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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import random
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import augly.audio.functional as F
import augly.utils as utils
import numpy as np
from augly.audio.utils import RNGSeed
"""
Base Classes for Transforms
"""
"""
Non-Random Transforms
These classes below are essentially class-based versions of the augmentation
functions previously defined. These classes were developed such that they can
be used with Composition operators (such as `torchvision`'s) and to support
use cases where a specific transform with specific attributes needs to be
applied multiple times.
Example:
>>> audio_array = np.array([...])
>>> pitch_shift_tsfm = PitchShift(n_steps=4.0, p=0.5)
>>> shifted_audio = pitch_shift_tsfm(audio_array, sample_rate)
"""
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] | 3.438247 | 251 |
# Copyright (c) 2021 - present, Timur Shenkao
# All rights reserved.
#
# 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 collections import OrderedDict
from typing import List
# 412. Fizz Buzz https://leetcode.com/problems/fizz-buzz/
# Given an integer n, return a string array resultwer (1-indexed) where:
# resultwer[i] == "FizzBuzz" if i is divisible by 3 and 5.
# resultwer[i] == "Fizz" if i is divisible by 3.
# resultwer[i] == "Buzz" if i is divisible by 5.
# resultwer[i] == i (as a string) if none of the above conditions are true.
# 1 <= n <= 104
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] | 3.443114 | 334 |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.7 on 2016-11-08 18:16
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2010 British Broadcasting Corporation and Kamaelia Contributors(1)
#
# (1) Kamaelia Contributors are listed in the AUTHORS file and at
# http://www.kamaelia.org/AUTHORS - please extend this file,
# not this notice.
#
# 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.
# -------------------------------------------------------------------------
# Simple topography viewer server - takes textual commands from a single socket
# and renders the appropriate graph
import pygame
from Physics import Particle as BaseParticle
class RenderingParticle(BaseParticle):
"""Version of Physics.Particle with added rendering functions. """
def render(self, surface):
"""Rendering passes. A generator method that renders in multiple passes.
Use yields to specify a wait until the pass the next stage of rendering
should take place at.
Example, that renders bonds 'behind' the blobs.
def render(self, surface):
yield 1
self.renderBonds(surface) # render bonds on pass 1
yield 5
self.renderSelf(surface) # render 'blob' on pass 5
If another particle type rendered, for example, on pass 3, then it
would be rendered on top of the bonds, but behind the blobs.
Use this mechanism to order rendering into layers.
"""
x = int(self.pos[0]) - self.left
y = int(self.pos[1]) - self.top
yield 1
for p in self.bondedTo:
pygame.draw.line(surface, (128,128,255), (x,y), (int(p.pos[0] -self.left),int(p.pos[1] - self.top)) )
yield 2
pygame.draw.circle(surface, (255,128,128), (x,y), self.radius)
if self.selected:
pygame.draw.circle(surface, (0,0,0), (x,y), self.radius, 2)
surface.blit(self.label, (x - self.label.get_width()/2, y - self.label.get_height()/2))
def setOffset( self, (left,top) ):
"""Inform of a change to the coords of the top left of the drawing surface,
so that this entity can render, as if the top left had moved
"""
self.left = left
self.top = top
def select( self ):
"""Tell this particle it is selected"""
self.selected = True
def deselect( self ):
"""Tell this particle it is selected"""
self.selected = False
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220,
220,
220,
220,
220,
220,
220,
2116,
13,
34213,
796,
6407,
628,
220,
220,
220,
825,
748,
9509,
7,
2116,
15179,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
24446,
428,
18758,
340,
318,
6163,
37811,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
34213,
796,
10352,
198
] | 2.626866 | 1,139 |
import datetime
import marisa
import sys
time_begin = datetime.datetime.now()
keys = []
for line in sys.stdin:
keys.append(line.rstrip())
time_end = datetime.datetime.now()
print "input:", time_end - time_begin
time_begin = datetime.datetime.now()
dic = dict()
for i in range(len(keys)):
dic[keys[i]] = i
time_end = datetime.datetime.now()
print "dict_build:", time_end - time_begin
time_begin = datetime.datetime.now()
for key in keys:
dic.get(key)
time_end = datetime.datetime.now()
print "dict_lookup:", time_end - time_begin
time_begin = datetime.datetime.now()
keyset = marisa.Keyset()
for key in keys:
keyset.push_back(key)
time_end = datetime.datetime.now()
print "keyset_build:", time_end - time_begin
time_begin = datetime.datetime.now()
trie = marisa.Trie()
trie.build(keyset)
time_end = datetime.datetime.now()
print "trie_build:", time_end - time_begin
time_begin = datetime.datetime.now()
agent = marisa.Agent()
for key in keys:
agent.set_query(key)
trie.lookup(agent)
agent.key_id()
time_end = datetime.datetime.now()
print "trie_agent_lookup:", time_end - time_begin
time_begin = datetime.datetime.now()
for key in keys:
trie.lookup(key)
time_end = datetime.datetime.now()
print "trie_lookup:", time_end - time_begin
time_begin = datetime.datetime.now()
for i in range(len(keys)):
agent.set_query(i)
trie.reverse_lookup(agent)
agent.key_str()
time_end = datetime.datetime.now()
print "trie_agent_reverse_lookup:", time_end - time_begin
time_begin = datetime.datetime.now()
for i in range(len(keys)):
trie.reverse_lookup(i)
time_end = datetime.datetime.now()
print "trie_reverse_lookup:", time_end - time_begin
time_begin = datetime.datetime.now()
for key in keys:
agent.set_query(key)
while trie.common_prefix_search(agent):
agent.key_str()
time_end = datetime.datetime.now()
print "trie_agent_common_prefix_search:", time_end - time_begin
time_begin = datetime.datetime.now()
for key in keys:
agent.set_query(key)
while trie.predictive_search(agent):
agent.key_str()
time_end = datetime.datetime.now()
print "trie_agent_predictive_search:", time_end - time_begin
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] | 2.600733 | 819 |
"""
Copyright 2021 Lukas Kreisköther
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
https://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 __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import PIL.Image
import numpy as np
import os
import random
class RandomConceptBuilder:
"""RandomConceptBuilder objects capsule the functionality for building random concept images necessary for using the
TCAV framework in industrial usecases. For that random crops from defined sets of images (e.g. from good class
when testing the bad class) with size crop_size are build. The random concept images are stored in folders
with name prefix 'random500_' so that they can be used for the TCAV framework.
"""
def __init__(self, path, folders_for_building, store_fmt, image_shape, crop_size, num_fold=30,
num_imgs_per_fold=100):
"""Initializes a RandomConceptBuilder object.
Args:
path (str): path which leads to the directory in which the folders are laying based upon which the random
concept images should be build (e.g. '/home/lukas/Documents/02_Data/FGUSS_subsets_grey/').
folders_for_building (list of str): list of strings for all folders in the directory from which the algorithm should
choose images to build the random concept images (e.g. ['good'] or ['one', 'two', 'three'])
image_shape (list of int): list with len=2 which defines the shape the produced images should have
(normally equals the input size of the model to investigate).
crop_size (list of int): list with len=3 defining the size of the random crops (e.g. [56, 56, 3]).
num_fold (int): number of folders of random concept images the algorithm should build.
num_imgs_per_fold (int): number of images per folder for the folders of random concept images.
store_fmt (str): store format of produced images.
"""
self.path = path
self.folders_for_building = folders_for_building
self.name_prefix = 'random500_'
self.store_fmt = store_fmt
self.image_shape = image_shape
self.crop_size = crop_size
self.num_fold = num_fold
self.num_imgs_per_fold = num_imgs_per_fold
if len(self.folders_for_building) == 1:
self.X_names = [str(self.folders_for_building[0] + '/' + name) for name in
os.listdir(self.path + self.folders_for_building[0])
if not os.path.isdir(self.path + self.folders_for_building[0] + '/' + name)]
else:
X_temp = []
for folder_name in self.folders_for_building:
X_temp = X_temp + ([str(folder_name + '/' + name) for name in os.listdir(self.path + folder_name)
if not os.path.isdir(self.path + self.folders_for_building[0] + '/' + name)])
self.X_names = X_temp
np.random.shuffle(self.X_names)
self.img_tensor = tf.placeholder(tf.float32, shape=(self.image_shape[0], self.image_shape[1], 3))
self.out = tf.image.random_crop(value=self.img_tensor, size=self.crop_size)
def build_random_concept_image(self, img):
"""Method for building the random concept image from an input image.
Args:
img (numpy.ndarray[float]): image to build a random concept image from.
Returns: PIL.Image: Random concept image as PIL.Image.
"""
img = np.array(img, dtype=np.float32)
with tf.Session():
i = self.out.eval(feed_dict={self.img_tensor: img})
i = np.tile(i, (int(img.shape[0] / i.shape[0]), int(img.shape[1] / i.shape[1]), 1))
img = np.pad(array=i, pad_width=((0, img.shape[0] % i.shape[0]), (0, img.shape[1] % i.shape[1]), (0, 0)),
mode='wrap')
return PIL.Image.fromarray(img.astype(np.uint8))
def build(self):
"""Method to call to start building the concept images. Function looks how many
images are already in the folders and fills the folders respectively.
"""
for i in range(self.num_fold):
sub_fold = self.name_prefix + str(i)
if not os.path.isdir(self.path + sub_fold):
try:
os.mkdir(self.path + sub_fold + '/')
except Exception as e:
print("Creation of the directory %s failed" % sub_fold)
print(e)
else:
print("Successfully created the directory %s " % sub_fold)
num_files = len([name for name in os.listdir(self.path + sub_fold) if
os.path.isfile(os.path.join(self.path + sub_fold, name))])
if not (num_files == self.num_imgs_per_fold):
for j in range(self.num_imgs_per_fold - num_files):
img = random.choice(self.X_names)
img = np.array(PIL.Image.open(tf.gfile.Open(self.path + '/' + img, 'rb')).convert('RGB'),
dtype=np.float32)
# todo: resize (right now, we don't do it since images have to be in right size for TCAV anyway)
img_ran = self.build_random_concept_image(img)
img_ran.save(self.path + sub_fold + '/' + str(num_files + j) + '.' + self.store_fmt)
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] | 2.376671 | 2,469 |
# -*- coding: utf-8 -*-
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
from functools import partial
import numpy as np
import jieba
import paddle
from paddlenlp.data import Stack, Tuple, Pad, Vocab
from paddlenlp.transformers import BertTokenizer
from paddlenlp.datasets import load_dataset
from utils import convert_example_for_lstm, convert_example_for_distill, convert_pair_example
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = {}
with open(vocab_file, "r", encoding="utf-8") as reader:
tokens = reader.readlines()
for index, token in enumerate(tokens):
token = token.rstrip("\n").split("\t")[0]
vocab[token] = index
return vocab
def apply_data_augmentation(data,
task_name,
tokenizer,
n_iter=20,
p_mask=0.1,
p_ng=0.25,
ngram_range=(2, 6),
whole_word_mask=False,
seed=0):
"""
Data Augmentation contains Masking and n-gram sampling. Tokenization and
Masking are performed at the same time, so that the masked token can be
directly replaced by `mask_token`, after what sampling is performed.
"""
np.random.seed(seed)
new_data = []
for example in data:
if task_name == 'qqp':
data_list = tokenizer.tokenize(example['sentence1'])
data_list_2 = tokenizer.tokenize(example['sentence2'])
new_data.append({
"sentence1": data_list,
"sentence2": data_list_2,
"labels": example['labels']
})
else:
data_list = tokenizer.tokenize(example['sentence'])
new_data.append({
"sentence": data_list,
"labels": example['labels']
})
for example in data:
for _ in range(n_iter):
if task_name == 'qqp':
words = _data_augmentation(example['sentence1'], data_list)
words_2 = _data_augmentation(example['sentence2'], data_list_2)
new_data.append({
"sentence1": words,
"sentence2": words_2,
"labels": example['labels']
})
else:
words = _data_augmentation(example['sentence'], data_list)
new_data.append({
"sentence": words,
"labels": example['labels']
})
return new_data
def apply_data_augmentation_for_cn(data,
tokenizer,
vocab,
n_iter=20,
p_mask=0.1,
p_ng=0.25,
ngram_range=(2, 10),
seed=0):
"""
Because BERT and jieba have different `tokenize` function, it returns
jieba_tokenizer(example['text'], bert_tokenizer(example['text']) and
example['label]) for each example in data.
jieba tokenization and Masking are performed at the same time, so that the
masked token can be directly replaced by `mask_token`, and other tokens
could be tokenized by BERT's tokenizer, from which tokenized example for
student model and teacher model would get at the same time.
"""
np.random.seed(seed)
new_data = []
for example in data:
text_tokenized = list(jieba.cut(example['text']))
lstm_tokens = text_tokenized
bert_tokens = tokenizer.tokenize(example['text'])
new_data.append({
"lstm_tokens": lstm_tokens,
"bert_tokens": bert_tokens,
"label": example['label']
})
for _ in range(n_iter):
# 1. Masking
lstm_tokens, bert_tokens = [], []
for word in text_tokenized:
if np.random.rand() < p_mask:
lstm_tokens.append([vocab.unk_token])
bert_tokens.append([tokenizer.unk_token])
else:
lstm_tokens.append([word])
bert_tokens.append(tokenizer.tokenize(word))
# 2. N-gram sampling
lstm_tokens, bert_tokens = ngram_sampling(lstm_tokens, bert_tokens,
p_ng, ngram_range)
lstm_tokens, bert_tokens = flatten(lstm_tokens), flatten(
bert_tokens)
new_data.append({
"lstm_tokens": lstm_tokens,
"bert_tokens": bert_tokens,
"label": example['label']
})
return new_data
def create_data_loader_for_small_model(task_name,
vocab_path,
model_name=None,
batch_size=64,
max_seq_length=128,
shuffle=True):
"""Data loader for bi-lstm, not bert."""
if task_name == 'chnsenticorp':
train_ds, dev_ds = load_dataset(task_name, splits=["train", "dev"])
else:
train_ds, dev_ds = load_dataset(
'glue', task_name, splits=["train", "dev"])
if task_name == 'chnsenticorp':
vocab = Vocab.load_vocabulary(
vocab_path,
unk_token='[UNK]',
pad_token='[PAD]',
bos_token=None,
eos_token=None, )
pad_val = vocab['[PAD]']
else:
vocab = BertTokenizer.from_pretrained(model_name)
pad_val = vocab.pad_token_id
trans_fn = partial(
convert_example_for_lstm,
task_name=task_name,
vocab=vocab,
max_seq_length=max_seq_length,
is_test=False)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=pad_val), # input_ids
Stack(dtype="int64"), # seq len
Stack(dtype="int64") # label
): fn(samples)
train_ds = train_ds.map(trans_fn, lazy=True)
dev_ds = dev_ds.map(trans_fn, lazy=True)
train_data_loader, dev_data_loader = create_dataloader(
train_ds, dev_ds, batch_size, batchify_fn, shuffle)
return train_data_loader, dev_data_loader
def create_distill_loader(task_name,
model_name,
vocab_path,
batch_size=64,
max_seq_length=128,
shuffle=True,
n_iter=20,
whole_word_mask=False,
seed=0):
"""
Returns batch data for bert and small model.
Bert and small model have different input representations.
"""
tokenizer = BertTokenizer.from_pretrained(model_name)
if task_name == 'chnsenticorp':
train_ds, dev_ds = load_dataset(task_name, splits=["train", "dev"])
vocab = Vocab.load_vocabulary(
vocab_path,
unk_token='[UNK]',
pad_token='[PAD]',
bos_token=None,
eos_token=None, )
pad_val = vocab['[PAD]']
data_aug_fn = partial(
apply_data_augmentation_for_cn,
tokenizer=tokenizer,
vocab=vocab,
n_iter=n_iter,
seed=seed)
else:
train_ds, dev_ds = load_dataset(
'glue', task_name, splits=["train", "dev"])
vocab = tokenizer
pad_val = tokenizer.pad_token_id
data_aug_fn = partial(
apply_data_augmentation,
task_name=task_name,
tokenizer=tokenizer,
n_iter=n_iter,
whole_word_mask=whole_word_mask,
seed=seed)
train_ds = train_ds.map(data_aug_fn, batched=True)
print("Data augmentation has been applied.")
trans_fn = partial(
convert_example_for_distill,
task_name=task_name,
tokenizer=tokenizer,
label_list=train_ds.label_list,
max_seq_length=max_seq_length,
vocab=vocab)
trans_fn_dev = partial(
convert_example_for_distill,
task_name=task_name,
tokenizer=tokenizer,
label_list=train_ds.label_list,
max_seq_length=max_seq_length,
vocab=vocab,
is_tokenized=False)
if task_name == 'qqp':
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # bert input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # bert segment
Pad(axis=0, pad_val=pad_val), # small input_ids
Stack(dtype="int64"), # small seq len
Pad(axis=0, pad_val=pad_val), # small input_ids
Stack(dtype="int64"), # small seq len
Stack(dtype="int64") # small label
): fn(samples)
else:
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # bert input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # bert segment
Pad(axis=0, pad_val=pad_val), # small input_ids
Stack(dtype="int64"), # small seq len
Stack(dtype="int64") # small label
): fn(samples)
train_ds = train_ds.map(trans_fn, lazy=True)
dev_ds = dev_ds.map(trans_fn_dev, lazy=True)
train_data_loader, dev_data_loader = create_dataloader(
train_ds, dev_ds, batch_size, batchify_fn, shuffle)
return train_data_loader, dev_data_loader
def create_pair_loader_for_small_model(task_name,
model_name,
vocab_path,
batch_size=64,
max_seq_length=128,
shuffle=True,
is_test=False):
"""Only support QQP now."""
tokenizer = BertTokenizer.from_pretrained(model_name)
train_ds, dev_ds = load_dataset('glue', task_name, splits=["train", "dev"])
vocab = Vocab.load_vocabulary(
vocab_path,
unk_token='[UNK]',
pad_token='[PAD]',
bos_token=None,
eos_token=None, )
trans_func = partial(
convert_pair_example,
task_name=task_name,
vocab=tokenizer,
is_tokenized=False,
max_seq_length=max_seq_length,
is_test=is_test)
train_ds = train_ds.map(trans_func, lazy=True)
dev_ds = dev_ds.map(trans_func, lazy=True)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=vocab['[PAD]']), # input
Stack(), # length
Pad(axis=0, pad_val=vocab['[PAD]']), # input
Stack(), # length
Stack(dtype="int64" if train_ds.label_list else "float32") # label
): fn(samples)
train_data_loader, dev_data_loader = create_dataloader(
train_ds, dev_ds, batch_size, batchify_fn, shuffle)
return train_data_loader, dev_data_loader
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62,
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628
] | 1.906173 | 6,107 |
from .pametis import *
__all__ = [
'OPT',
'configure',
'reset',
'sitemap',
'PametisException',
'AmbiguousOptions',
'BadParam',
'PametisCacheError',
'BadDomain',
'CantRemove',
'Pametis_cache',
'Sql_cache',
'postgres',
'sqlite',
'Pametis_spider',
'file_spider',
'sitemap_spider',
]
__version__ = "0.4"
__version_info__ = ( 0, 4, 0 )
| [
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220,
220,
220,
220,
220,
220,
705,
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220,
2361,
198,
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9641,
834,
796,
366,
15,
13,
19,
1,
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834,
9641,
62,
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834,
796,
357,
657,
11,
604,
11,
657,
1267,
198
] | 1.722628 | 274 |
#coding: utf-8
import re
from pyquery import PyQuery as pq
from lxml import etree
page = '''
'''
doc = pq(page)
div = doc('div').find('.proxylistitem')
div.each(perser)
#print d('p') #返回<p>test 1</p><p>test 2</p>
#print d('p').html() #返回test 1
#print d('p').eq(1).html() #返回test 2 | [
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252,
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352,
198,
2,
4798,
288,
10786,
79,
27691,
27363,
7,
16,
737,
6494,
3419,
1303,
32573,
242,
32368,
252,
9288,
362
] | 2.125926 | 135 |
"""Classes and functions used by multiple modules in the system."""
import uuid
from hashlib import md5
import bcrypt
from voluptuous import Invalid, MultipleInvalid
def token():
"""
Generate a random but insecure token.
Returns:
The randomly generated token
"""
return str(uuid.uuid4().hex)
def hash(string):
"""
Hash a string.
Args:
string: string to be hashed.
Returns:
The hex digest of the string.
"""
return md5(string.encode("utf-8")).hexdigest()
class PicoException(Exception):
"""
General class for exceptions in the picoCTF API.
Allows specification of a message and response code to display to the
client, as well as an optional field for arbitrary data.
The 'data' field will not be displayed to clients but will be stored
in the database, making it ideal for storing stack traces, etc.
"""
def __init__(self, message, status_code=500, data=None):
"""Initialize a new PicoException."""
Exception.__init__(self)
self.message = message
self.status_code = status_code
self.data = data
def to_dict(self):
"""Convert a PicoException to a dict for serialization."""
rv = dict()
rv["message"] = self.message
return rv
def check(*callback_tuples):
"""
Voluptuous wrapper function to raise our PicoException.
Args:
callback_tuples: a callback_tuple should contain
(status, msg, callbacks)
Returns:
Returns a function callback for the Schema
"""
def v(value):
"""
Try to validate the value with the given callbacks.
Args:
value: the item to validate
Raises:
PicoException with 400 status code and error msg.
Returns:
The value if the validation callbacks are satisfied.
"""
for msg, callbacks in callback_tuples:
for callback in callbacks:
try:
result = callback(value)
if not result and type(result) == bool:
raise Invalid()
except Exception:
raise PicoException(msg, 400)
return value
return v
def validate(schema, data):
"""
Wrap the call to voluptuous schema to raise the proper exception.
Args:
schema: The voluptuous Schema object
data: The validation data for the schema object
Raises:
PicoException with 400 status code and the voluptuous error message
"""
try:
schema(data)
except MultipleInvalid as error:
raise PicoException(error.msg, 400)
def hash_password(password):
"""
Hash plaintext password.
Args:
password: plaintext password
Returns:
Secure hash of password.
"""
return bcrypt.hashpw(password.encode("utf-8"), bcrypt.gensalt(8))
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7,
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40477,
12,
23,
12340,
275,
29609,
13,
70,
641,
2501,
7,
23,
4008,
198
] | 2.499157 | 1,186 |
import numpy as np
import matplotlib.pyplot as plt
import os
from keras import layers, optimizers
from keras.models import Model, Sequential
from keras.layers import Dense, LSTM, Dropout
from keras import optimizers, regularizers
from tensorflow import keras
from tensorflow.keras import layers
from train_model import *
if __name__ == "__main__":
learning_cycle = 0
for _ in range(learning_cycle):
mymodel = train()
multi_step_inference()
new_exp()
query_new_data()
| [
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220,
220,
220,
220,
220,
12405,
62,
3605,
62,
7890,
3419,
198
] | 2.678571 | 196 |
from firebase_admin import storage | [
6738,
2046,
8692,
62,
28482,
1330,
6143
] | 4.857143 | 7 |
# Do not REMOVE
| [
2,
2141,
407,
22657,
46,
6089,
198
] | 2.285714 | 7 |
from gym.envs.registration import register
register(
id='SimpleFlappy-v0',
entry_point='gym_simpleflappy.envs:FlappyEnv',
)
register(
id='SimpleFlappyDistance-v0',
entry_point='gym_simpleflappy.envs:FlappyEnvDistance',
)
| [
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25,
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7774,
4834,
85,
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3256,
198,
8,
198
] | 2.463918 | 97 |
from gym.scoreboard.registration import add_task, add_group
add_group(
id='bandits',
name='Bandits',
description='Various N-Armed Bandit environments'
)
add_task(
id='BanditTwoArmedDeterministicFixed-v0',
group='bandits',
experimental=True,
contributor='jkcooper2',
summary="Simplest bandit where one action always pays, and the other never does.",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = [1, 0]
r_dist = [1, 1]
""",
background=""
)
add_task(
id='BanditTwoArmedHighHighFixed-v0',
group='bandits',
experimental=True,
contributor='jkcooper2',
summary="Stochastic version with a small difference between which bandit pays where both are likely",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = [0.8, 0.9]
r_dist = [1, 1]
""",
background="Bandit B Figure 2.3 from Reinforcement Learning: An Introduction (Sutton & Barto) [link](https://webdocs.cs.ualberta.ca/~sutton/book/ebook/node18.html)"
)
add_task(
id='BanditTwoArmedLowLowFixed-v0',
group='bandits',
experimental=True,
contributor='jkcooper2',
summary="Stochastic version with a small difference between which bandit pays where both are unlikley",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = [0.1, 0.2]
r_dist = [1, 1]
""",
background="Bandit A Figure 2.3 from Reinforcement Learning: An Introduction (Sutton & Barto) [link](https://webdocs.cs.ualberta.ca/~sutton/book/ebook/node18.html)"
)
add_task(
id='BanditTwoArmedHighLowFixed-v0',
group='bandits',
experimental=True,
contributor='jkcooper2',
summary="Stochastic version with a large difference between which bandit pays out of two choices",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = [0.8, 0.2]
r_dist = [1, 1]
""",
background=""
)
add_task(
id='BanditTenArmedGaussian-v0',
group='bandits',
experimental=True,
contributor='jkcooper2',
summary="10 armed bandit mentioned with reward based on a Gaussian distribution",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = [1] (* 10)
r_dist = [numpy.random.normal(0, 1), 1] (* 10)
Every bandit always pays out
Each action has a reward mean (selected from a normal distribution with mean 0 and std 1), and the actual
reward returns is selected with a std of 1 around the selected mean
""",
background="Described on page 30 of Sutton and Barto's [Reinforcement Learning: An Introduction](https://www.dropbox.com/s/b3psxv2r0ccmf80/book2015oct.pdf?dl=0)"
)
add_task(
id='BanditTenArmedRandomRandom-v0',
group='bandits',
experimental=True,
contributor='jkcooper2',
summary="10 armed bandit with random probabilities assigned to both payouts and rewards",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = numpy.random.uniform(size=10)
r_dist = numpy.random.uniform(size=10)
Bandits have uniform probability of paying out and payout a reward of uniform probability
""",
background=""
)
add_task(
id='BanditTenArmedRandomFixed-v0',
group='bandits',
experimental=True,
contributor='jkcooper2',
summary="10 armed bandit with random probabilities assigned to how often the action will provide a reward",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = numpy.random.uniform(size=10)
r_dist = numpy.full(bandits, 1)
Bandits have a uniform probability of rewarding and always reward 1
""",
background=""
)
add_task(
id='BanditTenArmedUniformDistributedReward-v0',
group='bandits',
experimental=True,
contributor='jkcooper2',
summary="10 armed bandit with that always pays out with a reward selected from a uniform distribution",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = numpy.full(bandits, 1)
r_dist = numpy.random.uniform(size=10)
Bandits always pay out. Reward is selected from uniform distribution
""",
background="Based on comparisons from http://sudeepraja.github.io/Bandits/"
)
add_task(
id='BanditTwoArmedIndependentUniform-v0',
group='bandits',
experimental=True,
contributor='Thomas_Lecat',
summary="Simple two independent armed bandit giving a reward of one with probabilities p_1 and p_2",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p_dist = np.random.uniform(2)
r_dist = [1, 1]
""",
background="For the first experience, called 'Bandit with independent arms' of https://arxiv.org/abs/1611.05763"
add_task(
id='BanditTwoArmedDependentUniform-v0',
group='bandits',
experimental=True,
contributor='Thomas_Lecat',
summary="Two armed bandit giving a reward of one with probabilities p_1 ~ U[0,1] and p_2 = 1 - p_1",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p = np.random.uniform()
p_dist = [p, 1-p]
r_dist = [1, 1]
""",
background="For the experience called 'Bandits with dependent arms (I)' of https://arxiv.org/abs/1611.05763"
add_task(
id='BanditTwoArmedDependentEasy-v0',
group='bandits',
experimental=True,
contributor='Thomas_Lecat',
summary="Two armed bandit giving a reward of one with probabilities p_1 ~ U[0.1,0.9] and p_2 = 1 - p_1",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p = [0.1,0,9][np.random.randint(0,2)]
p_dist = [p, 1-p]
r_dist = [1, 1]
""",
background="For the experience called 'Bandits with dependent arms (I)' of https://arxiv.org/abs/1611.05763"
add_task(
id='BanditTwoArmedDependentMedium-v0',
group='bandits',
experimental=True,
contributor='Thomas_Lecat',
summary="Two armed bandit giving a reward of one with probabilities p_1 ~ U[0.25,0.75] and p_2 = 1 - p_1",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p = [0.25,0,75][np.random.randint(0,2)]
p_dist = [p, 1-p]
r_dist = [1, 1]
""",
background="For the experience called 'Bandits with dependent arms (I)' of https://arxiv.org/abs/1611.05763"
add_task(
id='BanditTwoArmedDependentHard-v0',
group='bandits',
experimental=True,
contributor='Thomas_Lecat',
summary="Two armed bandit giving a reward of one with probabilities p_1 ~ U[0.4,0.6] and p_2 = 1 - p_1",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
p = [0.4,0,6][np.random.randint(0,2)]
p_dist = [p, 1-p]
r_dist = [1, 1]
""",
background="For the experience called 'Bandits with dependent arms (I)' of https://arxiv.org/abs/1611.05763"
add_task(
id='BanditEleveArmedWithIndex-v0',
group='bandits',
experimental=True,
contributor='Thomas_Lecat',
summary="11 armed bandit with deterministic payouts. \
Nine 'non-target' return a reward of 1.1, \
one 'target' returns a reward of 5, \
the 11th arm has reward = 0.1 * index of the target arm (ranging from 0.1 to 1.0)",
description="""
Each bandit takes in a probability distribution, which is the likelihood of the action paying out,
and a reward distribution, which is the value or distribution of what the agent will be rewarded
the bandit does payout.
index = np.random.randint(0,10)
p_dist = np.full(11,1)
r_dist = np.full(11,1.1)
r_dist[index] = 5
r_dist[-1] = 0.1*index
BanditEnv.__init__(self, p_dist = p_dist, r_dist = r_dist)
""",
background="For the experience called 'Bandits with dependent arms (II)' of https://arxiv.org/abs/1611.05763"
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430,
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1998,
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351,
4795,
5101,
6,
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3740,
1378,
283,
87,
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14,
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14,
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220,
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220,
4469,
2625,
1890,
262,
1998,
1444,
705,
31407,
896,
351,
10795,
5101,
357,
40,
33047,
286,
3740,
1378,
283,
87,
452,
13,
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14,
8937,
14,
1433,
1157,
13,
2713,
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1,
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2860,
62,
35943,
7,
198,
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220,
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31407,
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7571,
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1150,
35,
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28406,
12,
85,
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198,
220,
220,
220,
1448,
11639,
3903,
896,
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11992,
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220,
220,
18920,
11639,
22405,
62,
43,
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265,
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220,
220,
10638,
2625,
7571,
6936,
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39522,
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1988,
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11,
24,
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13,
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17,
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79,
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198,
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220,
220,
13538,
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198,
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220,
220,
4469,
2625,
1890,
262,
1998,
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705,
31407,
896,
351,
10795,
5101,
357,
40,
33047,
286,
3740,
1378,
283,
87,
452,
13,
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14,
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14,
1433,
1157,
13,
2713,
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1,
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198,
2860,
62,
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7,
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220,
220,
4686,
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31407,
270,
7571,
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35,
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31205,
12,
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198,
220,
220,
220,
1448,
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3903,
896,
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11992,
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220,
220,
220,
18920,
11639,
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62,
43,
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265,
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220,
220,
10638,
2625,
7571,
6936,
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3501,
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530,
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39522,
279,
62,
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543,
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1988,
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286,
644,
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5797,
481,
307,
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262,
4097,
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40055,
13,
628,
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220,
220,
279,
796,
685,
15,
13,
1495,
11,
15,
11,
2425,
7131,
37659,
13,
25120,
13,
25192,
600,
7,
15,
11,
17,
15437,
198,
220,
220,
220,
279,
62,
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796,
685,
79,
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198,
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220,
220,
13538,
1600,
198,
220,
220,
220,
4469,
2625,
1890,
262,
1998,
1444,
705,
31407,
896,
351,
10795,
5101,
357,
40,
33047,
286,
3740,
1378,
283,
87,
452,
13,
2398,
14,
8937,
14,
1433,
1157,
13,
2713,
49641,
1,
198,
198,
2860,
62,
35943,
7,
198,
220,
220,
220,
4686,
11639,
31407,
270,
7571,
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1150,
35,
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17309,
12,
85,
15,
3256,
198,
220,
220,
220,
1448,
11639,
3903,
896,
3256,
198,
220,
220,
220,
11992,
28,
17821,
11,
198,
220,
220,
220,
18920,
11639,
22405,
62,
43,
721,
265,
3256,
198,
220,
220,
220,
10638,
2625,
7571,
6936,
4097,
270,
3501,
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530,
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39522,
279,
62,
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5299,
471,
58,
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13,
19,
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21,
60,
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62,
17,
796,
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532,
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220,
6764,
2625,
15931,
198,
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220,
220,
5501,
4097,
270,
2753,
287,
257,
12867,
6082,
11,
543,
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1988,
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11,
21,
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13,
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13,
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600,
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17,
15437,
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220,
279,
62,
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198,
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220,
220,
13538,
1600,
198,
220,
220,
220,
4469,
2625,
1890,
262,
1998,
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705,
31407,
896,
351,
10795,
5101,
357,
40,
33047,
286,
3740,
1378,
283,
87,
452,
13,
2398,
14,
8937,
14,
1433,
1157,
13,
2713,
49641,
1,
628,
220,
220,
220,
751,
62,
35943,
7,
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220,
220,
4686,
11639,
31407,
270,
28827,
303,
3163,
1150,
3152,
15732,
12,
85,
15,
3256,
198,
220,
220,
220,
1448,
11639,
3903,
896,
3256,
198,
220,
220,
220,
11992,
28,
17821,
11,
198,
220,
220,
220,
18920,
11639,
22405,
62,
43,
721,
265,
3256,
198,
220,
220,
220,
10638,
2625,
1157,
6936,
4097,
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2206,
49228,
1414,
5269,
13,
3467,
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16431,
705,
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12,
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3467,
198,
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220,
220,
220,
220,
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530,
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6,
5860,
257,
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286,
642,
11,
3467,
198,
220,
220,
220,
220,
220,
220,
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9,
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220,
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834,
7,
944,
11,
279,
62,
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796,
279,
62,
17080,
11,
374,
62,
17080,
796,
374,
62,
17080,
8,
198,
220,
220,
220,
13538,
1600,
198,
220,
220,
220,
4469,
2625,
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262,
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87,
452,
13,
2398,
14,
8937,
14,
1433,
1157,
13,
2713,
49641,
1,
198
] | 2.93978 | 3,454 |
from django import forms
from django.contrib.auth import forms as auth_forms
from django.contrib.auth.models import User
from books.widgets import NoNameTextInput
# TODO: Might be good to update this later to update the username too so we aren't doing two database saves
PRODUCTS = [
('ebook', 'eBook Only'),
('paperback', 'Paperback'),
('video', 'Video'),
]
| [
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] | 3.228814 | 118 |
# -*- coding: utf-8 -*-
"""
It generates plots that shows similarity for anomalies in each dataset.
"""
import copy
import math
import numpy as np
import matplotlib
import matplotlib.mlab
import matplotlib.pyplot as plt
from matplotlib import gridspec
import nslkdd.preprocessing as preprocessing
import nslkdd.data.model as model
if __name__ == '__main__':
import time
start = time.time()
df_training_20, df_training_full, gmms_training_20, gmms_training_full = preprocessing.get_preprocessed_training_data()
df_test_plus, df_test_21, gmms_test_plus, gmms_test_21 = preprocessing.get_preprocessed_test_data()
generate_plots_for_df(df_training_20, gmms_training_20, "training20")
generate_plots_for_df(df_training_full, gmms_training_full, "trainingfull")
generate_plots_for_df(df_test_plus, gmms_test_plus, "testplus")
generate_plots_for_df(df_test_21, gmms_test_21, "test21")
| [
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11,
308,
76,
907,
62,
9288,
62,
2481,
11,
366,
9288,
2481,
4943,
628
] | 2.670554 | 343 |
from nose.tools import assert_equal
from nose import SkipTest
import random
from pbtools.pbdagcon.aligngraph import *
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] | 3.529412 | 34 |
from django.utils import timezone
from django.utils.translation import gettext_lazy as _
from rest_framework import serializers
from rest_framework.exceptions import ValidationError
from ggongsul.member.models import Member
from ggongsul.member.serializers import MemberSerializer
from ggongsul.partner.models import Partner
from ggongsul.partner.serializers import PartnerShortInfoSerializer
from ggongsul.visitation.models import Visitation
| [
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] | 3.725 | 120 |
import json
import os
import random
import bottle
from api import ping_response, start_response, move_response, end_response
# Moving towards a tail is safe as long as that snake does not have food witihn reach.
# If it is te only possible move, that move should be made anyway
@bottle.route('/')
@bottle.route('/static/<path:path>')
def static(path):
"""
Given a path, return the static file located relative
to the static folder.
This can be used to return the snake head URL in an API response.
"""
return bottle.static_file(path, root='static/')
@bottle.post('/ping')
def ping():
"""
A keep-alive endpoint used to prevent cloud application platforms,
such as Heroku, from sleeping the application instance.
"""
return ping_response()
@bottle.post('/start')
@bottle.post('/move')
# int x,y or tuple (NEXT STEP)
##Only looks for dead end
##def snake_head_area(snake_heads, my_head):
## avoid_heads = []
## snake_heads1 = snake_heads
## snake_heads1.remove(my_head)
##
## for heads in snake_heads1:
## avoid_heads.append((heads[0]+1, heads[1]))
## avoid_heads.append((heads[0] - 1, heads[1]))
## avoid_heads.append((heads[0], heads[1] + 1))
## avoid_heads.append((heads[0], heads[1] - 1))
##
## return avoid_heads
# def safetyLevel(x,y, stuffToAvoid):
@bottle.post('/end')
# Expose WSGI app (so gunicorn can find it)
application = bottle.default_app()
if __name__ == '__main__':
bottle.run(
application,
host=os.getenv('IP', '0.0.0.0'),
port=os.getenv('PORT', '8080'),
debug=os.getenv('DEBUG', True)
)
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30531,
3256,
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8,
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220,
220,
220,
1267,
198
] | 2.645367 | 626 |
""" Internal model of a report during generation """
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# in this software.
# 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 or in the LICENSE file in the root pyGSTi directory.
#***************************************************************************************************
import time as _time
import warnings as _warnings
from pathlib import Path as _Path
import shutil as _shutil
from collections import defaultdict as _defaultdict, OrderedDict as _OrderedDict
import pickle as _pickle
from . import autotitle as _autotitle
from . import merge_helpers as _merge
from .. import _version, tools as _tools
from ..objects import VerbosityPrinter as _VerbosityPrinter, ExplicitOpModel as _ExplicitOpModel
from . import workspace as _ws
from .notebook import Notebook as _Notebook
# TODO this whole thing needs to be rewritten with different reports as derived classes
class Report:
""" The internal model of a report.
This class should never be instantiated directly. Instead, users
should use the appropriate factory method in
`pygsti.report.factory`.
"""
def _build(self, build_options=None):
""" Render all sections to a map of report elements for templating """
full_params = {
'results': self._results,
**self._report_params
}
full_params.update(self._build_defaults)
full_params.update(build_options or {})
qtys = self._global_qtys.copy()
for section in self._sections:
qtys.update(section.render(self._workspace, **full_params))
return qtys
def write_html(self, path, auto_open=False, link_to=None,
connected=False, build_options=None, brevity=0,
precision=None, resizable=True, autosize='initial',
single_file=False, verbosity=0):
""" Write this report to the disk as a collection of HTML documents.
Parameters
----------
path : str or path-like object
The filesystem path of a directory to write the report
to. If the specified directory does not exist, it will be
created automatically
auto_open : bool, optional
Whether the output file should be automatically opened in a web browser.
link_to : list, optional
If not None, a list of one or more items from the set
{"tex", "pdf", "pkl"} indicating whether or not to
create and include links to Latex, PDF, and Python pickle
files, respectively.
connected : bool, optional
Whether output HTML should assume an active internet connection. If
True, then the resulting HTML file size will be reduced because it
will link to web resources (e.g. CDN libraries) instead of embedding
them.
build_options : dict
Dict of options for building plots. Expected values are
defined during construction of this report object.
brevity : int, optional
Amount of detail to include in the report. Larger values mean smaller
"more briefr" reports, which reduce generation time, load time, and
disk space consumption. In particular:
- 1: Plots showing per-sequences quantities disappear at brevity=1
- 2: Reference sections disappear at brevity=2
- 3: Germ-level estimate tables disappear at brevity=3
- 4: Everything but summary figures disappears at brevity=4
precision : int or dict, optional
The amount of precision to display. A dictionary with keys
"polar", "sci", and "normal" can separately specify the
precision for complex angles, numbers in scientific notation, and
everything else, respectively. If an integer is given, it this
same value is taken for all precision types. If None, then
`{'normal': 6, 'polar': 3, 'sci': 0}` is used.
resizable : bool, optional
Whether plots and tables are made with resize handles and can be
resized within the report.
autosize : {'none', 'initial', 'continual'}
Whether tables and plots should be resized, either initially --
i.e. just upon first rendering (`"initial"`) -- or whenever
the browser window is resized (`"continual"`).
single_file : bool, optional
If true, the report will be written to a single HTML
document, with external dependencies baked-in. This mode
is not recommended for large reports, because this file
can grow large enough that major web browsers may struggle
to render it.
verbosity : int, optional
Amount of detail to print to stdout.
"""
build_options = build_options or {}
toggles = _defaultdict(lambda: False)
toggles.update(
{k: True for k in self._flags}
)
for k in range(brevity, 4):
toggles['BrevityLT' + str(k + 1)] = True
# Render sections
qtys = self._build(build_options)
# TODO this really should be a parameter of this method
embed_figures = self._report_params.get('embed_figures', True)
if single_file:
assert(embed_figures), \
"Single-file mode requires `embed_figures` to be True"
_merge.merge_jinja_template(
qtys, path, templateDir=self._templates['html'],
auto_open=auto_open, precision=precision,
link_to=link_to, connected=connected, toggles=toggles,
renderMath=True, resizable=resizable,
autosize=autosize, verbosity=verbosity
)
else:
_merge.merge_jinja_template_dir(
qtys, path, templateDir=self._templates['html'],
auto_open=auto_open, precision=precision,
link_to=link_to, connected=connected, toggles=toggles,
renderMath=True, resizable=resizable,
autosize=autosize, embed_figures=embed_figures,
verbosity=verbosity
)
def write_notebook(self, path, auto_open=False, connected=False, verbosity=0):
""" Write this report to the disk as an IPython notebook
A notebook report allows the user to interact more flexibly with the data
underlying the figures, and to easily generate customized variants on the
figures. As such, this type of report will be most useful for experts
who want to tinker with the standard analysis presented in the static
HTML or LaTeX format reports.
Parameters
----------
path : str or path-like object
The filesystem path to write the report to. By convention,
this should use the `.ipynb` file extension.
auto_open : bool, optional
If True, automatically open the report in a web browser after it
has been generated.
connected : bool, optional
Whether output notebook should assume an active internet connection. If
True, then the resulting file size will be reduced because it will link
to web resources (e.g. CDN libraries) instead of embedding them.
verbosity : int, optional
How much detail to send to stdout.
"""
# TODO this only applies to standard reports; rewrite generally
title = self._global_qtys['title']
confidenceLevel = self._report_params['confidence_level']
path = _Path(path)
printer = _VerbosityPrinter.build_printer(verbosity)
templatePath = _Path(__file__).parent / 'templates' / self._templates['notebook']
outputDir = path.parent
#Copy offline directory into position
if not connected:
_merge.rsync_offline_dir(outputDir)
#Save results to file
# basename = _os.path.splitext(_os.path.basename(filename))[0]
basename = path.stem
results_file_base = basename + '_results.pkl'
results_file = outputDir / results_file_base
with open(str(results_file), 'wb') as f:
_pickle.dump(self._results, f)
nb = _Notebook()
nb.add_markdown('# {title}\n(Created on {date})'.format(
title=title, date=_time.strftime("%B %d, %Y")))
nb.add_code("""\
import pickle
import pygsti""")
dsKeys = list(self._results.keys())
results = self._results[dsKeys[0]]
#Note: `results` is always a single Results obj from here down
nb.add_code("""\
#Load results dictionary
with open('{infile}', 'rb') as infile:
results_dict = pickle.load(infile)
print("Available dataset keys: ", ', '.join(results_dict.keys()))\
""".format(infile=results_file_base))
nb.add_code("""\
#Set which dataset should be used below
results = results_dict['{dsKey}']
print("Available estimates: ", ', '.join(results.estimates.keys()))\
""".format(dsKey=dsKeys[0]))
estLabels = list(results.estimates.keys())
estimate = results.estimates[estLabels[0]]
nb.add_code("""\
#Set which estimate is to be used below
estimate = results.estimates['{estLabel}']
print("Available gauge opts: ", ', '.join(estimate.goparameters.keys()))\
""".format(estLabel=estLabels[0]))
goLabels = list(estimate.goparameters.keys())
nb.add_code("""\
gopt = '{goLabel}'
ds = results.dataset
gssFinal = results.circuit_structs['final']
Ls = results.circuit_structs['final'].Ls
gssPerIter = results.circuit_structs['iteration'] #ALL_L
prepStrs = results.circuit_lists['prep fiducials']
effectStrs = results.circuit_lists['meas fiducials']
germs = results.circuit_lists['germs']
strs = (prepStrs, effectStrs)
params = estimate.parameters
objective = estimate.parameters['objective']
if objective == "logl":
mpc = estimate.parameters['minProbClip']
else:
mpc = estimate.parameters['minProbClipForWeighting']
clifford_compilation = estimate.parameters.get('clifford_compilation',None)
effective_ds, scale_subMxs = estimate.get_effective_dataset(True)
scaledSubMxsDict = {{'scaling': scale_subMxs, 'scaling.colormap': "revseq"}}
models = estimate.models
mdl = models[gopt] #FINAL
mdl_final = models['final iteration estimate'] #ITER
target_model = models['target']
mdlPerIter = models['iteration estimates']
mdl_eigenspace_projected = pygsti.tools.project_to_target_eigenspace(mdl, target_model)
goparams = estimate.goparameters[gopt]
confidenceLevel = {CL}
if confidenceLevel is None:
cri = None
else:
crfactory = estimate.get_confidence_region_factory(gopt)
region_type = "normal" if confidenceLevel >= 0 else "non-markovian"
cri = crfactory.view(abs(confidenceLevel), region_type)\
""".format(goLabel=goLabels[0], CL=confidenceLevel))
nb.add_code("""\
from pygsti.report import Workspace
ws = Workspace()
ws.init_notebook_mode(connected={conn}, autodisplay=True)\
""".format(conn=str(connected)))
nb.add_notebook_text_files([
templatePath / 'summary.txt',
templatePath / 'goodness.txt',
templatePath / 'gauge_invariant.txt',
templatePath / 'gauge_variant.txt'])
#Insert multi-dataset specific analysis
if len(dsKeys) > 1:
nb.add_markdown(('# Dataset comparisons\n'
'This report contains information for more than one data set.'
'This page shows comparisons between different data sets.'))
nb.add_code("""\
dslbl1 = '{dsLbl1}'
dslbl2 = '{dsLbl2}'
dscmp_gss = results_dict[dslbl1].circuit_structs['final']
ds1 = results_dict[dslbl1].dataset
ds2 = results_dict[dslbl2].dataset
dscmp = pygsti.obj.DataComparator([ds1, ds2], DS_names=[dslbl1, dslbl2])
""".format(dsLbl1=dsKeys[0], dsLbl2=dsKeys[1]))
nb.add_notebook_text_files([
templatePath / 'data_comparison.txt'])
#Add reference material
nb.add_notebook_text_files([
templatePath / 'input.txt',
templatePath / 'meta.txt'])
printer.log("Report Notebook created as %s" % path)
if auto_open:
port = "auto" if auto_open is True else int(auto_open)
nb.launch(str(path), port=port)
else:
nb.save_to(str(path))
def write_pdf(self, path, latex_cmd='pdflatex', latex_flags=None,
build_options=None,
brevity=0, precision=None, auto_open=False,
comm=None, verbosity=0):
""" Write this report to the disk as a PDF document.
Parameters
----------
path : str or path-like object
The filesystem path to write the report to. By convention,
this should use the `.pdf` file extension.
latex_cmd : str, optional
Shell command to run to compile a PDF document from the
generated LaTeX source.
latex_flags : [str], optional
List of flags to pass when calling `latex_cmd`.
build_options : dict
Dict of options for building plots. Expected values are
defined during construction of this report object.
brevity : int, optional
Amount of detail to include in the report. Larger values mean smaller
"more briefr" reports, which reduce generation time, load time, and
disk space consumption. In particular:
- 1: Plots showing per-sequences quantities disappear at brevity=1
- 2: Reference sections disappear at brevity=2
- 3: Germ-level estimate tables disappear at brevity=3
- 4: Everything but summary figures disappears at brevity=4
precision : int or dict, optional
The amount of precision to display. A dictionary with keys
"polar", "sci", and "normal" can separately specify the
precision for complex angles, numbers in scientific notation, and
everything else, respectively. If an integer is given, it this
same value is taken for all precision types. If None, then
`{'normal': 6, 'polar': 3, 'sci': 0}` is used.
auto_open : bool, optional
Whether the output file should be automatically opened in a web browser.
comm : mpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation
across multiple processors.
verbosity : int, optional
Amount of detail to print to stdout.
"""
if not self._pdf_available:
raise ValueError(("PDF output unavailable. (Usually this is because this report"
" has multiple gauge optimizations and/or datasets.)"))
toggles = _defaultdict(lambda: False)
toggles.update(
{k: True for k in self._flags}
)
for k in range(brevity, 4):
toggles['BrevityLT' + str(k + 1)] = True
printer = _VerbosityPrinter.build_printer(verbosity, comm=comm)
path = _Path(path)
latex_flags = latex_flags or ["-interaction=nonstopmode", "-halt-on-error", "-shell-escape"]
# Render sections
qtys = self._build(build_options)
# TODO: filter while generating plots to remove need for sanitization
qtys = {k: v for k, v in qtys.items()
if not(isinstance(v, _ws.Switchboard) or isinstance(v, _ws.SwitchboardView))}
printer.log("Generating LaTeX source...")
_merge.merge_latex_template(
qtys, self._templates['pdf'], str(path.with_suffix('.tex')),
toggles, precision, printer
)
printer.log("Compiling with `{} {}`".format(latex_cmd, ' '.join(latex_flags)))
_merge.compile_latex_report(str(path.parent / path.stem), [latex_cmd] + latex_flags, printer, auto_open)
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611,
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4808,
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17660,
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3108,
13,
927,
828,
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87,
62,
28758,
60,
1343,
47038,
62,
33152,
11,
20632,
11,
8295,
62,
9654,
8,
198
] | 2.399412 | 7,148 |
import pytest
from datetime import datetime
from update import lambda_handler
import boto3
import os
import json
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table(os.environ['TABLE_NAME'])
# ------------------------------------------
# valid pattern
# ------------------------------------------
@pytest.mark.parametrize("word,is_done,priority", [
(word, is_done, priority)
for word in [None, "", "修正後内容"]
for is_done in ['true', 'false', True, False]
for priority in ['high', 'medium', 'low']
])
# ------------------------------------------
# not found pattern
# ------------------------------------------
@pytest.fixture()
# ------------------------------------------
# invalid pattern
# ------------------------------------------
INVALID_PAYLOAD_LIST = [
{
"title": ""
},
{
"title": None
},
{
"title": "a" * 101
},
{
"content": "a" * 2001
},
{
"priority": "invalid_priority_value"
},
{
"is_done": "invalid_is_done_value"
},
]
@pytest.fixture(params=INVALID_PAYLOAD_LIST)
| [
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31,
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7,
37266,
28,
1268,
23428,
2389,
62,
4537,
56,
35613,
62,
45849,
8,
628
] | 2.574944 | 447 |
from bot import DOWNLOAD_DIR, LOGGER
from bot.helper.ext_utils.bot_utils import MirrorStatus, get_readable_file_size, get_readable_time
from time import sleep
| [
6738,
10214,
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62,
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198,
6738,
640,
1330,
3993,
628
] | 3.404255 | 47 |
#!/usr/bin/env python3
import pyaudio
import sys
sys.path.insert(0, "../")
from pwmaudio import noALSAerror
with noALSAerror():
p = pyaudio.PyAudio()
info = p.get_host_api_info_by_index(0)
print(p.get_host_api_count())
print(info)
numdevices = info.get('deviceCount')
for i in range(0, numdevices):
if (p.get_device_info_by_host_api_device_index(0, i).get('maxOutputChannels')) > 0:
# print("Output Device id ", i, " - ", p.get_device_info_by_host_api_device_index(0, i).get('name'))
print("Output Device id ", i, " - ", p.get_device_info_by_host_api_device_index(0, i))
| [
2,
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6,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
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62,
4774,
62,
15042,
62,
25202,
62,
9630,
7,
15,
11,
1312,
4008,
198
] | 2.229167 | 288 |
import sys
import math
import random
import imp
from java import jclass
from controller.leds import leds
from controller.message import message
from controller.sensor import sensor
from controller.motion_sensor import motion_sensor
from controller.button import button
from controller.color_sensor import color_sensor
from controller.infrared_sensor import infrared_sensor
from controller.sound_sensor import sound_sensor
from controller.timer import timer
imp.load_source('controllerleds', '/data/data/com.matatalab.matatacode/run/controller/leds.py')
imp.load_source('controllermessage', '/data/data/com.matatalab.matatacode/run/controller/message.py')
imp.load_source('controllesensor', '/data/data/com.matatalab.matatacode/run/controller/sensor.py')
imp.load_source('controllemotion_sensor', '/data/data/com.matatalab.matatacode/run/controller/motion_sensor.py')
imp.load_source('controllebutton', '/data/data/com.matatalab.matatacode/run/controller/button.py')
imp.load_source('controllecolor_sensor', '/data/data/com.matatalab.matatacode/run/controller/color_sensor.py')
imp.load_source('controlleinfrared_sensor', '/data/data/com.matatalab.matatacode/run/controller/infrared_sensor.py')
imp.load_source('controllesound_sensor', '/data/data/com.matatalab.matatacode/run/controller/sound_sensor.py')
imp.load_source('controlletimer', '/data/data/com.matatalab.matatacode/run/controller/timer.py')
| [
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14,
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14,
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62,
82,
22854,
13,
9078,
11537,
198,
11011,
13,
2220,
62,
10459,
10786,
13716,
1616,
22723,
3256,
31051,
7890,
14,
7890,
14,
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265,
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14,
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13,
9078,
11537,
628
] | 3.083516 | 455 |
# -*- coding: utf-8 -*-
"""OpenCTI CrowdStrike actor importer module."""
from typing import Any, Generator, List, Mapping, Optional
from crowdstrike_client.api.intel.actors import Actors
from crowdstrike_client.api.models import Response
from crowdstrike_client.api.models.actor import Actor
from pycti.connector.opencti_connector_helper import OpenCTIConnectorHelper # type: ignore # noqa: E501
from stix2 import Bundle, Identity, MarkingDefinition # type: ignore
from crowdstrike.actor.builder import ActorBundleBuilder
from crowdstrike.importer import BaseImporter
from crowdstrike.utils import datetime_to_timestamp, paginate, timestamp_to_datetime
class ActorImporter(BaseImporter):
"""CrowdStrike actor importer."""
_LATEST_ACTOR_TIMESTAMP = "latest_actor_timestamp"
def __init__(
self,
helper: OpenCTIConnectorHelper,
actors_api: Actors,
update_existing_data: bool,
author: Identity,
default_latest_timestamp: int,
tlp_marking: MarkingDefinition,
) -> None:
"""Initialize CrowdStrike actor importer."""
super().__init__(helper, author, tlp_marking, update_existing_data)
self.actors_api = actors_api
self.default_latest_timestamp = default_latest_timestamp
def run(self, state: Mapping[str, Any]) -> Mapping[str, Any]:
"""Run importer."""
self._info("Running actor importer with state: {0}...", state)
fetch_timestamp = state.get(
self._LATEST_ACTOR_TIMESTAMP, self.default_latest_timestamp
)
latest_fetched_actor_timestamp = None
for actors_batch in self._fetch_actors(fetch_timestamp):
if not actors_batch:
break
if latest_fetched_actor_timestamp is None:
first_in_batch = actors_batch[0]
created_date = first_in_batch.created_date
if created_date is None:
self._error(
"Missing created date for actor {0} ({1})",
first_in_batch.name,
first_in_batch.id,
)
break
latest_fetched_actor_timestamp = datetime_to_timestamp(created_date)
self._process_actors(actors_batch)
state_timestamp = latest_fetched_actor_timestamp or fetch_timestamp
self._info(
"Actor importer completed, latest fetch {0}.",
timestamp_to_datetime(state_timestamp),
)
return {self._LATEST_ACTOR_TIMESTAMP: state_timestamp}
| [
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7,
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23518,
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62,
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27823,
92,
198
] | 2.326165 | 1,116 |
from datetime import date
from zerobouncesdk import zerobouncesdk, ZBApiException, \
ZBMissingApiKeyException
test()
| [
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] | 2.847826 | 46 |
#!/usr/bin/python
#################################################################################
# MIT License
#
# Copyright (c) 2019 Aaron Jense, Amy Heidner, Dennis Heidner
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
#################################################################################
from third_party.Adafruit_I2C import *
from IAQ_Exceptions import *
import struct
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2878,
201,
198
] | 3.801061 | 377 |
import numpy as np
import math
import logging
log = logging.getLogger(__name__)
def fairness_reward(actions, queues=None):
"""Compute Jain"s fairness index for a list of values.
See http://en.wikipedia.org/wiki/Fairness_measure for fairness equations.
@param values: list of values
@return fairness: JFI
"""
if len(actions) == 0:
return 1.0
num = sum(actions) ** 2
denom = len(actions) * sum([i ** 2 for i in actions])
return num / float(denom)
def gini_reward(actions, queues=None):
"""Calculate the Gini coefficient of a numpy array."""
# based on bottom eq:
# http://www.statsdirect.com/help/generatedimages/equations/equation154.svg
# from:
# http://www.statsdirect.com/help/default.htm#nonparametric_methods/gini.htm
# All values are treated equally, arrays must be 1d:
# Values must be sorted:
actions = np.sort(actions)
# Number of array elements:
n = actions.shape[0]
# Index per array element:
index = np.arange(1, n + 1)
# Gini coefficient:
return ((np.sum((2 * index - n - 1) * actions)) / (n * np.sum(actions)))
# small script to visualize the reward output
if __name__ == "__main__":
import matplotlib.pyplot as plt
queues = [i * 0.1 for i in range(0, 11)]
actions = [i * .001 for i in range(0, 1000)]
for queue in queues:
rewards = []
queue_input = np.array([queue])
for action in actions:
action_input = np.array([action])
rewards.append((joint_queue_reward(action_input, queue_input)))
plt.plot(actions, rewards, label="Queue Size %f" % queue)
plt.xlabel("Action Input")
plt.ylabel("Reward")
plt.legend()
plt.show()
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220,
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62,
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7,
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62,
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11,
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220,
220,
220,
220,
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458,
83,
13,
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7,
4658,
11,
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11,
6167,
2625,
34991,
12849,
4064,
69,
1,
4064,
16834,
8,
198,
220,
220,
220,
458,
83,
13,
87,
18242,
7203,
12502,
23412,
4943,
198,
220,
220,
220,
458,
83,
13,
2645,
9608,
7203,
48123,
4943,
198,
220,
220,
220,
458,
83,
13,
1455,
437,
3419,
198,
220,
220,
220,
458,
83,
13,
12860,
3419,
198
] | 2.580357 | 672 |
# -*- coding: utf-8 -*-
import os
| [
2,
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9,
12,
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25,
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69,
12,
23,
532,
9,
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28686,
628,
628
] | 2.055556 | 18 |
print('vamos calcular seu IMC')
a = float(input('Sua altura: '))
p = float(input('Seu peso: '))
n = (p/(a**2))
print(f'Seu IMC e: {n:.1f}')
if n < 18.5:
print('Abaixo do peso.')
elif n <= 25 and n > 18.5:
print('Peso ideal.')
elif n < 30 and n > 25:
print('Sobrepeso.')
elif n <= 40 and 30 < n:
print('obsidade.')
else:
print('obsidade mórbida.')
| [
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285,
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3755,
2637,
8,
198,
220,
220,
220,
220
] | 1.994624 | 186 |
from unittest import TestCase
import logging
from supplychainpy._helpers import _data_cleansing
from supplychainpy.sample_data.config import ABS_FILE_PATH
#logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
| [
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532,
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7,
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8,
82,
11537,
198
] | 2.988095 | 84 |
switchApp("System Preferences.app")
click("1273526123226.png")
click("1273526171905.png")
thumbs = findAll("1273527194228.png")
for t in list(thumbs)[:2]: # only take the first two
dragLeft(t) # off
#dragRight(t) # on
#dragToMute(t)
| [
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] | 2.454545 | 99 |
import os
import sys
from .tracking import FunctionIndexer, get_full_function_name
def pytest_addoption(parser):
"""
Pytest hook - register command line arguments. We want to register the
--func_cov argument to explicitly pass the location of the package to
discover and the ignore_func_names ini setting.
Args:
parser:
"""
group = parser.getgroup("func_cov")
group.addoption(
"--func_cov",
dest="func_cov_source",
action="append",
default=[],
metavar="SOURCE",
nargs="?",
const=True,
)
group.addoption(
"--func_cov_report",
dest="func_cov_report",
action="append",
default=[],
metavar="SOURCE",
nargs="?",
const=True,
)
parser.addini("ignore_func_names", "function names to ignore", "linelist", [])
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220,
220,
220,
220,
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28,
17821,
11,
198,
220,
220,
220,
1267,
628,
220,
220,
220,
30751,
13,
2860,
5362,
7203,
46430,
62,
20786,
62,
14933,
1600,
366,
8818,
3891,
284,
8856,
1600,
366,
2815,
46331,
1600,
685,
12962,
628,
198
] | 2.339572 | 374 |
#! /usr/bin/env python
from .toy_problem_test import ToyProblemTest
from .reconciliation_problem_test import ReconciliationProblemTest
from .reconciliation_problem_2_test import ReconciliationProblem2Test
from .recon3_test import Recon3Test
from .optgapc1_test import OptGapC1Test
from .optgapc2_test import OptGapC2Test
from .optgapc3_test import OptGapC3Test
from .optgap4_test import OptGap4Test
from .single_edge_b import SingleEdgeBTest
from .feasibility_test import FeasibilityTest
from .flow_path_construction_test import FlowPathConstructionTest
from .we_need_to_fix_this_test import WeNeedToFixThisTest
from .abstract_test import bcolors
import argparse
ALL_TESTS = [ToyProblemTest(), ReconciliationProblemTest(),
ReconciliationProblem2Test(), Recon3Test(), OptGapC1Test(),
OptGapC2Test(), OptGapC3Test(), FeasibilityTest(),
OptGap4Test(), FlowPathConstructionTest(), WeNeedToFixThisTest(),
SingleEdgeBTest()]
TEST_NAME_DICT = {test.name: test for test in ALL_TESTS}
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--tests', nargs='+', required=False)
args = parser.parse_args()
if args.tests is not None:
tests_to_run = [TEST_NAME_DICT[name] for name in args.tests]
else:
tests_to_run = ALL_TESTS
print('RUNNING THE FOLLOWING TESTS: {}'.format(
[test.name for test in tests_to_run]))
run_tests(tests_to_run)
| [
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220,
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220,
220,
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220,
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220,
220,
220,
1057,
62,
41989,
7,
41989,
62,
1462,
62,
5143,
8,
628
] | 2.642086 | 556 |
"""Test getting/setting variables and subjacs with promoted/relative/absolute names."""
import unittest
import numpy as np
from openmdao.api import Problem, Group, ExecComp, IndepVarComp, DirectSolver, ParallelGroup
from openmdao.utils.mpi import MPI
try:
from openmdao.vectors.petsc_vector import PETScVector
except ImportError:
PETScVector = None
@unittest.skipUnless(MPI and PETScVector, "MPI and PETSc are required.")
if __name__ == '__main__':
unittest.main()
| [
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10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3.044025 | 159 |
from django.test import TestCase
from heltour.tournament.models import *
from django.core.urlresolvers import reverse
# For now we just have sanity checks for the templates used
# This could be enhanced by verifying the context data
| [
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] | 3.966102 | 59 |
from skydb import SkydbTable
from random import choice
from string import ascii_letters
table_name = ''.join([choice(ascii_letters) for i in range(20)])
import time
print("Creating table")
table = SkydbTable(table_name, columns=['c1','c2','c3'], seed="some_random", verbose=1)
print("Added table successfully")
| [
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] | 3.088235 | 102 |
from rabbitai.db_engine_specs.athena import AthenaEngineSpec
from tests.db_engine_specs.base_tests import TestDbEngineSpec
| [
6738,
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] | 3.263158 | 38 |
import os
import sys
import errno
import math
from math import *
sys.path.append('../../common')
from env_indigo import *
if not os.path.exists(joinPathPy("out", __file__)):
try:
os.makedirs(joinPathPy("out", __file__))
except OSError as e:
if e.errno != errno.EEXIST:
raise
indigo = Indigo()
indigo.setOption("molfile-saving-skip-date", "1")
indigo.setOption("treat-x-as-pseudoatom", "1")
indigo.setOption("smart-layout", "1")
ref_path = getRefFilepath("template_layout.sdf")
ref = indigo.iterateSDFile(ref_path)
print("**** Test template layout *****")
saver = indigo.writeFile(joinPathPy("out/template_layout.sdf", __file__))
for idx, item in enumerate(indigo.iterateSDFile(joinPathPy("molecules/template_layout.sdf", __file__))):
try:
mol = item.clone()
mol.layout()
res = moleculeLayoutDiff(indigo, mol, ref.at(idx).rawData(), ref_is_file = False)
print(" Item #{}: Result: {}".format(idx, res))
saver.sdfAppend(mol)
except IndigoException as e:
print("Exception for #%s: %s" % (idx, getIntemplate_layout.sdfdigoExceptionText(e)))
print("**** Test rings templates layout *****")
ref_path = getRefFilepath("rings_templates.sdf")
ref = indigo.iterateSDFile(ref_path)
saver = indigo.writeFile(joinPathPy("out/rings_templates.sdf", __file__))
for idx, item in enumerate(ref):
try:
mol = item.clone()
mol.layout()
res = moleculeLayoutDiff(indigo, mol, item.rawData(), ref_is_file = False)
print(" Item #{}: Result: {}".format(idx, res))
saver.sdfAppend(mol)
except IndigoException as e:
print("Exception for #%s: %s" % (idx, getIntemplate_layout.sdfdigoExceptionText(e))) | [
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90,
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25,
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7,
312,
87,
11,
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220,
220,
220,
220,
220,
220,
220,
473,
332,
13,
82,
7568,
4677,
437,
7,
43132,
8,
198,
220,
220,
220,
2845,
40673,
16922,
355,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
16922,
329,
1303,
4,
82,
25,
4064,
82,
1,
4064,
357,
312,
87,
11,
651,
5317,
368,
6816,
62,
39786,
13,
82,
7568,
12894,
78,
16922,
8206,
7,
68,
22305
] | 2.410292 | 719 |
from builtins import str
from django import forms
from django.forms.widgets import TextInput
from .version import Version
from .constants import DEFAULT_NUMBER_BITS
from .utils import convert_version_int_to_string
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'''
Created on Oct 20, 2021
@author: mballance
'''
from tblink_rpc_utils.idl_spec import IDLSpec
from tblink_rpc_utils.input_reader import InputReader
from tblink_rpc_utils.input_spec import InputSpec
from tblink_rpc_utils.yaml_idl_parser import YamlIDLParser
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import misaka as m
from django import template
from django.template.defaultfilters import stringfilter
from django.utils.safestring import mark_safe
from MedusaII.settings import MARKDOWNX_MARKDOWN_EXTENSIONS
register = template.Library()
@register.filter(is_safe=True)
@stringfilter
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#! /usr/bin/env python
import sys
from rpython.jit.codewriter.policy import JitPolicy
from rsqueakvm import model, objspace, interpreter, squeakimage
# This loads an image file in advance and includes it in the
# translation-output. At run-time, the defined selector is sent
# to the defined SmallInteger. This way we get an RPython
# "image" frozen into the executable, mmap'ed by the OS from
# there and loaded lazily when needed :-)
# Besides testing etc., this can be used to create standalone
# binaries executing a smalltalk program.
sys.setrecursionlimit(100000)
imagefile = "images/mini.image"
selector = "loopTest"
receiver = 0
interp, s_frame = setup()
# _____ Define and setup target ___
if __name__ == "__main__":
entry_point(sys.argv)
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198,
220,
220,
220,
5726,
62,
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7,
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13,
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85,
8,
198
] | 3.358407 | 226 |
from model.modelfactory import * | [
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] | 4 | 8 |
from cursor import Cursor
from node import Node
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220,
220,
220,
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] | 2.275862 | 29 |
load(
"@com_googlesource_gerrit_bazlets//tools:classpath.bzl",
"classpath_collector",
)
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] | 2.232558 | 43 |
# -*- coding: utf-8 -*-
import inspect
import llbc
class pyllbcStream(object):
"""
Stream class encapsulation, use to pack/unpack data sequence.
"""
@property
def endian(self):
"""
Get stream endian setting(see llbc.Endian module).
"""
return llbc.inl.GetPyStreamEndian(self.__c_obj)
@endian.setter
def endian(self, e):
"""
Set stream endian(see llbc.Endian module).
"""
llbc.inl.SetPyStreamEndian(self.__c_obj, e)
@property
def pos(self):
"""
Get stream current reading/writing position.
"""
return llbc.inl.GetPyStreamPos(self.__c_obj)
@pos.setter
def pos(self, p):
"""
Set stream current reading/writing position.
"""
llbc.inl.SetPyStreamPos(self.__c_obj, p)
@property
def size(self):
"""
Get stream size(unsafe method, size will automatic adjust by stream).
"""
return llbc.inl.GetPyStreamSize(self.__c_obj)
@size.setter
def size(self, s):
"""
Set stream size(unsafe method, size will automatic adjust by stream).
"""
llbc.inl.SetPyStreamSize(self.__c_obj, s)
@property
def raw(self):
"""
Get stream memery view as buffer.
"""
return llbc.inl.PyStreamGetRaw(self.__c_obj)
@raw.setter
def raw(self, r):
"""
Set stream raw memory from str/buffer/bytearray.
"""
llbc.inl.PyStreamSetRaw(self.__c_obj, r)
@property
def cobj(self):
"""
Get raw pyllbc stream object(calling by c/c++ layer).
"""
return self.__c_obj
def __str__(self):
"""
Get human readable stream data's string representation.
"""
import binascii
return binascii.hexlify(self.raw)
@staticmethod
@staticmethod
@staticmethod
@staticmethod
@staticmethod
def unpack(self, fmt):
"""
Unpack data according to the given format. the result is a tuple even if it contents exactly one item.
format strings:
c: char value(like b).
b: byte value(like c).
B: boolean value.
s: short value.
i: integer value.
q: signed long long value.
f: float value.
d: double value(only support Fomat method).
S: string value.
S#: string value, use another pack/unpack algorithm, 4 bytes length + string content(not include NULL character).
S$: string value, will read stream to end as string content, write like 'S', but not append string end character '\0'.
U: unicode value.
A: byte array value.
F: buffer value.
N: None value.
C: class type, will automatic call class.encode() method to decode must tell stream this class name,
use C<ClassName> semantic.
(): tuple type, if only has one element, it represent tuple all element type is the given type, otherwise
the tuple size must equal your given element count.
[]: list type, the same as tuple type: ().
{key:value}: dictionary type.
The format examples:
iiS
(i)
(U)
[i]
{i:S}
{i:(C<int>)}
([SC<int>NA(i)]{int:S}B
"""
return self.__unpack(fmt)
def pack(self, fmt, *values):
"""
Pack values according to the given format, the arguments must match the values required by the format exactly.
format strings:
c: char value(like b).
b: byte value(like c).
B: boolean value.
s: short value.
i: integer value.
q: signed long long value.
f: float value.
d: double value(only support Fomat method).
S: string value.
S#: string value, use another pack/unpack algorithm, 4 bytes length + string content(not include NULL character).
S$: string value, will read stream to end as string content, write like 'S', but not append string end character '\0'.
U: unicode value.
A: byte array value.
F: buffer value.
N: None value.
C: class type, will automatic call class.encode() method to decode, must tell stream this class name,
use C<ClassName> semantic.
(): tuple type, if only has one element, it represent tuple all element type is the given type, otherwise
the tuple size must equal your given element count.
[]: list type, the same as tuple type: ().
{key:value}: dictionary type.
"""
caller_env = None
if fmt.find('C') >= 0 and not llbc.inl.PyStreamIsExprCompiled(fmt):
caller_env = inspect.stack()[1][0].f_globals
return llbc.inl.PyStreamFmtWrite(self.__c_obj, fmt, values, caller_env)
llbc.Stream = pyllbcStream
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292,
979,
72,
13,
33095,
75,
1958,
7,
944,
13,
1831,
8,
628,
220,
220,
220,
2488,
12708,
24396,
628,
220,
220,
220,
2488,
12708,
24396,
628,
220,
220,
220,
2488,
12708,
24396,
628,
220,
220,
220,
2488,
12708,
24396,
628,
220,
220,
220,
2488,
12708,
24396,
628,
220,
220,
220,
825,
555,
8002,
7,
944,
11,
46996,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
791,
8002,
1366,
1864,
284,
262,
1813,
5794,
13,
262,
1255,
318,
257,
46545,
772,
611,
340,
10154,
3446,
530,
2378,
13,
198,
220,
220,
220,
220,
220,
220,
220,
5794,
13042,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
25,
1149,
1988,
7,
2339,
275,
737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
275,
25,
18022,
1988,
7,
2339,
269,
737,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
347,
25,
25131,
1988,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
264,
25,
1790,
1988,
13,
198,
220,
220,
220,
220,
220,
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220,
220,
220,
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1312,
25,
18253,
1988,
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10662,
25,
4488,
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890,
1988,
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220,
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220,
220,
220,
277,
25,
12178,
1988,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
25,
4274,
1988,
7,
8807,
1104,
376,
296,
265,
2446,
737,
628,
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1988,
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220,
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311,
2,
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4731,
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1194,
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14,
403,
8002,
11862,
11,
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9881,
4129,
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4731,
2695,
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2291,
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2095,
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3,
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284,
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355,
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2695,
11,
3551,
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50,
3256,
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24443,
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59,
15,
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198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
471,
25,
28000,
1098,
1988,
13,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
25,
18022,
7177,
1988,
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1988,
13,
628,
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220,
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220,
220,
220,
220,
220,
220,
399,
25,
6045,
1988,
13,
628,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
327,
25,
1398,
2099,
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428,
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1988,
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1988,
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1988,
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25,
4488,
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1988,
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1988,
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1988,
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11,
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198,
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25,
28000,
1098,
1988,
13,
628,
220,
220,
220,
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317,
25,
18022,
7177,
1988,
13,
198,
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220,
220,
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220,
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376,
25,
11876,
1988,
13,
628,
220,
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399,
25,
6045,
1988,
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628,
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220,
220,
220,
220,
220,
220,
220,
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220,
327,
25,
1398,
2099,
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3419,
2446,
284,
36899,
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1560,
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428,
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1438,
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220,
198,
220,
220,
220,
220,
220,
220,
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327,
27,
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29,
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] | 2.215063 | 2,297 |
#import hashlib
#from random import randint
#
# def hash2(self, str1):
#
# result=0
# b_str1=str.encode(str1)
# h=hashlib.sha1(b_str1).hexdigest()
# for c in str1:
# result += ord(c)
# return result % self.filter_len
# if __name__ == '__main__':
# dataset=["0123456789", "1234567890", "sdfsdfsdf", "sdf2143124", "hophey", "abirvaolg", "8901234567", "2356sdfqix,ed", "9012345678"]
# dataset2=["012345678932", "12345623e47890", "sdfdsfq1sdfsdf", "sdf2gs2143124", "qwerhophey", "atgxcvbirvaolg", "8sdgaw901234567", "321452356sdfqix,ed", "5124e39012345678"]
# BLOOM_TEST=BloomFilter(32)
# for data in dataset:
# BLOOM_TEST.add(data)
# for data in dataset2:
# if BLOOM_TEST.is_value(data):
# print(f'It seems {data} is here')
# else:
# print(f'No {data} by the name of bloom filter ')
# for data in dataset:
# if BLOOM_TEST.is_value(data):
# print(f'It seems {data} is here')
# else:
# print(f'No {data} by the name of bloom filter ')
# print( BLOOM_TEST.bloom_array) | [
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8
] | 1.900498 | 603 |
import os.path
| [
11748,
28686,
13,
6978,
628
] | 3.2 | 5 |
"""
.. module:: Facemovie
:platform: Unix, Windows
:synopsis: Main class of the application. Contains the core image processing functions.Plays the role of a controller for the application, as it supports the communication layer.
.. moduleauthor:: Julien Lengrand-Lambert <[email protected]>
"""
import threading
import logging
import Facemovie_lib
from util.Notifier import Observer
from util.Notifier import Observable
class FacemovieThread(threading.Thread, Observable, Observer):
'''
Creates a Thread version of Facemovie using the facemovie_lib.
This class can then be run anywhere, from a GUI, script, ...
'''
def __init__(self, face_params):
"""
Initializes all parameters of the application. Input and output folders
are defined, together with the classifier profile.
:param face_params: A faceparams object that contains all needed information to run the Facemovie.
:type face_params: FaceParams
"""
threading.Thread.__init__(self)
Observable.__init__(self)
Observer.__init__(self, "Application")
self.stop_process = False
self.face_params = face_params
self.facemovie = Facemovie_lib.FaceMovie(self.face_params)
self.facemovie.subscribe(self) # Subscribing to facemovie reports
self.subscribe(self.facemovie) # Used to send request to stop
self.my_logger = logging.getLogger('IvolutionFile.Thread')
#self.console_logger = logging.getLogger('ConsoleLog')
def update(self, message):
"""
Trigerred by IvolutionWindow.
Uses the Observer pattern to inform the user about the progress of the GUI.
"""
if len(message) == 1: # system commands
if message[0] == "STOP":
#self.console_logger.debug("Facemovie is going to stop")
self.my_logger.debug("Facemovie is going to stop")
self.stop_process = True
self.notify(["Lib", ["STOP"]])
else:
#self.console_logger.debug("Unrecognized system command")
self.my_logger.debug("Unrecognized system command")
##self.console_logger.debug(message)
self.my_logger.debug(message)
elif len(message) == 2: # notifications
##self.console_logger.debug(message)
self.my_logger.debug(message)
if message[0] == "FILEADD":
self.notify(["Interface", [message[0], message[1], 0]])
else:
# notify gui about small updates
self.notify(["Interface", ["STATUS", message[0], message[1]]])
# checking for fatal error
if message[0] == "Error":
#self.console_logger.debug("Fatal Error detected")
self.my_logger.debug("Fatal Error detected")
self.stop_process = True
self.notify(["Lib", ["STOP"]])
elif len(message) == 3: # notifications
if message[0] == "FILEDONE":
self.notify(["Interface", message])
else:
#self.console_logger.debug("Unrecognized command")
self.my_logger.debug("Unrecognized command")
#self.console_logger.debug(message)
self.my_logger.debug(message)
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] | 2.355384 | 1,421 |
#Question 24
#Implement a queue class in Python: It should support 3 APIs:
#queue.top(): prints current element at front of queue
#queue.pop(): takes out an element from front of queue
#queue.add(): adds a new element at end of stack
queue_1 = Queue()
queue_1.add(12)
queue_1.add(11)
queue_1.add(55)
queue_1.add(66)
queue_1.add(56)
queue_1.add(43)
queue_1.add(33)
queue_1.add(88)
queue_1.add(56)
queue_1.add(34)
print queue_1
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
print queue_1.top()
queue_1.pop()
| [
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] | 2.430303 | 330 |
from typing import Any, Dict, List, Optional, cast
from uuid import UUID
from tortoise.query_utils import Q
from app import models, schemas
from app.services.searchers import AbstractSearcher
| [
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# -*- coding: utf-8 -*-
"""
Just for test
""" | [
2,
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] | 2.142857 | 21 |
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
# tcfile = './Thermal_conductivity_Se.txt'
tcfile = './Thermal_conductivity_S.txt'
plt_tc(tcfile) | [
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] | 2.402597 | 77 |
"""
Scatter plot between
"""
import pandas as pd
import numpy as np
from numpy.random import random
from math import pi
from bokeh.io import output_notebook
output_notebook()
from bokeh.io import show, output_file
from bokeh.palettes import RdYlGn6
from bokeh.models import (
BasicTicker,
ColorBar,
LinearColorMapper,
PrintfTickFormatter,
ColumnDataSource,
HoverTool,
Span,
)
from bokeh.plotting import figure, save, show, output_file
from bokeh.palettes import BuGn, Blues8, Oranges256
def plot_vs_population(districts_budget_df):
"""
From district budget to scatter plots vs total population
"""
for q in districts_budget_df.budget_type.unique():
df = districts_budget_df.query("budget_type == '{}'".format(q))
df["num_total"] = df["num_male"] + df["num_female"]
df = df.groupby(["dname", "num_total"])["budget"].sum().reset_index()
source = ColumnDataSource(
data=dict(
x=df["num_total"] / 10000, y=df["budget"] / 1000000, desc=df["dname"]
)
)
p = figure(title="", tools="hover,box_zoom,reset")
vline = Span(
location=df.num_total.mean() / 10000,
dimension="height",
line_color="gold",
line_width=1.5,
)
hline = Span(
location=df["budget"].mean() / 1000000,
dimension="width",
line_color="gold",
line_width=1.5,
)
p.circle(
"x", "y", source=source, fill_alpha=0.2, size=10,
)
p.xaxis.axis_label = "จำนวนผู้อยู่อาศัย (หมื่นคน)"
p.yaxis.axis_label = f"งบประมาณ{q} (ล้านบาท)"
p.xaxis.axis_label_text_font_size = "15pt"
p.yaxis.axis_label_text_font_size = "15pt"
p.xaxis.major_label_text_font_size = "12pt"
p.yaxis.major_label_text_font_size = "12pt"
hover = HoverTool(
tooltips=[
("เขต", "@desc"),
(f"งบ{q}", "@y ล้านบาท"),
("จำนวนผู้อาศัย", "@x หมื่นคน"),
]
)
p.add_tools(hover)
p.renderers.extend([vline, hline])
output_file(f"plots/scatter-{q_map[q]}-budget.html", mode="inline")
save(p)
def plot_vs_area(districts_budget_df):
"""
From district budget to scatter plots vs area size
"""
for q in districts_budget_df.budget_type.unique():
df = districts_budget_df.query("budget_type == '{}'".format(q))
df = df.groupby(["dname", "AREA"])["budget"].sum().reset_index()
source = ColumnDataSource(
data=dict(
x=df["AREA"] / 1000000, y=df["budget"] / 1000000, desc=df["dname"]
)
)
p = figure(title="", tools="hover,box_zoom,reset")
vline = Span(
location=df.AREA.mean() / 1000000,
dimension="height",
line_color="gold",
line_width=1.5,
)
hline = Span(
location=df["budget"].mean() / 1000000,
dimension="width",
line_color="gold",
line_width=1.5,
)
p.circle(
"x", "y", source=source, fill_alpha=0.2, size=10,
)
p.xaxis.axis_label = "ขนาดพื้นที่ (ตร.กม.)"
p.yaxis.axis_label = f"งบประมาณ{q} (ล้านบาท)"
p.xaxis.axis_label_text_font_size = "15pt"
p.yaxis.axis_label_text_font_size = "15pt"
p.xaxis.major_label_text_font_size = "12pt"
p.yaxis.major_label_text_font_size = "12pt"
hover = HoverTool(
tooltips=[
("เขต", "@desc"),
(f"งบ{q}", "@y ล้านบาท"),
("ขนาดพื้นที่", "@x ตร.กม."),
]
)
p.add_tools(hover)
p.renderers.extend([vline, hline])
output_file(f"plots/scatter-{q_map[q]}-budget-area.html", mode="inline")
save(p)
if __name__ == "__main__":
districts_budget_df = pd.read_csv("data/districts_budget.csv")[
["dname", "ประเภทแผนงาน", "งบแผนงาน", "AREA", "num_male", "num_female"]
]
districts_budget_df["num_total"] = (
districts_budget_df.num_male + districts_budget_df.num_female
)
districts_budget_df.rename(
columns={"ประเภทแผนงาน": "budget_type", "งบแผนงาน": "budget"}, inplace=True
)
q_map = {
"ทั่วไป/บริหาร/อื่นๆ": "gen",
"การคลัง": "treasury",
"เทศกิจ/รักษาความสะอาด": "clean",
"โยธา/ก่อสร้าง/จราจร": "civil",
"น้ำท่วม/ทางเท้า": "pedes",
"สิ่งแวดล้อม": "env",
"พัฒนาชุมชน/อาชีพ": "enh",
"อนามัย/สาธารณะสุข": "health",
"การศึกษา": "edu",
}
plot_vs_population(districts_budget_df)
plot_vs_area(districts_budget_df)
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7,
17080,
2012,
82,
62,
37315,
62,
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8,
198
] | 1.650313 | 2,874 |
APItoken = 'ffb1bf6df27099919ca9ab63da88b1929016a7f7468d477f65241f61e1f457ab4' \
'f53c50ead0371ce632b283b5dc803fae33b34b3601053d2bde24f4ebc921b1b'
config = {
'url': 'https://q-console-api.mybluemix.net/api',
'hub': 'ibmq',
'group': 'qc-ware',
'project': 'default'
}
| [
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10354,
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6,
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] | 1.735632 | 174 |
#!/usr/bin/python
# 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 ansible.module_utils.basic import * # noqa
if __name__ == '__main__':
main()
| [
2,
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12417,
834,
10354,
198,
220,
220,
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1388,
3419,
198
] | 3.521505 | 186 |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import unittest
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession, SQLContext, Row
from pyspark.sql.functions import col
from pyspark.testing.sqlutils import ReusedSQLTestCase
from pyspark.testing.utils import PySparkTestCase
# We can't include this test into SQLTests because we will stop class's SparkContext and cause
# other tests failed.
# This test is separate because it's closely related with session's start and stop.
# See SPARK-23228.
if __name__ == "__main__":
from pyspark.sql.tests.test_session import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
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28,
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49493,
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8,
198
] | 3.444444 | 477 |
import argparse
import numpy as np
import os
import sys
sys.path.append('../data')
sys.path.append('../plot')
import torch
from load import sigmoid, quadratic, chf, parkinsons, load_data_format
from data_utils import parse_data, change_missing
from plot_utils import plot_subtypes, plot_latent
from models import Sublign
if __name__=='__main__':
main() | [
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] | 3.050847 | 118 |
from Bio.PDB import PDBParser as BioPDBParser
import io
import itertools
from operator import itemgetter
from utils.exceptions import InvalidFormat
VALID_AMINOACIDS = {"A", "R", "N", "D", "C", "C", "Q", "E", "G", "H", "I", "L", "K", "M", "M", "F", "P", "O", "S", "U",
"T", "W", "Y", "V", "B", "Z", "X", "X", "J"}
def get_chain_contacts(chain):
"""Credits to Felix Simkovic; code taken from GitHub rigdenlab/conkit/conkit/io/pdb.py"""
contacts = []
residue_range = list(range(1, len(chain) + 1))
assert len(residue_range) == len(chain)
iterator = itertools.product(list(zip(residue_range, chain)), list(zip(residue_range, chain)))
for (resseq1_alt, residue1), (resseq2_alt, residue2) in iterator:
seq_distance = int(residue1.id[1]) - int(residue2.id[1])
if seq_distance <= 4:
continue
for atom1, atom2 in itertools.product(residue1, residue2):
xyz_distance = atom1 - atom2
if xyz_distance > 20:
d_bin = 9
elif xyz_distance <= 4:
d_bin = 0
else:
d_bin = int(round((xyz_distance - 4) / 2, 0))
if xyz_distance < 8:
contact = (int(residue1.id[1]), int(residue2.id[1]), round(1.0 - (xyz_distance / 100), 6), d_bin, 1)
else:
contact = (int(residue1.id[1]), int(residue2.id[1]), 0, d_bin, 1)
contacts.append(contact)
return contacts
def remove_atoms(chain):
"""Credits to Felix Simkovic; code taken from GitHub rigdenlab/conkit/conkit/io/pdb.py"""
for residue in chain.copy():
if residue.id[0].strip() and residue.resname not in VALID_AMINOACIDS:
chain.detach_child(residue.id)
continue
for atom in residue.copy():
# if atom.is_disordered():
# chain[residue.id].detach_child(atom.id)
if residue.resname == "GLY" and atom.id == "CA":
continue
elif atom.id != "CB":
chain[residue.id].detach_child(atom.id)
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312,
4083,
15255,
620,
62,
9410,
7,
37696,
13,
312,
8,
628
] | 2.034213 | 1,023 |
import re
from salmon.search.base import IdentData, SearchMixin
from salmon.sources import DiscogsBase
SOURCES = {
"Vinyl": "Vinyl",
"File": "WEB",
"CD": "CD",
}
def sanitize_artist_name(name):
"""
Remove parenthentical number disambiguation bullshit from artist names,
as well as the asterisk stuff.
"""
name = re.sub(r" \(\d+\)$", "", name)
return re.sub(r"\*+$", "", name)
def parse_source(formats):
"""
Take the list of format strings provided by Discogs and iterate over them
to find a possible source for the release.
"""
for format_s, source in SOURCES.items():
if any(format_s in f for f in formats):
return source
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] | 2.605166 | 271 |
import RPi.GPIO as GPIO
| [
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] | 2.666667 | 9 |
import logging
from django.utils.html import strip_tags
from . import settings as sendgrid_settings
from .signals import message_composed
logger = logging.getLogger('threaded_messages')
if sendgrid_settings.THREADED_MESSAGES_USE_SENDGRID:
from sendgrid_parse_api.signals import email_received
else:
email_received = None
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] | 3.073394 | 109 |
# coding:utf-8
'''
@author = super_fazai
@File : free_api_utils.py
@connect : [email protected]
'''
"""
一些免费api 接口的封装
"""
from pprint import pprint
import re
# from fzutils.ip_pools import tri_ip_pool
# from fzutils.spider.fz_requests import Requests
# from fzutils.common_utils import json_2_dict
# from fzutils.internet_utils import (
# get_base_headers,)
from .ip_pools import tri_ip_pool
from .spider.fz_requests import Requests
from .common_utils import json_2_dict
from .internet_utils import (
get_base_headers,)
__all__ = [
'get_jd_one_goods_price_info', # 获取京东单个商品价格
'get_express_info', # 获取快递信息
'get_phone_num_info', # 获取手机号信息
'get_baidu_baike_info', # 获取某关键字的百度百科信息
# map
'get_bd_map_shop_info_list_by_keyword_and_area_name', # 根据关键字和区域检索店铺信息(百度api 关键字搜索服务)[测试最多前400个]
'get_gd_map_shop_info_list_by_keyword_and_area_name', # 根据关键字和区域检索店铺信息(高德api 关键字搜索服务)
'get_gd_input_prompt_info', # 根据关键字和城市名获取输入提示(高德api)
'get_gd_reverse_geocode_info', # 根据地址str获取逆向地理编码(高德api)
'get_gd_map_shop_info_list_by_lng_and_lat_and_keyword', # 根据经纬度(主要根据), 关键字(附加条件)等条件检索附近店铺信息(高德api 关键字搜索服务)
'get_gd_map_shop_info_list_by_gd_id', # 根据gd_id来得到指定的shop info list(一般为第一个)[测试发现不准确, 根据id, 常返回不相干商家]
]
def get_jd_one_goods_price_info(goods_id) -> list:
'''
获取京东单个商品价格
:param goods_id: 商品id
:return:
'''
base_url = 'http://p.3.cn/prices/mgets'
params = (
('skuIds', 'J_' + goods_id),
)
body = Requests.get_url_body(
url=base_url,
use_proxy=False,
params=params)
return json_2_dict(body, default_res=[])
def get_express_info(express_type, express_id) -> dict:
'''
获取快递信息
express_type: ps: 传字典对应的value
{
'申通': 'shentong',
'ems': 'ems',
'顺丰': 'shunfeng',
'圆通': 'yuantong',
'中通': 'zhongtong',
'韵达': 'yunda',
'天天': 'tiantian',
'汇通': 'huitongkuaidi',
'全峰': 'quanfengkuaidi',
'德邦': 'debangwuliu',
'宅急送': 'zhaijisong',
...
}
:param express_type: 快递公司名
:param express_id: 快递号
:return:
'''
base_url = 'http://www.kuaidi100.com/query'
params = (
('type', express_type),
('postid', express_id),
)
body = Requests.get_url_body(
url=base_url,
use_proxy=False,
params=params,)
return json_2_dict(body)
def get_phone_num_info(phone_num) -> dict:
'''
获取手机号信息
:param phone_num: 手机号
:return:
'''
url = 'https://tcc.taobao.com/cc/json/mobile_tel_segment.htm'
params = (
('tel', str(phone_num)),
)
body = Requests.get_url_body(
url=url,
params=params,
use_proxy=False)
try:
res = re.compile('__GetZoneResult_ = (.*)').findall(body)[0]
return json_2_dict(res)
except IndexError:
return {}
def get_baidu_baike_info(keyword, bk_length=1000) -> dict:
'''
获取某关键字的百度百科信息
:param keyword:
:return:
'''
url = 'http://baike.baidu.com/api/openapi/BaikeLemmaCardApi'
params = (
('scope', '103'),
('format', 'json'),
('appid', '379020'),
('bk_key', str(keyword)),
('bk_length', str(bk_length)),
)
body = Requests.get_url_body(
url=url,
params=params,
use_proxy=False)
return json_2_dict(body)
def get_bd_map_shop_info_list_by_keyword_and_area_name(ak:str,
keyword:str,
area_name:str,
page_num:int,
page_size:int=20,
use_proxy=True,
ip_pool_type=tri_ip_pool,
num_retries=6,
timeout=20,
logger=None,) -> list:
"""
根据关键字和区域检索店铺信息(百度api 关键字搜索服务)[测试最多前400个]
:param ak: 百度地图申请的ak
:param keyword: eg: '鞋子'
:param area_name: eg: '杭州' 待搜索的区域, 多为省份, 城市, 具体区域
:param page_num: start 1, 最大20
:param page_size: 固定
:param ip_pool_type:
:param num_retries:
:return:
"""
headers = get_base_headers()
headers.update({
'Connection': 'keep-alive',
'Cache-Control': 'max-age=0',
'Upgrade-Insecure-Requests': '1',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
})
params = (
('query', str(keyword)),
('region', str(area_name)),
('output', 'json'),
('ak', str(ak)),
('page_num', str(page_num)),
('page_size', str(page_size)),
)
url = 'http://api.map.baidu.com/place/v2/search'
body = Requests.get_url_body(
url=url,
headers=headers,
params=params,
use_proxy=use_proxy,
ip_pool_type=ip_pool_type,
num_retries=num_retries,
timeout=timeout,)
# print(body)
data = json_2_dict(
json_str=body,
default_res={},
logger=logger,).get('results', [])
# pprint(data)
return data
def get_gd_map_shop_info_list_by_keyword_and_area_name(gd_key:str,
keyword:str,
area_name:str,
page_num: int,
page_size: int=20,
use_proxy=True,
ip_pool_type=tri_ip_pool,
num_retries=6,
timeout=20,
children=0,
extensions='all',
poi_type='',
logger=None,) -> list:
"""
根据关键字和区域检索店铺信息(高德api 关键字搜索服务)
:param gd_key: 申请的key
:param keyword: 关键字 eg: '鞋子'
:param area_name: eg: '杭州' 待搜索的区域, 城市名
:param page_num: 最大翻页数100
:param page_size: 默认值'20'
:param use_proxy:
:param ip_pool_type:
:param num_retries:
:param timeout:
:param children: 按照层级展示子POI数据, 取值0 or 1
:param extensions: 返回结果控制
:param poi_type: 查询POI类型, eg: '061205', 可默认为空值!
:return:
"""
headers = get_base_headers()
headers.update({
'Connection': 'keep-alive',
'Cache-Control': 'max-age=0',
'Upgrade-Insecure-Requests': '1',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
})
params = (
('key', str(gd_key)),
('keywords', str(keyword)),
('types', str(poi_type)),
('city', str(area_name)),
('citylimit', 'true'),
('children', str(children)),
('offset', str(page_size)),
('page', str(page_num)),
('extensions', str(extensions)),
)
url = 'http://restapi.amap.com/v3/place/text'
body = Requests.get_url_body(
use_proxy=use_proxy,
url=url,
headers=headers,
params=params,
ip_pool_type=ip_pool_type,
timeout=timeout,
num_retries=num_retries,)
# print(body)
data = json_2_dict(
json_str=body,
default_res={},
logger=logger,).get('pois', [])
# pprint(data)
return data
def get_gd_input_prompt_info(gd_key:str,
keyword,
city_name:str,
poi_type='',
lng:float=0.,
lat:float=0.,
ip_pool_type=tri_ip_pool,
num_retries=6,
timeout=20,
use_proxy=True,
logger=None,) -> list:
"""
根据关键字和城市名获取输入提示(高德api)
:param gd_key: 申请的key
:param keyword: eg: '美食'
:param city_name: eg: '杭州'
:param poi_type: eg: '050301'
:param lng:
:param lat:
:return:
"""
headers = get_base_headers()
headers.update({
'Connection': 'keep-alive',
'Cache-Control': 'max-age=0',
'Upgrade-Insecure-Requests': '1',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
})
# eg: '116.481488,39.990464' 经纬度
location = ','.join([str(lng), str(lat)]) if lng != 0. or lat != 0. else ''
params = (
('key', str(gd_key)),
('keywords', str(keyword)),
('type', poi_type),
('location', location),
('city', str(city_name)),
('datatype', 'all'),
)
url= 'https://restapi.amap.com/v3/assistant/inputtips'
body = Requests.get_url_body(
use_proxy=use_proxy,
url=url,
headers=headers,
params=params,
ip_pool_type=ip_pool_type,
timeout=timeout,
num_retries=num_retries,)
# print(body)
data = json_2_dict(
json_str=body,
logger=logger,).get('tips', [])
# pprint(data)
return data
def get_gd_reverse_geocode_info(gd_key:str,
address:str,
city_name:str,
ip_pool_type=tri_ip_pool,
num_retries=6,
timeout=20,
use_proxy=True,
logger=None,) -> list:
"""
根据地址str获取逆向地理编码(高德api)
:param gd_key:
:param address: eg: '方恒国际中心A座'
:param city_name: eg: '北京'
:param ip_pool_type:
:param num_retries:
:param timeout:
:param use_proxy:
:param logger:
:return:
"""
headers = get_base_headers()
headers.update({
'Connection': 'keep-alive',
'Cache-Control': 'max-age=0',
'Upgrade-Insecure-Requests': '1',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
})
params = (
('key', str(gd_key)),
('address', str(address)),
('city', str(city_name)),
)
url= 'https://restapi.amap.com/v3/geocode/geo'
body = Requests.get_url_body(
use_proxy=use_proxy,
url=url,
headers=headers,
params=params,
ip_pool_type=ip_pool_type,
timeout=timeout,
num_retries=num_retries,)
# print(body)
data = json_2_dict(
json_str=body,
logger=logger,).get('geocodes', [])
# pprint(data)
return data
def get_gd_map_shop_info_list_by_lng_and_lat_and_keyword(gd_key:str,
lng:float,
lat:float,
keyword:str='',
radius:int=1000,
page_num:int=1,
page_size:int=20,
poi_type='',
extensions='all',
use_proxy=True,
ip_pool_type=tri_ip_pool,
num_retries=6,
timeout=20,
logger=None,) -> list:
"""
根据经纬度(主要根据), 关键字(附加条件)等条件检索附近店铺信息(高德api 关键字搜索服务)
:param gd_key: 申请的key
:param lng: 经度
:param lat: 纬度
:param keyword: 关键字 eg: '鞋子', 默认空值!
:param radius: 半径 (如果已知的经纬度能准确定位到某家店铺, 可将radius=100, 来提高定位返回信息精确度!!)
:param page_num: 最大翻页数100
:param page_size: 默认值'20'
:param poi_type: 查询POI类型, eg: '061205', 可默认为空值!
:param extensions: 返回结果控制
:param use_proxy:
:param ip_pool_type:
:param num_retries:
:param timeout:
:param logger:
:return:
"""
headers = get_base_headers()
headers.update({
'Connection': 'keep-alive',
'Cache-Control': 'max-age=0',
'Upgrade-Insecure-Requests': '1',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
})
params = (
('key', str(gd_key)),
('location', ','.join([str(lng), str(lat)])),
('keywords', str(keyword)),
('types', str(poi_type)),
('radius', str(radius)),
('offset', str(page_size)),
('page', str(page_num)),
('extensions', str(extensions)),
)
url = 'https://restapi.amap.com/v3/place/around'
body = Requests.get_url_body(
use_proxy=use_proxy,
url=url,
headers=headers,
params=params,
ip_pool_type=ip_pool_type,
timeout=timeout,
num_retries=num_retries,)
# print(body)
data = json_2_dict(
json_str=body,
default_res={},
logger=logger,).get('pois', [])
# pprint(data)
return data
def get_gd_map_shop_info_list_by_gd_id(gd_key:str,
gd_id:str,
use_proxy=True,
ip_pool_type=tri_ip_pool,
num_retries=6,
timeout=20,
logger=None,) -> list:
"""
根据gd_id来得到指定的shop info list(一般为第一个)[测试发现不准确, 根据id, 常返回不相干商家]
:param gd_key: 申请的key
:param gd_id: eg: 'B0FFIR6P0B'
:param use_proxy:
:param ip_pool_type:
:param num_retries:
:param timeout:
:param logger:
:return:
"""
headers = get_base_headers()
headers.update({
'Connection': 'keep-alive',
'Cache-Control': 'max-age=0',
'Upgrade-Insecure-Requests': '1',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8',
})
params = (
('id', gd_id),
('output', ''),
('key', gd_key),
)
url = 'https://restapi.amap.com/v3/place/detail'
body = Requests.get_url_body(
use_proxy=use_proxy,
url=url,
headers=headers,
params=params,
ip_pool_type=ip_pool_type,
timeout=timeout,
num_retries=num_retries,)
# print(body)
data = json_2_dict(
json_str=body,
default_res={},
logger=logger,).get('pois', [])
# pprint(data)
return data
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628,
220,
220,
220,
1441,
1366,
198
] | 1.523067 | 10,383 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
test_axpy_weather
----------------------------------
Tests for `axpy_weather` module.
"""
import sys
import unittest
from axpy_weather import axpy_weather
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from setuptools import setup, find_packages
setup(
name="jupyterhub-configurator",
version="1.0",
packages=find_packages(),
license="3-BSD",
author="yuvipanda",
author_email="[email protected]",
install_requires=["tornado", "aiohttp", "jupyterhub", "deepmerge", "pluggy"],
include_package_data=True,
entry_points={
"jupyterhub_configurator": ["z2jh = jupyterhub_configurator.schemas.z2jh"]
},
)
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17,
73,
71,
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198,
220,
220,
220,
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8,
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] | 2.310881 | 193 |
from __future__ import annotations
from typing import List, Tuple, Dict
try:
import urequests as requests
except ImportError:
import requests
try:
import ujson as json
except ImportError:
import json
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] | 3.483333 | 60 |
# Start your code below (tip: Make sure to indent your code)
| [
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] | 3.3 | 20 |
import sys
sys.path.append("../../")
from unittest import TestCase
from pmst.geometry import Point, Ray
from pmst.component import Lens
from pmst.microscope import Microscope
import pmst.source
import numpy as np
# self.assertTrue(self.s.ray_list.get_ray(1) == Ray(Point(0, .5, 1), Point(0, .5, 2)))
# self.assertTrue(self.s.ray_list.get_ray(2) == Ray(Point(.1, .1, 1), Point(.1, .1, 2)))
# Plane source converges
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3212,
198,
220,
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220,
220,
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220,
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] | 2.469274 | 179 |
import os
import inspect
import typing
import threading
from contextlib import suppress
from functools import wraps
def enforce_types(callable):
"""
From:
https://stackoverflow.com/questions/50563546/validating-detailed-types-in-python-dataclasses
"""
spec = inspect.getfullargspec(callable)
if inspect.isclass(callable):
callable.__init__ = decorate(callable.__init__)
return callable
return decorate(callable)
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] | 2.791667 | 168 |
from django.db.models.signals import post_save
from django.dispatch import receiver
from .models import Vote
from .serializers import VoteSerializer
from asgiref.sync import async_to_sync
import channels.layers
@receiver(post_save, sender=Vote)
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] | 3.351351 | 74 |
#!/usr/bin/env python
"""MariaDB slave status checker"""
import sys
import argparse
import MySQLdb
class NagiosPlugin(object):
"""Nagios Plugin base class"""
class SlaveStatusCheck(NagiosPlugin):
"""Class to help us run slave status queries against MariaDB"""
REPLICATION_LAG_MODE = 'replication_lag'
SLAVESQL_MODE = 'slave_sql'
SLAVEIO_MODE = 'slave_io'
MODES = (REPLICATION_LAG_MODE,
SLAVESQL_MODE,
SLAVEIO_MODE)
def run_check(self):
"""Execute the check against the given mode"""
check_fn = getattr(self, self.mode)
check_fn()
def replication_lag(self):
"""Check replication lag thresholds"""
lag = self._slave_status.get('Seconds_Behind_Master')
if lag is None:
self.unknown_state("No replication lag reported")
if not self.warning or not self.critical:
self.unknown_state("Warning and critical thresholds undefined")
lag = int(lag)
warning = int(self.warning)
critical = int(self.critical)
lag_performance_msg = "log={0}s;{1};{2};0".format(lag,warning,critical)
lag_display_msg = "Slave is {0} seconds behinds master".format(lag)
lag_msg = "{0} | {1}".format(lag_display_msg,lag_performance_msg)
if lag >= warning and lag < critical:
self.warning_state(lag_msg)
elif lag >= critical:
self.critical_state(lag_msg)
self.ok_state(lag_msg)
def slave_sql(self):
"""Check that Slave_SQL_Running = Yes"""
if self._slave_status.get('Slave_SQL_Running') == "No":
msg = "Slave sql is not running. Last error: {0}".format(
self._slave_status.get('Last_SQL_Error'))
self.critical_state(msg)
self.ok_state("Slave sql is running")
def slave_io(self):
"""Check that Slave_IO_Running = Yes"""
if self._slave_status.get('Slave_IO_Running') == "No":
msg = "Slave io is not running. Last error: {0}".format(
self._slave_status.get('Last_IO_Error'))
self.critical_state(msg)
self.ok_state("Slave io is running")
def get_slave_status(self):
"""Run the query!"""
try:
sql = 'SHOW SLAVE "{0}" STATUS'.format(self.connection_name)
conn = None
conn = MySQLdb.Connection(
self.hostname,
self.username,
self.password)
curs = conn.cursor(MySQLdb.cursors.DictCursor)
curs.execute(sql)
conn.commit()
self._slave_status = curs.fetchall()[0]
if self.verbose:
print self._slave_status
except MySQLdb.Error, exc:
msg = "{0}: {1}".format(exc.args[0], exc.args[1])
self.unknown_state(msg)
finally:
if conn:
conn.close()
def main(args=None):
"""starter method"""
if args is None:
args = sys.argv[1:]
parser = argparse.ArgumentParser(description='MariaDB slave status checker')
parser.add_argument('--hostname', default='localhost', type=str,
help="MariaDB hostname")
parser.add_argument('--username', type=str, help="MariaDB username")
parser.add_argument('--password', type=str, help="MariaDB password")
parser.add_argument('--connection', required=True, type=str,
help="MariaDB slave connection name")
parser.add_argument('--mode', type=str, required=True,
choices=SlaveStatusCheck.MODES,
help="slave state to check")
parser.add_argument('-w', '--warning', type=int, default=None,
help="warning limit")
parser.add_argument('-c', '--critical', type=int, default=None,
help="critical limit")
parser.add_argument('--verbose', action='store_true', default=False,
help="enable verbose mode")
args = parser.parse_args(args)
ssc = SlaveStatusCheck(args.hostname, args.username, args.password,
args.connection, args.mode, args.verbose,
args.warning, args.critical)
ssc.get_slave_status()
ssc.run_check()
if __name__ == '__main__':
main() # pragma: no cover
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10354,
198,
220,
220,
220,
1388,
3419,
1303,
23864,
2611,
25,
645,
3002,
198
] | 2.201621 | 1,974 |
import sys, os, platform
import ocr_image_analyzer as OCR
try:
import PIL.Image
import PIL.ImageTk
except ModuleNotFoundError:
print('Required libraries not found, please install PIL')
if __name__ == "__main__":
raise Exception('Cannot be called as main script')
debug = True
#******************************************** Program state independent logic
filepathSlash = '\\' if isWindowsOS() else '/'
#******************************************** Object that contains program state
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46068,
220,
220,
9515,
326,
4909,
1430,
1181,
201,
198,
201,
198
] | 3.222892 | 166 |
# exercise 10.2.1
from matplotlib.pyplot import figure, show
from scipy.io import loadmat
from toolbox_02450 import clusterplot
from scipy.cluster.hierarchy import linkage, fcluster, dendrogram
# Load Matlab data file and extract variables of interest
mat_data = loadmat('../Data/synth1.mat')
X = mat_data['X']
y = mat_data['y'].squeeze()
attributeNames = [name[0] for name in mat_data['attributeNames'].squeeze()]
classNames = [name[0][0] for name in mat_data['classNames']]
N, M = X.shape
C = len(classNames)
# Perform hierarchical/agglomerative clustering on data matrix
Method = 'single'
Metric = 'euclidean'
Z = linkage(X, method=Method, metric=Metric)
# Compute and display clusters by thresholding the dendrogram
Maxclust = 4
cls = fcluster(Z, criterion='maxclust', t=Maxclust)
figure(1)
clusterplot(X, cls.reshape(cls.shape[0],1), y=y)
# Display dendrogram
max_display_levels=6
figure(2,figsize=(10,4))
dendrogram(Z, truncate_mode='level', p=max_display_levels)
show()
print('Ran Exercise 10.2.1') | [
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3419,
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198,
4798,
10786,
49,
272,
32900,
838,
13,
17,
13,
16,
11537
] | 2.782967 | 364 |
from js9 import j
app = j.tools.prefab._getBaseAppClass()
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] | 2.727273 | 22 |
#!/usr/bin/env python
# Copyright (c) 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Python utility that triggers and waits for tasks to complete on CTFE."""
import base64
import hashlib
import json
import math
import optparse
import requests
import sys
import time
CTFE_HOST = "https://ct.skia.org"
CTFE_QUEUE = CTFE_HOST + '/queue/'
CHROMIUM_PERF_TASK_POST_URI = CTFE_HOST + "/_/webhook_add_chromium_perf_task"
GET_CHROMIUM_PERF_RUN_STATUS_URI = CTFE_HOST + "/get_chromium_perf_run_status"
CHROMIUM_PERF_RUNS_HISTORY = CTFE_HOST + "/chromium_perf_runs/"
GCE_WEBHOOK_SALT_METADATA_URI = (
"http://metadata/computeMetadata/v1/project/attributes/"
"webhook_request_salt")
CTFE_CONNECTION_RETRIES = 5
CONNECTION_WAIT_BASE = 5
POLLING_FREQUENCY_SECS = 30 # 30 seconds.
TRYBOT_DEADLINE_SECS = 24 * 60 * 60 # 24 hours.
def retry():
"""A retry decorator with exponential backoff."""
return decorator
@retry()
@retry()
def _CreateTaskJSON(options):
"""Creates a JSON representation of the requested task."""
task_params = {}
task_params["username"] = options.requester
task_params["benchmark"] = options.benchmark
task_params["platform"] = "Linux"
task_params["page_sets"] = "10k"
task_params["repeat_runs"] = "3"
task_params["run_in_parallel"] = str(options.parallel)
task_params["benchmark_args"] = "--output-format=csv-pivot-table"
task_params["browser_args_nopatch"] = (
"--disable-setuid-sandbox --enable-threaded-compositing "
"--enable-impl-side-painting")
task_params["browser_args_withpatch"] = (
"--disable-setuid-sandbox --enable-threaded-compositing "
"--enable-impl-side-painting")
trybot_params = {}
trybot_params["issue"] = options.issue
trybot_params["patchset"] = options.patchset
trybot_params["task"] = task_params
return json.dumps(trybot_params)
def _GetWebhookSaltFromMetadata():
"""Gets webhook_request_salt from GCE's metadata server."""
headers = {"Metadata-Flavor": "Google"}
resp = requests.get(GCE_WEBHOOK_SALT_METADATA_URI, headers=headers)
if resp.status_code != 200:
raise CtTrybotException(
'Return code from %s was %s' % (GCE_WEBHOOK_SALT_METADATA_URI,
resp.status_code))
return base64.standard_b64decode(resp.text)
def _TriggerTask(options):
"""Triggers the requested task on CTFE and returns the new task's ID."""
task = _CreateTaskJSON(options)
m = hashlib.sha512()
m.update(task)
m.update('notverysecret' if options.local else _GetWebhookSaltFromMetadata())
encoded = base64.standard_b64encode(m.digest())
headers = {
"Content-type": "application/x-www-form-urlencoded",
"Accept": "application/json",
"X-Webhook-Auth-Hash": encoded}
resp = _AddTaskToCTFE(task, headers)
if resp.status_code != 200:
raise CtTrybotException(
'Return code from %s was %s' % (CHROMIUM_PERF_TASK_POST_URI,
resp.status_code))
try:
ret = json.loads(resp.text)
except ValueError, e:
raise CtTrybotException(
'Did not get a JSON response from %s: %s' % (
CHROMIUM_PERF_TASK_POST_URI, e))
return ret["taskID"]
if '__main__' == __name__:
option_parser = optparse.OptionParser()
option_parser.add_option(
'', '--issue',
help='The Rietveld CL number to get the patch from.')
option_parser.add_option(
'', '--patchset',
help='The Rietveld CL patchset to use.')
option_parser.add_option(
'', '--requester',
help='Email address of the user who requested this run.')
option_parser.add_option(
'', '--benchmark',
help='The CT benchmark to run on the patch.')
option_parser.add_option(
'', '--parallel', default=False, action='store_true',
help='Whether to run this benchmark in parallel.')
option_parser.add_option(
'', '--local', default=False, action='store_true',
help='Uses a dummy metadata salt if this flag is true else it tries to '
'get the salt from GCE metadata.')
options, unused_args = option_parser.parse_args()
if (not options.issue or not options.patchset or not options.requester
or not options.benchmark):
option_parser.error('Must specify issue, patchset, requester and benchmark')
sys.exit(TriggerAndWait(options))
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1870,
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] | 2.589025 | 1,713 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
| [
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] | 3.727273 | 22 |
import functools
from flask_appify.util import request_wants_json
try:
from google.appengine.api import datastore_errors
except ImportError:
# not on the AppEngine platform
datastore_errors = None
__all__ = [
'init_app',
]
html_response = {
503: """<!doctype html><html><head><title>503 Service
Unavailable</title></head><body><h1>Service Unavailable</h1><p>Service is
temporarily unavailable. Please try again later.</p></body></html>"""
}
json_response = {
503: """{"status":"error","code":503,"message":"Service is
temporarily unavailable. Please try again later."}"""
}
def handle_temp_error(app, err):
"""
This is a Flask `errorhandler` handling `datastore_errors.InternalError`.
According to `https://cloud.google.com/appengine/docs/standard/python/
datastore/exceptions` this exception does not necessarily mean that the
underlying operation failed.
:param app: The flask app that received the error.
:param err: The exception that was raised.
"""
response = app.make_response(
json_response[503] if request_wants_json() else html_response[503]
)
response.status_code = 503
return response
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62,
8189,
796,
44541,
628,
220,
220,
220,
1441,
2882,
628
] | 3.038265 | 392 |
from datetime import datetime
from pathlib import Path
import pytest
from my_receipts.apps.receipts.parsers import TaxcomParser
CURRENT_DIR = Path(__file__).resolve(strict=True).parent
pytestmark = pytest.mark.django_db
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] | 2.848101 | 79 |
# Generated by Django 3.2.9 on 2021-11-18 17:50
from django.db import migrations
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] | 2.766667 | 30 |
import sublime
import sublime_plugin
from structs.thread_handler import *
from api.inspect import highlighting
from lookup import file_type as lookup_file_type
| [
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] | 4.05 | 40 |
from src import *
| [
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] | 3.6 | 5 |
from django.contrib.auth import login, logout
from django.contrib.auth.decorators import login_required
from django.db import transaction
from django.shortcuts import render, redirect
from django.views.generic import TemplateView
from grocery_store.grocery_auth.forms import SignInForm, SignUpForm
from grocery_store.product.models import Category
from grocery_store.profiles.forms import ProfileForm, ProfileAddressForm
@login_required
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] | 3.7 | 120 |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 30 17:56:58 2016
@author: Vivian Zhong
"""
import click
import pymc3 as pm
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import logging
@click.command()
@click.option('--filename', default='data.csv',
help='File name of the data in CSV format.')
@click.option('--output_col', default='output',
help='Name of column that contains data.')
@click.option('--sample_col', default='sample_name',
help='Name of column that contains sample names.')
@click.option('--baseline_name', default='control',
help='Name of positive control in sample names column.')
@click.option('--n_steps', default=300000,
help='Number of iterations for ADVI.')
class BEST(object):
"""BEST Model, based on Kruschke (2013).
Parameters
----------
data : pandas DataFrame
A pandas dataframe which has the following data:
- Each row is one replicate measurement.
- There is a column that records the treatment name.
- There is a column that records the measured value for that replicate.
sample_col : str
The name of the column containing sample names.
output_col : str
The name of the column containing values to estimate.
baseline_name : str
The name of the "control" or "baseline".
Output
------
model : PyMC3 model
Returns the BEST model containing
"""
def _convert_to_indices(self):
"""
Adds the "indices" column to self.data (DataFrame). This is necessary
for the simplified model specification in the "fit" function below.
"""
sample_names = dict()
for i, name in enumerate(
list(np.unique(self.data[self.sample_col].values))):
logging.info('Sample name {0} has the index {1}'.format(name, i))
sample_names[name] = i
self.data['indices'] = self.data[self.sample_col].apply(
lambda x: sample_names[x])
def fit(self, n_steps=50000):
"""
Creates a Bayesian Estimation model for replicate measurements of
treatment(s) vs. control.
Parameters
----------
n_steps : int
The number of steps to run ADVI.
"""
sample_names = set(self.data[self.sample_col].values)
# mean_test = self.data.groupby('indices').mean()[self.output_col].values
# sd_test = self.data.groupby('indices').std()[self.output_col].values
# print(mean_test, sd_test)
with pm.Model() as model:
# Hyperpriors
# upper = pm.Exponential('upper', lam=0.05)
nu = pm.Exponential('nu_minus_one', 1/29.) + 1
# "fold", which is the estimated fold change.
fold = pm.Flat('fold', shape=len(sample_names))
# Assume that data have heteroskedastic (i.e. variable) error but
# are drawn from the same HalfCauchy distribution.
sigma = pm.HalfCauchy('sigma', beta=1, shape=len(sample_names))
# Model prediction
mu = fold[self.data['indices']]
sig = sigma[self.data['indices']]
# Data likelihood
like = pm.StudentT('like', nu=nu, mu=mu, sd=sig**-2,
observed=self.data[self.output_col])
# Sample from posterior
v_params = pm.variational.advi(n=n_steps)
start = pm.variational.sample_vp(v_params, 1)[0]
cov = np.power(model.dict_to_array(v_params.stds), 2)
step = pm.NUTS(scaling=cov, is_cov=True)
logging.info('Starting MCMC sampling')
trace = pm.sample(step=step, start=start, draws=2000)
self.trace = trace
self.model = model
def plot_posterior(self, rotate_xticks=False):
"""
Plots a swarm plot of the data overlaid on top of the 95% HPD and IQR
of the posterior distribution.
"""
# Make summary plot #
fig = plt.figure()
ax = fig.add_subplot(111)
# 1. Get the lower error and upper errorbars for 95% HPD and IQR.
lower, lower_q, upper_q, upper = np.percentile(self.trace['fold'][500:],
[2.5, 25, 75, 97.5],
axis=0)
summary_stats = pd.DataFrame()
summary_stats['mean'] = self.trace['fold'].mean(axis=0)
err_low = summary_stats['mean'] - lower
err_high = upper - summary_stats['mean']
iqr_low = summary_stats['mean'] - lower_q
iqr_high = upper_q - summary_stats['mean']
# 2. Plot the swarmplot and errorbars.
summary_stats['mean'].plot(ls='', ax=ax,
yerr=[err_low, err_high])
summary_stats['mean'].plot(ls='', ax=ax,
yerr=[iqr_low, iqr_high],
elinewidth=4, color='red')
sns.swarmplot(data=self.data, x=self.sample_col, y=self.output_col,
ax=ax, alpha=0.5)
if rotate_xticks:
logging.info('rotating xticks')
plt.xticks(rotation='vertical')
plt.ylabel(self.output_col)
return fig, ax
def plot_elbo(self):
"""
Plots the ELBO values to help check for convergence.
"""
fig = plt.figure()
plt.plot(-np.log10(-self.params.elbo_vals))
return fig
if __name__ == '__main__':
main() | [
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705,
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12417,
834,
10354,
198,
220,
220,
220,
1388,
3419
] | 2.167895 | 2,579 |
#!/usr/local/bin/python3
if __name__ == "__main__":
arm(2)
| [
2,
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14,
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14,
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18,
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] | 2.096774 | 31 |
'''
TO-DO:
GET BETTER RESULTS
CLEAN UP THIS CODE TO GET THE SINGLE PROCESSING EASIER TO USE
REDO AS A PR TO ORIGINAL REPO
'''
import os
import argparse
from solver import Solver
from data_loader import get_loader
from torch.backends import cudnn
# celeba_loader = get_loader(config.celeba_image_dir, config.attr_path, config.selected_attrs,
# config.celeba_crop_size, config.image_size, config.batch_size,
# 'CelebA', config.mode, config.num_workers)
if __name__ == '__main__':
config = Alt_config()
celeba_loader = get_loader(config.celeba_image_dir, config.attr_path, config.selected_attrs,
config.celeba_crop_size, config.image_size, config.batch_size,
'CelebA', config.mode, config.num_workers)
solver = Solver(celeba_loader, None, config)
solver.test_single()
main(config)
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] | 2.277641 | 407 |
# modified from https://github.com/tkipf/pygcn/blob/master/pygcn/layers.py
import math
import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
| [
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] | 2.663551 | 107 |
OC_OKAPI_KEY = "xxx"
OC_USERNAME = "xxx"
OC_PASSWORD = "xxx"
OC_QUERYID = "xxx"
| [
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] | 2 | 40 |
# 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.
import os
from data_validation import data_validation, consts
TERADATA_PASSWORD = os.getenv("TERADATA_PASSWORD")
TERADATA_HOST = os.getenv("TERADATA_HOST")
PROJECT_ID = os.getenv("PROJECT_ID")
conn = {
"source_type": "Teradata",
"host": TERADATA_HOST,
"user_name": "udf",
"password": TERADATA_PASSWORD,
"port": 1025,
}
TERADATA_CONFIG = {
# Specific Connection Config
consts.CONFIG_SOURCE_CONN: conn,
consts.CONFIG_TARGET_CONN: conn,
# Validation Type
consts.CONFIG_TYPE: "Column",
# Configuration Required Depending on Validator Type
consts.CONFIG_SCHEMA_NAME: "Sys_Calendar",
consts.CONFIG_TABLE_NAME: "CALENDAR",
consts.CONFIG_AGGREGATES: [
{
consts.CONFIG_TYPE: "count",
consts.CONFIG_SOURCE_COLUMN: "year_of_calendar",
consts.CONFIG_TARGET_COLUMN: "year_of_calendar",
consts.CONFIG_FIELD_ALIAS: "count",
},
],
consts.CONFIG_FORMAT: "table",
consts.CONFIG_FILTERS: [
{
consts.CONFIG_TYPE: consts.FILTER_TYPE_EQUALS,
consts.CONFIG_FILTER_SOURCE_COLUMN: "year_of_calendar",
consts.CONFIG_FILTER_SOURCE_VALUE: 2010,
consts.CONFIG_FILTER_TARGET_COLUMN: "year_of_calendar",
consts.CONFIG_FILTER_TARGET_VALUE: 2010,
},
],
}
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] | 2.407035 | 796 |
#-------------------------------------------------------------------------------
# Name: MNIST TensorFlow example
# Purpose: Experiments with TensorFlow
#
# Author: kol
#
# Created: 09.01.2020
# Copyright: (c) kol 2020
#-------------------------------------------------------------------------------
import tensorflow as tf
import matplotlib.pyplot as plt
from pathlib import Path
import numpy as np
from random import randrange
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
if Path('./mnist.m').exists():
print("Loading pre-trained model")
model = tf.keras.models.load_model('mnist.m')
else:
print("Training the model")
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.save('./mnist.m')
model.evaluate(x_test, y_test, verbose=2)
predictions = model.predict(x_test)
max_count = 10
num_rows = 5
fig = plt.figure(figsize=(8,4))
for i in range(max_count):
n = randrange(0, predictions.shape[0]-1)
fig.add_subplot(num_rows, max_count / num_rows, i+1)
plot_image(predictions[n], y_test[n], x_test[n])
if i >= max_count-1:
break
plt.tight_layout()
plt.show()
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10295,
396,
13,
76,
11537,
198,
198,
19849,
13,
49786,
7,
87,
62,
9288,
11,
220,
331,
62,
9288,
11,
15942,
577,
28,
17,
8,
198,
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9278,
796,
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13,
79,
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7,
87,
62,
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8,
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62,
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220,
220,
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796,
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9521,
7,
15,
11,
16277,
13,
43358,
58,
15,
45297,
16,
8,
198,
220,
220,
220,
2336,
13,
2860,
62,
7266,
29487,
7,
22510,
62,
8516,
11,
3509,
62,
9127,
1220,
997,
62,
8516,
11,
1312,
10,
16,
8,
198,
220,
220,
220,
7110,
62,
9060,
7,
28764,
9278,
58,
77,
4357,
331,
62,
9288,
58,
77,
4357,
2124,
62,
9288,
58,
77,
12962,
198,
220,
220,
220,
611,
1312,
18189,
3509,
62,
9127,
12,
16,
25,
198,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
198,
489,
83,
13,
33464,
62,
39786,
3419,
198,
489,
83,
13,
12860,
3419,
198
] | 2.462963 | 648 |
"""
test data for orientation functions
"""
# global
import numpy as np
| [
37811,
198,
9288,
1366,
329,
12852,
5499,
198,
37811,
198,
198,
2,
3298,
198,
11748,
299,
32152,
355,
45941,
628
] | 3.7 | 20 |
from dataclasses import dataclass
from moonleap import Resource
@dataclass
| [
6738,
4818,
330,
28958,
1330,
4818,
330,
31172,
198,
198,
6738,
8824,
293,
499,
1330,
20857,
628,
198,
31,
19608,
330,
31172,
198
] | 3.391304 | 23 |
from typing import Any, Iterable, Iterator, List, Optional, Union
AnyString = Union[str, Iterable[Any]]
def split_string(string: Optional[AnyString], separator: str = ",") -> List[str]:
"""
Breaks given *string* by the specified *separator*.
If *string* is a non-``str`` iterable, then return a list if it is not already.
>>> split_string('A, B, C') # Str
['A', 'B', 'C']
>>> split_string(['A', 'B', 'C']) # List, a non-str iterable
['A', 'B', 'C']
>>> split_string(('A', 'B', 'C')) # Tuple, a non-str iterable
['A', 'B', 'C']
"""
return list(iter_split_string(string=string, separator=separator))
def iter_split_string(string: Optional[AnyString], separator: str = ",") -> Iterator[str]:
"""Generator version of :func:`split_string`."""
if string is None:
return
elif isinstance(string, str):
parts = str(string).split(separator)
for part in parts:
part = part.strip()
if part:
yield part
elif isinstance(string, Iterable):
# NOTE: Text is also an Iterable, so this should always be after the Text check.
for part in string:
part = str(part).strip()
if part:
yield part
else:
raise TypeError("Cannot split string of {!r}".format(type(string)))
def is_instance_of_all(obj, classes: Iterable[type]) -> bool:
"""
Returns ``True`` if the ``obj`` argument is an instance of all of the
classes in the ``classes`` argument.
:raises TypeError: If any element of classes is not a type.
"""
if any(not isinstance(classinfo, type) for classinfo in classes):
raise TypeError("classes must contain types")
return all(isinstance(obj, classinfo) for classinfo in classes)
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7,
26801,
11,
1398,
10951,
8,
329,
1398,
10951,
287,
6097,
8,
198
] | 2.567568 | 703 |
#=======================================================================
# verilator_cffi.py
#=======================================================================
from __future__ import print_function
import os
import shutil
import verilog_structural
from ...tools.simulation.vcd import get_vcd_timescale
from subprocess import check_output, STDOUT, CalledProcessError
from ...model.signals import InPort, OutPort
from ...model.PortBundle import PortBundle
from exceptions import VerilatorCompileError
#-----------------------------------------------------------------------
# verilog_to_pymtl
#-----------------------------------------------------------------------
# Create a PyMTL compatible interface for Verilog HDL.
#-----------------------------------------------------------------------
# verilate_model
#-----------------------------------------------------------------------
# Convert Verilog HDL into a C++ simulator using Verilator.
# http://www.veripool.org/wiki/verilator
#-----------------------------------------------------------------------
# create_c_wrapper
#-----------------------------------------------------------------------
# Generate a C wrapper file for Verilated C++.
#-----------------------------------------------------------------------
# create_shared_lib
#-----------------------------------------------------------------------
# Compile the cpp wrapper into a shared library.
#
# Verilator suggests:
#
# For best performance, run Verilator with the "-O3 --x-assign=fast
# --noassert" flags. The -O3 flag will require longer compile times, and
# --x-assign=fast may increase the risk of reset bugs in trade for
# performance; see the above documentation for these flags.
#
# Minor Verilog code changes can also give big wins. You should not have
# any UNOPTFLAT warnings from Verilator. Fixing these warnings can
# result in huge improvements; one user fixed their one UNOPTFLAT
# warning by making a simple change to a clock latch used to gate clocks
# and gained a 60% performance improvement.
#
# Beyond that, the performance of a Verilated model depends mostly on
# your C++ compiler and size of your CPU's caches.
#
# By default, the lib/verilated.mk file has optimization
# turned off. This is for the benefit of new users, as it improves
# compile times at the cost of runtimes. To add optimization as the
# default, set one of three variables, OPT, OPT_FAST, or OPT_SLOW
# lib/verilated.mk. Or, use the -CFLAGS and/or -LDFLAGS option on the
# verilator command line to pass the flags directly to the compiler or
# linker. Or, just for one run, pass them on the command line to make:
#
# make OPT_FAST="-O2" -f Vour.mk Vour__ALL.a
# OPT_FAST specifies optimizations for those programs that are part of
# the fast path, mostly code that is executed every cycle. OPT_SLOW
# specifies optimizations for slow-path files (plus tracing), which
# execute only rarely, yet take a long time to compile with optimization
# on. OPT specifies overall optimization and affects all compiles,
# including those OPT_FAST and OPT_SLOW affect. For best results, use
# OPT="-O2", and link with "-static". Nearly the same results can be had
# with much better compile times with OPT_FAST="-O1 -fstrict-aliasing".
# Higher optimization such as "-O3" may help, but gcc compile times may
# be excessive under O3 on even medium sized designs. Alternatively,
# some larger designs report better performance using "-Os".
#
# http://www.veripool.org/projects/verilator/wiki/Manual-verilator
# I have added a new feature which compiles all of the standard Verilator
# code into a static library and then simply links this in. This reduces
# compile times.
#-----------------------------------------------------------------------
# create_verilator_py_wrapper
#-----------------------------------------------------------------------
#-----------------------------------------------------------------------
# get_indices
#-----------------------------------------------------------------------
# Utility function for determining assignment of wide ports
#-----------------------------------------------------------------------
# set_input_stmt
#-----------------------------------------------------------------------
#-----------------------------------------------------------------------
# set_output_stmt
#-----------------------------------------------------------------------
# TODO: no way to distinguish between combinational and sequential
# outputs, so we set outputs both ways...
# This seems broken, but I can't think of a better way.
#-----------------------------------------------------------------------
# verilator_mangle
#-----------------------------------------------------------------------
#-----------------------------------------------------------------------
# pymtl_wrapper_from_ports
#-----------------------------------------------------------------------
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] | 4.640112 | 1,067 |
from django.conf.urls import include, url
from django.contrib.sitemaps.views import sitemap
from django.contrib.sitemaps import FlatPageSitemap, GenericSitemap, Sitemap
from django.core.urlresolvers import reverse
from django.contrib import admin
from blog.models import Article, News, Category, Column
from demoproject import views
import suit;
import xadmin;
xadmin.autodiscover();
from xadmin.plugins import xversion
xversion.register_models()
sitemaps = {
'article-is-top': GenericSitemap(
{
'queryset': Article.objects.filter(
status=0, is_top=True
).all(),
'date_field': 'pub_time'
},
priority=1.0,
changefreq='daily'
),
'article-is-not-top': GenericSitemap(
{
'queryset': Article.objects.filter(status=0).all(),
'date_field': 'pub_time'
},
priority=0.8,
changefreq='daily'
),
'news': GenericSitemap(
{
'queryset': News.objects.all(),
'data_field': 'pub_time'
},
priority=0.6,
changefreq='daily'
),
'category': GenericSitemap(
{
'queryset': Category.objects.all()
},
priority=0.9,
changefreq='daily'
),
'column': GenericSitemap(
{
'queryset': Column.objects.all()
},
priority=0.9,
changefreq='daily'
),
'static': StaticViewSitemap
}
urlpatterns = [
url(r'^admin/', include(admin.site.urls)),
url(r'', include('blog.urls')),
url(r'', include('vmaig_comments.urls')),
url(r'', include('vmaig_auth.urls')),
url(r'^sitemap\.xml$', sitemap, {'sitemaps': sitemaps},
name='django.contrib.sitemaps.views.sitemap'),
url(r'^piechart/', views.demo_piechart, name='demo_piechart'),
url(r'^linechart/', views.demo_linechart, name='demo_linechart'),
url(r'^linechart_without_date/', views.demo_linechart_without_date, name='demo_linechart_without_date'),
url(r'^linewithfocuschart/', views.demo_linewithfocuschart, name='demo_linewithfocuschart'),
url(r'^multibarchart/', views.demo_multibarchart, name='demo_multibarchart'),
url(r'^stackedareachart/', views.demo_stackedareachart, name='demo_stackedareachart'),
url(r'^multibarhorizontalchart/', views.demo_multibarhorizontalchart, name='demo_multibarhorizontalchart'),
url(r'^lineplusbarchart/', views.demo_lineplusbarchart, name='demo_lineplusbarchart'),
url(r'^cumulativelinechart/', views.demo_cumulativelinechart, name='demo_cumulativelinechart'),
url(r'^discretebarchart/', views.demo_discretebarchart, name='demo_discretebarchart'),
url(r'^discretebarchart_with_date/', views.demo_discretebarchart_with_date, name='demo_discretebarchart_date'),
url(r'^scatterchart/', views.demo_scatterchart, name='demo_scatterchart'),
url(r'^linechart_with_ampm/', views.demo_linechart_with_ampm, name='demo_linechart_with_ampm'),
url(r'^charthome$', views.home, name='charthome'),
url(r'^xadmin/', include(xadmin.site.urls)),
]
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# -*- coding: utf-8 -*-
#
# Copyright 2012-2015 Spotify AB
#
# 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 __future__ import absolute_import
import luigi
class SparkeyExportTask(luigi.Task):
"""
A luigi task that writes to a local sparkey log file.
Subclasses should implement the requires and output methods. The output
must be a luigi.LocalTarget.
The resulting sparkey log file will contain one entry for every line in
the input, mapping from the first value to a tab-separated list of the
rest of the line.
To generate a simple key-value index, yield "key", "value" pairs from the input(s) to this task.
"""
# the separator used to split input lines
separator = '\t'
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10354,
604,
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] | 1.540572 | 1,713 |
import logging
from .resource import BaseResource
logger = logging.getLogger(__name__)
| [
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198
] | 3.52 | 25 |
from django import forms
from django.core.mail.message import EmailMessage
from django.forms.widgets import Textarea | [
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13,
28029,
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8255,
20337
] | 3.866667 | 30 |
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