrimitiveExp,
PrimitiveLog,
PrimitiveAbs,
PrimitiveSquare,
PrimitiveSqrt,
# Aggregate layers
[PrimitiveMeanBWH,
PrimitiveMeanWHC,
PrimitiveMeanWH,
PrimitiveMeanWHCGrouped,],
[PrimitiveStdBWH,
PrimitiveStdWHC,
PrimitiveStdWH,
PrimitiveStdWHCGrouped,],
[PrimitiveStdcenteredBWH,
PrimitivestdcenteredWHC,
PrimitivestdcenteredWH,
PrimitivestdcenteredWHCGrouped]
]
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452,
395,
67,
38050,
12418,
34,
13247,
276,
60,
198,
60,
198
] | 2.817699 | 1,130 |
import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
from dotenv import load_dotenv
load_dotenv()
lz_uri = 'spotify:artist:36QJpDe2go2KgaRleHCDTp'
client_credentials_manager = SpotifyClientCredentials(client_id='CLIENT_ID',
client_secret='CLIENT_SECRET')
spotify = spotipy.Spotify(client_credentials_manager=client_credentials_manager)
results = spotify.artist_top_tracks(lz_uri)
for track in results['tracks'][:10]:
print('track : ' + track['name'])
print('audio : ' + track['preview_url'])
print('cover art: ' + track['album']['images'][0]['url'])
print()
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] | 2.371747 | 269 |
import logging
| [
11748,
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] | 4.25 | 4 |
# Copyright 2010-2016, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# This build file is expected to be used on Windows only.
{
'variables': {
'relative_dir': 'win32/base',
'gen_out_dir': '<(SHARED_INTERMEDIATE_DIR)/<(relative_dir)',
},
'targets': [
{
'target_name': 'win32_base_dummy',
'type': 'none',
'sources': [
# empty
],
},
],
'conditions': [
['OS=="win"', {
# TODO(yukawa): Refactor following targets when the implementation of
# TSF Mozc is completed.
'targets': [
{
'target_name': 'input_dll_import_lib',
'type': 'shared_library',
'sources': [
'input_dll.cc',
'input_dll.def',
],
'dependencies': [
'../../base/base.gyp:base',
],
'msvs_settings': {
'VCLinkerTool': {
'AdditionalOptions': [
'/ignore:4070',
],
},
},
},
{
'target_name': 'imframework_util',
'type': 'static_library',
'sources': [
'imm_reconvert_string.cc',
'imm_registrar.cc',
'imm_util.cc',
'keyboard_layout_id.cc',
'tsf_profile.cc',
'tsf_registrar.cc',
],
'dependencies': [
'../../base/base.gyp:base',
'input_dll_import_lib',
],
},
{
'target_name': 'imframework_util_test',
'type': 'executable',
'sources': [
'imm_reconvert_string_test.cc',
'input_dll_test.cc',
'keyboard_layout_id_test.cc',
],
'dependencies': [
'../../base/base.gyp:base',
'../../protocol/protocol.gyp:commands_proto',
'../../testing/testing.gyp:gtest_main',
'imframework_util',
],
'variables': {
'test_size': 'small',
},
},
{
'target_name': 'ime_impl_base',
'type': 'static_library',
'sources': [
'accessible_object.cc',
'accessible_object_info.cc',
'browser_info.cc',
'config_snapshot.cc',
'conversion_mode_util.cc',
'deleter.cc',
'focus_hierarchy_observer.cc',
'indicator_visibility_tracker.cc',
'input_state.cc',
'keyboard.cc',
'keyevent_handler.cc',
'string_util.cc',
'surrogate_pair_observer.cc',
'win32_window_util.cc',
],
'dependencies': [
'../../base/base.gyp:base',
'../../config/config.gyp:config_handler',
'../../protocol/protocol.gyp:commands_proto',
'../../protocol/protocol.gyp:config_proto',
'../../session/session_base.gyp:key_info_util',
'../../session/session_base.gyp:output_util',
],
'link_settings': {
'msvs_settings': {
'VCLinkerTool': {
'AdditionalDependencies': [
'oleacc.lib',
],
},
},
},
},
{
'target_name': 'ime_impl_base_test',
'type': 'executable',
'sources': [
'conversion_mode_util_test.cc',
'deleter_test.cc',
'indicator_visibility_tracker_test.cc',
'keyboard_test.cc',
'keyevent_handler_test.cc',
'string_util_test.cc',
'surrogate_pair_observer_test.cc',
],
'dependencies': [
'../../base/base_test.gyp:clock_mock',
'../../client/client.gyp:client',
'../../testing/testing.gyp:gtest_main',
'ime_impl_base',
],
},
{
'target_name': 'ime_base',
'type': 'static_library',
'sources': [
'migration_util.cc',
'uninstall_helper.cc',
],
'conditions': [
['branding=="GoogleJapaneseInput"', {
'sources': [
'omaha_util.cc',
],
}],
],
'dependencies': [
'../../base/base.gyp:base',
'imframework_util',
],
},
{
'target_name': 'win32_base_test',
'type': 'executable',
'sources': [
'uninstall_helper_test.cc',
],
'dependencies': [
'../../base/base.gyp:base',
'../../protocol/protocol.gyp:commands_proto',
'../../testing/testing.gyp:gtest_main',
'ime_base',
],
'conditions': [
['branding=="GoogleJapaneseInput"', {
'sources': [
'omaha_util_test.cc',
],
}],
],
'variables': {
'test_size': 'small',
},
},
{
'target_name': 'text_icon',
'type': 'static_library',
'sources': [
'text_icon.cc',
],
'dependencies': [
'../../base/base.gyp:base',
],
},
{
'target_name': 'text_icon_test',
'type': 'executable',
'sources': [
'text_icon_test.cc',
],
'dependencies': [
'../../base/base.gyp:win_font_test_helper',
'../../testing/testing.gyp:gtest_main',
'text_icon',
],
},
{
'target_name': 'win32_file_verifier',
'type': 'static_library',
'sources': [
'file_verifier.cc',
],
'dependencies': [
'../../base/base.gyp:base',
],
'link_settings': {
'msvs_settings': {
'VCLinkerTool': {
'AdditionalDependencies': [
'crypt32.lib',
'imagehlp.lib',
'wintrust.lib',
],
'DelayLoadDLLs': [
'imagehlp.dll',
'wintrust.dll',
],
},
},
},
},
{
'target_name': 'win32_file_verifier_test',
'type': 'executable',
'sources': [
'file_verifier_test.cc',
],
'dependencies': [
'../../testing/testing.gyp:gtest_main',
'../../testing/testing.gyp:mozctest',
'win32_file_verifier',
],
'variables': {
'test_size': 'small',
# Copy the test data for character set test.
'test_data_subdir': 'data/test/win32/integrity',
'test_data': [
'../../<(test_data_subdir)/mozc_test_binary.exe',
'../../<(test_data_subdir)/mozc_test_binary_modified.exe',
'../../<(test_data_subdir)/mozc_test_binary_modified_signed.exe',
'../../<(test_data_subdir)/mozc_test_binary_no_checksum.exe',
'../../<(test_data_subdir)/mozc_test_binary_signed.exe',
],
},
'includes': [ '../../gyp/install_testdata.gypi' ],
},
],
}],
],
'targets': [
# Test cases meta target: this target is referred from gyp/tests.gyp
{
'target_name': 'win32_base_all_test',
'type': 'none',
'conditions': [
['OS=="win"', {
'dependencies': [
'ime_impl_base_test',
'imframework_util_test',
'win32_base_test',
'win32_file_verifier_test',
],
}],
],
},
],
}
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] | 1.848193 | 4,980 |
from ..graph import Arc, ArcType, Node, NodeType, Graph, GraphType, GraphSet
from ..gcn import GCN
import tensorflow as tf
import numpy as np
tf.compat.v1.disable_eager_execution()
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#!/usr/bin/env python
#-----------------------------------------------------------------------
# twitter-user-search
# - performs a search for users matching a certain query
#-----------------------------------------------------------------------
from twitter import *
#-----------------------------------------------------------------------
# load our API credentials
#-----------------------------------------------------------------------
import sys
sys.path.append(".")
import config
#-----------------------------------------------------------------------
# create twitter API object
#-----------------------------------------------------------------------
twitter = Twitter(auth = OAuth(config.access_key,
config.access_secret,
config.consumer_key,
config.consumer_secret))
#-----------------------------------------------------------------------
# perform a user search
# twitter API docs: https://dev.twitter.com/rest/reference/get/users/search
#-----------------------------------------------------------------------
results = twitter.users.search(q = '"New Cross"')
#-----------------------------------------------------------------------
# loop through each of the users, and print their details
#-----------------------------------------------------------------------
for user in results:
print("@%s (%s): %s" % (user["screen_name"], user["name"], user["location"]))
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198,
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31,
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1,
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14692,
3672,
33116,
2836,
14692,
24886,
8973,
4008,
198
] | 4.94863 | 292 |
#!/usr/bin/env python3
# You are probably well aware of the 'birthday paradox'
# https://en.wikipedia.org/wiki/Birthday_problem
# Let's try simulating it
# You will need a list for the 365 day calendar
# You will need a set number of people (e.g. 25)
# During each 'trial' you do the following
# Choose a person
# Git them a random birthday
# Store it in the calendar
# Once you have stored all birthdays, check to see if any have the same day
# Do this for many trials to see what the probability of sharing a birthday is
import random
trial=10000
people=23
share=0
for i in range(trial):
calendar = [0]*365
count = 0
for j in range(people):
birthday=random.randint(0,364)
calendar[birthday] += 1
for j in range(365):
if count < calendar[j]:
count = calendar[j]
if count >= 2:
share += 1
print(share/trial)
"""
python3 33birthday.py
0.571
"""
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25,
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220,
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7,
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8,
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220,
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18,
4747,
24280,
820,
13,
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198,
15,
13,
42875,
198,
37811,
628
] | 2.737463 | 339 |
from datetime import datetime, timedelta
import numpy as np
import pytest
from pytest import fixture
from vanilla_option_pricing.option import VanillaOption, option_list_to_pandas_dataframe, \
pandas_dataframe_to_option_list
@fixture
@fixture
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] | 3.083333 | 84 |
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from flask_caching import Cache
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] | 4.041667 | 24 |
from inspect import formatargvalues
import discord, datetime, db, time, os
import pandas as pd
from discord import Embed
from discord.ext.commands import Cog, command
from apscheduler.schedulers.asyncio import AsyncIOScheduler
from apscheduler.triggers.cron import CronTrigger
# from discord_components import Button, ButtonStyle, InteractionType
# import asyncio
from db import servers_con, version
from discord.utils import get
class Help(Cog):
"""
> Show this help message and controlling help for commands.
"""
@command(name = "help")
@Cog.listener()
@Cog.listener() | [
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220,
220,
220,
2488,
34,
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13,
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] | 2.653226 | 248 |
import logging
from ._version import __version__
__all__ = [
"__version__",
]
logger = logging.getLogger(__name__)
logging.basicConfig()
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from pygame_rl.scenario.gridworld.envs.gridworld_v0 import GridworldV0
from pygame_rl.scenario.gridworld.envs.gridworld_v1 import GridworldV1
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# 该脚本平均每小时提交 4万5千多条URL
# 推荐 0.3s,方便及时停止抓取， 一天提交10万~15万 URL PS：因为URL一旦提交到队列中，就不可修改
# python qiniu_big_fetch.py <ak> <sk> <bucket> <zone(z0 z1)> <file_path> <sleep_time>
import urlparse, hmac, hashlib, base64, commands, sys, time
def Qiniu_token(access_key, secret_key,
method,
host,
url,
content_type=None,
body=None):
"""
<Method> <PathWithRawQuery>
Host: <Host>
Content-Type: <ContentType>
[<X-Qiniu-*> Headers]
[<Body>] #这里的 <Body> 只有在 <ContentType> 存在且不为 application/octet-stream 时才签进去。
"""
parsed_url = urlparse.urlsplit(url)
netloc = parsed_url.netloc
path = parsed_url.path
query = parsed_url.query
if not host:
host = netloc
path_with_query = path
if query != '':
path_with_query = ''.join([path_with_query, '?', query])
data = ''.join(["%s %s" %
(method, path_with_query), "\n", "Host: %s" %
host, "\n"])
if content_type:
data += "Content-Type: %s" % (content_type) + "\n"+"\n"
if content_type and content_type != "application/octet-stream" and body:
data += body.decode(encoding='UTF-8')
return '{0}:{1}'.format(access_key, __token(data, secret_key))
if __name__ == '__main__':
try:
AccessKey = sys.argv[1]
SecretKey = sys.argv[2]
bucket_name= sys.argv[3]
zone = sys.argv[4] # z0,z1,z2,na0,as0 对应 华东，华北，华南，北美，东南亚
file_name = sys.argv[5] # 第一项为URL，保存的文件名默认为URI，每项用'\t'分割
time_sleep = float(sys.argv[6])
except Exception:
print 'version: 2018-11-21'
print './qiniu_big_fetch.py <ak> <sk> <bucket_name> <zone> <url_file_name> <time_sleep> \n'
else:
fout = open( file_name )
while 1:
line = fout.readline().strip()
if not line: break
line_list=line.split('\t')
fetch_url_single = line_list[0].strip()
if len(line_list) == 1: file_key_single = urlparse.urlsplit(fetch_url_single)[2].replace('/','',1)
if len(line_list) > 1: file_key_single=line_list[1].strip()
path = '/sisyphus/fetch'
host = 'api-%s.qiniu.com' % zone
url = 'http://'+host+path
body = '{"ignore_same_key":true, "url": "%s", "bucket": "%s", "key": "%s" }' % (fetch_url_single, bucket_name, file_key_single )
if len(line_list) == 3:
file_md5_check=line_list[2].strip()
body = '{"ignore_same_key":true, "url": "%s", "bucket": "%s", "key": "%s", "md5": "%s" }' % (fetch_url_single, bucket_name, file_key_single , file_md5_check, )
try:
Token = 'Qiniu ' + Qiniu_token( AccessKey, SecretKey, method='POST', host='', url=url, content_type='application/json' , body=body )
post_fetch_request( Token, url, fetch_url_single, bucket_name, file_key_single , body)
except Exception:
print ("there must be error in token or post")
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] | 1.947143 | 1,400 |
# Exercício Python 041: A Confederação Nacional de Natação precisa de um programa que leia o ano de nascimento de um atleta e mostre sua categoria, de acordo com a idade:
# - Até 9 anos: MIRIM
# - Até 14 anos: INFANTIL
# - Até 19 anos: JÚNIOR
# - Até 25 anos: SÊNIOR
# - Acima de 25 anos: MASTER
from datetime import date
n = int(input('Digite o ano de nascimento: ').strip())
a = date.today().year - n
if a <= 9:
print('Você tem {} anos e sua categoria é MIRIM'.format(a))
elif a >= 10 and a <= 14:
print('Você tem {} anos e sua categoria é INFANTIL'.format(a))
elif a >= 15 and a <= 19:
print('Você tem {} anos e sua categoria é JÙNIOR'.format(a))
elif a >= 16 and a <= 25:
print('Você tem {} anos e sua categoria é SÊNIOR'.format(a))
else:
print('Você tem {} anos e sua categoria é MASTER'.format(a))
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] | 2.383285 | 347 |
"""
Description:
Author: Jiaqi Gu ([email protected])
Date: 2021-03-31 17:48:41
LastEditors: Jiaqi Gu ([email protected])
LastEditTime: 2021-09-26 00:51:50
"""
from typing import Tuple
import torch
import torch.nn as nn
from pyutils.config import configs
from pyutils.datasets import get_dataset
from pyutils.loss import AdaptiveLossSoft, KLLossMixed
from pyutils.lr_scheduler.warmup_cosine_restart import CosineAnnealingWarmupRestarts
from pyutils.optimizer.sam import SAM
from pyutils.typing import DataLoader, Optimizer, Scheduler
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.conv import _ConvNd
from torch.types import Device
from core.datasets import CIFAR10Dataset, FashionMNISTDataset, MNISTDataset, SVHNDataset
from core.models import *
__all__ = [
"make_dataloader",
"make_model",
"make_weight_optimizer",
"make_arch_optimizer",
"make_optimizer",
"make_scheduler",
"make_criterion",
]
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220,
220,
220,
366,
15883,
62,
22213,
28019,
1600,
198,
60,
628,
628,
628,
628
] | 2.677871 | 357 |
"""
Graphviz extensions for Markdown.
Renders the output inline, eliminating the need to configure an output
directory.
Supports outputs types of SVG and PNG. The output will be taken from the
filename specified in the tag. Example:
{% dot attack_plan.svg
digraph G {
rankdir=LR
Earth [peripheries=2]
Mars
Earth -> Mars
}
%}
Requires the graphviz library (http://www.graphviz.org/)
Inspired by jawher/markdown-dot (https://github.com/jawher/markdown-dot)
Modified to work with Python 3 and cleaned-up SVG output
"""
import re
import markdown
import subprocess
import base64
# Global vars
BLOCK_RE = re.compile(
r'^\{% (?P<command>\w+)\s+(?P<filename>[^\s]+)\s*\n(?P<content>.*?)%}\s*$',
re.MULTILINE | re.DOTALL)
# Command whitelist
SUPPORTED_COMMAMDS = ['dot', 'neato', 'fdp', 'sfdp', 'twopi', 'circo']
| [
37811,
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8,
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2,
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3256,
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66,
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628,
628
] | 2.565476 | 336 |
from django.conf import settings
from django.core.management import call_command, get_commands, load_command_class
from django.core.management.base import BaseCommand, CommandError
from django.db import connection
from django_tenants.utils import get_tenant_model, get_public_schema_name
class BaseTenantCommand(BaseCommand):
"""
Generic command class useful for iterating any existing command
over all schemata. The actual command name is expected in the
class variable COMMAND_NAME of the subclass.
"""
def __new__(cls, *args, **kwargs):
"""
Sets option_list and help dynamically.
"""
obj = super().__new__(cls, *args, **kwargs)
app_name = get_commands()[obj.COMMAND_NAME]
if isinstance(app_name, BaseCommand):
# If the command is already loaded, use it directly.
cmdclass = app_name
else:
cmdclass = load_command_class(app_name, obj.COMMAND_NAME)
# prepend the command's original help with the info about schemata iteration
obj.help = "Calls %s for all registered schemata. You can use regular %s options. " \
"Original help for %s: %s" % (obj.COMMAND_NAME, obj.COMMAND_NAME, obj.COMMAND_NAME,
getattr(cmdclass, 'help', 'none'))
return obj
def handle(self, *args, **options):
"""
Iterates a command over all registered schemata.
"""
if options['schema_name']:
# only run on a particular schema
connection.set_schema_to_public()
self.execute_command(get_tenant_model().objects.get(schema_name=options['schema_name']), self.COMMAND_NAME,
*args, **options)
else:
for tenant in get_tenant_model().objects.all():
if not (options['skip_public'] and tenant.schema_name == get_public_schema_name()):
self.execute_command(tenant, self.COMMAND_NAME, *args, **options)
class TenantWrappedCommand(InteractiveTenantOption, BaseCommand):
"""
Generic command class useful for running any existing command
on a particular tenant. The actual command name is expected in the
class variable COMMAND_NAME of the subclass.
"""
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] | 2.465315 | 937 |
########################################################
# run_rt.py
# Author: Jamie Zhu <jimzhu@GitHub>
# Created: 2014/2/6
# Last updated: 2014/5/8
# Implemented approach: CloudPred
# Evaluation metrics: MAE, NMAE, RMSE, MRE, NPRE
########################################################
import numpy as np
import os, sys, time
import multiprocessing
sys.path.append('src')
# Build external model
if not os.path.isfile('src/core.so'):
print 'Lack of core.so (built from the C++ module).'
print 'Please first build the C++ code into core.so by using: '
print '>> python setup.py build_ext --inplace'
sys.exit()
from utilities import *
import evaluator
import dataloader
#########################################################
# config area
#
para = {'dataType': 'rt', # set the dataType as 'rt' or 'tp'
'dataPath': '../data/dataset#1/',
'outPath': 'result/',
'metrics': ['MAE', 'NMAE', 'RMSE', 'MRE', 'NPRE'], # delete where appropriate
'density': list(np.arange(0.05, 0.31, 0.05)), # matrix density
'rounds': 20, # how many runs are performed at each matrix density
'dimension': 20, # dimenisionality of the latent factors
'lambda': 30, # regularization parameter
'topK': 10, # the parameter of TopK similar users or services, the default value is
# topK = 10 as in the reference paper
'weight': 0.5, # the combination weight of UPCC and IPCC, the default value is
# weight = 0.5 as in the reference paper
'maxIter': 300, # the max iterations
'saveTimeInfo': False, # whether to keep track of the running time
'saveLog': False, # whether to save log into file
'debugMode': False, # whether to record the debug info
'parallelMode': True # whether to leverage multiprocessing for speedup
}
initConfig(para)
#########################################################
startTime = time.clock() # start timing
logger.info('==============================================')
logger.info('CloudPred: [Zhang et al, SRDS\'2011].')
# load the dataset
dataMatrix = dataloader.load(para)
logger.info('Loading data done.')
# run for each density
if para['parallelMode']: # run on multiple processes
pool = multiprocessing.Pool()
for density in para['density']:
pool.apply_async(evaluator.execute, (dataMatrix, density, para))
pool.close()
pool.join()
else: # run on single processes
for density in para['density']:
evaluator.execute(dataMatrix, density, para)
logger.info(time.strftime('All done. Total running time: %d-th day - %Hhour - %Mmin - %Ssec.',
time.gmtime(time.clock() - startTime)))
logger.info('==============================================')
sys.path.remove('src')
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] | 2.948332 | 929 |
import torch
import torch.nn as nn
import torch.nn.functional as functional
class upsampling(nn.Module):
'''
net from Deeper depth prediction with fully convolutional residual
networks
Args of __init__():
in_channels: the # of channels of the input
out_channels: the # of channels of the output (result of upsampling)
Args of forward():
x: the input
upsample_size: the output size (H x W) of the feature map
Do notice the upsample is about resolution not # of channels
'''
def forward(self, x, upsample_size):
'''
In forward pass, the (..., H, W) will be changed
it will be upsampled to increase the feature maps' resolution
'''
x = functional.interpolate(x,
size=upsample_size,
mode='bilinear')
_x = self.conv1(x)
# x_branch2 = self.bn2(self.conv2(x))
x_branch2 = self.gn2(self.conv2(x))
# _x = self.bn1(_x)
_x = self.gn1(_x)
_x = self.relu(_x)
# x_branch1 = self.bn1_2(self.conv1_2(_x))
x_branch1 = self.gn1_2(self.conv1_2(_x))
merge = self.relu(x_branch1 + x_branch2)
return merge
class Decoder(nn.Module):
'''
Decoder use a series of upsampling layers,
compressing the features and enlarging the map's scale
'''
# resnet50's layer4 has out_channels of 2048
class MFF(nn.Module):
'''
Multiscale Feature Fusion Module
Take the output of different layers of encoder
upsample them and concat them, fuse the feature maps together
Note that the upsampling layer also has conv layers
'''
class RefineBlock(nn.Module):
'''
Do the refinement work,
take the concat of MFF output and decoder output as input,
output the one channel (depth) image, i.e. the depth estimation image
dimension recap:
input:
default:
MFF output: 64 channels
Decoder output: 64 channels
in args:
MFF output: out_channels
Decoder output: in_channels // 32
so after concat:
input dim = 64 + 64 = 128 channels
'''
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220,
220,
220,
220,
220,
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25,
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220,
220,
220,
220,
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34580,
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25,
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62,
354,
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3373,
3933,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
523,
706,
1673,
265,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5128,
5391,
796,
5598,
1343,
5598,
796,
13108,
9619,
198,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
220,
220,
220,
220,
220
] | 2.10559 | 1,127 |
import math
import torch
import torch.nn as nn
from .GeneralModels import BasicBlock
| [
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] | 3.73913 | 23 |
#CodeFights
#Given a year, return the century it is in. The first century spans from the year 1 up to and including the year 100, the second - from the year 101 up to and including the year 200, etc. | [
2,
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] | 3.98 | 50 |
from raspador import Pilot, UserInteractor, BrowserInteractor
from typing import Dict, List | [
6738,
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] | 4.136364 | 22 |
# GENERATED VERSION FILE
# TIME: Thu May 6 12:37:04 2021
__version__ = '1.2.0+unknown'
short_version = '1.2.0'
version_info = (1, 2, 0)
| [
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] | 2.262295 | 61 |
import pybullet_data
import pybullet as p
import numpy as np
if __name__ == '__main__':
p.connect(p.GUI)
p.setAdditionalSearchPath(pybullet_data.getDataPath())
plane = p.createCollisionShape(p.GEOM_PLANE)
p.changeDynamics(plane, -1, lateralFriction=1)
p.createMultiBody(0, plane)
p.setGravity(0, 0, -10)
p.setRealTimeSimulation(0)
sphereUid = TextureRope.load(length=40)
while True:
p.stepSimulation()
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220,
220,
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220,
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220,
279,
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1741,
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628,
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] | 2.34359 | 195 |
from get_classification_file_with_original_annotation import get_image_label_dict_from_original_annotation, get_classification_file_based_on_label
original_annotation_path="/git/PaDiM-master/assets_new_new/data/2021-03-05/json_for_classification"
image_data_root="/git/PaDiM-master/kangqiang_result/segment_image_result_wide_resnet50_2/image/**/*.jpg"
save_image_path="/git/PaDiM-master/kangqiang_result/croped_images_with_padim_segment_for_classification"
original_image_label_dict= get_image_label_dict_from_original_annotation(original_annotation_path)
get_classification_file_based_on_label(original_image_label_dict,image_data_root,save_image_path)
| [
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] | 2.793249 | 237 |
#!/usr/bin/python
"""
This application reads key value pairs from the command line input and writes to a dictionary, which is then written to a config file
The preferred file extension is .cfg
"""
import signal
import sys
import HackPSUconfig as config
#Initialize a dictionary and define the save and configFile variables
#save is initialized to False to prevent the signal handler form saving if the program quits before a file is given
dict = {}
configFile = None
save = False
def signal_handler(signal, frame):
"""
void signal_handler (signal, frame)
This function handles the system signal SIGINT
The purpose of this function is to save before quitting
Args:
signal: The signal from the OS
frame: The current stack frame
"""
print('\nSIGINT captured; save and quit initiated.')
#If we have a
if configFile != None:
config.setProperties(configFile, dict)
sys.exit(0)
#Register our signal handler
signal.signal(signal.SIGINT, signal_handler)
#Read the input file
configFile = input('Enter a configuration file name: ')
print('Press Ctrl+C to save and quit\n')
#Loop until program is force killed
while(True):
#Read in a key, value pair and load it into the dictionary
key = input('Please enter a key: ')
value = input('Please enter a value: ')
dict[key] = value
print('Dictionary entry for ' + key+ ':' + dict[key] + ' created successfully')
print('')
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8,
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198,
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60,
1343,
705,
2727,
7675,
11537,
198,
197,
4798,
7,
7061,
8,
198,
197
] | 3.431034 | 406 |
# Generated by Django 3.1.6 on 2021-02-20 12:26
from django.db import migrations, models
| [
2,
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] | 2.84375 | 32 |
import logging
from abc import ABC
from abc import abstractmethod
from typing import List
import torch
logger = logging.getLogger(__name__)
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7,
834,
3672,
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8,
628,
198
] | 3.512195 | 41 |
import os
from src.utils.utils import load_config
def test_load_config():
"""Test the load_config function"""
config = load_config(".env.test")
assert config["app_name"] == os.getenv("APP_NAME")
assert config["parameters_path"] == os.getenv("PARAMETERS_PATH")
assert config["artifact_path"] == os.getenv("ARTIFACT_PATH")
assert config["models_path"] == os.getenv("MODELS_PATH")
assert config["logs_path"] == os.getenv("LOGS_PATH")
assert config["mlflow_tracking_db"] == os.getenv("MLFLOW_TRACKING_DB")
assert config["mlflow_tracking_uri"] == os.getenv("MLFLOW_TRACKING_URI")
assert config["mlflow_experiment"] == os.getenv("MLFLOW_EXPERIMENT")
assert config["train_test_raw_path"] == os.getenv("TRAIN_TEST_RAW_PATH")
assert config["holdout_raw_path"] == os.getenv("HOLDOUT_RAW_PATH")
| [
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13,
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51,
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51,
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62,
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62,
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220,
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448,
62,
1831,
62,
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8973,
6624,
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13,
1136,
24330,
7203,
39,
15173,
12425,
62,
20530,
62,
34219,
4943,
198
] | 2.64557 | 316 |
import logging
from django import forms
from django.core.files import File
from django.core.files.storage import default_storage
from django.core.files.uploadedfile import UploadedFile
from django.utils.crypto import get_random_string
from django.utils.translation import gettext_lazy as _
logger = logging.getLogger(__name__)
class HierarkeyForm(forms.Form):
"""
This is a custom subclass of ``django.forms.Form`` that you can use to set
values for any keys. See the Forms chapter of the documentation for more details.
"""
BOOL_CHOICES = (
('False', _('disabled')),
('True', _('enabled')),
)
def save(self) -> None:
"""
Saves all changed values to the database.
"""
for name, field in self.fields.items():
value = self.cleaned_data[name]
if isinstance(value, UploadedFile):
# Delete old file
fname = self._s.get(name, as_type=File)
if fname:
try:
default_storage.delete(fname.name)
except OSError: # pragma: no cover
logger.error('Deleting file %s failed.' % fname.name)
# Create new file
newname = default_storage.save(self.get_new_filename(value.name), value)
value._name = newname
self._s.set(name, value)
elif isinstance(value, File):
# file is unchanged
continue
elif not value and isinstance(field, forms.FileField):
# file is deleted
fname = self._s.get(name, as_type=File)
if fname:
try:
default_storage.delete(fname.name)
except OSError: # pragma: no cover
logger.error('Deleting file %s failed.' % fname.name)
del self._s[name]
elif value is None:
del self._s[name]
elif self._s.get(name, as_type=type(value)) != value:
self._s.set(name, value)
def get_new_filename(self, name: str) -> str:
"""
Returns the file name to use based on the original filename of an uploaded file.
By default, the file name is constructed as::
<model_name>-<attribute_name>/<primary_key>/<original_basename>.<random_nonce>.<extension>
"""
nonce = get_random_string(length=8)
return '%s-%s/%s/%s.%s.%s' % (
self.obj._meta.model_name, self.attribute_name,
self.obj.pk, name, nonce, name.split('.')[-1]
)
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] | 2.058778 | 1,293 |
#!/usr/bin/python
import sys
import re
import pandas as pd
import csv
if sys.argv[1].startswith("-f"):
col_number = int(sys.argv[1][2]) - 1
input_file = sys.argv[2]
else:
col_number = 0
input_file = sys.argv[1]
name_dict = {}
with open("Homo_sapiens.GRCh37.75.gtf", "r") as f:
for line in f:
if not line.startswith('#'):
if line.split("\t")[2] == "gene":
gene_id = re.findall(r'ENSG[0-9]{11}', line)[0]
gene_name = line.split("\t")[8].split("\"")[3]
name_dict[gene_id] = gene_name
expression = pd.read_csv(input_file, quoting=csv.QUOTE_NONE)
hugo_list = []
for i in range(len(expression)):
gene_id = expression.iloc[i, col_number][1:16]
if gene_id in name_dict.keys():
hugo_list.append('"' + name_dict[gene_id] + '"')
else:
hugo_list.append("\"Unknown\"")
expression.iloc[:, col_number] = hugo_list
print(expression.to_csv(quoting=csv.QUOTE_NONE,index=False,line_terminator='\n'))
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13,
33295,
10786,
30543,
1343,
1438,
62,
11600,
58,
70,
1734,
62,
312,
60,
1343,
705,
1,
11537,
201,
198,
197,
17772,
25,
201,
198,
197,
197,
71,
1018,
78,
62,
4868,
13,
33295,
7203,
7879,
20035,
7879,
4943,
201,
198,
197,
201,
198,
38011,
13,
346,
420,
58,
45299,
951,
62,
17618,
60,
796,
16225,
78,
62,
4868,
201,
198,
4798,
7,
38011,
13,
1462,
62,
40664,
7,
421,
10720,
28,
40664,
13,
10917,
23051,
62,
45,
11651,
11,
9630,
28,
25101,
11,
1370,
62,
23705,
1352,
11639,
59,
77,
6,
4008,
201,
198
] | 2.069717 | 459 |
#Array
"""
Idea:
let forward[i] = nums[0]*nums[1]*...*nums[i-1] ,backward[i] = backward[i] = nums[i+1]*nums[i+2]*...*nums[n],
then,
res[i] = forward[i]*backward[i]
"""
import unittest
unittest.main()
| [
2,
19182,
198,
198,
37811,
198,
41452,
64,
25,
198,
1616,
2651,
58,
72,
60,
796,
997,
82,
58,
15,
60,
9,
77,
5700,
58,
16,
60,
9,
986,
9,
77,
5700,
58,
72,
12,
16,
60,
837,
1891,
904,
58,
72,
60,
796,
19528,
58,
72,
60,
796,
997,
82,
58,
72,
10,
16,
60,
9,
77,
5700,
58,
72,
10,
17,
60,
9,
986,
9,
77,
5700,
58,
77,
4357,
198,
8524,
11,
198,
411,
58,
72,
60,
796,
2651,
58,
72,
60,
9,
1891,
904,
58,
72,
60,
198,
37811,
198,
198,
11748,
555,
715,
395,
198,
198,
403,
715,
395,
13,
12417,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
220
] | 1.827586 | 116 |
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