content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
#!/usr/bin/env python
#-*- coding: UTF-8 -*-
import os
import time
import subprocess
import shutil
import sys
os.chdir(sys.path[0])
print(os.getcwd())
cacheFolder = os.getcwd() + "/temp/"
cacheFile = cacheFolder + "temp"
caches = []
generalSize = "640X640"
if(len(sys.argv) > 1) :
wishSize = 640 * int(sys.argv[1])
generalSize = "%dx%d" % (wishSize, wishSize)
print("开始...")
checkTempFileExist()
loadCache()
initRunner()
print("已结束.")
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# Note taken from --> https://gist.github.com/JungeAlexander/6ce0a5213f3af56d7369 & https://stackoverflow.com/questions/714063/importing-modules-from-parent-folder/11158224#11158224
import os, sys, inspect
current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parent_dir = os.path.dirname(current_dir)
sys.path.insert(0, parent_dir)
import time
import datetime
import json
from wtforms import fields
from _compat import text_type, as_unicode
# from widgets import * as admin_widgets
import form.widgets as admin_widgets
__all__ = ['DateTimeField', 'TimeField', 'Select2Field', 'Select2TagsField', 'JSONField']
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220,
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220,
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220,
220,
198
] | 2.721774 | 248 |
from datetime import datetime
from os.path import dirname, join
import pytest
from city_scrapers_core.constants import COMMISSION, PASSED, TENTATIVE
from city_scrapers_core.utils import file_response
from freezegun import freeze_time
from scrapy.settings import Settings
from city_scrapers.spiders.chi_ssa_23 import ChiSsa23Spider
test_response = file_response(
join(dirname(__file__), "files", "chi_ssa_23.html"),
url="https://www.lincolnparkchamber.com/clark-street-ssa-administration/",
)
spider = ChiSsa23Spider()
spider.settings = Settings(values={"CITY_SCRAPERS_ARCHIVE": False})
freezer = freeze_time("2020-05-11")
freezer.start()
parsed_items = sorted(
[item for item in spider.parse(test_response)],
key=lambda i: i["start"],
reverse=True,
)
freezer.stop()
@pytest.mark.parametrize("item", parsed_items)
@pytest.mark.parametrize("item", parsed_items)
@pytest.mark.parametrize("item", parsed_items)
@pytest.mark.parametrize("item", parsed_items)
@pytest.mark.parametrize("item", parsed_items)
@pytest.mark.parametrize("item", parsed_items)
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] | 2.7325 | 400 |
import subprocess
filename = "../data/crackdump-01.csv"
read()
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12,
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1,
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961,
3419,
198
] | 2.64 | 25 |
import logging
from base64 import b64encode
from asn1crypto.cms import CMSAttribute, ContentInfo, IssuerAndSerialNumber
from cryptography.hazmat.primitives.asymmetric import padding
from .asn1 import SCEPCMSAttributeType
from .cryptoutils import digest_for_data, decrypt, digest_function_for_type
from .enums import MessageType, PKIStatus
from .certificate import Certificate
CMSAttribute._fields = [
('type', SCEPCMSAttributeType),
('values', None),
]
logger = logging.getLogger(__name__)
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1362,
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628,
198
] | 3.104294 | 163 |
import pytest
from sort import *
@pytest.mark.parametrize(
"input,expected",
[
pytest.param(
[4], [4]
),
pytest.param(
[5, 7, 6, 4], [4, 5, 6, 7]
),
],
)
@pytest.mark.parametrize(
"input,expected",
[
pytest.param(
[4], [4]
),
pytest.param(
[5, 7, 6, 4], [4, 5, 6, 7]
),
],
)
@pytest.mark.parametrize(
"input,expected",
[
pytest.param(
[4], [4]
),
pytest.param(
[4, 2], [2, 4]
),
pytest.param(
[5, 7, 6, 4], [4, 5, 6, 7]
),
],
)
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17,
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9288,
13,
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7,
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220,
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220,
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685,
20,
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11,
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19,
11,
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11,
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11,
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60,
198,
220,
220,
220,
220,
220,
220,
220,
10612,
198,
220,
220,
220,
16589,
198,
8,
198
] | 1.543981 | 432 |
"""
Created on 26 Dec 2016
@author: Bruno Beloff ([email protected])
https://learn.adafruit.com/setting-up-io-python-library-on-beaglebone-black/port
"""
import serial
import time
from scs_core.sys.serial import Serial
from scs_host.lock.lock import Lock
# --------------------------------------------------------------------------------------------------------------------
class HostSerial(Serial):
"""
classdocs
"""
# ----------------------------------------------------------------------------------------------------------------
def __init__(self, device_path, baud_rate, hard_handshake=False):
"""
Constructor
"""
super().__init__(device_path, baud_rate, hard_handshake)
# ----------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------
@property
# ----------------------------------------------------------------------------------------------------------------
@property
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220,
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220,
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7,
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11,
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62,
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11,
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62,
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11,
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62,
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28,
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220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
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220,
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273,
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220,
220,
220,
220,
220,
37227,
198,
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220,
220,
220,
220,
220,
220,
2208,
22446,
834,
15003,
834,
7,
25202,
62,
6978,
11,
275,
3885,
62,
4873,
11,
1327,
62,
4993,
32431,
8,
628,
198,
220,
220,
220,
1303,
16529,
47232,
628,
628,
220,
220,
220,
1303,
16529,
47232,
628,
220,
220,
220,
2488,
26745,
628,
198,
220,
220,
220,
1303,
16529,
47232,
628,
220,
220,
220,
2488,
26745,
198
] | 4.559524 | 252 |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
from random import random, seed
from bertsum.others.utils import test_rouge
def get_rouge(predictions, targets, temp_dir, random_seed=42):
"""
function to get the rouge metric for the prediction and the reference.
Args:
predictions (list of strings): Predictions to be compared.
target (list of strings): References
temp_dir (str): Path where temporary folders are created to host the files
generated by ROUGE application.
seed (int, optional): Random seed. Defaults to 42.
Return:
dictionary: rouge metric
"""
seed(random_seed)
random_number = random()
os.makedirs(temp_dir, exist_ok=True)
candidate_path = os.path.join(temp_dir, "candidate" + str(random_number))
gold_path = os.path.join(temp_dir, "gold" + str(random_number))
_write_list_to_file(predictions, candidate_path)
_write_list_to_file(targets, gold_path)
rouge = test_rouge(temp_dir, candidate_path, gold_path)
return rouge
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7,
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8,
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220,
220,
4808,
13564,
62,
4868,
62,
1462,
62,
7753,
7,
83,
853,
1039,
11,
3869,
62,
6978,
8,
198,
220,
220,
220,
13805,
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62,
472,
469,
7,
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62,
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3869,
62,
6978,
8,
198,
220,
220,
220,
1441,
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469,
198
] | 2.784615 | 390 |
from flask import Flask
from .extensions import db
from .models import Tree
app = Flask(__name__)
db.init_app(app)
db.app = app
# Create dummy secrey key so we can use sessions
app.config['SECRET_KEY'] = '123456790'
# Create in-memory database
# app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite://data.sqlite'
from .views import * # noqa
@app.before_first_request
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628,
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198,
31,
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13,
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62,
11085,
62,
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] | 2.810606 | 132 |
import torch
# https://discuss.pytorch.org/t/covariance-and-gradient-support/16217
| [
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2743,
590,
12,
392,
12,
49607,
12,
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] | 2.625 | 32 |
from django.apps import AppConfig
| [
6738,
42625,
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13,
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1330,
2034,
16934,
628
] | 3.888889 | 9 |
# Copyright (c) 2009 Doug Hellmann All rights reserved.
#
"""
"""
# end_pymotw_header
import compileall
import sys
sys.path[:] = ["examples", "notthere"]
print("sys.path =", sys.path)
compileall.compile_path()
| [
2,
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] | 2.789474 | 76 |
import yaml
import argparse
from datasets import get_dataset
from diffusion.trainers import get_trainer
# The first arg parser parses out only the --config argument, this argument is used to
# load a yaml file containing key-values that override the defaults for the main parser below
config_parser = parser = argparse.ArgumentParser(
description="Training Config", add_help=False
)
parser.add_argument(
"-c",
"--config",
default="",
type=str,
metavar="FILE",
help="YAML config file specifying default arguments",
)
if __name__ == "__main__":
args, args_text = _parse_args()
print(args_text)
# Get Dataset
trainloader, testloader = get_dataset(args)
# Get trainer and train
trainer = get_trainer(args)
trainer.train(trainloader, testloader)
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8,
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198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
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198,
220,
220,
220,
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11,
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62,
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796,
4808,
29572,
62,
22046,
3419,
198,
220,
220,
220,
3601,
7,
22046,
62,
5239,
8,
198,
220,
220,
220,
1303,
3497,
16092,
292,
316,
198,
220,
220,
220,
4512,
29356,
11,
1332,
29356,
796,
651,
62,
19608,
292,
316,
7,
22046,
8,
628,
220,
220,
220,
1303,
3497,
21997,
290,
4512,
198,
220,
220,
220,
21997,
796,
651,
62,
2213,
10613,
7,
22046,
8,
198,
220,
220,
220,
21997,
13,
27432,
7,
27432,
29356,
11,
1332,
29356,
8,
198
] | 3.037879 | 264 |
#
# PySNMP MIB module CIENA-CES-ACL-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CIENA-CES-ACL-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:31:34 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols("ASN1", "OctetString", "ObjectIdentifier", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, ValueSizeConstraint, ConstraintsIntersection, ValueRangeConstraint, SingleValueConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "ValueSizeConstraint", "ConstraintsIntersection", "ValueRangeConstraint", "SingleValueConstraint")
cienaCesConfig, = mibBuilder.importSymbols("CIENA-SMI", "cienaCesConfig")
CienaGlobalState, = mibBuilder.importSymbols("CIENA-TC", "CienaGlobalState")
InetAddress, InetAddressType, InetAddressPrefixLength = mibBuilder.importSymbols("INET-ADDRESS-MIB", "InetAddress", "InetAddressType", "InetAddressPrefixLength")
ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup")
ModuleIdentity, ObjectIdentity, Unsigned32, Counter64, IpAddress, iso, Bits, MibScalar, MibTable, MibTableRow, MibTableColumn, Integer32, TimeTicks, MibIdentifier, Counter32, Gauge32, NotificationType = mibBuilder.importSymbols("SNMPv2-SMI", "ModuleIdentity", "ObjectIdentity", "Unsigned32", "Counter64", "IpAddress", "iso", "Bits", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Integer32", "TimeTicks", "MibIdentifier", "Counter32", "Gauge32", "NotificationType")
TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString")
cienaCesAclMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25))
cienaCesAclMIB.setRevisions(('2012-11-21 00:00', '2012-05-01 00:00',))
if mibBuilder.loadTexts: cienaCesAclMIB.setLastUpdated('201211210000Z')
if mibBuilder.loadTexts: cienaCesAclMIB.setOrganization('Ciena, Inc')
cienaCesAclMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1))
cienaCesAclGlobal = MibIdentifier((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1))
cienaCesAclRules = MibIdentifier((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2))
cienaCesAclMIBConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 3))
cienaCesAclMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 3, 1))
cienaCesAclMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 3, 2))
cienaCesAclAdminState = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 1), CienaGlobalState()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclAdminState.setStatus('current')
cienaCesAclCacheHit = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 2), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclCacheHit.setStatus('current')
cienaCesAclNoHit = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclNoHit.setStatus('current')
cienaCesAclBadPort = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclBadPort.setStatus('current')
cienaCesAclBadDscp = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclBadDscp.setStatus('current')
cienaCesAclOperState = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 6), CienaGlobalState()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclOperState.setStatus('current')
cienaCesAclInUseEntries = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclInUseEntries.setStatus('current')
cienaCesAclMaxEntries = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 8), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclMaxEntries.setStatus('current')
cienaCesAclBadProtocol = MibScalar((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 1, 9), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclBadProtocol.setStatus('current')
cienaCesAclTable = MibTable((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1), )
if mibBuilder.loadTexts: cienaCesAclTable.setStatus('deprecated')
cienaCesAclEntry = MibTableRow((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1), ).setIndexNames((0, "CIENA-CES-ACL-MIB", "cienaCesAclEntryInetAddrType"), (0, "CIENA-CES-ACL-MIB", "cienaCesAclEntryInetAddr"), (0, "CIENA-CES-ACL-MIB", "cienaCesAclEntryInetPrefixLength"))
if mibBuilder.loadTexts: cienaCesAclEntry.setStatus('deprecated')
cienaCesAclEntryInetAddrType = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 1), InetAddressType())
if mibBuilder.loadTexts: cienaCesAclEntryInetAddrType.setStatus('deprecated')
cienaCesAclEntryInetAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 2), InetAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryInetAddr.setStatus('deprecated')
cienaCesAclEntryInetPrefixLength = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 3), InetAddressPrefixLength())
if mibBuilder.loadTexts: cienaCesAclEntryInetPrefixLength.setStatus('deprecated')
cienaCesAclEntryHits = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryHits.setStatus('deprecated')
cienaCesAclEntryBadPort = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryBadPort.setStatus('deprecated')
cienaCesAclEntryDscpMask = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 6), OctetString().subtype(subtypeSpec=ValueSizeConstraint(8, 8)).setFixedLength(8)).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryDscpMask.setStatus('deprecated')
cienaCesAclEntryBadDscp = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryBadDscp.setStatus('deprecated')
cienaCesAclEntryPortBitMask = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 8), OctetString().subtype(subtypeSpec=ValueSizeConstraint(8, 8)).setFixedLength(8)).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryPortBitMask.setStatus('deprecated')
cienaCesAclEntryNotifInetAddrType = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 9), InetAddressType()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryNotifInetAddrType.setStatus('deprecated')
cienaCesAclEntryNotifInetAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 10), InetAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryNotifInetAddr.setStatus('deprecated')
cienaCesAclEntryNotifInetPrefixLength = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 1, 1, 11), InetAddressPrefixLength()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesAclEntryNotifInetPrefixLength.setStatus('deprecated')
cienaCesExtAclTable = MibTable((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2), )
if mibBuilder.loadTexts: cienaCesExtAclTable.setStatus('current')
cienaCesExtAclEntry = MibTableRow((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1), ).setIndexNames((0, "CIENA-CES-ACL-MIB", "cienaCesExtAclEntrySrcInetAddrType"), (0, "CIENA-CES-ACL-MIB", "cienaCesExtAclEntrySrcInetAddr"), (0, "CIENA-CES-ACL-MIB", "cienaCesExtAclEntrySrcInetPrefixLen"), (0, "CIENA-CES-ACL-MIB", "cienaCesExtAclEntryDstInetAddrType"), (0, "CIENA-CES-ACL-MIB", "cienaCesExtAclEntryDstInetAddr"), (0, "CIENA-CES-ACL-MIB", "cienaCesExtAclEntryDstInetPrefixLen"))
if mibBuilder.loadTexts: cienaCesExtAclEntry.setStatus('current')
cienaCesExtAclEntrySrcInetAddrType = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 1), InetAddressType())
if mibBuilder.loadTexts: cienaCesExtAclEntrySrcInetAddrType.setStatus('current')
cienaCesExtAclEntrySrcInetAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 2), InetAddress().subtype(subtypeSpec=ValueSizeConstraint(16, 16)).setFixedLength(16))
if mibBuilder.loadTexts: cienaCesExtAclEntrySrcInetAddr.setStatus('current')
cienaCesExtAclEntrySrcInetPrefixLen = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 3), InetAddressPrefixLength())
if mibBuilder.loadTexts: cienaCesExtAclEntrySrcInetPrefixLen.setStatus('current')
cienaCesExtAclEntryDstInetAddrType = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 4), InetAddressType())
if mibBuilder.loadTexts: cienaCesExtAclEntryDstInetAddrType.setStatus('current')
cienaCesExtAclEntryDstInetAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 5), InetAddress().subtype(subtypeSpec=ValueSizeConstraint(16, 16)).setFixedLength(16))
if mibBuilder.loadTexts: cienaCesExtAclEntryDstInetAddr.setStatus('current')
cienaCesExtAclEntryDstInetPrefixLen = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 6), InetAddressPrefixLength())
if mibBuilder.loadTexts: cienaCesExtAclEntryDstInetPrefixLen.setStatus('current')
cienaCesExtAclEntryNotifSrcInetAddrType = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 7), InetAddressType()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryNotifSrcInetAddrType.setStatus('current')
cienaCesExtAclEntryNotifSrcInetAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 8), InetAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryNotifSrcInetAddr.setStatus('current')
cienaCesExtAclEntryNotifSrcInetPrefixLen = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 9), InetAddressPrefixLength()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryNotifSrcInetPrefixLen.setStatus('current')
cienaCesExtAclEntryNotifDstInetAddrType = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 10), InetAddressType()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryNotifDstInetAddrType.setStatus('current')
cienaCesExtAclEntryNotifDstInetAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 11), InetAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryNotifDstInetAddr.setStatus('current')
cienaCesExtAclEntryNotifDstInetPrefixLen = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 12), InetAddressPrefixLength()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryNotifDstInetPrefixLen.setStatus('current')
cienaCesExtAclEntryHits = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 13), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryHits.setStatus('current')
cienaCesExtAclEntryBadPort = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 14), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryBadPort.setStatus('current')
cienaCesExtAclEntryDscpMask = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 15), OctetString().subtype(subtypeSpec=ValueSizeConstraint(8, 8)).setFixedLength(8)).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryDscpMask.setStatus('current')
cienaCesExtAclEntryBadDscp = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 16), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryBadDscp.setStatus('current')
cienaCesExtAclEntryPortBitMask = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 17), OctetString().subtype(subtypeSpec=ValueSizeConstraint(8, 8)).setFixedLength(8)).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryPortBitMask.setStatus('current')
cienaCesExtAclEntryProtocol = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 18), Bits().clone(namedValues=NamedValues(("icmp", 0), ("tcp", 1), ("udp", 2), ("all", 15)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryProtocol.setStatus('current')
cienaCesExtAclEntryBadProtocol = MibTableColumn((1, 3, 6, 1, 4, 1, 1271, 2, 1, 25, 1, 2, 2, 1, 19), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: cienaCesExtAclEntryBadProtocol.setStatus('current')
mibBuilder.exportSymbols("CIENA-CES-ACL-MIB", cienaCesAclNoHit=cienaCesAclNoHit, PYSNMP_MODULE_ID=cienaCesAclMIB, cienaCesExtAclEntryPortBitMask=cienaCesExtAclEntryPortBitMask, cienaCesExtAclEntryDstInetAddrType=cienaCesExtAclEntryDstInetAddrType, cienaCesAclEntryBadPort=cienaCesAclEntryBadPort, cienaCesAclInUseEntries=cienaCesAclInUseEntries, cienaCesExtAclEntrySrcInetAddr=cienaCesExtAclEntrySrcInetAddr, cienaCesExtAclEntryNotifDstInetAddrType=cienaCesExtAclEntryNotifDstInetAddrType, cienaCesExtAclEntryHits=cienaCesExtAclEntryHits, cienaCesAclTable=cienaCesAclTable, cienaCesAclBadProtocol=cienaCesAclBadProtocol, cienaCesAclEntry=cienaCesAclEntry, cienaCesExtAclEntryBadDscp=cienaCesExtAclEntryBadDscp, cienaCesExtAclEntryDstInetAddr=cienaCesExtAclEntryDstInetAddr, cienaCesAclEntryHits=cienaCesAclEntryHits, cienaCesExtAclEntryProtocol=cienaCesExtAclEntryProtocol, cienaCesAclMIBConformance=cienaCesAclMIBConformance, cienaCesAclEntryInetPrefixLength=cienaCesAclEntryInetPrefixLength, cienaCesAclMIBCompliances=cienaCesAclMIBCompliances, cienaCesAclEntryNotifInetAddr=cienaCesAclEntryNotifInetAddr, cienaCesExtAclEntryNotifSrcInetPrefixLen=cienaCesExtAclEntryNotifSrcInetPrefixLen, cienaCesExtAclEntryBadProtocol=cienaCesExtAclEntryBadProtocol, cienaCesAclEntryBadDscp=cienaCesAclEntryBadDscp, cienaCesAclMIBObjects=cienaCesAclMIBObjects, cienaCesAclOperState=cienaCesAclOperState, cienaCesExtAclTable=cienaCesExtAclTable, cienaCesAclEntryNotifInetPrefixLength=cienaCesAclEntryNotifInetPrefixLength, cienaCesAclEntryInetAddr=cienaCesAclEntryInetAddr, cienaCesExtAclEntryNotifSrcInetAddr=cienaCesExtAclEntryNotifSrcInetAddr, cienaCesAclMIBGroups=cienaCesAclMIBGroups, cienaCesAclGlobal=cienaCesAclGlobal, cienaCesAclEntryInetAddrType=cienaCesAclEntryInetAddrType, cienaCesExtAclEntryNotifDstInetAddr=cienaCesExtAclEntryNotifDstInetAddr, cienaCesAclEntryPortBitMask=cienaCesAclEntryPortBitMask, cienaCesExtAclEntryDstInetPrefixLen=cienaCesExtAclEntryDstInetPrefixLen, cienaCesExtAclEntryNotifSrcInetAddrType=cienaCesExtAclEntryNotifSrcInetAddrType, cienaCesExtAclEntryBadPort=cienaCesExtAclEntryBadPort, cienaCesExtAclEntrySrcInetAddrType=cienaCesExtAclEntrySrcInetAddrType, cienaCesExtAclEntryDscpMask=cienaCesExtAclEntryDscpMask, cienaCesAclRules=cienaCesAclRules, cienaCesAclEntryDscpMask=cienaCesAclEntryDscpMask, cienaCesAclEntryNotifInetAddrType=cienaCesAclEntryNotifInetAddrType, cienaCesAclMIB=cienaCesAclMIB, cienaCesAclCacheHit=cienaCesAclCacheHit, cienaCesAclBadPort=cienaCesAclBadPort, cienaCesExtAclEntry=cienaCesExtAclEntry, cienaCesExtAclEntrySrcInetPrefixLen=cienaCesExtAclEntrySrcInetPrefixLen, cienaCesAclAdminState=cienaCesAclAdminState, cienaCesExtAclEntryNotifDstInetPrefixLen=cienaCesExtAclEntryNotifDstInetPrefixLen, cienaCesAclBadDscp=cienaCesAclBadDscp, cienaCesAclMaxEntries=cienaCesAclMaxEntries)
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11,
513,
11,
718,
11,
352,
11,
604,
11,
352,
11,
1105,
4869,
11,
362,
11,
352,
11,
1679,
11,
352,
11,
362,
4008,
198,
979,
8107,
34,
274,
32,
565,
8895,
2749,
261,
10367,
796,
337,
571,
33234,
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19510,
16,
11,
513,
11,
718,
11,
352,
11,
604,
11,
352,
11,
1105,
4869,
11,
362,
11,
352,
11,
1679,
11,
513,
4008,
198,
979,
8107,
34,
274,
32,
565,
8895,
2749,
6316,
16097,
796,
337,
571,
33234,
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19510,
16,
11,
513,
11,
718,
11,
352,
11,
604,
11,
352,
11,
1105,
4869,
11,
362,
11,
352,
11,
1679,
11,
513,
11,
352,
4008,
198,
979,
8107,
34,
274,
32,
565,
8895,
40469,
14459,
796,
337,
571,
33234,
7483,
19510,
16,
11,
513,
11,
718,
11,
352,
11,
604,
11,
352,
11,
1105,
4869,
11,
362,
11,
352,
11,
1679,
11,
513,
11,
362,
4008,
198,
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8107,
34,
274,
32,
565,
46787,
9012,
796,
337,
571,
3351,
282,
283,
19510,
16,
11,
513,
11,
718,
11,
352,
11,
604,
11,
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11,
1105,
4869,
11,
362,
11,
352,
11,
1679,
11,
352,
11,
352,
11,
352,
828,
327,
2013,
64,
22289,
9012,
3419,
737,
2617,
11518,
15457,
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961,
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4943,
198,
361,
285,
571,
32875,
13,
2220,
8206,
82,
25,
269,
2013,
64,
34,
274,
32,
565,
46787,
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13,
2617,
19580,
10786,
14421,
11537,
198,
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8107,
34,
274,
32,
565,
30562,
17889,
796,
337,
571,
3351,
282,
283,
19510,
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11,
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11,
718,
11,
352,
11,
604,
11,
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11,
1105,
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11,
362,
11,
352,
11,
1679,
11,
352,
11,
352,
11,
362,
828,
15034,
2624,
3419,
737,
2617,
11518,
15457,
7203,
961,
8807,
4943,
198,
361,
285,
571,
32875,
13,
2220,
8206,
82,
25,
269,
2013,
64,
34,
274,
32,
565,
30562,
17889,
13,
2617,
19580,
10786,
14421,
11537,
198,
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34,
274,
32,
565,
2949,
17889,
796,
337,
571,
3351,
282,
283,
19510,
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11,
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718,
11,
352,
11,
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11,
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1105,
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362,
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1679,
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352,
11,
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11,
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828,
15034,
2624,
3419,
737,
2617,
11518,
15457,
7203,
961,
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4943,
198,
361,
285,
571,
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13,
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82,
25,
269,
2013,
64,
34,
274,
32,
565,
2949,
17889,
13,
2617,
19580,
10786,
14421,
11537,
198,
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8107,
34,
274,
32,
565,
22069,
13924,
796,
337,
571,
3351,
282,
283,
19510,
16,
11,
513,
11,
718,
11,
352,
11,
604,
11,
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11,
1105,
4869,
11,
362,
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1679,
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352,
11,
352,
11,
604,
828,
15034,
2624,
3419,
737,
2617,
11518,
15457,
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961,
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4943,
198,
361,
285,
571,
32875,
13,
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8206,
82,
25,
269,
2013,
64,
34,
274,
32,
565,
22069,
13924,
13,
2617,
19580,
10786,
14421,
11537,
198,
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8107,
34,
274,
32,
565,
22069,
35,
1416,
79,
796,
337,
571,
3351,
282,
283,
19510,
16,
11,
513,
11,
718,
11,
352,
11,
604,
11,
352,
11,
1105,
4869,
11,
362,
11,
352,
11,
1679,
11,
352,
11,
352,
11,
642,
828,
15034,
2624,
3419,
737,
2617,
11518,
15457,
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4943,
198,
361,
285,
571,
32875,
13,
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8206,
82,
25,
269,
2013,
64,
34,
274,
32,
565,
22069,
35,
1416,
79,
13,
2617,
19580,
10786,
14421,
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34,
274,
32,
565,
18843,
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283,
19510,
16,
11,
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11,
718,
11,
352,
11,
604,
11,
352,
11,
1105,
4869,
11,
362,
11,
352,
11,
1679,
11,
352,
11,
352,
11,
718,
828,
327,
2013,
64,
22289,
9012,
3419,
737,
2617,
11518,
15457,
7203,
961,
8807,
4943,
198,
361,
285,
571,
32875,
13,
2220,
8206,
82,
25,
269,
2013,
64,
34,
274,
32,
565,
18843,
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13,
2617,
19580,
10786,
14421,
11537,
198,
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34,
274,
32,
565,
818,
11041,
14539,
1678,
796,
337,
571,
3351,
282,
283,
19510,
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11,
513,
11,
718,
11,
352,
11,
604,
11,
352,
11,
1105,
4869,
11,
362,
11,
352,
11,
1679,
11,
352,
11,
352,
11,
767,
828,
15034,
2624,
3419,
737,
2617,
11518,
15457,
7203,
961,
8807,
4943,
198,
361,
285,
571,
32875,
13,
2220,
8206,
82,
25,
269,
2013,
64,
34,
274,
32,
565,
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11041,
14539,
1678,
13,
2617,
19580,
10786,
14421,
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1105,
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362,
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11,
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11,
807,
828,
15034,
2624,
3419,
737,
2617,
11518,
15457,
7203,
961,
8807,
4943,
198,
361,
285,
571,
32875,
13,
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8206,
82,
25,
269,
2013,
64,
34,
274,
32,
565,
11518,
14539,
1678,
13,
2617,
19580,
10786,
14421,
11537,
198,
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8107,
34,
274,
32,
565,
22069,
19703,
4668,
796,
337,
571,
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282,
283,
19510,
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11,
513,
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718,
11,
352,
11,
604,
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198,
361,
285,
571,
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82,
25,
269,
2013,
64,
34,
274,
32,
565,
22069,
19703,
4668,
13,
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19580,
10786,
14421,
11537,
198,
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8107,
34,
274,
32,
565,
10962,
796,
337,
571,
10962,
19510,
16,
11,
513,
11,
718,
11,
352,
11,
604,
11,
352,
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1105,
4869,
11,
362,
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352,
11,
362,
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352,
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198,
361,
285,
571,
32875,
13,
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82,
25,
269,
2013,
64,
34,
274,
32,
565,
10962,
13,
2617,
19580,
10786,
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31023,
11537,
198,
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34,
274,
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565,
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571,
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11,
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718,
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11,
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352,
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2013,
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274,
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565,
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13,
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274,
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565,
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571,
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604,
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352,
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571,
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269,
2013,
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274,
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565,
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316,
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81,
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13,
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274,
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565,
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2013,
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274,
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565,
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316,
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13,
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274,
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565,
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2013,
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274,
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565,
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316,
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2013,
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274,
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565,
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13,
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565,
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269,
2013,
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274,
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565,
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13924,
13,
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565,
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79,
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571,
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11,
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2013,
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2013,
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274,
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565,
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1416,
79,
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10786,
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274,
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565,
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13924,
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571,
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269,
2013,
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34,
274,
32,
565,
30150,
13924,
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45195,
13,
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10786,
10378,
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274,
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565,
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571,
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11,
513,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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2013,
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818,
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844,
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] | 2.313321 | 6,546 |
import argparse
from functools import partial
from multiprocessing import Pool
from pathlib import Path
from PIL import Image
from tqdm import tqdm
parser = argparse.ArgumentParser()
parser.add_argument(dest='base_dir', type=Path)
parser.add_argument(dest='out_dir', type=Path)
args = parser.parse_args()
if __name__ == '__main__':
main()
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] | 3.070175 | 114 |
import requests
from helpers.logHelper import logger
symbolNamePairs = {
"BITCOIN": "BTC",
"ETHEREUM": "ETH",
"DOGECOIN": "DOGE",
}
setting = settings()
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"""Operation on Streams that leave the shape of the stream unchanged"""
import numpy as np
import pandas as pd
from vmpy.utils import cast_array_to_original_type
# FTP based 7-zones with left bind edge set to -0.001
POWER_ZONES_THRESHOLD = [-0.001, 0.55, 0.75, 0.9, 1.05, 1.2, 1.5, 10.0]
POWER_ZONES_THRESHOLD_DESC = ["Active Recovery", "Endurance", "Tempo",
"Threshold", "VO2Max", "Anaerobic", "Neuromuscular",]
POWER_ZONES_THRESHOLD_ZNAME = ["Z1", "Z2", "Z3", "Z4", "Z5", "Z6", "Z7"]
# LTHR based 5-zones with left bind edge set to -0.001
HEART_RATE_ZONES = [-0.001, 0.68, 0.83, 0.94, 1.05, 10.0]
HEART_RATE_ZONES_DESC = ["Active recovery", "Endurance", "Tempo", "Threshold", "VO2Max",]
HEART_RATE_ZONES_ZNAME = ["Z1", "Z2", "Z3", "Z4", "Z5"]
def compute_zones(arg, **kwargs):
"""Convert stream into respective zones stream
Watts streams can be converted either into ftp based 7-zones or into custom zones
HR streams can be converted either in lthr based 5-zones or into custom zones
One of three *ftp*, *lthr* or *zone* keyword parameters must be provided
Parameters
----------
arg : array-like
ftp : number, optional
Value for FTP, will be used for 7-zones calculation
lthr: number, optional
Value for LTHR, will be used for 5-zones calculation
zones: list, optional
List of custom defined zones with left edge set to -1 and right edge to 10000
Returns
-------
array-like of int, the same type as arg
"""
arg_s = pd.Series(arg)
if kwargs.get('zones', None):
abs_zones = kwargs.get('zones')
elif kwargs.get('ftp', None):
abs_zones = np.asarray(POWER_ZONES_THRESHOLD) * kwargs.get('ftp')
elif kwargs.get('lthr', None):
abs_zones = np.asarray(HEART_RATE_ZONES) * kwargs.get('lthr')
else:
raise ValueError
labels = kwargs.get('labels', list(range(1, len(abs_zones))))
assert len(abs_zones) == (len(labels) + 1)
y = pd.cut(arg_s, bins=abs_zones, labels=labels)
y = cast_array_to_original_type(y, type(arg))
return y
def wpk(power, weight):
"""Watts per kilo
Parameters
----------
power : list, ndarray, series
weight : number
Returns
-------
array-like
"""
rv = pd.Series(power, dtype=float)/ weight
rv = cast_array_to_original_type(rv, type(power))
return rv
def mask_fill(arg, mask=None, value=0.0, **kwargs):
"""Replace masked values
Parameters
----------
arg : array-like
mask : array-like of bools, optional
Default value is None, which means no masking will be applied
value : number, optional
Value to use for replacement, default=0.0
Returns
-------
y: type of input argument
In case the arg is an ndarray all operations will be performed on the original array.
To preserve original array pass a copy to the function
"""
if mask is None:
return arg
y = np.array(arg)
mask = np.array(mask, dtype=bool)
y[~mask] = value
rv = cast_array_to_original_type(y, type(arg))
return rv
def median_filter(arg, window=31, threshold=1, value=None, **kwargs):
"""Outlier replacement using median filter
Detect outliers using median filter and replace with rolling median or specified value
Parameters
----------
arg : array-like
window : int, optional
Size of window (including the sample; default=31 is equal to 15 on either side of value)
threshold : number, optional
default=3 and corresponds to 2xSigma
value : float, optional
Value to be used for replacement, default=None, which means replacement by rolling median value
Returns
-------
y: type of input argument
In case the arg is an ndarray all operations will be performed on the original array.
To preserve original array pass a copy to the function
"""
y = pd.Series(arg)
rolling_median = y.rolling(window, min_periods=1).median()
difference = np.abs(y - rolling_median)
median_abs_deviation = difference.rolling(window, min_periods=1).median()
outlier_idx = difference > 1.4826 * threshold * median_abs_deviation
""" The factor 1.4826 makes the MAD scale estimate
an unbiased estimate of the standard deviation for Gaussian data.
"""
if value:
y[outlier_idx] = value
else:
y[outlier_idx] = rolling_median[outlier_idx]
y = y.as_matrix()
y = cast_array_to_original_type(y, type(arg))
return y
def rolling_mean(arg, window=10, mask=None, value=0.0, **kwargs):
"""Compute rolling mean
Compute *uniform* or *ewma* rolling mean of the stream. In-process masking with replacement is
controlled by optional keyword parameters
Parameters
----------
arg : array-like
window : int
Size of the moving window in sec, default=10
mask : array-like of boolean, optional
Default value is None, which means no masking will be applied
value : number, optional
Value to use for replacement, default=0.0
type : {"uniform", "emwa"}, optional
Type of averaging, default="uniform"
Returns
-------
y: type of input argument
The moving array will indicate which samples to set to zero before
applying rolling mean.
"""
if mask is not None:
arg = mask_fill(arg, mask, value, **kwargs)
y = pd.Series(arg)
if kwargs.get('type', 'uniform') == 'ewma':
y = y.ewm(span=window, min_periods=1).mean().values
else:
y = y.rolling(window, min_periods=1).mean().values
y = cast_array_to_original_type(y, type(arg))
return y
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] | 2.617109 | 2,186 |
from cms.plugin_base import CMSPluginBase
from cms.plugin_pool import plugin_pool
from cms.models.pluginmodel import CMSPlugin
from django.utils.translation import ugettext_lazy as _
from cms.models import Page
from django.conf import settings
from django.contrib.sites.shortcuts import get_current_site
from arividam.utils import get_page_by_slug
from .models import PromotedNews
import logging
logger = logging.getLogger(__name__)
plugin_pool.register_plugin(NewsPlugin)
plugin_pool.register_plugin(FeaturedNewsPlugin)
| [
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] | 3.33758 | 157 |
import os
import re
import requests
import subprocess
filename = 'requirements.txt'
new_packages = []
with open(filename, 'r') as file:
pattern = '(.*) == (.*)'
packages = re.findall(pattern, file.read())
for package, version in packages:
response = requests.get(f'https://pypi.org/pypi/{package}/json')
keys = response.json()['releases'].keys()
releases = [key for key in keys if key.replace('.', '').isdigit()]
latest = sorted(
releases,
key=lambda release: [
int(number) for number in release.split('.')
]).pop()
if latest != version:
print(f'Upgrading {package} ({version} => {latest})')
CI = os.environ.get('CI')
python = 'python' if CI else 'python3'
cmd = f'{python} -m pip install {package}=={latest}'
code = subprocess.run(cmd, shell=True).returncode
if code:
exit(code)
version = latest
new_packages.append((package, version))
with open(filename, 'w') as file:
for package, version in new_packages:
file.write(f'{package} == {version}\n')
| [
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6,
90,
26495,
92,
6624,
1391,
9641,
32239,
77,
11537,
201,
198
] | 2.175943 | 557 |
n = int(input())
num = list(map(int , input().split()))
d,m = map(int , input().split())
c= 0
for i in range(0,n-m+1):
d_ = 0
for j in range(0,m):
d_ += num[i+j]
if d_ == d:
c += 1
print(c) | [
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] | 1.85 | 120 |
from collections import namedtuple
from functools import partial
import pytest
import torch
from sklearn.metrics import accuracy_score
from sklearn.metrics import r2_score as sk_r2score
from tests.helpers import seed_all
from tests.helpers.testers import BATCH_SIZE, NUM_BATCHES, NUM_CLASSES, MetricTester
from torchmetrics import Metric
from torchmetrics.classification import Accuracy
from torchmetrics.regression import R2Score
from torchmetrics.wrappers.multioutput import MultioutputWrapper
seed_all(42)
class _MultioutputMetric(Metric):
"""Test class that allows passing base metric as a class rather than its instantiation to the wrapper."""
def _update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
"""Update the each pair of outputs and predictions."""
return self.metric.update(preds, target)
def _compute(self) -> torch.Tensor:
"""Compute the R2 score between each pair of outputs and predictions."""
return self.metric.compute()
@torch.jit.unused
def forward(self, *args, **kwargs):
"""Run forward on the underlying metric."""
return self.metric(*args, **kwargs)
def reset(self) -> None:
"""Reset the underlying metric state."""
self.metric.reset()
num_targets = 2
Input = namedtuple("Input", ["preds", "target"])
_multi_target_regression_inputs = Input(
preds=torch.rand(NUM_BATCHES, BATCH_SIZE, num_targets),
target=torch.rand(NUM_BATCHES, BATCH_SIZE, num_targets),
)
_multi_target_classification_inputs = Input(
preds=torch.rand(NUM_BATCHES, BATCH_SIZE, NUM_CLASSES, num_targets),
target=torch.randint(NUM_CLASSES, (NUM_BATCHES, BATCH_SIZE, num_targets)),
)
def _multi_target_sk_r2score(preds, target, adjusted=0, multioutput="raw_values"):
"""Compute R2 score over multiple outputs."""
sk_preds = preds.view(-1, num_targets).numpy()
sk_target = target.view(-1, num_targets).numpy()
r2_score = sk_r2score(sk_target, sk_preds, multioutput=multioutput)
if adjusted != 0:
r2_score = 1 - (1 - r2_score) * (sk_preds.shape[0] - 1) / (sk_preds.shape[0] - adjusted - 1)
return r2_score
def _multi_target_sk_accuracy(preds, target, num_outputs):
"""Compute accuracy over multiple outputs."""
accs = []
for i in range(num_outputs):
accs.append(accuracy_score(torch.argmax(preds[:, :, i], dim=1), target[:, i]))
return accs
@pytest.mark.parametrize(
"base_metric_class, compare_metric, preds, target, num_outputs, metric_kwargs",
[
(
R2Score,
_multi_target_sk_r2score,
_multi_target_regression_inputs.preds,
_multi_target_regression_inputs.target,
num_targets,
{},
),
(
Accuracy,
partial(_multi_target_sk_accuracy, num_outputs=2),
_multi_target_classification_inputs.preds,
_multi_target_classification_inputs.target,
num_targets,
dict(num_classes=NUM_CLASSES),
),
],
)
class TestMultioutputWrapper(MetricTester):
"""Test the MultioutputWrapper class with regression and classification inner metrics."""
@pytest.mark.parametrize("ddp", [True, False])
@pytest.mark.parametrize("dist_sync_on_step", [True, False])
def test_multioutput_wrapper(
self, base_metric_class, compare_metric, preds, target, num_outputs, metric_kwargs, ddp, dist_sync_on_step
):
"""Test that the multioutput wrapper properly slices and computes outputs along the output dimension for
both classification and regression metrics."""
self.run_class_metric_test(
ddp,
preds,
target,
_MultioutputMetric,
compare_metric,
dist_sync_on_step,
metric_args=dict(num_outputs=num_outputs, base_metric_class=base_metric_class, **metric_kwargs),
)
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] | 2.426897 | 1,621 |
import hashlib
import json
import logging
import re
import uuid
from django.db import models
from django.db.models.signals import pre_save
from django.dispatch import receiver
from oldp.apps.cases.models import Case
from oldp.apps.laws.models import Law
logger = logging.getLogger(__name__)
class ReferenceMarker(models.Model):
"""
Abstract class for reference markers, i.e. the actual reference within a text "§§ 12-14 BGB".
Marker has a position (start, end, line), unique identifier (uuid, randomly generated), text of the marker as in
the text, list of references (can be law, case, ...). Implementations of abstract class (LawReferenceMarker, ...)
have the corresponding source object (LawReferenceMarker: referenced_by = a law object).
"""
text = models.CharField(max_length=250) # Text of marker
uuid = models.CharField(max_length=36)
start = models.IntegerField(default=0)
end = models.IntegerField(default=0)
line = models.CharField(blank=True, max_length=200)
referenced_by = None
referenced_by_type = None
references = []
@staticmethod
@staticmethod
def make_markers_clickable(value):
"""
TODO Replace ref marker number with db id
"""
return re.sub(r'\[ref=([-a-z0-9]+)\](.*?)\[\/ref\]', r'<a href="#refs" onclick="clickRefMarker(this);" data-ref-uuid="\1" class="ref">\2</a>', value)
class LawReferenceMarker(ReferenceMarker):
"""
A reference marker in a law content object.
"""
referenced_by_type = Law
referenced_by = models.ForeignKey(Law, on_delete=models.CASCADE)
@receiver(pre_save, sender=LawReferenceMarker)
class CaseReferenceMarker(ReferenceMarker):
"""
A reference marker in a case content object.
"""
referenced_by_type = Case
referenced_by = models.ForeignKey(Case, on_delete=models.CASCADE)
@receiver(pre_save, sender=CaseReferenceMarker)
class Reference(models.Model):
"""
A reference connecting two content objects (1:1 relation). The object that is referenced is either "law", "case"
or ... (reference target). The referencing object (the object which text contains the reference) can be derived
via marker.
Abstract class: Depending on the referencing object (its marker) the corresponding implementation is used.
If the referenced object is not defined, the reference is "not assigned" (is_assigned method)
"""
law = models.ForeignKey(Law, null=True, on_delete=models.SET_NULL)
case = models.ForeignKey(Case, null=True, on_delete=models.SET_NULL)
to = models.CharField(max_length=250) # to as string, if case or law cannot be assigned (ref id)
to_hash = models.CharField(max_length=100, null=True)
marker = None
count = None
def get_url(self):
"""
Returns Url to law or case item (if exist) otherwise return search Url.
:return:
"""
if self.law is not None:
return self.law.get_url()
elif self.case is not None:
return self.case.get_url()
else:
return '/search/?q=%s' % self.marker.text
class LawReference(Reference):
"""
A reference from a law to any content object (law, case, ...)
"""
marker = models.ForeignKey(LawReferenceMarker, on_delete=models.CASCADE)
@receiver(pre_save, sender=LawReference)
class CaseReference(Reference):
"""
A reference from a case to any content object (law, case, ...)
"""
marker = models.ForeignKey(CaseReferenceMarker, on_delete=models.CASCADE)
@receiver(pre_save, sender=CaseReference)
# @receiver(pre_save, sender=Reference)
# def json_dumps_reference(sender, instance, *args, **kwargs):
# if not isinstance(instance.to, str):
# instance.to = json.dumps(instance.to)
# @receiver(post_init, sender=LawReference)
# def json_loads_reference(sender, instance, *args, **kwargs):
# print(instance.ids)
# exit(0)
# if instance.ids is not None and isinstance(instance.ids, str):
# instance.ids = json.loads(instance.ids)
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] | 2.85084 | 1,428 |
#!/usr/bin/env python3
import os
if os.geteuid() != 0:
exit('This script requires root privileges.\nPlease try again with sudo.')
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10786,
1212,
4226,
4433,
6808,
18850,
13,
59,
77,
5492,
1949,
757,
351,
21061,
2637,
8,
628
] | 2.956522 | 46 |
import argparse
from pathlib import Path
import typing
import numpy as np
import scipy.spatial.distance
from encoder.inference import Model as EncoderModel
from synthesizer.inference import Synthesizer
_NUM_ENROLLMENTS = 3
_NUM_VERIFICATIONS = 5
_WAV_FODLER = Path('/Users/dalei/Downloads/VCTK-Corpus/wav48')
_TXT_FODLER = Path('/Users/dalei/Downloads/VCTK-Corpus/txt')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
args, _ = parser.parse_known_args()
run(args)
| [
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8,
628
] | 2.697297 | 185 |
## system-config-printer
## Copyright (C) 2008, 2011 Red Hat, Inc.
## Authors:
## Tim Waugh <[email protected]>
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
__all__ = ['set_debugprint_fn',
'Device', 'Printer', 'activateNewPrinter',
'copyPPDOptions', 'getDevices', 'getPrinters',
'missingPackagesAndExecutables', 'missingExecutables',
'parseDeviceID',
'setPPDPageSize',
'ppds',
'openprinting']
_debugprint_fn = _no_debug
def set_debugprint_fn (debugprint):
"""
Set debugging hook.
@param debugprint: function to print debug output
@type debugprint: fn (str) -> None
"""
global _debugprint_fn
_debugprint_fn = debugprint
from cupshelpers import \
Device, \
Printer, \
activateNewPrinter, \
copyPPDOptions, \
getDevices, \
getPrinters, \
missingPackagesAndExecutables, \
missingExecutables, \
parseDeviceID, \
setPPDPageSize
import ppds
import openprinting
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11,
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59,
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11,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
3467,
198,
220,
220,
220,
21136,
24728,
2389,
11,
197,
197,
197,
197,
59,
198,
220,
220,
220,
900,
47,
5760,
9876,
10699,
198,
198,
11748,
9788,
9310,
198,
11748,
1280,
4798,
278,
198
] | 2.677966 | 649 |
import mock
import time
import redis
from pyramid import testing
from kinto.core.utils import sqlalchemy
from kinto.core.storage import exceptions
from kinto.core.cache import (CacheBase, postgresql as postgresql_backend,
redis as redis_backend, memory as memory_backend,
heartbeat)
from .support import unittest, skip_if_no_postgresql
@skip_if_no_postgresql
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48267,
62,
361,
62,
3919,
62,
7353,
34239,
13976,
198
] | 2.45977 | 174 |
# Copyright (c) 2020, Xilinx
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of FINN nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import onnx.helper as oh
from onnx import TensorProto
import finn.analysis.topology as ta
from finn.core.modelwrapper import ModelWrapper
from finn.transformation.infer_shapes import InferShapes
| [
2,
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77,
13,
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13,
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1435,
355,
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198,
6738,
957,
77,
13,
7295,
13,
19849,
48553,
1330,
9104,
36918,
2848,
198,
6738,
957,
77,
13,
7645,
1161,
13,
259,
2232,
62,
1477,
7916,
1330,
554,
2232,
2484,
7916,
628,
198
] | 3.516393 | 488 |
from selenium import webdriver as seledriver
class WebDriver(object):
"""
The base class for controlling the browser in the webbrowser class.
Selenium Webdriver wrapper class.
"""
def __init__(self, options = None):
"""
Initialize Class
Parameters
----
options: str|list
arguments of webdriver
"""
self._webdriver = None
self.options = []
if options is not None:
self.add_options(options)
def add_options(self, value):
"""
add options
Parameters
----
value: str|list
arguments
"""
if type(value) == str:
self.options.append(value)
elif type(value == list):
for v in value:
if type(v) == str:
self.options.append(v)
else:
raise ValueError("Invalid Value")
else:
raise ValueError("Invalid Value")
def get_browser(self):
"""
get browser object
Returns
----
driver: selenium.webdriver
browser's driver object
"""
raise NotImplementedError
class ChromeDriver(WebDriver):
"""
Google Chrome's driver
require chromedriver_binary
`pip install chromedriver_binary`
This class does not currently support using Chrome with an existing profile.
The option does not specify User-data-dir because "Selenium.common.exceptions.webdriverexception" occurs.
"""
def get_browser(self):
"""
get browser object
Returns
----
driver: selenium.webdriver
browser's driver object
"""
import chromedriver_binary
options = seledriver.ChromeOptions()
for o in self.options:
options.add_argument(o)
return seledriver.Chrome(options=options) | [
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] | 2.75082 | 610 |
# -*- coding: utf-8 -*-
from collections import defaultdict
import click
from mygeotab import API, dates
from mygeotab.ext import feed
@click.command(help="A console data feeder example")
@click.argument("database", nargs=1, required=True)
@click.option("--user", "-u", prompt=True, help="A MyGeotab username")
@click.option("--password", "-p", prompt=True, hide_input=True, help="A MyGeotab password")
@click.option("--server", default=None, help="The server (default is my.geotab.com)")
@click.option(
"--interval",
"-i",
type=click.IntRange(5, 300),
default=60,
help="The data feed interval in seconds (default is 60 seconds)",
)
if __name__ == "__main__":
main()
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198
] | 2.818548 | 248 |
from config import Config
import psycopg2
from psycopg2.extras import Json, DictCursor
import pdb
import pandas as pd
import os
import time
import cv2
from gesture_recognition import featurizer
def orchestrator():
"""Pull frames with confidence, accurate predictions from database and use them to generate new model."""
# define database names
db_frames = 'frames'
db_model_scores = 'model_scores'
db_users = 'users'
db_conf_preds = 'confident_preds'
# define feature names
instance = 'instance'
user_id = 'user_id'
root_dir = 'root_dir'
pred_gest = 'pred_gest'
true_gest = 'true_gest'
pred_conf= 'pred_conf'
processed_path = 'processed_path'
# select all high-scoring predictions. These will be used to train new models.
conn = psycopg2.connect(host=Config.DB_HOST, database=Config.DB_NAME, user=Config.DB_USER, password=Config.DB_PASS)
cur = conn.cursor(cursor_factory=DictCursor)
confidence_threshold = 0 # way too low; used for testing
features = f'{instance}, {user_id}, {true_gest}, {pred_conf}, {root_dir}, {processed_path}'
query = 'SELECT ' + features + f' FROM {db_frames} WHERE {pred_conf} > {confidence_threshold} AND {pred_gest} = {true_gest}'
cur.execute(query)
conn.commit()
rows = cur.fetchall()
# make dataframe for high-scoring predictions that includes rotated images. Rotated images will enhance training results.
columns = [feature.strip() for feature in features.split(",")]
df = pd.DataFrame(rows, columns=columns)
df = df.drop(pred_conf, axis=1)
df = df[df.notnull()]
# exit if no frames in database
if df.empty:
print(f'[ERROR] No accurately predicted frames with prediction confidence > {confidence_threshold} in {db_frames}.')
cur.close()
else:
print(f'[INFO] Confident predictions pulled from {db_frames} table.')
# generate rotated images, save files to file storage system, append paths to dataframe
processed_path = 'processed_path'
flipped_path = 'flipped_path'
mirrored_path = 'mirrored_path'
mirrored_flipped_path = 'mirrored_flipped_path'
rotated_image_path_feats = [flipped_path, mirrored_path, mirrored_flipped_path]
for feat in rotated_image_path_feats:
df[feat] = None
df_feats = [instance, user_id, root_dir]
start_time = time.time()
for i in range(len(df)):
orig_path = df[processed_path][i]
frame_orig = cv2.imread(orig_path)
(_, frame_orig) = cv2.threshold(frame_orig, 127, 255, cv2.THRESH_BINARY)
row_orig = df.iloc[i]
rotate_dict = featurizer.rotate(frame_orig, row_orig, df_feats)
rotate_keys = list(rotate_dict.keys())
root_dir_path = row_orig[root_dir]
rotated_dir = os.path.join(root_dir_path, 'rotated')
if os.path.isdir(rotated_dir) == False:
print('[INFO] Creating directory for rotated images.')
os.mkdir(rotated_dir)
user_id_num = str(row_orig[user_id])
user_dir = os.path.join(rotated_dir, str(user_id_num))
if os.path.isdir(user_dir) == False:
print(f'[INFO] Creating directory for rotated images from user {user_id_num}.')
os.mkdir(user_dir)
for key in rotate_keys:
frame = rotate_dict[key]['frame']
path = rotate_dict[key]['path']
cv2.imwrite(path, frame)
try:
column = key + '_path'
df[column][i] = path
except:
print('[ERROR] Unable to save rotated image path to database or dataframe')
print(f'[INFO] Processing rotated images took {time.time() - start_time} seconds')
# drop user_id and root_dir from data frame
df = df.drop([user_id, root_dir], axis=1)
df = df.rename(columns={'true_gest': 'gesture'})
# add table of confident predictions to database
from sqlalchemy import create_engine
engine = create_engine("postgresql://{user}:{pw}@{host}/{name}".format(host=Config.DB_HOST, user=Config.DB_USER, pw=Config.DB_PASS, name=Config.DB_NAME))
table = 'conf_preds'
df.to_sql(table, con=engine, if_exists='replace', index=False) # would be better to append existing table conf_preds but current design processes all images from database rather than just new ones. Will update in the future.
print(f'[INFO] Table of confident predictions updated.')
# check if sufficient number of each gesture present in table of confident predictions. If not, exit since a new model cannot be trained
from objects import gestures_map # may place gestures_map on database. stored models should be saved with gestures_map they correspond with. example: train new model with additional gestures
gestures_list = list(gestures_map.values())
df_gestures_list = list(df['gesture'].unique())
differing_gestures = [gesture for gesture in gestures_list if gesture not in df_gestures_list]
if differing_gestures != []:
print(f'[ERROR] Not enough confident predictions have been made for {differing_gestures}. Unable to split data.')
return
# generate new table with image paths transposed for convenient model training
df_conf_preds = pd.DataFrame()
for i in range(len(df)):
row = df.iloc[i]
instance_val = row[instance]
gesture_val = row['gesture']
# append row for each file path. the predicted and true gestures of each file are the same
df_conf_preds = df_conf_preds.append([[instance_val + '_og', gesture_val, row[processed_path]]], ignore_index=True)
df_conf_preds = df_conf_preds.append([[instance_val + '_f', gesture_val, row[flipped_path]]], ignore_index=True)
df_conf_preds = df_conf_preds.append([[instance_val + '_m', gesture_val, row[mirrored_path]]], ignore_index=True)
df_conf_preds = df_conf_preds.append([[instance_val + '_mf', gesture_val, row[mirrored_flipped_path]]], ignore_index=True)
df_conf_preds = df_conf_preds.rename(columns={0: instance, 1: 'gesture', 2: 'path'})
# form y_data from confident predictions dataframe
from keras.utils import to_categorical
y_data = df_conf_preds['gesture']
for cat in list(gestures_map.keys()):
gesture_name = gestures_map[cat]
y_data = y_data.replace(gesture_name, cat)
y_data = to_categorical(y_data, num_classes=len(gestures_map.keys()))
y_data = pd.DataFrame(y_data)
# reduce table size to count of least occurring gesture
import random
driving_count = -1
for i in y_data.columns:
gesture_count = len(y_data[y_data[i] == 1][i])
if gesture_count < driving_count or driving_count == -1:
driving_count = gesture_count
indices = []
for i in y_data.columns:
gesture_indices = list(y_data[y_data[i] == 1][i].index);
sample_indices = random.sample(gesture_indices, driving_count);
indices.extend(sample_indices)
y_data = y_data.iloc[indices]
# form x_data from confident predictions dataframe. Size of x_data driven by least occuring gesture
x_data = df_conf_preds['path'].iloc[indices]
# split data into training (72%), validation (8%), and testing (20%) sets
test_size = 0.2
if len(x_data) < len(gestures_list)/test_size:
print(f'[ERROR] Not enough confident predictions have been made. Unable to split data.')
return
from sklearn.model_selection import train_test_split
x_train_paths, x_test_paths, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2, stratify=y_data)
x_train_paths, x_val_paths, y_train, y_val = train_test_split(x_train_paths, y_train, test_size=0.1, stratify=y_train)
print(f'[INFO] Prepared training data. Building model...')
# build model
from .builder import build_and_save_model
from objects import gestures_map # ideally, the gesture map should be capable of dynamically impacting the training cycle. However, I am assuming the set of predicted gestures will not change
model_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'models')
[model_path, training_date] = build_and_save_model(x_train_paths, x_val_paths, y_train, y_val, model_dir) # wait until data collection infrastructure in place to train new models
# determine model_name based on entries in database
query = 'SELECT model_name from models'
cur.execute(query)
conn.commit()
rows = cur.fetchall()
if rows == []:
model_name = 'model_0'
else:
name_start = 'model_'
last_num = int(rows[-1][0].split(name_start)[1])
model_name = name_start + str(last_num+1)
# push model to database
gestures_map = Json(gestures_map)
model = open(model_path, 'rb').read()
model_blob = psycopg2.Binary(model)
table = 'models'
query = f"INSERT INTO {table}(model_name, training_date, gestures_map, model, model_path) VALUES('{model_name}', '{training_date}', {gestures_map}, {model_blob}, '{model_path}')"
cur.execute(query)
conn.commit()
print(f'[INFO] New model stored in database.')
# make dataframe containing all instances used to train new model
new_instances = df_conf_preds.loc[list(x_train_paths.index)]['instance'].sort_index()
df_new_instances = pd.DataFrame(new_instances)
df_new_instances[model_name] = 1
# update table that contains which frame instances were used to train which model(s)
# In the long run, this table may be helpful for determining which training images correspond with accurate models
# There is likely a cleaner way to accomplish this
table = 'model_train_data_map'
query = f"SELECT {instance} FROM {table}"
cur.execute(query)
conn.commit()
sql_instances = cur.fetchall()
if sql_instances != []:
df_sql_instances = pd.DataFrame(sql_instances).rename(columns={0: "instance"})
df_new_instances = df_new_instances.merge(df_sql_instances, how='outer', on='instance')
# push temporary table to database that contains all training instances with instances used to train new model indicated with "1"
new_instance = 'new_instance'
df_new_instances = df_new_instances.rename(columns={instance: new_instance})
new_table = 'new_table'
df_new_instances.to_sql(new_table, con=engine, if_exists='replace', index=False)
engine.dispose()
# on database, merge newly created temporary table with original one
temp_table = 'temp_table'
query = f"""
DROP TABLE IF EXISTS {temp_table};
SELECT * INTO {temp_table} FROM {new_table} LEFT JOIN {table} ON {instance}={new_instance};
DROP TABLE IF EXISTS {new_table};
ALTER TABLE {temp_table} DROP COLUMN {instance};
ALTER TABLE {temp_table} RENAME COLUMN {new_instance} to {instance};
DROP TABLE IF EXISTS {table};
ALTER TABLE {temp_table} RENAME TO {table}
"""
cur.execute(query)
conn.commit()
print(f'[INFO] Model / training data mapping table updated.')
# evaluate model performance and compare with performance of other models
from . import evaluator
table = 'model_scores'
query = f"SELECT * FROM {table}"
cur.execute(query)
conn.commit()
sql_model_scores = pd.DataFrame(cur.fetchall())
# close database connection
cur.close()
# evaluate new model and append scores to model score table
[f1, eval_date, eval_time, y_true, y_pred] = evaluator.evaluate_model(model_path, x_test_paths, y_test)
rank = 1
model_results = [model_name, f1, rank, eval_date, eval_time, y_true, y_pred]
sql_model_scores = sql_model_scores.append([model_results], ignore_index=True)
sql_model_scores = sql_model_scores.rename(columns={0:'model_name', 1:'f1_score', 2:'rank', 3:'evaluation_date', 4:'evaluation_time', 5:'true_gestures', 6:'predicted_gestures'})
# rank models by f1 score
sql_model_scores = sql_model_scores.sort_values('f1_score', ascending=False, ignore_index=True)
rank_vals = []
for i in range(len(sql_model_scores)):
rank_vals.append(i+1)
sql_model_scores['rank'] = rank_vals
# replace database table with new model scores
engine = create_engine("postgresql://{user}:{pw}@{host}/{name}".format(host=Config.DB_HOST, user=Config.DB_USER, pw=Config.DB_PASS, name=Config.DB_NAME))
sql_model_scores.to_sql(table, con=engine, if_exists='replace', index=False)
engine.dispose() | [
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] | 2.404623 | 5,494 |
import nmap
import sys
import os
import multiprocessing
import socket
from colorama import Fore, Back, Style
scanner = nmap.PortScanner()
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] | 2.867925 | 53 |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.15 on 2018-11-22 14:09
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
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] | 2.753623 | 69 |
import re
import time
from datetime import datetime, timedelta
def test_mode_replay(eventgen_test_helper):
"""Test normal replay mode settings"""
events = eventgen_test_helper("eventgen_replay.conf").get_events()
# assert the event length is the same as sample file size
assert len(events) == 12
pattern = re.compile(r"\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}")
for event in events:
# assert that integer token is replaced
assert "@@integer" not in event
result = pattern.match(event)
assert result is not None
def test_mode_replay_end_1(eventgen_test_helper):
"""Test normal replay mode with end = 2 which will replay the sample twice and exit"""
events = eventgen_test_helper("eventgen_replay_end_1.conf").get_events()
# assert the event length is twice of the events in the sample file
assert len(events) == 24
def test_mode_replay_end_2(eventgen_test_helper):
"""Test normal replay mode with end = -1 which will replay the sample forever"""
helper = eventgen_test_helper("eventgen_replay_end_2.conf")
time.sleep(60)
assert helper.is_alive()
def test_mode_replay_backfill(eventgen_test_helper):
"""Test normal replay mode with backfill = -5s which should be ignore since backfill < interval"""
events = eventgen_test_helper("eventgen_replay_backfill.conf").get_events()
# assert the events length is twice of the events in the sample file
assert len(events) == 24
def test_mode_replay_backfill_greater_interval(eventgen_test_helper):
"""Test normal replay mode with backfill = -120s"""
current_datetime = datetime.now()
events = eventgen_test_helper(
"eventgen_replay_backfill_greater_interval.conf"
).get_events()
# assert the events length is twice of the events in the sample file
assert len(events) == 24
pattern = re.compile(r"\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}")
for event in events:
result = pattern.match(event)
assert result is not None
event_datetime = datetime.strptime(result.group(), "%Y-%m-%d %H:%M:%S")
assert event_datetime < current_datetime
def test_mode_replay_tutorial1(eventgen_test_helper):
"""Test the replay mode with csv for sample file sample.tutorial1.csv"""
events = eventgen_test_helper("eventgen_tutorial1.conf").get_events()
assert len(events) == 2019
def test_mode_replay_timemultiple(eventgen_test_helper):
"""Test normal replay mode with timeMultiple = 0.5 which will replay the sample with half time interval"""
current_datetime = datetime.now()
events = eventgen_test_helper("eventgen_replay_timeMultiple.conf").get_events()
pattern = re.compile(r"\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}")
for event in events:
result = pattern.match(event)
assert result is not None
event_datetime = datetime.strptime(result.group(), "%Y-%m-%d %H:%M:%S")
delter_seconds = (event_datetime - current_datetime).total_seconds()
# assert the event time is after (now - earliest) time
assert delter_seconds < 14
def test_mode_replay_csv(eventgen_test_helper):
"""Test normal replay mode with sampletype = csv which will get _raw row from the sample"""
events = eventgen_test_helper("eventgen_replay_csv.conf").get_events()
# assert the events equals to the sample csv file
assert len(events) == 10
def test_mode_replay_with_timezone(eventgen_test_helper):
"""Test normal replay mode with sampletype = csv which will get _raw row from the sample"""
events = eventgen_test_helper("eventgen_replay_csv_with_tz.conf").get_events()
# assert the events equals to the sample csv file
assert len(events) == 4
now_ts = datetime.utcnow() + timedelta(hours=-1)
for event in events:
event_ts = datetime.strptime(event.split(" ")[0], "%Y-%m-%dT%H:%M:%S,%f")
d = now_ts - event_ts
assert d.seconds < 60, "timestamp with timezone check fails."
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] | 2.685348 | 1,481 |
#!/usr/bin/env python
# Invalidates CDNs so the caches are refreshed
import boto3
import os
import time
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import pandas as pd
import pytest
from powersimdata.tests.mock_grid import MockGrid
from powersimdata.tests.mock_scenario import MockScenario
from powersimdata.tests.mock_scenario_info import MockScenarioInfo
period_num = 4
# plant_id is the index
mock_plant = {
"plant_id": [101, 102, 103, 104, 105, 106],
"bus_id": [1001, 1002, 1003, 1004, 1005, 1006],
"type": ["solar", "wind", "ng", "coal", "dfo", "hydro"],
"zone_id": [1, 2, 3, 1, 3, 2],
"GenFuelCost": [0, 0, 3.3, 4.4, 5.5, 0],
"Pmin": [0, 0, 0, 0, 0, 0],
"Pmax": [40, 80, 50, 150, 80, 60],
}
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
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#!/usr/bin/env python3
# Copyright 2009-2017 BHG http://bw.org/
# Class inheritance is the fundamental part of OOP
# allows you to extend your class by deriving properties/variables and methods from parent classes.
# no longer providing default values.
# it is bcz this is going to be the base class and it's going too be inherited in order to be used.
# bcz of this we need to do extra checking in our getters and setters.
# we cannot just return a value, we need to check and see whether the value is actually there.
# so, using exceptions here - exception tries to return a value and if it fails it returns None instead.
# using duck class to inherit base class animal.
# using kitten class to inherit base class animal.
# s - string that will identify the target of its predator
if __name__ == '__main__': main()
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] | 3.708696 | 230 |
"""Here are the db connection."""
import importlib
import logging
from mixer.settings import db_type
from mixer.glogger import logger
class Reader(object):
"""Helper to gen the reader class."""
def __init__(self, db_name):
"""Constructor."""
DB = getattr(
importlib.import_module(
"utilities.database.{}".format(db_type)
), "DB"
)
logger.log(logging.INFO, "Initialize DB connection")
self.db = DB(db_name)
self.db_name = db_name
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] | 2.18254 | 252 |
# --------------
# import the libraries
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
# Code starts here
df = pd.read_csv(path)
print(df.head())
X = df.drop('insuranceclaim',axis=1)
y = df['insuranceclaim']
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=6)
# Code ends here
# --------------
import matplotlib.pyplot as plt
# Code starts here
plt.boxplot(X_train['bmi'])
q_value = X_train['bmi'].quantile(0.95)
y_train.value_counts()
# Code ends here
# --------------
# Code starts here
relation = X_train.corr()
print(relation)
sns.pairplot(X_train)
# Code ends here
# --------------
import seaborn as sns
import matplotlib.pyplot as plt
# Code starts here
cols = ['children','sex','region','smoker']
fig, axes = plt.subplots(2, 2, figsize=(10,10))
for i in range(2):
for j in range(2):
ax = axes[i,j]
col = cols[i*2+j]
sns.countplot(x=X_train[col],hue=y_train,ax=ax)
# Code ends here
# --------------
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# parameters for grid search
parameters = {'C':[0.1,0.5,1,5]}
# Code starts here
lr = LogisticRegression()
grid = GridSearchCV(lr,parameters)
grid.fit(X_train,y_train)
y_pred = grid.predict(X_test)
accuracy = accuracy_score(y_test,y_pred)
print(accuracy)
# Code ends here
# --------------
from sklearn.metrics import roc_auc_score, auc
from sklearn import metrics
# Code starts here
#y_scores = grid.decision_function(X_test)
score = roc_auc_score(y_pred,y_test)
y_pred_proba = grid.predict_proba(X_test)[:,1]
fpr, tpr,_ = metrics.roc_curve(y_test,y_pred)
roc_auc = roc_auc_score(y_test, y_pred_proba)
auc = auc(fpr,tpr)
plt.plot(fpr,tpr,label='Logistic model, auc='+str(auc))
# Code ends here
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# Definition for a binary tree node.
# Do in order traversal. The in order traversal is monotonically increase
# O(1) Space, can not use iterative method or recursive solution, both use space
# class Solution(object):
# first = TreeNode(None)
# second = TreeNode(None)
# prev = TreeNode(None)
# def recoverTree(self, root):
# """
# :type root: TreeNode
# :rtype: None Do not return anything, modify root in-place instead.
# """
# # Recursion Method
# if root is None:
# return
# def helper(self, curr):
# if curr is None:
# return
# helper(curr.left)
# if prev is not None and prev.val >= curr.val:
# # have mistake first is the prev node, second is the curr node
# Morris Traversal O(1) solution
| [
2,
30396,
329,
257,
13934,
5509,
10139,
13,
198,
198,
2,
2141,
287,
1502,
33038,
282,
13,
383,
287,
1502,
33038,
282,
318,
937,
18970,
1146,
2620,
198,
2,
440,
7,
16,
8,
4687,
11,
460,
407,
779,
11629,
876,
2446,
393,
45115,
4610,
11,
1111,
779,
2272,
198,
198,
2,
1398,
28186,
7,
15252,
2599,
198,
2,
220,
220,
220,
220,
717,
796,
12200,
19667,
7,
14202,
8,
198,
2,
220,
220,
220,
220,
1218,
796,
12200,
19667,
7,
14202,
8,
198,
2,
220,
220,
220,
220,
8654,
796,
12200,
19667,
7,
14202,
8,
198,
198,
2,
220,
220,
220,
220,
825,
8551,
27660,
7,
944,
11,
6808,
2599,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
1058,
4906,
6808,
25,
12200,
19667,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
1058,
81,
4906,
25,
6045,
2141,
407,
1441,
1997,
11,
13096,
6808,
287,
12,
5372,
2427,
13,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3311,
24197,
11789,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
611,
6808,
318,
6045,
25,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
198,
2,
220,
220,
220,
220,
825,
31904,
7,
944,
11,
1090,
81,
2599,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1090,
81,
318,
6045,
25,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
220,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
31904,
7,
22019,
81,
13,
9464,
8,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
611,
8654,
318,
407,
6045,
290,
8654,
13,
2100,
18189,
1090,
81,
13,
2100,
25,
220,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
423,
7457,
717,
318,
262,
8654,
10139,
11,
1218,
318,
262,
1090,
81,
10139,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
198,
2,
14433,
4759,
690,
282,
440,
7,
16,
8,
4610,
198,
220,
220,
220,
220,
198
] | 2.269129 | 379 |
import os
from utils.Template_directory import *
from utils.utilities import *
import sys
sys.path.append('../')
from core.Renderer.FileRenderer import Renderer
layer1 = [Experiment,Production]
layer1_names = ['Experiment','Production']
Files = File
| [
11748,
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62,
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315,
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1330,
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220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
220
] | 2.988636 | 88 |
# problem7.py
# By listing the first six prime numbers: 2, 3, 5, 7, 11, and 13, we can see that the 6th prime is 13.
# What is the 10 001st prime number?
i = 1
z = 1
while i < 10002:
z += 1
if z > 1:
for j in range(2, z):
if z % j == 0:
break
else:
i += 1
print(z)
| [
2,
1917,
22,
13,
9078,
198,
198,
2,
2750,
13487,
262,
717,
2237,
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3146,
25,
362,
11,
513,
11,
642,
11,
767,
11,
1367,
11,
290,
1511,
11,
356,
460,
766,
326,
262,
718,
400,
6994,
318,
1511,
13,
198,
2,
1867,
318,
262,
838,
3571,
16,
301,
6994,
1271,
30,
198,
198,
72,
796,
352,
198,
89,
796,
352,
198,
4514,
1312,
1279,
8576,
17,
25,
198,
220,
220,
220,
1976,
15853,
352,
198,
220,
220,
220,
611,
1976,
1875,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
2837,
7,
17,
11,
1976,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1976,
4064,
474,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1312,
15853,
352,
198,
198,
4798,
7,
89,
8,
198
] | 1.964497 | 169 |
import re
import os
if __name__ == '__main__':
# Check if file with name Cache.txt exists
if os.path.isfile('Cache.txt'):
# If file exists, delete it
os.remove('Cache.txt')
# Create file with name Cache.txt
name = input('Enter your name (Enter -1 to skip): ')
while not name:
name = input('Enter your name (Enter -1 to skip): ')
if name == '-1':
name = ''
skip_column = input('Enter column number to skip in the excel file (Enter -1 to skip): ')
while (not skip_column or not skip_column.isdigit()) and skip_column != '-1':
if not skip_column.isdigit():
print("Invalid input :(\nEnter an integer")
skip_column = ""
skip_column = input(
'Enter column number to skip in the excel file (Enter -1 to skip): ')
if skip_column == '-1':
skip_column = ''
skip_row = input('Enter row number to skip in the excel file (Enter -1 to skip): ')
while (not skip_row or not skip_row.isdigit()) and skip_row != '-1':
if not skip_row.isdigit():
print("Invalid input :(\nEnter an integer")
skip_row = ""
skip_row = input('Enter row number to skip in the excel file (Enter -1 to skip): ')
if skip_row == '-1':
skip_row = ''
# Write name, skip_column and skip_row to Cache.txt
color = input(
'Enter hex value of the color with which you want to color the cell (Enter -1 to skip): ')
while not isValidHexaCode(color) and color != '-1':
color = input(
'Enter hex value of the color with which you want to color the cell (Enter -1 to skip): ')
print("Choose how do you want to extract names from the name list:")
print("Enter 1 to get names from Excel file",
"Enter 2 to get names from txt file", "Enter -1 to skip", sep='\n')
file_input = input("Enter your choice: ")
while file_input not in ['1', '2', '-1']:
print("Enter 1 to get names from Excel file",
"Enter 2 to get names from txt file", "Enter -1 to skip", sep='\n')
file_input = input("Enter your choice: ")
if file_input == '-1':
file_input = ''
if color == '-1':
color = ''
with open('Cache.txt', 'w') as f:
f.write(name + "|Name" + '\n' + skip_column +
"|No. of columns to skip" + '\n' + skip_row + '|No. of rows to skip\n'+color+'|Cell Color\n' + file_input + '|File Input')
| [
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62,
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328,
270,
33529,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
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44651,
5128,
36147,
59,
77,
17469,
281,
18253,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14267,
62,
28665,
796,
13538,
198,
220,
220,
220,
220,
220,
220,
220,
14267,
62,
28665,
796,
5128,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
17469,
5721,
1271,
284,
14267,
287,
262,
27336,
2393,
357,
17469,
532,
16,
284,
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2599,
705,
8,
198,
220,
220,
220,
611,
14267,
62,
28665,
6624,
705,
12,
16,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
14267,
62,
28665,
796,
10148,
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220,
220,
14267,
62,
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10786,
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284,
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220,
220,
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62,
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220,
220,
220,
220,
220,
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62,
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13,
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328,
270,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
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44651,
5128,
36147,
59,
77,
17469,
281,
18253,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14267,
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220,
220,
220,
220,
220,
220,
220,
14267,
62,
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27336,
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532,
16,
284,
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2599,
705,
8,
198,
220,
220,
220,
611,
14267,
62,
808,
6624,
705,
12,
16,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
14267,
62,
808,
796,
10148,
198,
220,
220,
220,
1303,
19430,
1438,
11,
14267,
62,
28665,
290,
14267,
62,
808,
284,
34088,
13,
14116,
198,
220,
220,
220,
3124,
796,
5128,
7,
198,
220,
220,
220,
220,
220,
220,
220,
705,
17469,
17910,
1988,
286,
262,
3124,
351,
543,
345,
765,
284,
3124,
262,
2685,
357,
17469,
532,
16,
284,
14267,
2599,
705,
8,
198,
220,
220,
220,
981,
407,
318,
47139,
39,
1069,
64,
10669,
7,
8043,
8,
290,
3124,
14512,
705,
12,
16,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
3124,
796,
5128,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
17469,
17910,
1988,
286,
262,
3124,
351,
543,
345,
765,
284,
3124,
262,
2685,
357,
17469,
532,
16,
284,
14267,
2599,
705,
8,
198,
220,
220,
220,
3601,
7203,
31851,
703,
466,
345,
765,
284,
7925,
3891,
422,
262,
1438,
1351,
25,
4943,
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220,
220,
220,
3601,
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352,
284,
651,
3891,
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220,
220,
220,
220,
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11639,
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77,
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220,
220,
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62,
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7203,
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534,
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25,
366,
8,
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220,
220,
981,
2393,
62,
15414,
407,
287,
37250,
16,
3256,
705,
17,
3256,
705,
12,
16,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
17469,
352,
284,
651,
3891,
422,
24134,
2393,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
17469,
362,
284,
651,
3891,
422,
256,
742,
2393,
1600,
366,
17469,
532,
16,
284,
14267,
1600,
41767,
11639,
59,
77,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
2393,
62,
15414,
796,
5128,
7203,
17469,
534,
3572,
25,
366,
8,
198,
220,
220,
220,
611,
2393,
62,
15414,
6624,
705,
12,
16,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
2393,
62,
15414,
796,
10148,
198,
220,
220,
220,
611,
3124,
6624,
705,
12,
16,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
3124,
796,
10148,
198,
220,
220,
220,
351,
1280,
10786,
30562,
13,
14116,
3256,
705,
86,
11537,
355,
277,
25,
198,
220,
220,
220,
220,
220,
220,
220,
277,
13,
13564,
7,
3672,
1343,
366,
91,
5376,
1,
1343,
705,
59,
77,
6,
1343,
14267,
62,
28665,
1343,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
91,
2949,
13,
286,
15180,
284,
14267,
1,
1343,
705,
59,
77,
6,
1343,
14267,
62,
808,
1343,
705,
91,
2949,
13,
286,
15274,
284,
14267,
59,
77,
6,
10,
8043,
10,
6,
91,
28780,
5315,
59,
77,
6,
1343,
2393,
62,
15414,
1343,
705,
91,
8979,
23412,
11537,
198
] | 2.466332 | 995 |
#================================================#
# vector_scalar.py
# based on: gsn_vec_scal_1.ncl,
# gsn_vec_scal_2.ncl,
# gsn_vec_scal_3.ncl
#================================================#
from pathlib import Path
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
#=================================================#
# open file and read in data
#=================================================#
data_location = Path("/Users/brianpm/Documents/www.ncl.ucar.edu/Applications/Data/cdf/")
data_file = data_location / "uvt.nc"
f1 = xr.open_dataset(data_file)
u = f1['U'][0,0,:,:] # read in example data [2D only here]
v = f1['V'][0,0,:,:]
speed = (u**2 + v**2)**0.5
#=================================================#
# PLOT 1 - Vector field colored by a scalar.
#=================================================#
outfile_ext = "png"
outfilename = "gsn_vec_scal"
wks, ax = plt.subplots()
plot = ax.quiver(u,v,speed)
# you can change the relative size of the arrows
# with the scale kwarg, but it requires quite
# a bit of tuning.
# plot = ax.quiver(u,v,speed, scale=350)
# you can still concatenate strings with +:
wks.savefig("/Users/brianpm/Desktop/"+outfilename+"."+outfile_ext)
#=================================================#
# PLOT 2 - Contour plot with vectors on top
#=================================================#
wks2, ax2 = plt.subplots()
plot2 = ax2.contourf(speed[10:30,20:40]) # contour the variable
plotV = ax2.quiver(u[10:30, 20:40], v[10:30, 20:40])
wks2.savefig("/Users/brianpm/Desktop/"+outfilename+"2."+outfile_ext)
#=================================================#
# Plot 3 - Put it on a map
#=================================================#
wks3, ax3 = plt.subplots(subplot_kw={"projection":ccrs.PlateCarree()})
lon = f1['lon']
lat = f1['lat']
lons, lats = np.meshgrid(lon, lat)
plot3 = ax3.quiver(lons, lats, u, v, speed, transform=ccrs.PlateCarree())
ax3.set_title("Basic Vector/Scalar/Map Plot")
ax3.set_extent([lon.min(), lon.max(), lat.min(), lat.max()])
ax3.coastlines()
ax3.set_xticks(np.arange(-180, 180, 30))
ax3.set_yticks(np.arange(-90, 90, 30))
ax3.grid()
wks3.savefig("/Users/brianpm/Desktop/"+outfilename+"3."+outfile_ext)
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] | 2.761614 | 818 |
# -*- encoding: utf-8 -*-
###
# Copyright 2019 Joël Perras <[email protected]>
# 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.
###
###
# Prevent commands from being mistakenly printed to buffers instead of being
# executed due to leading spaces or tabs.
#
# Upon hitting enter with an input that has leading spaces before a slash e.g.
# ` /nick vulpine`, the input will be halted and a message will be printed in
# the core weechat buffer.
#
# There are currently no commands or settings. Simply install and activate this
# script and you're good to go.
###
import re
import weechat
SCRIPT_NAME = "command_cop"
SCRIPT_AUTHOR = "Joël Perras <[email protected]>"
SCRIPT_VERSION = "0.1"
SCRIPT_LICENSE = "MIT"
SCRIPT_DESC = "Prevent entering of leading spaces before /command."
def command_run_input(data, buffer, command):
""" Function called when a command "/input xxxx" is run."""
if command == "/input return": # As in enter was pressed.
# Get input contents.
input_s = weechat.buffer_get_string(buffer, 'input')
# Match leading spaces before commands (slashes) and spaces just after a
# command slash.
matches = re.match(r'(?:\s+/|/\s+)(.*)', input_s)
if matches is not None:
# Alert in weechat buffer.
weechat.prnt("", "%sLeading spaces detected in command!" % weechat.color('red'))
return weechat.WEECHAT_RC_OK_EAT
return weechat.WEECHAT_RC_OK
if __name__ == '__main__':
if weechat.register(SCRIPT_NAME, SCRIPT_AUTHOR, SCRIPT_VERSION, SCRIPT_LICENSE, SCRIPT_DESC, '', ''):
weechat.hook_command_run('/input return', 'command_run_input', '')
| [
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3256,
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] | 3.054484 | 881 |
from redis import StrictRedis
import logging
# 准备配置类
class Config(object):
"""app配置类"""
# DEBUG = True
# 配置MySQL:指定数据库位置
SQLALCHEMY_DATABASE_URI = 'mysql://root:mysql@[email protected]:3306/information_new'
# 禁用追踪mysql:因为mysql的性能差，如果再去追踪mysql的所有的修改，会再次浪费性能
SQLALCHEMY_TRACK_MODIFICATIONS = False
# 配置redis
REDIS_HOST = '127.0.0.1'
REDIS_PORT = 6379
# 准备秘钥
SECRET_KEY = 'ajkhdflhslfjlfh'
# 配置Session:将flask的session数据引导到redis
SESSION_TYPE = 'redis' # 存储到redis
# 配置redis的位置
SESSION_REDIS=StrictRedis(host=REDIS_HOST,port=REDIS_PORT)
# 使用签名将session的明文转成密文
SESSION_USE_SIGNER = True
# 设置session有效期：一天，以秒为单位
PERMANENT_SESSION_LIFETIME = 60*60*24
class DevelopmentConfig(Config):
"""开发环境配置类
如果开发环境的配置和父类一致，可以直接pass
"""
DEBUG = True
# 开发环境的日志等级为调试模式
LOGGING_LEVEL = logging.DEBUG
class ProductionConfig(Config):
"""生产环境配置类
实际开发中，需要额外配置生产环境下的数据库和其他的信息
"""
DEBUG = False
# 生产环境的日志等级为调试模式
LOGGING_LEVEL = logging.WARNING
# 工厂方法需要的原材料
configs = {
'dev':DevelopmentConfig,
'prod':ProductionConfig
} | [
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] | 1.279955 | 893 |
from datetime import datetime
from flask_wtf import Form
from wtforms import (
BooleanField,
DateTimeField,
SelectField,
SelectMultipleField,
StringField,
)
from wtforms.validators import DataRequired, URL
from constants import GENRES, STATES
| [
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] | 3.204819 | 83 |
import math
import numpy as np
""" A controller class which implements a joint feedforward
controller by compensating for the desired acceleration torque and the desired gravity torque."""
| [
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# Copyright 2019 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
#
# 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.
"""Main logic for training the A2N model.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gc
import math
import os
from absl import app
from absl import flags
from absl import logging
import clueweb_text_graph
import dataset
import graph
import losses
import metrics
import models
import numpy as np
import slim
from tensorboard.plugins import projector
import tensorflow as tf
from tensorflow.python.training.summary_io import SummaryWriterCache
import text_graph
import utils
FLAGS = flags.FLAGS
flags.DEFINE_string("kg_file", None, "path to kg file")
flags.DEFINE_string("output_dir", None, "output dir for summaries/logs")
flags.DEFINE_string("dev_kg_file", None, "path to dev kg file")
flags.DEFINE_string("test_kg_file", None, "path to test kg file")
flags.DEFINE_string("model_path", None, "path to model if testing only")
flags.DEFINE_boolean("evaluate", False, "run eval loop")
flags.DEFINE_boolean("test_only", False, "if test only")
flags.DEFINE_integer("global_step", None,
"global_step to restore model for testing")
flags.DEFINE_integer("num_epochs", 5, "number of train epochs")
flags.DEFINE_integer("batchsize", 64, "batchsize for training")
flags.DEFINE_integer("test_batchsize", 10, "batchsize for testing")
flags.DEFINE_integer("max_neighbors", None,
"maximum neighbors to use during training")
flags.DEFINE_integer("max_negatives", None,
"maximum number of negative entities to sample")
flags.DEFINE_integer("emb_dim", 100,
"dimension of entity and relation embeddings")
flags.DEFINE_float("entity_encoder_dropout", 1.0,
"dropout for entity embeddings")
flags.DEFINE_float("relation_encoder_dropout", 1.0,
"dropout for relation embeddings")
flags.DEFINE_float("init_entity_encoder_dropout", 1.0,
"dropout for init entity embeddings in attention")
flags.DEFINE_float("attention_encoder_dropout", 1.0,
"dropout for attention encoder")
flags.DEFINE_boolean("use_separate_attention_emb", False,
"use separate entity embeddings for computing attention")
flags.DEFINE_integer("num_parallel_preprocess", 64,
"number of processes to use in dataset preprocessing")
flags.DEFINE_integer("prefetch_examples", 10, "number of examples to prefetch")
flags.DEFINE_integer("shuffle_buffer", 50000,
"buffer for shuffling training examples")
flags.DEFINE_float("learning_rate", 0.001, "learning for optimizer")
flags.DEFINE_float("grad_clip", None, "Clip gradient norm during training")
flags.DEFINE_integer("save_every", 100, "save model every this many steps")
flags.DEFINE_string("entity_names_file", None,
"mapping of Freebase mid to names")
flags.DEFINE_enum("model", "attention",
["distmult", "attention", "source_attention",
"source_rel_attention", "source_path_attention"],
"the model to use")
flags.DEFINE_bool("use_tanh", False, "use tanh non-linearity on embeddings")
flags.DEFINE_enum("attention_type", "bilinear",
["bilinear", "cosine", "sigmoid_bilinear",
"sigmoid_avg_bilinear", "relation"],
"type of attention to use for attention model")
flags.DEFINE_bool("analyze", False, "analyze model")
flags.DEFINE_integer("max_path_length", None,
"maximum path length for path attention models")
flags.DEFINE_string("text_kg_file", None, "path to text data")
flags.DEFINE_integer("max_text_len", None, "max length of text")
flags.DEFINE_integer("max_vocab_size", None, "max number of text words")
flags.DEFINE_integer("min_word_freq", None, "min freq threshold for text words")
flags.DEFINE_integer("max_text_neighbors", None, "max text neighbors")
flags.DEFINE_float("text_encoder_dropout", 1.0, "dropout for text cnn")
flags.DEFINE_list("text_encoder_filter_widths", ["3", "5", "7"],
"filter widths for cnn")
flags.DEFINE_enum("text_encoder_nonlinearity", "tanh", ["relu", "tanh"],
"non-linearity to use for TextCNN")
flags.DEFINE_integer("text_encoder_num_filters", 64, "num filters for cnn")
flags.DEFINE_string("clueweb_sentences", None,
"path to clueweb sentences (or data formatted like cw)")
flags.DEFINE_string("clueweb_data", None,
"path to clueweb data (or data formatted like cw)")
flags.DEFINE_string("clueweb_embeddings", None,
"path to clueweb embeddings (or data formatted like cw)")
flags.DEFINE_integer("text_emb_dim", None, "embedding dim for clueweb text")
flags.DEFINE_integer("subsample_text_rels", None,
"subsample text to max this many per pair")
flags.DEFINE_string("master", "local",
"""BNS name of the TensorFlow master to use.""")
flags.DEFINE_integer("task", 0,
"""Task id of the replica running the training.""")
flags.DEFINE_integer("ps_tasks", 0, """Number of tasks in the ps job.
If 0 no ps job is used.""")
flags.mark_flag_as_required("kg_file")
flags.mark_flag_as_required("output_dir")
def get_train_op(loss, optimizer, grad_clip=None, global_step=None):
"""Make a train_op apply gradients to loss using optimizer.
Args:
loss: the loss function to optimize
optimizer: the optimizer to compute and apply gradients
grad_clip: clip gradient norms by the value supplied (default dont clip)
global_step: tf.placeholder for global_step
Returns:
train_op: the training op to run
grads_and_vars: the gradients and variables for debugging
var_names: the variable names for debugging
capped_grads_and_vars: for debugging
"""
variables = tf.trainable_variables()
grads_and_vars = optimizer.compute_gradients(loss, variables)
var_names = [v.name for v in variables]
logging.info("Trainable variables:")
for var in var_names:
logging.info("\t %s", var)
logging.debug(grads_and_vars)
grad_var_norms = [(tf.global_norm([gv[1]]), tf.global_norm([gv[0]]))
for gv in grads_and_vars]
if grad_clip:
capped_grads_and_vars = [(tf.clip_by_norm(gv[0], grad_clip), gv[1])
for gv in grads_and_vars]
else:
capped_grads_and_vars = grads_and_vars
# norms of gradients for debugging
# grad_norms = [tf.sqrt(tf.reduce_sum(tf.square(grad)))
# for grad, _ in grads_and_vars]
train_op = optimizer.apply_gradients(capped_grads_and_vars,
global_step=global_step)
return train_op, grad_var_norms, var_names, capped_grads_and_vars
def read_graph_data(
kg_file, add_reverse_graph, add_inverse_edge, mode,
num_epochs, batchsize, max_neighbors, max_negatives,
train_graph=None, text_kg_file=None, val_graph=None
):
"""Read graph, create dataset and build model."""
# Read graphs and create datasets
entity_vocab = relation_vocab = None
if train_graph:
entity_vocab = train_graph.entity_vocab
relation_vocab = train_graph.relation_vocab
if FLAGS.clueweb_data and mode == "train":
graph_type = clueweb_text_graph.CWTextGraph
text_kg_file = FLAGS.clueweb_data
elif text_kg_file and mode == "train":
graph_type = text_graph.TextGraph
text_kg_file = FLAGS.text_kg_file
else:
graph_type = graph.Graph
text_kg_file = None
k_graph = graph_type(
text_kg_file=text_kg_file,
skip_new=True,
max_text_len=FLAGS.max_text_len,
max_vocab_size=FLAGS.max_vocab_size,
min_word_freq=FLAGS.min_word_freq,
kg_file=kg_file,
add_reverse_graph=add_reverse_graph,
add_inverse_edge=add_inverse_edge, mode=mode,
entity_vocab=entity_vocab, relation_vocab=relation_vocab,
max_path_length=FLAGS.max_path_length if mode == "train" else None,
embeddings_file=FLAGS.clueweb_embeddings,
sentence_vocab_file=FLAGS.clueweb_sentences,
subsample=FLAGS.subsample_text_rels
)
if FLAGS.text_kg_file:
max_text_len = FLAGS.max_text_len
if mode == "train":
max_text_len = max_text_len or k_graph.max_text_len
elif train_graph:
max_text_len = max_text_len or train_graph.max_text_len
else:
max_text_len = None
k_data = dataset.Dataset(data_graph=k_graph, train_graph=train_graph,
mode=mode, num_epochs=num_epochs,
batchsize=batchsize,
max_neighbors=max_neighbors,
max_negatives=max_negatives,
model_type=FLAGS.model,
max_text_len=max_text_len,
max_text_neighbors=FLAGS.max_text_neighbors,
val_graph=val_graph)
# Create the training data iterator and return the input tensors
# with tf.device("/job:worker"):
k_data.create_dataset_iterator(
num_parallel=FLAGS.num_parallel_preprocess,
prefetch=FLAGS.prefetch_examples,
shuffle_buffer=FLAGS.shuffle_buffer
# , device="worker" if FLAGS.master != "local" else "cpu"
)
return k_graph, k_data
def create_model(train_graph, iterator):
"""Create model and placeholders."""
if FLAGS.clueweb_data:
s, nbrs_s, text_nbrs_s, r, candidates, nbrs_candidates, labels, text_nbrs_s_emb = iterator.get_next()
elif FLAGS.text_kg_file:
s, nbrs_s, text_nbrs_s, r, candidates, nbrs_candidates, labels = \
iterator.get_next()
else:
s, nbrs_s, r, candidates, nbrs_candidates, labels = iterator.get_next()
# Create the attention model, this returns candidates scores and the model
# encoders in a dict for creating feed_dict for all encoders
is_train_ph = tf.placeholder_with_default(True, shape=[],
name="is_train_ph")
if FLAGS.model == "attention":
with tf.variable_scope("attention_model", reuse=False):
candidate_scores, model = models.attention_kbc_model(
FLAGS, train_graph, is_train_ph,
(s, nbrs_s, r, candidates, nbrs_candidates)
)
elif FLAGS.model == "source_attention":
with tf.variable_scope("s_attention_model", reuse=False):
candidate_scores, model = models.source_attention_kbc_model(
FLAGS, train_graph, is_train_ph,
(s, nbrs_s, r, candidates)
)
elif FLAGS.model in ["source_rel_attention", "source_path_attention"]:
if FLAGS.clueweb_data:
input_tensors = (s, nbrs_s, text_nbrs_s, text_nbrs_s_emb, r, candidates)
elif FLAGS.text_kg_file:
input_tensors = (s, nbrs_s, text_nbrs_s, r, candidates)
else:
input_tensors = (s, nbrs_s, r, candidates)
with tf.variable_scope("s_attention_model", reuse=False):
candidate_scores, model = models.source_attention_kbc_model(
FLAGS, train_graph, is_train_ph,
input_tensors, model_type=FLAGS.model
)
elif FLAGS.model == "distmult":
with tf.variable_scope("distmult_model", reuse=False):
candidate_scores, model = models.distmult_kbc_model(
FLAGS, train_graph, is_train_ph,
(s, r, candidates)
)
if FLAGS.clueweb_data:
inputs = (s, nbrs_s, text_nbrs_s, text_nbrs_s_emb,
r, candidates, nbrs_candidates)
elif FLAGS.text_kg_file:
inputs = (s, nbrs_s, text_nbrs_s, r, candidates, nbrs_candidates)
else:
inputs = (s, nbrs_s, r, candidates, nbrs_candidates)
return candidate_scores, candidates, labels, model, is_train_ph, inputs
def evaluate():
"""Run evaluation on dev or test data."""
add_inverse_edge = FLAGS.model in \
["source_rel_attention", "source_path_attention"]
if FLAGS.clueweb_data:
train_graph = clueweb_text_graph.CWTextGraph(
text_kg_file=FLAGS.clueweb_data,
embeddings_file=FLAGS.clueweb_embeddings,
sentence_vocab_file=FLAGS.clueweb_sentences,
skip_new=True,
kg_file=FLAGS.kg_file,
add_reverse_graph=not add_inverse_edge,
add_inverse_edge=add_inverse_edge,
subsample=FLAGS.subsample_text_rels
)
elif FLAGS.text_kg_file:
train_graph = text_graph.TextGraph(
text_kg_file=FLAGS.text_kg_file,
skip_new=True,
max_text_len=FLAGS.max_text_len,
max_vocab_size=FLAGS.max_vocab_size,
min_word_freq=FLAGS.min_word_freq,
kg_file=FLAGS.kg_file,
add_reverse_graph=not add_inverse_edge,
add_inverse_edge=add_inverse_edge,
max_path_length=FLAGS.max_path_length
)
else:
train_graph = graph.Graph(
kg_file=FLAGS.kg_file,
add_reverse_graph=not add_inverse_edge,
add_inverse_edge=add_inverse_edge,
max_path_length=FLAGS.max_path_length
)
# train_graph, _ = read_graph_data(
# kg_file=FLAGS.kg_file,
# add_reverse_graph=(FLAGS.model != "source_rel_attention"),
# add_inverse_edge=(FLAGS.model == "source_rel_attention"),
# mode="train", num_epochs=FLAGS.num_epochs, batchsize=FLAGS.batchsize,
# max_neighbors=FLAGS.max_neighbors,
# max_negatives=FLAGS.max_negatives
# )
val_graph = None
if FLAGS.dev_kg_file:
val_graph, eval_data = read_graph_data(
kg_file=FLAGS.dev_kg_file,
add_reverse_graph=not add_inverse_edge,
add_inverse_edge=add_inverse_edge,
# add_reverse_graph=False,
# add_inverse_edge=False,
mode="dev", num_epochs=1, batchsize=FLAGS.test_batchsize,
max_neighbors=FLAGS.max_neighbors,
max_negatives=FLAGS.max_negatives, train_graph=train_graph,
text_kg_file=FLAGS.text_kg_file
)
if FLAGS.test_kg_file:
_, eval_data = read_graph_data(
kg_file=FLAGS.test_kg_file,
add_reverse_graph=not add_inverse_edge,
add_inverse_edge=add_inverse_edge,
# add_reverse_graph=False,
# add_inverse_edge=False,
mode="test", num_epochs=1, batchsize=FLAGS.test_batchsize,
max_neighbors=FLAGS.max_neighbors,
max_negatives=None, train_graph=train_graph,
text_kg_file=FLAGS.text_kg_file,
val_graph=val_graph
)
if not FLAGS.dev_kg_file and not FLAGS.test_kg_file:
raise ValueError("Evalution without a dev or test file!")
iterator = eval_data.dataset.make_initializable_iterator()
candidate_scores, candidates, labels, model, is_train_ph, inputs = \
create_model(train_graph, iterator)
# Create eval metrics
# if FLAGS.dev_kg_file:
batch_rr = metrics.mrr(candidate_scores, candidates, labels)
mrr, mrr_update = tf.metrics.mean(batch_rr)
mrr_summary = tf.summary.scalar("MRR", mrr)
all_hits, all_hits_update, all_hits_summaries = [], [], []
for k in [1, 3, 10]:
batch_hits = metrics.hits_at_k(candidate_scores, candidates, labels, k=k)
hits, hits_update = tf.metrics.mean(batch_hits)
hits_summary = tf.summary.scalar("Hits_at_%d" % k, hits)
all_hits.append(hits)
all_hits_update.append(hits_update)
all_hits_summaries.append(hits_summary)
hits = tf.group(*all_hits)
hits_update = tf.group(*all_hits_update)
global_step = tf.Variable(0, name="global_step", trainable=False)
current_step = tf.Variable(0, name="current_step", trainable=False,
collections=[tf.GraphKeys.LOCAL_VARIABLES])
incr_current_step = tf.assign_add(current_step, 1)
reset_current_step = tf.assign(current_step, 0)
slim.get_or_create_global_step(graph=tf.get_default_graph())
# best_hits = tf.Variable(0., trainable=False)
# best_step = tf.Variable(0, trainable=False)
# with tf.control_dependencies([hits]):
# update_best_hits = tf.cond(tf.greater(hits, best_hits),
# lambda: tf.assign(best_hits, hits),
# lambda: 0.)
# update_best_step = tf.cond(tf.greater(hits, best_hits),
# lambda: tf.assign(best_step, global_step),
# lambda: 0)
# best_hits_summary = tf.summary.scalar("Best Hits@10", best_hits)
# best_step_summary = tf.summary.scalar("Best Step", best_step)
nexamples = eval_data.data_graph.tuple_store.shape[0]
if eval_data.data_graph.add_reverse_graph:
nexamples *= 2
num_batches = math.ceil(nexamples / float(FLAGS.test_batchsize))
local_init_op = tf.local_variables_initializer()
if FLAGS.analyze:
entity_names = utils.read_entity_name_mapping(FLAGS.entity_names_file)
session = tf.Session()
# summary_writer = tf.summary.FileWriter(FLAGS.output_dir, session.graph)
init_op = tf.global_variables_initializer()
session.run(init_op)
session.run(local_init_op)
saver = tf.train.Saver(tf.trainable_variables())
ckpt_path = FLAGS.model_path + "/model.ckpt-%d" % FLAGS.global_step
attention_probs = model["attention_encoder"].get_from_collection(
"attention_probs"
)
if FLAGS.clueweb_data:
s, nbrs_s, text_nbrs_s, text_nbrs_s_emb, r, candidates, _ = inputs
elif FLAGS.text_kg_file:
s, nbrs_s, text_nbrs_s, r, candidates, _ = inputs
else:
s, nbrs_s, r, candidates, _ = inputs
saver.restore(session, ckpt_path)
session.run(iterator.initializer)
num_attention = 5
nsteps = 0
outf_correct = open(FLAGS.output_dir + "/analyze_correct.txt", "w+")
outf_incorrect = open(
FLAGS.output_dir + "/analyze_incorrect.txt", "w+"
)
ncorrect = 0
analyze_outputs = [candidate_scores, s, nbrs_s, r, candidates, labels,
attention_probs]
if FLAGS.text_kg_file:
analyze_outputs.append(text_nbrs_s)
while True:
try:
analyze_vals = session.run(analyze_outputs, {is_train_ph: False})
if FLAGS.text_kg_file:
cscores, se, nbrs, qr, cands, te, nbr_attention_probs, text_nbrs = \
analyze_vals
else:
cscores, se, nbrs, qr, cands, te, nbr_attention_probs = analyze_vals
# import pdb; pdb.set_trace()
pred_ids = cscores.argmax(1)
for i in range(se.shape[0]):
sname = train_graph.inverse_entity_vocab[se[i]]
if sname in entity_names:
sname = entity_names[sname]
rname = train_graph.inverse_relation_vocab[qr[i]]
pred_target = cands[i, pred_ids[i]]
pred_name = train_graph.inverse_entity_vocab[pred_target]
if pred_name in entity_names:
pred_name = entity_names[pred_name]
tname = train_graph.inverse_entity_vocab[te[i][0]]
if tname in entity_names:
tname = entity_names[tname]
if te[i][0] == pred_target:
outf = outf_correct
ncorrect += 1
else:
outf = outf_incorrect
outf.write("\n(%d) %s, %s, ? \t Pred: %s \t Target: %s" %
(nsteps+i+1, sname, rname, pred_name, tname))
top_nbrs_index = np.argsort(nbr_attention_probs[i, :])[::-1]
outf.write("\nTop Nbrs:")
for j in range(num_attention):
nbr_index = top_nbrs_index[j]
if nbr_index < FLAGS.max_neighbors:
nbr_id = nbrs[i, nbr_index, :]
nbr_name = ""
for k in range(0, nbrs.shape[-1], 2):
ent_name = train_graph.inverse_entity_vocab[nbr_id[k+1]]
if ent_name in entity_names:
ent_name = entity_names[ent_name]
rel_name = train_graph.inverse_relation_vocab[nbr_id[k]]
nbr_name += "(%s, %s)" % (rel_name, ent_name)
else:
# Text Relation
text_nbr_ids = text_nbrs[i, nbr_index - FLAGS.max_neighbors, :]
text_nbr_ent = text_nbr_ids[0]
ent_name = train_graph.inverse_entity_vocab[text_nbr_ent]
if ent_name in entity_names:
ent_name = entity_names[ent_name]
rel_name = train_graph.get_relation_text(text_nbr_ids[1:])
nbr_name = "(%s, %s)" % (rel_name, ent_name)
outf.write("\n\t\t %s Prob: %.4f" %
(nbr_name, nbr_attention_probs[i, nbr_index]))
nsteps += se.shape[0]
tf.logging.info("Current hits@1: %.3f", ncorrect * 1.0 / (nsteps))
except tf.errors.OutOfRangeError:
break
outf_correct.close()
outf_incorrect.close()
return
if FLAGS.test_only:
ckpt_path = FLAGS.model_path + "/model.ckpt-%d" % FLAGS.global_step
slim.evaluation.evaluate_once(
master=FLAGS.master,
checkpoint_path=ckpt_path,
logdir=FLAGS.output_dir,
variables_to_restore=tf.trainable_variables() + [global_step],
initial_op=tf.group(local_init_op, iterator.initializer),
# initial_op=iterator.initializer,
num_evals=num_batches,
eval_op=tf.group(mrr_update, hits_update, incr_current_step),
eval_op_feed_dict={is_train_ph: False},
final_op=tf.group(mrr, hits),
final_op_feed_dict={is_train_ph: False},
summary_op=tf.summary.merge([mrr_summary]+ all_hits_summaries),
hooks=[DataInitHook(),
tf.train.LoggingTensorHook(
{"mrr": mrr, "hits": hits, "step": current_step},
every_n_iter=1
)]
)
else:
slim.evaluation.evaluation_loop(
master=FLAGS.master,
checkpoint_dir=FLAGS.model_path,
logdir=FLAGS.output_dir,
variables_to_restore=tf.trainable_variables() + [global_step],
initial_op=tf.group(local_init_op, iterator.initializer),
# initial_op=iterator.initializer,
num_evals=num_batches,
eval_op=tf.group(mrr_update, hits_update, incr_current_step),
eval_op_feed_dict={is_train_ph: False},
final_op=tf.group(mrr, hits),
final_op_feed_dict={is_train_ph: False},
summary_op=tf.summary.merge([mrr_summary] + all_hits_summaries),
max_number_of_evaluations=None,
eval_interval_secs=60,
hooks=[DataInitHook(),
tf.train.LoggingTensorHook(
{"mrr": mrr, "hits": hits, "step": current_step},
every_n_iter=1
)]
)
def train():
"""Running the main training loop with given parameters."""
if FLAGS.task == 0 and not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MakeDirs(FLAGS.output_dir)
# Read train/dev/test graphs, create datasets and model
add_inverse_edge = FLAGS.model in \
["source_rel_attention", "source_path_attention"]
train_graph, train_data = read_graph_data(
kg_file=FLAGS.kg_file,
add_reverse_graph=not add_inverse_edge,
add_inverse_edge=add_inverse_edge,
mode="train",
num_epochs=FLAGS.num_epochs, batchsize=FLAGS.batchsize,
max_neighbors=FLAGS.max_neighbors,
max_negatives=FLAGS.max_negatives,
text_kg_file=FLAGS.text_kg_file
)
worker_device = "/job:{}".format(FLAGS.brain_job_name)
with tf.device(
tf.train.replica_device_setter(
FLAGS.ps_tasks, worker_device=worker_device)):
iterator = train_data.dataset.make_one_shot_iterator()
candidate_scores, _, labels, model, is_train_ph, _ = create_model(
train_graph, iterator
)
# Create train loss and training op
loss = losses.softmax_crossentropy(logits=candidate_scores, labels=labels)
optimizer = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate)
global_step = tf.Variable(0, name="global_step", trainable=False)
train_op = get_train_op(loss, optimizer, FLAGS.grad_clip,
global_step=global_step)
tf.summary.scalar("Loss", loss)
run_options = tf.RunOptions(report_tensor_allocations_upon_oom=True)
session_config = tf.ConfigProto(log_device_placement=True)
# Create tf training session
scaffold = tf.train.Scaffold(saver=tf.train.Saver(max_to_keep=1000))
# ckpt_hook = tf.train.CheckpointSaverHook(
# checkpoint_dir=FLAGS.output_dir, scaffold=scaffold,
# save_steps=FLAGS.save_every
# )
# summary_hook = tf.train.SummarySaverHook(
# save_secs=60, output_dir=FLAGS.output_dir,
# summary_op=tf.summary.merge_all()
# )
session = tf.train.MonitoredTrainingSession(
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
checkpoint_dir=FLAGS.output_dir,
save_checkpoint_steps=FLAGS.save_every,
scaffold=scaffold,
save_summaries_secs=60,
# hooks=[summary_hook],
# chief_only_hooks=[ckpt_hook],
config=session_config
)
# Create embeddings visualization
if FLAGS.task == 0:
utils.save_embedding_vocabs(FLAGS.output_dir, train_graph,
FLAGS.entity_names_file)
pconfig = projector.ProjectorConfig()
add_embedding_to_projector(
pconfig, model["entity_encoder"].embeddings.name.split(":")[0],
os.path.join(FLAGS.output_dir, "entity_vocab.tsv")
)
add_embedding_to_projector(
pconfig, model["relation_encoder"].embeddings.name.split(":")[0],
os.path.join(FLAGS.output_dir, "relation_vocab.tsv")
)
if FLAGS.text_kg_file:
word_embeddings = model["text_encoder"].word_embedding_encoder.embeddings
add_embedding_to_projector(
pconfig, word_embeddings.name.split(":")[0],
os.path.join(FLAGS.output_dir, "word_vocab.tsv")
)
projector.visualize_embeddings(
SummaryWriterCache.get(FLAGS.output_dir), pconfig
)
# Main training loop
running_total_loss = 0.
nsteps = 0
gc.collect()
while True:
try:
current_loss, _, _ = session.run(
[loss, train_op, global_step],
# feed_dict={is_train_ph: True, handle: train_iterator_handle},
feed_dict={is_train_ph: True},
options=run_options
)
nsteps += 1
running_total_loss += current_loss
tf.logging.info("Step %d, loss: %.3f, running avg loss: %.3f",
nsteps, current_loss, running_total_loss / nsteps)
if nsteps %2 == 0:
gc.collect()
except tf.errors.OutOfRangeError:
tf.logging.info("End of Traning Epochs after %d steps", nsteps)
break
if __name__ == "__main__":
app.run(main)
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366,
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12417,
834,
1298,
198,
220,
598,
13,
5143,
7,
12417,
8,
198
] | 2.217247 | 12,083 |
from Module import AbstractModule
| [
6738,
19937,
1330,
27741,
26796,
628
] | 5.833333 | 6 |
from typing import Iterable
import sqlalchemy as sa
from scrapfishin.models import Recipe
def grocery_list(
s: sa.orm.Session,
recipes: Iterable[Recipe]
) -> str:
"""
Format an iterable of Recipes into a Grocery List.
Parameters
----------
s : sqlalchemy.orm.Session
database session to bind objects
recipes : [Recipe]
list of recipes to shop for
Returns
-------
grocery_page : str
page of sorted ingredients
"""
seen = []
for r in recipes:
for i in r.ingredient_amounts:
unit = f'{i.measurement.unit} of {i.ingredient.food}'
amount = i.amount
try:
existing = next(s for s in seen if unit in s)
except StopIteration:
pass
else:
amount += float(existing.split(' ')[0])
seen.remove(existing)
seen.append(f'{amount} {unit}')
return '\n'.join(sorted(seen, key=lambda i: i.split(' of ')[-1]))
def random_recipe(s: sa.orm.Session, *, n: int=1) -> Iterable(Recipe):
"""
Get `n` random recipes.
Parameters
----------
s : sqlalchemy.orm.Session
database session to bind objects
n : int = [default: 1]
number of recipes to return
"""
q = s.query(Recipe)\
.order_by(sa.func.random())\
.limit(n)
return iter(q.all())
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] | 2.231132 | 636 |
#!/usr/bin/env python
"""NDG Security Attribute Authority test harness for unit test site 'A'
NERC Data Grid Project
"""
__author__ = "P J Kershaw"
__date__ = "24/09/08"
__copyright__ = "(C) 2009 Science and Technology Facilities Council"
__contact__ = "[email protected]"
__revision__ = "$Id$"
from os import path
import optparse
from paste.script.util.logging_config import fileConfig
from paste.deploy import loadapp
from ndg.security.server.utils.wsgi_utils import GunicornServerApp
from ndg.security.server.test.base import NDGSEC_TEST_CONFIG_DIR
INI_FILENAME = 'attribute-service.ini'
CFG_FILEPATH = path.join(path.dirname(path.abspath(__file__)), INI_FILENAME)
if __name__ == '__main__':
def_cert_filepath = path.join(NDGSEC_TEST_CONFIG_DIR,
'pki',
'localhost.crt')
def_prikey_filepath = path.join(NDGSEC_TEST_CONFIG_DIR,
'pki',
'localhost.key')
parser = optparse.OptionParser()
parser.add_option("-p",
"--port",
dest="port",
default=5443,
type='int',
help="port number to run under")
parser.add_option("-c",
"--cert-file",
dest='cert_filepath',
default=def_cert_filepath,
help="SSL Certificate file")
parser.add_option("-k",
"--private-key-file",
dest='prikey_filepath',
default=def_prikey_filepath,
help="SSL private key file")
parser.add_option("-f",
"--conf",
dest="config_filepath",
default=CFG_FILEPATH,
help="Configuration file path")
opt = parser.parse_args()[0]
dir_name = path.dirname(__file__)
options = {
'bind': '{}:{}'.format('127.0.0.1', opt.port),
'keyfile': opt.prikey_filepath,
'certfile': opt.cert_filepath
}
fileConfig(opt.config_filepath)
app = loadapp('config:%s' % opt.config_filepath)
gunicorn_server_app = GunicornServerApp(app, options)
gunicorn_server_app.run() | [
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220,
2485,
291,
1211,
62,
15388,
62,
1324,
13,
5143,
3419
] | 1.908055 | 1,229 |
import random
from tyckiting_client.ai import base
from tyckiting_client import actions
from tyckiting_client.ai.strategies import pipelineEscaping
from tyckiting_client.ai.strategies import scanning
from tyckiting_client.ai.strategies import uncertainTracking
'''
Rules:
like robin but in certain situations endangered bots stay and do an other action
'''
STAY_PROB = 0.25
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] | 3.436364 | 110 |
"""
Code related to face detection and manipulation
"""
#pip install facenet_pytorch
from facenet_pytorch import MTCNN
mtcnn = MTCNN(image_size=256, margin=80)
# simplest ye olde trustworthy MTCNN for face detection with landmarks
# my version of isOdd, should make a separate repo for it :D
# the actual scaler function
"""
A useful scaler algorithm, based on face detection.
Takes PIL.Image, returns a uniformly scaled PIL.Image
boxes: a list of detected bboxes
_img: PIL.Image
max_res: maximum pixel area to fit into. Use to stay below the VRAM limits of your GPU.
target_face: desired face size. Upscale or downscale the whole image to fit the detected face into that dimension.
fixed_ratio: fixed scale. Ignores the face size, but doesn't ignore the max_res limit.
max_upscale: maximum upscale ratio. Prevents from scaling images with tiny faces to a blurry mess.
"""
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] | 3.314079 | 277 |
{%- if cookiecutter.copyright != "None" -%}
# Copyright (c) {% now "utc", '%Y' %}, {{ cookiecutter.copyright }}. Unauthorised use, distribution or duplication is prohibited
{% endif %}
"""
{{ cookiecutter.project_name }}.
{{ cookiecutter.library_name }}
"""
from flask import Blueprint, jsonify
from observability.logger import Logger
blueprint = Blueprint("health_check", __name__, url_prefix="/api/health")
logger = Logger.init("{{ cookiecutter.__project_name_slug }}")
@blueprint.route("/")
def health_check():
"""Check health status."""
logger.info("Health check")
return jsonify({"status": "ok"})
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366,
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8,
198
] | 3.014563 | 206 |
#!/usr/bin/env python
#-*- coding: utf-8 -*-
import sys
import os
import pytest
from mock import patch
sys.path.insert(0, os.path.abspath('./'))
from feuersoftware import PublicAPI
TOKEN = '2xgRoQfoMGb4IveCDJIZqOO1l8hZZ5jT5mAw7SSk1otrFSq50IA2HIYB3luEpv7Vw8BWwG'\
'Y2zV96VUkOF3FCZs2OP03qaTWF3CDrUHOKndvLIFTTgx0FCMBTFBRF1DfG4g3rs8BSMHB4'\
'6qph1AlxOZ6parmJlp90V3GQB4EoI6DFdKE4SZeBuu46mXoaDlSmpTTS3FCpeG7oEUJVgy'\
'pLZkZSFPRng5HdKhp6HG2XmNIMAtKTG3DAUWuKRi3cZ4JstLj05y4r7jt81g4DYXz9gVYc'\
'UWk2pOkIZ9RPmu0s4LlaXHEK3TJlxLIUt5eHIzPUVKXyhdJDckviPsTYNfRxkpcNGd0vAb'\
'zfzwMadgb4xaOi1v6ZpsRfXyOPgpudcnO6rwwi9TlAWNZ2075CO7HVFEP31yGhXmYsdFwj'\
'ne3UIraWovMWHqeyv2yQLigKLePDAgXYUFqQpZ9P5ScznSMUg0ZnxS0Miy0qKe9zDYtqTk'\
'qQVwrUGfGVFp4Ti83NJLCCGUOCmF0ovOB28mYyQIqGAi2MDaNIuAvz6HT1tGAo5nYdzOeu'
@patch("feuersoftware.logging.Logger.info")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.error")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.info")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.info")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.info")
@patch("feuersoftware.logging.Logger.warning")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.error")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.info")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.error")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.info")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.info")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.info")
@patch("feuersoftware.logging.Logger.warning")
@patch("feuersoftware.requests")
@patch("feuersoftware.logging.Logger.error")
@patch("feuersoftware.requests")
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] | 1.956432 | 964 |
# Generated by Django 3.1.2 on 2020-10-27 11:12
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
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"""Module for events adapter."""
import copy
import logging
import os
from typing import List
from aiohttp import ClientSession
from aiohttp import hdrs
from aiohttp import web
from multidict import MultiDict
EVENTS_HOST_SERVER = os.getenv("EVENTS_HOST_SERVER", "localhost")
EVENTS_HOST_PORT = os.getenv("EVENTS_HOST_PORT", "8082")
EVENT_SERVICE_URL = f"http://{EVENTS_HOST_SERVER}:{EVENTS_HOST_PORT}"
class DashboardAdapter:
"""Class representing events."""
async def get_all_events(self, token: str) -> List:
"""Get all events function."""
events = []
headers = MultiDict(
[
(hdrs.CONTENT_TYPE, "application/json"),
(hdrs.AUTHORIZATION, f"Bearer {token}"),
]
)
async with ClientSession() as session:
async with session.get(
f"{EVENT_SERVICE_URL}/events", headers=headers
) as resp:
logging.debug(f"get_all_events - got response {resp.status}")
if resp.status == 200:
events = await resp.json()
logging.debug(f"events - got response {events}")
elif resp.status == 401:
logging.info("TODO Performing new login")
# Perform login
else:
logging.error(f"Error {resp.status} getting events: {resp} ")
return events
async def get_event(self, token: str, id: str) -> dict:
"""Get event function."""
event = {}
headers = MultiDict(
[
(hdrs.CONTENT_TYPE, "application/json"),
(hdrs.AUTHORIZATION, f"Bearer {token}"),
]
)
async with ClientSession() as session:
async with session.get(
f"{EVENT_SERVICE_URL}/events/{id}", headers=headers
) as resp:
logging.debug(f"get_event {id} - got response {resp.status}")
if resp.status == 200:
event = await resp.json()
logging.debug(f"event - got response {event}")
else:
logging.error(f"Error {resp.status} getting events: {resp} ")
return event
async def create_event(self, token: str, event: dict) -> str:
"""Create new event function."""
id = ""
headers = MultiDict(
[
(hdrs.CONTENT_TYPE, "application/json"),
(hdrs.AUTHORIZATION, f"Bearer {token}"),
]
)
request_body = copy.deepcopy(event)
async with ClientSession() as session:
async with session.post(
f"{EVENT_SERVICE_URL}/events", headers=headers, json=request_body
) as resp:
if resp.status == 201:
logging.debug(f"result - got response {resp}")
location = resp.headers[hdrs.LOCATION]
id = location.split(os.path.sep)[-1]
else:
logging.error(f"create_event failed - {resp.status}")
raise web.HTTPBadRequest(reason="Create event failed.")
return id
async def delete_event(self, token: str, id: str) -> str:
"""Delete event function."""
headers = MultiDict(
[
(hdrs.CONTENT_TYPE, "application/json"),
(hdrs.AUTHORIZATION, f"Bearer {token}"),
]
)
url = f"{EVENT_SERVICE_URL}/events/{id}"
async with ClientSession() as session:
async with session.delete(url, headers=headers) as response:
pass
logging.debug(f"Delete event: {id} - res {response.status}")
if response.status == 204:
logging.debug(f"result - got response {response}")
else:
logging.error(f"delete_event failed - {response.status}, {response}")
raise web.HTTPBadRequest(reason="Delete event failed.")
return str(response.status)
async def update_event(self, token: str, id: str, request_body: dict) -> str:
"""Update event function."""
headers = MultiDict(
[
(hdrs.CONTENT_TYPE, "application/json"),
(hdrs.AUTHORIZATION, f"Bearer {token}"),
]
)
async with ClientSession() as session:
async with session.put(
f"{EVENT_SERVICE_URL}/events/{id}", headers=headers, json=request_body
) as resp:
if resp.status == 204:
logging.debug(f"update event - got response {resp}")
else:
logging.error(f"update_event failed - {resp.status}")
raise web.HTTPBadRequest(reason="Update event failed.")
logging.debug(f"Updated event: {id} - res {resp.status}")
return str(resp.status)
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] | 2.018753 | 2,453 |
#!/usr/bin/python3
DOC="""feots_compare
feots_compare is use to compare two FEOTS NetCDF output files and report simple statistics.
Currently feots_compare will generate a histogram of log_{10}( |f_1 - f_2| ) where f_1 and
f_2 are tracer fields from two FEOTS output files.
Usage:
feots_compare absdiff <file1> <file2> [--field=<tracerfield>]
Commands:
absdiff Compute statistics using absolute differences between two FEOTS files
Options:
-h --help Display this help screen
--field=<string> Specification of the field in the NetCDF file to compare [default: DyeTracer_01]
"""
import netCDF4 as nc
import numpy as np
from matplotlib import pyplot as plt
from docopt import docopt
#END parse_cli
#END main
if __name__ == '__main__':
main()
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] | 2.830935 | 278 |
import importlib
import json
import os
from oauth2client import client, crypt
from opendc.util import exceptions, parameter_checker
from opendc.util.exceptions import ClientError
class Request:
"""WebSocket message to REST request mapping."""
def __init__(self, message=None):
""""Initialize a Request from a socket message."""
# Get the Request parameters from the message
if message is None:
return
try:
self.message = message
self.id = message['id']
self.path = message['path']
self.method = message['method']
self.params_body = message['parameters']['body']
self.params_path = message['parameters']['path']
self.params_query = message['parameters']['query']
self.token = message['token']
except KeyError as exception:
raise exceptions.MissingRequestParameterError(exception)
# Parse the path and import the appropriate module
try:
self.path = message['path'].strip('/')
module_base = 'opendc.api.{}.endpoint'
module_path = self.path.replace('{', '').replace('}', '').replace('/', '.')
self.module = importlib.import_module(module_base.format(module_path))
except ImportError as e:
print(e)
raise exceptions.UnimplementedEndpointError('Unimplemented endpoint: {}.'.format(self.path))
# Check the method
if self.method not in ['POST', 'GET', 'PUT', 'PATCH', 'DELETE']:
raise exceptions.UnsupportedMethodError('Non-rest method: {}'.format(self.method))
if not hasattr(self.module, self.method):
raise exceptions.UnsupportedMethodError('Unimplemented method at endpoint {}: {}'.format(
self.path, self.method))
# Verify the user
if "OPENDC_FLASK_TESTING" in os.environ:
self.google_id = 'test'
return
try:
self.google_id = self._verify_token(self.token)
except crypt.AppIdentityError as e:
raise exceptions.AuthorizationTokenError(e)
def check_required_parameters(self, **kwargs):
"""Raise an error if a parameter is missing or of the wrong type."""
try:
parameter_checker.check(self, **kwargs)
except exceptions.ParameterError as e:
raise ClientError(Response(400, str(e)))
def process(self):
"""Process the Request and return a Response."""
method = getattr(self.module, self.method)
try:
response = method(self)
except ClientError as e:
e.response.id = self.id
return e.response
response.id = self.id
return response
def to_JSON(self):
"""Return a JSON representation of this Request"""
self.message['id'] = 0
self.message['token'] = None
return json.dumps(self.message)
@staticmethod
def _verify_token(token):
"""Return the ID of the signed-in user.
Or throw an Exception if the token is invalid.
"""
try:
id_info = client.verify_id_token(token, os.environ['OPENDC_OAUTH_CLIENT_ID'])
except Exception as e:
print(e)
raise crypt.AppIdentityError('Exception caught trying to verify ID token: {}'.format(e))
if id_info['aud'] != os.environ['OPENDC_OAUTH_CLIENT_ID']:
raise crypt.AppIdentityError('Unrecognized client.')
if id_info['iss'] not in ['accounts.google.com', 'https://accounts.google.com']:
raise crypt.AppIdentityError('Wrong issuer.')
return id_info['sub']
class Response:
"""Response to websocket mapping"""
def __init__(self, status_code, status_description, content=None):
"""Initialize a new Response."""
self.id = 0
self.status = {'code': status_code, 'description': status_description}
self.content = content
def to_JSON(self):
""""Return a JSON representation of this Response"""
data = {'id': self.id, 'status': self.status}
if self.content is not None:
data['content'] = self.content
return json.dumps(data, default=str)
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] | 2.403135 | 1,786 |
# uncompyle6 version 3.7.4
# Python bytecode 3.7 (3394)
# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]
# Embedded file name: T:\InGame\Gameplay\Scripts\Server\statistics\ranked_statistic.py
# Compiled at: 2020-08-11 17:51:45
# Size of source mod 2**32: 58267 bytes
from protocolbuffers import SimObjectAttributes_pb2 as protocols, Commodities_pb2
import contextlib, operator
from bucks.bucks_enums import BucksType
from bucks.bucks_utils import BucksUtils
from distributor.shared_messages import IconInfoData
from event_testing.resolver import SingleSimResolver
from event_testing.test_events import TestEvent
from interactions.utils.loot import LootActions
from interactions.utils.tunable_icon import TunableIcon
from sims4.localization import TunableLocalizedString, TunableLocalizedStringFactory
from sims4.math import Threshold
from sims4.tuning.instances import HashedTunedInstanceMetaclass
from sims4.tuning.tunable import HasTunableReference, OptionalTunable, TunableList, Tunable, TunableMapping, TunableTuple, TunableEnumEntry, TunableResourceKey, TunableRange, TunableReference, TunableColor
from sims4.tuning.tunable_base import ExportModes, GroupNames
from sims4.utils import constproperty, classproperty, flexmethod
from singletons import DEFAULT
from statistics.commodity_messages import send_sim_ranked_stat_update_message, send_sim_ranked_stat_change_rank_change_update_message
from statistics.progressive_statistic_callback_mixin import ProgressiveStatisticCallbackMixin
from statistics.statistic_enums import StatisticLockAction
from ui.ui_dialog import UiDialogResponse
from ui.ui_dialog_notification import UiDialogNotification
import event_testing, services, sims4.log, sims4.resources, statistics, tag, telemetry_helper, ui.screen_slam
logger = sims4.log.Logger('RankedStatistic', default_owner='rfleig')
TELEMETRY_GROUP_RANKED_STAT = 'RKST'
TELEMETRY_HOOK_RANKED_STAT_LEVEL_CHANGE = 'LEVE'
TELEMETRY_FIELD_RANKED_STAT_TYPE = 'type'
TELEMETRY_FIELD_RANKED_STAT_LEVEL = 'leve'
ranked_stat_telemetry_writer = sims4.telemetry.TelemetryWriter(TELEMETRY_GROUP_RANKED_STAT)
L. 786 0 LOAD_CONST 0
2 STORE_FAST 'batch_rank_levels'
L. 787 4 SETUP_LOOP 200 'to 200'
6_0 COME_FROM 184 '184'
6 LOAD_FAST 'old_level'
8 LOAD_FAST 'new_level'
10 COMPARE_OP <
12 POP_JUMP_IF_FALSE 198 'to 198'
L. 788 14 LOAD_FAST 'old_level'
16 LOAD_CONST 1
18 INPLACE_ADD
20 STORE_FAST 'old_level'
L. 790 22 LOAD_FAST 'self'
24 LOAD_ATTR event_data
26 LOAD_METHOD get
28 LOAD_FAST 'old_level'
30 CALL_METHOD_1 1 '1 positional argument'
32 STORE_FAST 'event_data'
L. 791 34 LOAD_FAST 'event_data'
36 LOAD_CONST None
38 COMPARE_OP is-not
40 POP_JUMP_IF_FALSE 172 'to 172'
L. 792 42 LOAD_FAST 'self'
44 LOAD_ATTR tracker
46 LOAD_ATTR owner
48 LOAD_ATTR is_simulating
50 POP_JUMP_IF_FALSE 158 'to 158'
L. 793 52 LOAD_GLOBAL SingleSimResolver
54 LOAD_FAST 'self'
56 LOAD_ATTR tracker
58 LOAD_ATTR owner
60 CALL_FUNCTION_1 1 '1 positional argument'
62 STORE_FAST 'resolver'
L. 794 64 LOAD_FAST 'old_level'
66 LOAD_FAST 'self'
68 LOAD_ATTR highest_level
70 COMPARE_OP >
72 STORE_FAST 'is_new_level'
L. 795 74 LOAD_FAST 'is_new_level'
76 POP_JUMP_IF_FALSE 110 'to 110'
L. 797 78 SETUP_LOOP 104 'to 104'
80 LOAD_FAST 'event_data'
82 LOAD_ATTR loot
84 GET_ITER
86 FOR_ITER 102 'to 102'
88 STORE_FAST 'loot'
L. 798 90 LOAD_FAST 'loot'
92 LOAD_METHOD apply_to_resolver
94 LOAD_FAST 'resolver'
96 CALL_METHOD_1 1 '1 positional argument'
98 POP_TOP
100 JUMP_BACK 86 'to 86'
102 POP_BLOCK
104_0 COME_FROM_LOOP 78 '78'
L. 801 104 LOAD_FAST 'old_level'
106 LOAD_FAST 'self'
108 STORE_ATTR highest_level
110_0 COME_FROM 76 '76'
L. 802 110 LOAD_FAST 'event_data'
112 LOAD_ATTR rank_up
114 POP_JUMP_IF_FALSE 130 'to 130'
L. 803 116 LOAD_FAST 'self'
118 LOAD_ATTR increase_rank_level
120 LOAD_FAST 'is_new_level'
122 LOAD_FAST 'from_add'
124 LOAD_CONST ('new_rank', 'from_add')
126 CALL_FUNCTION_KW_2 2 '2 total positional and keyword args'
128 POP_TOP
130_0 COME_FROM 114 '114'
L. 804 130 SETUP_LOOP 172 'to 172'
132 LOAD_FAST 'event_data'
134 LOAD_ATTR loot_always
136 GET_ITER
138 FOR_ITER 154 'to 154'
140 STORE_FAST 'loot'
L. 805 142 LOAD_FAST 'loot'
144 LOAD_METHOD apply_to_resolver
146 LOAD_FAST 'resolver'
148 CALL_METHOD_1 1 '1 positional argument'
150 POP_TOP
152 JUMP_BACK 138 'to 138'
154 POP_BLOCK
156 JUMP_FORWARD 172 'to 172'
158_0 COME_FROM 50 '50'
L. 806 158 LOAD_FAST 'event_data'
160 LOAD_ATTR rank_up
162 POP_JUMP_IF_FALSE 172 'to 172'
L. 807 164 LOAD_FAST 'batch_rank_levels'
166 LOAD_CONST 1
168 INPLACE_ADD
170 STORE_FAST 'batch_rank_levels'
172_0 COME_FROM 162 '162'
172_1 COME_FROM 156 '156'
172_2 COME_FROM_LOOP 130 '130'
172_3 COME_FROM 40 '40'
L. 813 172 LOAD_FAST 'self'
174 LOAD_ATTR tracker
176 LOAD_ATTR owner
178 LOAD_ATTR is_npc
180 POP_JUMP_IF_FALSE 186 'to 186'
182 LOAD_FAST 'from_add'
184 POP_JUMP_IF_TRUE 6 'to 6'
186_0 COME_FROM 180 '180'
L. 816 186 LOAD_FAST 'self'
188 LOAD_METHOD _handle_level_change_telemetry
190 LOAD_FAST 'old_level'
192 CALL_METHOD_1 1 '1 positional argument'
194 POP_TOP
196 JUMP_BACK 6 'to 6'
198_0 COME_FROM 12 '12'
198 POP_BLOCK
200_0 COME_FROM_LOOP 4 '4'
L. 818 200 LOAD_FAST 'batch_rank_levels'
202 LOAD_CONST 0
204 COMPARE_OP >
206 POP_JUMP_IF_FALSE 220 'to 220'
L. 819 208 LOAD_FAST 'self'
210 LOAD_METHOD increase_rank_levels
212 LOAD_FAST 'batch_rank_levels'
214 CALL_METHOD_1 1 '1 positional argument'
216 POP_TOP
218 JUMP_FORWARD 232 'to 232'
220_0 COME_FROM 206 '206'
L. 823 220 LOAD_FAST 'self'
222 LOAD_ATTR create_and_send_commodity_update_msg
224 LOAD_CONST False
226 LOAD_CONST ('is_rate_change',)
228 CALL_FUNCTION_KW_1 1 '1 total positional and keyword args'
230 POP_TOP
232_0 COME_FROM 218 '218'
Parse error at or near `COME_FROM_LOOP' instruction at offset 172_2
@contextlib.contextmanager
@sims4.utils.classproperty
@sims4.utils.classproperty
@sims4.utils.classproperty
@sims4.utils.classproperty
@flexmethod
@constproperty
@classmethod
@classmethod
@classmethod
@flexmethod
@classproperty
@classmethod
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] | 1.68128 | 5,657 |
# Import utils submodule
import api.api
# Decide to start seeing other people
api.api.we_need_to_talk(break_up=False)
import api
# Create instance of MyClass
my_instance = api.AppClass(value='class attribute value')
# Print out class attribute value
print(my_instance.attribute) | [
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] | 3.373494 | 83 |
import numba
import autogalaxy as ag
from autolens.point.point_dataset import PointDataset
from autolens.point.point_solver import PointSolver
from autolens.point.fit_point.fluxes import FitFluxes
from autolens.point.fit_point.positions_image import FitPositionsImage
from autolens.point.fit_point.positions_source import FitPositionsSource
from autolens.lens.ray_tracing import Tracer
from autolens import exc
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"""
Plot a quartz class map for a drill core HSI cube.
"""
from __future__ import print_function
import os
import os.path as osp
import matplotlib.pyplot as plt
import numpy as np
import pysptools.util as util
import pysptools.eea as eea
import pysptools.abundance_maps as amp
if __name__ == '__main__':
# Load the cube
data_path = os.environ['PYSPTOOLS_DATA']
home = os.environ['HOME']
result_path = os.path.join(home, 'results')
sample = 'hematite.hdr'
data_file = osp.join(data_path, sample)
data, header = util.load_ENVI_file(data_file)
if osp.exists(result_path) == False:
os.makedirs(result_path)
axes = parse_ENVI_header(header)
# Telops cubes are flipped left-right
# Flipping them again restore the orientation
data = np.fliplr(data)
U = get_endmembers(data, axes, 4, result_path)
amaps = gen_abundance_maps(data, U, result_path)
# EM4 == quartz
quartz = amaps[:,:,3]
plot(quartz, 'spectral', 'quartz', result_path)
# EM1 == background, we use the backgroud to isolate the drill core
# and define the mask
mask = (amaps[:,:,0] < 0.2)
plot(mask, 'spectral', 'mask', result_path)
# Plot the quartz in color and the hematite in gray
plot(np.logical_and(mask == 1, quartz <= 0.001) + quartz, 'spectral', 'hematite+quartz', result_path)
# pixels stat
rock_surface = np.sum(mask)
quartz_surface = np.sum(quartz > 0.16)
print('Some statistics')
print(' Drill core surface (mask) in pixels:', rock_surface)
print(' Quartz surface in pixels:', quartz_surface)
print(' Hematite surface in pixels:', rock_surface - quartz_surface)
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] | 2.440559 | 715 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import math
if __name__ == '__main__':
print(func(5, 3)())
print(func(8, 10, 1)())
print(func(3, 5, 0)())
print(func(2, 2, 1)())
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'''from django.contrib.auth import get_user_model
from django.test import TestCase #an extension of Python’s TestCase
from django.urls import reverse, resolve
from django.test import Client
from .models import PremiumBlog
from .views import (
BlogListView,
BlogDetailView,
)
class CustomUserTests(TestCase):
def test_create_user(self):
User = get_user_model()
user = User.objects.create_user(
username='partho',
email='[email protected]',
first_name='Partho',
last_name='Bhattacharjee',
country='Bangladesh',
city_or_district='Sylhet'
)
user.set_password('testpass123')
user.save()
self.assertEqual(user.email, '[email protected]')
self.assertEqual(user.country, 'Bangladesh')
self.assertEqual(user.city_or_district, 'Sylhet')
self.assertTrue(user.is_active)
self.assertFalse(user.is_staff)
self.assertFalse(user.is_superuser)
class BlogTests(TestCase):
def setUp(self):
c = Client()
c.login(email='[email protected]', password='testpass123')
url = reverse('blog_list')
self.response = self.client.get(url)
def test_job_post(self):
post = PremiumBlog.objects.create(
Author='Barun',
Title='What is Django?',
Description='Python Framework',
)
self.assertEqual(post.Author, 'Barun')
self.assertEqual(post.Title, 'What is Django?')
self.assertEqual(post.Description, 'Python Framework')
def test_job_list_template(self):
self.assertEqual(self.response.status_code, 200)
self.assertTemplateUsed(self.response, 'premium/blog_list.html')
self.assertContains(self.response, 'Search your blog here')
self.assertNotContains(
self.response, 'Hi there! I should not be on the page.')
def job_detail_view(self):
post = PremiumBlog.objects.create(
Author='Barun',
Title='What is Django?',
Description='Python Framework',
)
response = self.client.get(post.get_absolute_url())
no_response = self.client.get('/jobs/12345/')
self.assertEqual(response.status_code, 200)
self.assertEqual(no_response.status_code, 404)
self.assertContains(response, 'What is Django?')
self.assertTemplateUsed(response, 'premium/blog_detail.html')''' | [
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] | 2.295644 | 1,079 |
# Copyright 2018 DeepMind Technologies Limited. 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.
"""Tests for action_metrics_observers."""
from acme import specs
from acme.testing import fakes
from acme.utils.observers import action_metrics
import dm_env
import numpy as np
from absl.testing import absltest
_FAKE_ENV = _make_fake_env()
_TIMESTEP = _FAKE_ENV.reset()
if __name__ == '__main__':
absltest.main()
| [
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# -*- coding: utf-8 -*-
r""" A policy that acts as a wrapper on another policy `P`, assumed to be *horizon dependent* (has to known :math:`T`), by implementing a "doubling trick":
- starts to assume that :math:`T=T_0=1000`, and run the policy :math:`P(T_0)`, from :math:`t=1` to :math:`t=T_0`,
- if :math:`t > T_0`, then the "doubling trick" is performed, by either re-initializing or just changing the parameter `horizon` of the policy P, for instance with :math:`T_2 = 10 \times T_0`,
- and keep doing this until :math:`t = T`.
.. note::
This is implemented in a very generic way, with simply a function `next_horizon(horizon)` that gives the next horizon to try when crossing the current guess.
It can be a simple linear function (`next_horizon(horizon) = horizon + 100`), a geometric growth to have the "real" doubling trick (`next_horizon(horizon) = horizon * 10`), or even functions growing exponentially fast (`next_horizon(horizon) = horizon ** 1.1`, `next_horizon(horizon) = horizon ** 1.5`, `next_horizon(horizon) = horizon ** 2`).
.. note::
My guess is that this "doubling trick" wrapping policy can only be efficient (for stochastic problems) if:
- the underlying policy `P` is a very efficient horizon-dependent algorithm, e.g., the :class:`Policies.ApproximatedFHGittins`,
- the growth function `next_horizon` is growing faster than any geometric rate, so that the number of refresh is :math:`o(\log T)` and not :math:`O(\log T)`.
.. seealso::
Reference: [[What the Doubling Trick Can or Can't Do for Multi-Armed Bandits, Lilian Besson and Emilie Kaufmann, 2018]](https://hal.inria.fr/hal-01736357), to be presented soon.
.. warning::
Interface: If `FULL_RESTART=False` (default), the underlying algorithm is recreated at every breakpoint,
instead its attribute `horizon` or `_horizon` is updated. Be sure that this is enough to really
change the internal value used by the policy. Some policy use T only once to compute others parameters,
which should be updated as well. A manual implementation of the `__setattr__` method can help.
"""
from __future__ import division, print_function # Python 2 compatibility
__author__ = "Lilian Besson"
__version__ = "0.9"
import numpy as np
try:
from .BaseWrapperPolicy import BaseWrapperPolicy
from .UCBH import UCBH
except ImportError:
from BaseWrapperPolicy import BaseWrapperPolicy
from UCBH import UCBH
try:
from .usenumba import jit # Import numba.jit or a dummy jit(f)=f
except (ValueError, ImportError, SystemError):
from usenumba import jit # Import numba.jit or a dummy jit(f)=f
#: Default horizon-dependent policy
default_horizonDependent_policy = UCBH
#: Default constant to know what to do when restarting the underlying policy with a new horizon parameter.
#:
#: - `True` means that a new policy, initialized from scratch, will be created at every breakpoint.
#: - `False` means that the same policy object is used but just its attribute `horizon` is updated (default).
FULL_RESTART = True
FULL_RESTART = False
#: Default horizon, used for the first step.
DEFAULT_FIRST_HORIZON = 200
#: Default stepsize for the arithmetic horizon progression.
ARITHMETIC_STEP = 10 * DEFAULT_FIRST_HORIZON
ARITHMETIC_STEP = 1 * DEFAULT_FIRST_HORIZON
@jit
def next_horizon__arithmetic(i, horizon):
r""" The arithmetic horizon progression function:
.. math::
T &\mapsto T + 100,\\
T_i &:= T_0 + 100 \times i.
"""
return horizon + ARITHMETIC_STEP
next_horizon__arithmetic.__latex_name__ = "arithm"
next_horizon__arithmetic.__latex_name__ = r"$T_i = {} + {} \times i$".format(DEFAULT_FIRST_HORIZON, ARITHMETIC_STEP)
#: Default multiplicative constant for the geometric horizon progression.
GEOMETRIC_STEP = 2
@jit
def next_horizon__geometric(i, horizon):
r""" The geometric horizon progression function:
.. math::
T &\mapsto T \times 2,\\
T_i &:= T_0 2^i.
"""
return horizon * GEOMETRIC_STEP
next_horizon__geometric.__latex_name__ = "geom"
next_horizon__geometric.__latex_name__ = r"$T_i = {} \times {}^i$".format(DEFAULT_FIRST_HORIZON, GEOMETRIC_STEP)
#: Default exponential constant for the exponential horizon progression.
EXPONENTIAL_STEP = 1.5
@jit
def next_horizon__exponential(i, horizon):
r""" The exponential horizon progression function:
.. math::
T &\mapsto \left\lfloor T^{1.5} \right\rfloor,\\
T_i &:= \left\lfloor T_0^{1.5^i} \right\rfloor.
"""
return int(np.floor(horizon ** EXPONENTIAL_STEP))
next_horizon__exponential.__latex_name__ = "exp"
next_horizon__exponential.__latex_name__ = r"$T_i = {}^{}$".format(DEFAULT_FIRST_HORIZON, r"{%.3g^i}" % EXPONENTIAL_STEP)
#: Default exponential constant for the slow exponential horizon progression.
SLOW_EXPONENTIAL_STEP = 1.1
@jit
def next_horizon__exponential_slow(i, horizon):
r""" The exponential horizon progression function:
.. math::
T &\mapsto \left\lfloor T^{1.1} \right\rfloor,\\
T_i &:= \left\lfloor T_0^{1.1^i} \right\rfloor.
"""
return int(np.floor(horizon ** SLOW_EXPONENTIAL_STEP))
next_horizon__exponential_slow.__latex_name__ = "slow exp"
next_horizon__exponential_slow.__latex_name__ = r"$T_i = {}^{}$".format(DEFAULT_FIRST_HORIZON, r"{%.3g^i}" % SLOW_EXPONENTIAL_STEP)
#: Default exponential constant for the fast exponential horizon progression.
FAST_EXPONENTIAL_STEP = 2
@jit
def next_horizon__exponential_fast(i, horizon):
r""" The exponential horizon progression function:
.. math::
T &\mapsto \lfloor T^{2} \rfloor,\\
T_i &:= \lfloor T_0^{2^i} \rfloor.
"""
return int(np.floor(horizon ** 2))
next_horizon__exponential_fast.__latex_name__ = "fast exp"
next_horizon__exponential_fast.__latex_name__ = r"$T_i = {}^{}$".format(DEFAULT_FIRST_HORIZON, r"{%.3g^i}" % FAST_EXPONENTIAL_STEP)
#: Default constant :math:`\alpha` for the generic exponential sequence.
ALPHA = 2
#: Default constant :math:`\beta` for the generic exponential sequence.
BETA = 2
def next_horizon__exponential_generic(i, horizon):
r""" The generic exponential horizon progression function:
.. math:: T_i := \left\lfloor \frac{T_0}{a} a^{b^i} \right\rfloor.
"""
return int((DEFAULT_FIRST_HORIZON / ALPHA) * ALPHA ** (BETA ** i))
# return int(ALPHA * np.floor(horizon ** BETA))
next_horizon__exponential_generic.__latex_name__ = r"exp $a={:.3g}$, $b={:.3g}$".format(ALPHA, BETA)
next_horizon__exponential_generic.__latex_name__ = r"$T_i = ({}/{}) {}^{}$".format(DEFAULT_FIRST_HORIZON, ALPHA, ALPHA, r"{%.3g^i}" % BETA)
#: Chose the default horizon growth function.
# default_next_horizon = next_horizon__arithmetic
# default_next_horizon = next_horizon__geometric
# default_next_horizon = next_horizon__geometric
# default_next_horizon = next_horizon__exponential_fast
default_next_horizon = next_horizon__exponential_slow
# --- Utility function
def breakpoints(next_horizon, first_horizon, horizon, debug=False):
r""" Return the list of restart point (breakpoints), if starting from ``first_horizon`` to ``horizon`` with growth function ``next_horizon``.
- Also return the gap between the last guess for horizon and the true horizon. This gap should not be too large.
- Nicely print all the values if ``debug=True``.
- First examples:
>>> first_horizon = 1000
>>> horizon = 30000
>>> breakpoints(next_horizon__arithmetic, first_horizon, horizon) # doctest: +ELLIPSIS
([1000, 1200, 1400, ..., 29800, 30000], 0)
>>> breakpoints(next_horizon__geometric, first_horizon, horizon)
([1000, 2000, 4000, 8000, 16000, 32000], 2000)
>>> breakpoints(next_horizon__exponential, first_horizon, horizon)
([1000, 31622], 1622)
>>> breakpoints(next_horizon__exponential_slow, first_horizon, horizon)
([1000, 1995, 4265, 9838, 24671, 67827], 37827)
>>> breakpoints(next_horizon__exponential_fast, first_horizon, horizon)
([1000, 1000000], 970000)
- Second examples:
>>> first_horizon = 5000
>>> horizon = 1000000
>>> breakpoints(next_horizon__arithmetic, first_horizon, horizon) # doctest: +ELLIPSIS
([5000, 5200, ..., 999600, 999800, 1000000], 0)
>>> breakpoints(next_horizon__geometric, first_horizon, horizon)
([5000, 10000, 20000, 40000, 80000, 160000, 320000, 640000, 1280000], 280000)
>>> breakpoints(next_horizon__exponential, first_horizon, horizon)
([5000, 353553, 210223755], 209223755)
>>> breakpoints(next_horizon__exponential_slow, first_horizon, horizon)
([5000, 11718, 29904, 83811, 260394, 906137, 3572014], 2572014)
>>> breakpoints(next_horizon__exponential_fast, first_horizon, horizon)
([5000, 25000000], 24000000)
- Third examples:
>>> first_horizon = 10
>>> horizon = 1123456
>>> breakpoints(next_horizon__arithmetic, first_horizon, horizon) # doctest: +ELLIPSIS
([10, 210, 410, ..., 1123210, 1123410, 1123610], 154)
>>> breakpoints(next_horizon__geometric, first_horizon, horizon)
([10, 20, 40, 80, 160, 320, 640, 1280, 2560, 5120, 10240, 20480, 40960, 81920, 163840, 327680, 655360, 1310720], 187264)
>>> breakpoints(next_horizon__exponential, first_horizon, horizon)
([10, 31, 172, 2255, 107082, 35040856], 33917400)
>>> breakpoints(next_horizon__exponential_slow, first_horizon, horizon)
([10, 12, 15, 19, 25, 34, 48, 70, 107, 170, 284, 499, 928, 1837, 3895, 8903, 22104, 60106, 180638, 606024, 2294768], 1171312)
>>> breakpoints(next_horizon__exponential_fast, first_horizon, horizon)
([10, 100, 10000, 100000000], 98876544)
"""
i = 0
t = max(first_horizon, 2)
times = [t]
if debug: print("\n\nFor the growth function {}, named '{}', first guess of the horizon = {} and true horizon = {} ...\n ==> The times will be:".format(next_horizon, getattr(next_horizon, '__latex_name__', '?'), first_horizon, horizon))
while t < horizon:
t = next_horizon(i, t)
i += 1
times.append(t)
if debug: print(" The {}th breakpoint is {} ...".format(i, t)) # DEBUG
assert horizon <= t, "Error: the last guess for horizon = {} was found smaller than the true horizon = {}...".format(t, horizon) # DEBUG
gap = t - horizon
if debug: print("This last guess for horizon = {} gives a gap = {} against the true horizon {}. Relative difference = {:.3%}...".format(t, gap, horizon, gap / float(horizon))) # DEBUG
return times, gap
# --- Experimental code to plot some doubling sequences and
# check numerically some inequalities :
# like controlling a sum Sigma_i=0^n u_i by a constant times to last term u_n
# and controlling the last term u_{L_T} as a function of T.
#: The constant c in front of the function f.
constant_c_for_the_functions_f = 1.0
constant_c_for_the_functions_f = 0.1
constant_c_for_the_functions_f = 0.5
def function_f__for_geometric_sequences(i, c=constant_c_for_the_functions_f):
r""" For the *geometric* doubling sequences, :math:`f(i) = c \times \log(i)`."""
if i <= 0: return 0.0
return c * np.log(i)
def function_f__for_exponential_sequences(i, c=constant_c_for_the_functions_f):
r""" For the *exponential* doubling sequences, :math:`f(i) = c \times i`."""
return c * i
def function_f__for_generic_sequences(i, c=constant_c_for_the_functions_f, d=0.5, e=0.0):
r""" For a certain *generic* family of doubling sequences, :math:`f(i) = c \times i^{d} \times (\log(i))^{e}`.
- ``d, e = 0, 1`` gives :func:`function_f__for_geometric_sequences`,
- ``d, e = 1, 0`` gives :func:`function_f__for_geometric_sequences`,
- ``d, e = 0.5, 0`` gives an intermediate sequence, growing faster than any geometric sequence and slower than any exponential sequence,
- any other combination has not been studied yet.
.. warning:: ``d`` should most probably be smaller than 1.
"""
i = float(i)
if i <= 0: return 0.0
if e == 0:
assert d > 0, "Error: invalid value of d = {} for function_f__for_generic_sequences.".format(d) # DEBUG
return c * (i ** d)
if d == 0:
assert e > 0, "Error: invalid value of e = {} for function_f__for_generic_sequences.".format(e) # DEBUG
return c * ((np.log(i)) ** e)
return c * (i ** d) * ((np.log(i)) ** e)
#: Value of the parameter :math:`\alpha` for the :func:`Ti_from_f` function.
alpha_for_Ti = 0.1
alpha_for_Ti = 1.0
alpha_for_Ti = 0.5
def Ti_from_f(f, alpha=alpha_for_Ti, *args, **kwargs):
r""" For any non-negative and increasing function :math:`f: i \mapsto f(i)`, the corresponding sequence is defined by:
.. math:: \forall i\in\mathbb{N},\; T_i := \lfloor \exp(\alpha \times \exp(f(i))) \rfloor.
.. warning:: :math:`f(i)` can need other parameters, see the examples above. They can be given as ``*args`` or ``**kwargs`` to :func:`Ti_from_f`.
.. warning:: it should be computed otherwise, I should give :math:`i \mapsto \exp(f(i))` instead of :math:`f: i \mapsto f(i)`. I need to try as much as possible to reduce the risk of overflow errors!
"""
# WARNING don't forget the floor!
return Ti
def Ti_geometric(i, horizon, alpha=alpha_for_Ti, first_horizon=DEFAULT_FIRST_HORIZON, *args, **kwargs):
""" Sequence :math:`T_i` generated from the function :math:`f` = :func:`function_f__for_geometric_sequences`."""
f = function_f__for_geometric_sequences
this_Ti = first_horizon + np.floor(np.exp(alpha * np.exp(f(float(i), *args, **kwargs))))
if not (np.isinf(this_Ti) or np.isnan(this_Ti)): this_Ti = int(this_Ti)
return this_Ti
Ti_geometric.__latex_name__ = r"$f(i)=\log(i)$"
def Ti_exponential(i, horizon, alpha=alpha_for_Ti, first_horizon=DEFAULT_FIRST_HORIZON, *args, **kwargs):
""" Sequence :math:`T_i` generated from the function :math:`f` = :func:`function_f__for_exponential_sequences`."""
f = function_f__for_exponential_sequences
this_Ti = first_horizon + np.floor(np.exp(alpha * np.exp(f(float(i), *args, **kwargs))))
if not (np.isinf(this_Ti) or np.isnan(this_Ti)): this_Ti = int(this_Ti)
return this_Ti
Ti_exponential.__latex_name__ = r"$f(i)=i$"
def Ti_intermediate_sqrti(i, horizon, alpha=alpha_for_Ti, first_horizon=DEFAULT_FIRST_HORIZON, *args, **kwargs):
""" Sequence :math:`T_i` generated from the function :math:`f` = :func:`function_f__for_intermediate_sequences`."""
f = function_f__for_intermediate_sequences
this_Ti = first_horizon + np.floor(np.exp(alpha * np.exp(f(float(i), *args, **kwargs))))
if not (np.isinf(this_Ti) or np.isnan(this_Ti)): this_Ti = int(this_Ti)
return this_Ti
Ti_intermediate_sqrti.__latex_name__ = r"$f(i)=\sqrt{i}$"
def Ti_intermediate_i13(i, horizon, alpha=alpha_for_Ti, first_horizon=DEFAULT_FIRST_HORIZON, *args, **kwargs):
""" Sequence :math:`T_i` generated from the function :math:`f` = :func:`function_f__for_intermediate2_sequences`."""
f = function_f__for_intermediate2_sequences
this_Ti = first_horizon + np.floor(np.exp(alpha * np.exp(f(float(i), *args, **kwargs))))
if not (np.isinf(this_Ti) or np.isnan(this_Ti)): this_Ti = int(this_Ti)
return this_Ti
Ti_intermediate_i13.__latex_name__ = r"$f(i)=i^{1/3}$"
def Ti_intermediate_i23(i, horizon, alpha=alpha_for_Ti, first_horizon=DEFAULT_FIRST_HORIZON, *args, **kwargs):
""" Sequence :math:`T_i` generated from the function :math:`f` = :func:`function_f__for_intermediate3_sequences`."""
f = function_f__for_intermediate3_sequences
this_Ti = first_horizon + np.floor(np.exp(alpha * np.exp(f(float(i), *args, **kwargs))))
if not (np.isinf(this_Ti) or np.isnan(this_Ti)): this_Ti = int(this_Ti)
return this_Ti
Ti_intermediate_i23.__latex_name__ = r"$f(i)=i^{2/3}$"
def Ti_intermediate_i12_logi12(i, horizon, alpha=alpha_for_Ti, first_horizon=DEFAULT_FIRST_HORIZON, *args, **kwargs):
""" Sequence :math:`T_i` generated from the function :math:`f` = :func:`function_f__for_intermediate4_sequences`."""
f = function_f__for_intermediate4_sequences
this_Ti = first_horizon + np.floor(np.exp(alpha * np.exp(f(float(i), *args, **kwargs))))
if not (np.isinf(this_Ti) or np.isnan(this_Ti)): this_Ti = int(this_Ti)
return this_Ti
Ti_intermediate_i12_logi12.__latex_name__ = r"$f(i)=\sqrt{i \log(i)}$"
def Ti_intermediate_i_by_logi(i, horizon, alpha=alpha_for_Ti, first_horizon=DEFAULT_FIRST_HORIZON, *args, **kwargs):
""" Sequence :math:`T_i` generated from the function :math:`f` = :func:`function_f__for_intermediate5_sequences`."""
f = function_f__for_intermediate5_sequences
this_Ti = first_horizon + np.floor(np.exp(alpha * np.exp(f(float(i + 1), *args, **kwargs))))
if not (np.isinf(this_Ti) or np.isnan(this_Ti)): this_Ti = int(this_Ti)
return this_Ti
Ti_intermediate_i_by_logi.__latex_name__ = r"$f(i)=i / \log(i)$"
def last_term_operator_LT(Ti, max_i=10000):
r""" For a certain function representing a doubling sequence, :math:`T: i \mapsto T_i`, this :func:`last_term_operator_LT` function returns the function :math:`L: T \mapsto L_T`, defined as:
.. math:: \forall T\in\mathbb{N},\; L_T := \min\{ i \in\mathbb{N},\; T \leq T_i \}.
:math:`L_T` is the only integer which satisfies :math:`T_{L_T - 1} < T \leq T_{L_T}`.
"""
return LT
import matplotlib.pyplot as plt
import seaborn as sns
def plot_doubling_sequences(
i_min=1, i_max=30,
list_of_f=(
function_f__for_geometric_sequences,
function_f__for_intermediate_sequences,
function_f__for_intermediate2_sequences,
function_f__for_intermediate3_sequences,
function_f__for_intermediate4_sequences,
function_f__for_exponential_sequences,
),
label_of_f=(
"Geometric doubling (d=0, e=1)",
"Intermediate doubling (d=1/2, e=0)",
"Intermediate doubling (d=1/3, e=0)",
"Intermediate doubling (d=2/3, e=0)",
"Intermediate doubling (d=1/2, e=1/2)",
"Exponential doubling (d=1, e=0)",
),
*args, **kwargs
):
r""" Display a plot to illustrate the values of the :math:`T_i` as a function of :math:`i` for some i.
- Can accept many functions f (and labels).
"""
# Make unique markers
nb = len(list_of_f)
allmarkers = ['o', 'D', 'v', 'p', '<', 's', '^', '*', 'h', '>']
longlist = allmarkers * (1 + int(nb / float(len(allmarkers)))) # Cycle the good number of time
markers = longlist[:nb] # Truncate
# Make unique colors
colors = sns.hls_palette(nb + 1)[:nb]
fig = plt.figure()
# plt.hold(True)
i_s = np.arange(i_min, i_max)
# now for each function f
for num_f, (f, la) in enumerate(zip(list_of_f, label_of_f)):
print("\n\nThe {}th function is referred to as {} and is {}".format(num_f, la, f)) # DEBUG
Ti = Ti_from_f(f)
values_of_Ti = np.array([ Ti(i) for i in i_s ])
plt.plot(i_s, values_of_Ti, label=la, lw=3, ms=3, color=colors[num_f], marker=markers[num_f])
plt.legend()
plt.xlabel(r"Value of the time horizon $i = {},...,{}$".format(i_min, i_max))
plt.title(r"Comparison of the values of $T_i$")
plt.show()
return fig
def plot_quality_first_upper_bound(
Tmin=10, Tmax=int(1e8), nbTs=100,
gamma=0.0, delta=1.0, # XXX bound in RT <= log(T)
# gamma=0.5, delta=0.0, # XXX bound in RT <= sqrt(T)
# gamma=0.5, delta=0.5, # XXX bound in RT <= sqrt(T * log(T))
# gamma=0.66667, delta=1.0, # XXX another weird bound in RT <= T^2/3 * log(T)
list_of_f=(
function_f__for_geometric_sequences,
function_f__for_intermediate_sequences,
function_f__for_intermediate2_sequences,
function_f__for_intermediate3_sequences,
function_f__for_intermediate4_sequences,
function_f__for_exponential_sequences,
),
label_of_f=(
"Geometric doubling (d=0, e=1)",
"Intermediate doubling (d=1/2, e=0)",
"Intermediate doubling (d=1/3, e=0)",
"Intermediate doubling (d=2/3, e=0)",
"Intermediate doubling (d=1/2, e=1/2)",
"Exponential doubling (d=1, e=0)",
),
show_Ti_m_Tim1=True,
# show_Ti_m_Tim1=False, # DEBUG
*args, **kwargs
):
r""" Display a plot to compare numerically between the following sum :math:`S` and the upper-bound we hope to have, :math:`T^{\gamma} (\log T)^{\delta}`, as a function of :math:`T` for some values between :math:`T_{\min}` and :math:`T_{\max}`:
.. math:: S := \sum_{i=0}^{L_T} (T_i - T_{i-1})^{\gamma} (\log (T_i - T_{i-1}))^{\delta}.
- Can accept many functions f (and labels).
- Can use :math:`T_i` instead of :math:`T_i - T_{i-1}` if ``show_Ti_m_Tim1=False`` (default is to use the smaller possible bound, with difference of sequence lengths, :math:`T_i - T_{i-1}`).
.. warning:: This is still ON GOING WORK.
"""
# Make unique markers
nb = len(list_of_f)
allmarkers = ['o', 'D', 'v', 'p', '<', 's', '^', '*', 'h', '>']
longlist = allmarkers * (1 + int(nb / float(len(allmarkers)))) # Cycle the good number of time
markers = longlist[:nb] # Truncate
# Make unique colors
colors = sns.hls_palette(nb + 1)[:nb]
fig = plt.figure()
# plt.hold(True)
Ts = np.floor(np.linspace(Tmin, Tmax, num=nbTs))
the_bound_we_want = (Ts ** gamma) * (np.log(Ts) ** delta)
# plt.plot(Ts, the_bound_we_want, label=r"$T^{\gamma} (\log T)^{\delta}$", lw=3, ms=3, color=colors[0], marker=markers[0])
# compute the sequence lengths to use, either T_i or T_i - T_{i-1}
Ts_for_f = np.copy(Ts)
if show_Ti_m_Tim1: Ts_for_f[1:] = np.diff(Ts)
# now for each function f
for num_f, (f, la) in enumerate(zip(list_of_f, label_of_f)):
print("\n\nThe {}th function is referred to as {} and is {}".format(num_f, la, f)) # DEBUG
Ti = Ti_from_f(f)
LT = last_term_operator_LT(Ti)
the_sum_we_have = np.zeros_like(Ts_for_f)
for j, (Tj, dTj) in enumerate(zip(Ts, Ts_for_f)):
LTj = LT(Tj)
the_sum_we_have[j] = sum(
(dTj ** gamma) * (np.log(dTj) ** delta)
for i in range(0, LTj + 1)
)
print("For j = {}, Tj = {}, dTj = {}, gives LTj = {}, and the value of the sum from i=0 to LTj is = {}.".format(j, Tj, dTj, LTj, the_sum_we_have[j])) # DEBUG
print("the_sum_we_have =", the_sum_we_have) # DEBUG
plt.plot(Ts, the_sum_we_have / the_bound_we_want, label=la, lw=3, ms=3, color=colors[num_f], marker=markers[num_f])
plt.legend()
plt.xlabel(r"Value of the time horizon $T = {},...,{}$".format(Tmin, Tmax))
str_of_Tj_or_dTj = "T_i - T_{i-1}" if show_Ti_m_Tim1 else "T_i"
plt.title(r"Ratio of the sum $\sum_{i=0}^{L_T} (%s)^{\gamma} (\log(%s))^{\delta}$ and the upper-bound $T^{\gamma} \log(T)^{\delta}$, for $\gamma=%.3g$, $\delta=%.3g$." % (str_of_Tj_or_dTj, str_of_Tj_or_dTj, gamma, delta)) # DEBUG
plt.show()
return fig
# --- The interesting class
#: If the sequence Ti does not grow enough, artificially increase i until T_inext > T_i
MAX_NB_OF_TRIALS = 500
class DoublingTrickWrapper(BaseWrapperPolicy):
r""" A policy that acts as a wrapper on another policy `P`, assumed to be *horizon dependent* (has to known :math:`T`), by implementing a "doubling trick".
- Reference: [[What the Doubling Trick Can or Can't Do for Multi-Armed Bandits, Lilian Besson and Emilie Kaufmann, 2018]](https://hal.inria.fr/hal-01736357), to be presented soon.
"""
# --- pretty printing
# --- Start game by creating new underlying policy
def startGame(self):
""" Initialize the policy for a new game."""
super(BaseWrapperPolicy, self).startGame()
# super(DoublingTrickWrapper, self).startGame() # WARNING no
self._i = 0 # reinitialize this
self.horizon = self._first_horizon #: Last guess for the horizon
try:
self.policy = self._policy(self.nbArms, horizon=self.horizon, lower=self.lower, amplitude=self.amplitude, *self._args, **self._kwargs)
except Exception as e:
print("WARNING: Received exception {} when trying to create the underlying policy... maybe the 'horizon={}' keyword argument was not understood correctly? Retrying without it...".format(e, self.horizon)) # DEBUG
self.policy = self._policy(self.nbArms, lower=self.lower, amplitude=self.amplitude, *self._args, **self._kwargs)
# now also start game for the underlying policy
self.policy.startGame()
# --- Pass the call to the subpolicy
def getReward(self, arm, reward):
""" Pass the reward, as usual, update t and sometimes restart the underlying policy."""
# print(" - At time t = {}, got a reward = {} from arm {} ...".format(self.t, arm, reward)) # DEBUG
# super(DoublingTrickWrapper, self).getReward(arm, reward)
self.t += 1
self.policy.getReward(arm, reward)
# Maybe we have to update the horizon?
if self.t > self.horizon:
self._i += 1
new_horizon = self._next_horizon(self._i, self.horizon)
# XXX <!-- small hack if the sequence is not growing fast enough
nb_of_trials = 1
while nb_of_trials < MAX_NB_OF_TRIALS and new_horizon <= self.horizon:
self._i += 1
nb_of_trials += 1
new_horizon = self._next_horizon(self._i, self.horizon)
# XXX end of small hack -->
assert new_horizon > self.horizon, "Error: the new_horizon = {} is not > the current horizon = {} ...".format(new_horizon, self.horizon) # DEBUG
# print(" - At time t = {}, a DoublingTrickWrapper class was running with current horizon T_i = {} and decided to use {} as a new horizon...".format(self.t, self.horizon, new_horizon)) # DEBUG
self.horizon = new_horizon
# now we have to update or restart the underlying policy
if self.full_restart:
try:
self.policy = self._policy(self.nbArms, horizon=self.horizon, lower=self.lower, amplitude=self.amplitude, *self._args, **self._kwargs)
except Exception as e:
# print("Received exception {} when trying to create the underlying policy... maybe the 'horizon={}' keyword argument was not understood correctly? Retrying without it...".format(e, self.horizon)) # DEBUG
self.policy = self._policy(self.nbArms, lower=self.lower, amplitude=self.amplitude, *self._args, **self._kwargs)
# now also start game for the underlying policy
self.policy.startGame()
# print(" ==> Fully restarting the underlying policy by creating a new object... Now it is = {} ...".format(self.policy)) # DEBUG
else:
if hasattr(self.policy, 'horizon'):
try:
self.policy.horizon = self.horizon
except AttributeError:
pass
# print("Warning: unable to update the parameter 'horizon' of the underlying policy {}... Trying '_horizon' ...".format(self.policy)) # DEBUG
# print(" ==> Just updating the horizon parameter of the underlying policy... Now it is = {} ...".format(self.policy)) # DEBUG
# else:
# print(" ==> Nothing to do, as the underlying policy DOES NOT have a 'horizon' or '_horizon' parameter that could have been updated... Maybe you are not using a good policy? I suggest UCBH or ApproximatedFHGittins.") # DEBUG
# # --- Debugging
if __name__ == "__main__":
import sys
if "plot" in sys.argv[1:]:
plt.ion()
# plot_doubling_sequences()
for gamma, delta in [
(0.0, 1.0), # XXX bound in RT <= log(T)
(0.5, 0.0), # XXX bound in RT <= sqrt(T)
(0.5, 0.5), # XXX bound in RT <= sqrt(T * log(T))
(0.66667, 1.0), # XXX another weird bound in RT <= T^2/3 * log(T)
]:
plot_quality_first_upper_bound(gamma=gamma, delta=delta, show_Ti_m_Tim1=True)
plot_quality_first_upper_bound(gamma=gamma, delta=delta, show_Ti_m_Tim1=False)
sys.exit(0)
# Code for debugging purposes.
from doctest import testmod
print("\nTesting automatically all the docstring written in each functions of this module :")
testmod(verbose=True)
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220,
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7,
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28,
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8,
198
] | 2.43969 | 11,731 |
myVarRed= "Red"
myVarBlue= "Blue"
print("Roses are Red. " + "Violets are Blue.")
print("Roses are " + myVarRed + ". Violets are " + myVarBlue)
myStr = "Roses are Red. " + "Violets are Blue."
varStr = "Roses are " + myVarRed + ". Violets are " + myVarBlue
print(myStr)
print(varStr)
name = "Joe"
feet= 6
inches= 2
print("My name is " + name + ". I'm " + str(feet) + " feet " + str(inches) + " inches tall.")
myStr = "My name is " + name + ". I'm " + str(feet) + " feet " + str(inches) + " inches tall."
print(myStr)
print(myVarRed + " roses can grow up to " + str(feet) + " feet!")
myStr = myVarBlue + " violets can grow up to " + str(inches) + " inches!"
print(myStr)
print("The " + myVarBlue + " sky turned " + myVarRed + "!") | [
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] | 2.589474 | 285 |
__all__ = [
'builder_android',
] | [
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] | 2.1875 | 16 |
from configparser import RawConfigParser
CONTEXT = Context()
| [
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#!/usr/bin/env python
import os
import re
import unasync # requires pip>=10.0 for PEP 518 support
from setuptools import setup
# Get the version (borrowed from SQLAlchemy)
base_path = os.path.dirname(__file__)
with open(os.path.join(base_path, "src", "urllib3", "__init__.py")) as fp:
version = re.match(r".*__version__ = \"(.*?)\"", fp.read(), re.S).group(1)
setup(version=version, cmdclass={"build_py": unasync.build_py})
| [
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555,
292,
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13,
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62,
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198
] | 2.568047 | 169 |
from amplification.tasks.equals import EqualsTask
from amplification.tasks.graph import GraphTask, MidpointTask
from amplification.tasks.sum import SumTask
from amplification.tasks.eval import EvalTask, EvalSumTask
from amplification.tasks.iterate import IterTask
from amplification.tasks.sat import SatTask
| [
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class TestSimpleClass(object):
"""
Classes can still be used to organize collections of test cases, with
each test being a Method on the Class, rather than a standalone function.
"""
x = 1
y = 2
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] | 3.235294 | 68 |
# coding: utf8
from pybo import Config
from pathlib import Path
| [
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from globals_consts import NAMESPACE, cname | [
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from city_scrapers_core.spiders import CityScrapersSpider
from city_scrapers.mixins.wayne_commission import WayneCommissionMixin
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#!/usr/bin/env python
__copyright__ = """
Copyright (c) 2020 Tananaev Denis
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 pylatex import (
Document,
Command,
Section,
Subsection,
LongTable,
MultiColumn,
Figure,
SubFigure,
)
from pylatex.utils import italic, bold, NoEscape
import os
def create_long_table(
doc,
parameters,
skip_parameters=[],
table_specs=r"|p{0.45\linewidth}|p{0.45\linewidth}|",
header=[bold("Parameter"), bold("Value")],
):
"""
Helper function to create long table for parameters
Arguments:
doc: document to add table
parameters: parameters dict
skip_parameters: list of parameters to skip
table_specs: latex specific table settings
header: list with column names
"""
columns = len(header)
with doc.create(LongTable(table_spec=table_specs)) as data_table:
# Table header
data_table.add_hline()
data_table.add_row(header)
data_table.add_hline()
data_table.end_table_header()
data_table.add_row(
(MultiColumn(columns, align="r", data="Continued on Next Page"),)
)
data_table.end_table_footer()
data_table.add_row((MultiColumn(columns, align="r", data="End of Table"),))
data_table.end_table_last_footer()
for item in parameters:
if item not in skip_parameters:
data_table.add_row([item, str(parameters[item])])
data_table.add_hline()
def add_figure(doc, graphics_dir, image_name, width=r"0.5\linewidth"):
"""
Helper function to create figure
Arguments:
doc: document to add figure
graphics_dir: directory containing .png image
image_name: the name of image without extension
width: width of image in docement page
"""
image_filename = os.path.join(
os.path.dirname(__file__), graphics_dir, image_name + ".png"
)
with doc.create(Figure(position="h!")) as pic:
pic.add_image(image_filename, width=NoEscape(width))
pic.add_caption(image_name)
def add_sub_figure(doc, graphics_dir, image_names=[], captioning="Metrics"):
"""
Helper function to create multiple sub figures
Arguments:
doc: document to add figure
graphics_dir: directory containing .png image
image_names: the list of image names without extension
captioning: global captioning for the figure
"""
num_figures = len(image_names)
scale = 1.0 / num_figures
sub_width = str(scale) + r"\linewidth"
with doc.create(Figure(position="h!")) as fig:
for image in image_names:
image_filename = os.path.join(
os.path.dirname(__file__), graphics_dir, image + ".png"
)
with doc.create(
SubFigure(position="b", width=NoEscape(sub_width))
) as sub_fig:
sub_fig.add_image(image_filename, width=NoEscape(r"\linewidth"))
sub_fig.add_caption(image)
fig.add_caption(captioning)
def generate_latex_pdf(
graphics_dir,
output_dir,
report_dict,
report_name="experiment_report",
clean_tex=True,
):
"""
The function generates latex/pdf report from json dictionary
Arguments:
graphics_dir: directory containing .png images for report
output_dir: the directory to output report
report_dict: dictionary with report information
report_name: the name of output latex/pdf report
clean_tex: remove latex specific files
"""
output_filename = os.path.join(output_dir, report_name)
parameters = report_dict["parameters"]
report_name = parameters["experiment_info"]["experiment_name"].strip()
description = parameters["experiment_info"]["description"].strip()
authors = parameters["experiment_info"]["authors"].strip()
best_epoch = report_dict["best_epoch"]
main_metric = report_dict["main_metric"]
metric_value = float(report_dict["epoch_metrics"][best_epoch][main_metric]) * 100
result = "\nResult: Best epoch {} with {:.2f}% {}.".format(
best_epoch, metric_value, main_metric
)
# More dertails about page options: https://www.overleaf.com/learn/latex/page_size_and_margins
geometry_options = {
"tmargin": "1cm",
"bmargin": "3cm",
"lmargin": "2cm",
"rmargin": "2cm",
"includeheadfoot": True,
}
doc = Document(geometry_options=geometry_options, page_numbers=True)
doc.preamble.append(Command("title", "Experiment Report"))
doc.preamble.append(Command("author", authors))
doc.preamble.append(Command("date", report_dict["date"]))
doc.append(NoEscape(r"\maketitle"))
# We should handle in unique way in report each parameter which is not correspod {param : single_value}
skip_parameters = set(["experiment_info", "optimizer", "scheduler", "augment"])
with doc.create(Section(report_name)):
doc.append(italic("Description:\n"))
doc.append(description)
doc.append(bold(result))
with doc.create(Subsection("Parameters")):
create_long_table(doc, parameters, skip_parameters)
with doc.create(Subsection("Optimizer")):
create_long_table(doc, parameters["optimizer"])
with doc.create(Subsection("Scheduler")):
create_long_table(doc, parameters["scheduler"])
add_figure(doc, graphics_dir, "learning_rate_scheduler")
with doc.create(Subsection("Augmentations")):
create_long_table(doc, parameters["augment"])
with doc.create(Section("Data plots")):
image_names = ["loss_epoch_metrics"]
add_sub_figure(
doc, graphics_dir, image_names=image_names, captioning="Epoch metrics"
)
# add_figure(doc, graphics_dir, "accuracy_epoch_metrics")
doc.generate_pdf(output_filename, clean_tex=clean_tex)
| [
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11,
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62,
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28,
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62,
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8,
198
] | 2.565697 | 2,717 |
import pytest
@pytest.fixture(params=[None, False])
def sort(request):
"""
Valid values for the 'sort' parameter used in the Index
setops methods (intersection, union, etc.)
Caution:
Don't confuse this one with the "sort" fixture used
for DataFrame.append or concat. That one has
parameters [True, False].
We can't combine them as sort=True is not permitted
in in the Index setops methods.
"""
return request.param
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] | 2.824561 | 171 |
# Copyright (c) 2018 Intel Corporation
#
# 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 yardstick.common import exceptions
from yardstick.common.messaging import payloads
from yardstick.tests.unit import base as ut_base
| [
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] | 3.851852 | 189 |
# -*- coding: utf-8 -*-
r"""
# .---. .-----------
# / \ __ / ------
# / / \( )/ ----- (`-') _ _(`-') <-. (`-')_
# ////// '\/ ` --- ( OO).-/( (OO ).-> .-> \( OO) ) .->
# //// / // : : --- (,------. \ .'_ (`-')----. ,--./ ,--/ ,--.' ,-.
# // / / / `\/ '-- | .---' '`'-..__)( OO).-. ' | \ | | (`-')'.' /
# // //..\\ (| '--. | | ' |( _) | | | | . '| |)(OO \ /
# ============UU====UU==== | .--' | | / : \| |)| | | |\ | | / /)
# '//||\\` | `---. | '-' / ' '-' ' | | \ | `-/ /`
# ''`` `------' `------' `-----' `--' `--' `--'
# ######################################################################################
#
# Author: edony - [email protected]
#
# twitter : @edonyzpc
#
# Last modified: 2015-05-10 15:02
#
# Filename: filebuf.py
#
# Description: All Rights Are Reserved
#
"""
class PyColor(object):
""" This class is for colored print in the python interpreter!
"F3" call Addpy() function to add this class which is defined
in the .vimrc for vim Editor."""
@property
def new(self):
"""
Customized Python Print Color.
"""
return self._newcolor
@new.setter
def new(self,color_str):
"""
New Color.
"""
self._newcolor = color_str
def disable(self):
"""
Disable Color Print.
"""
self.warningcolor = ''
self.endcolor = ''
class FileBuf(object):
"""
FILEBUF: class to write the each different lines into buffer file named `tmp`.
"""
def __init__(self, file1, file2):
"""
Initialize the instance attributes: [file1, file2, file1_line_num, file2_line_num]
"""
self.file1 = file1
self.file2 = file2
self.file1_line_num = len(open(self.file1).readlines())
self.file2_line_num = len(open(self.file2).readlines())
self.buffer = []
def mark_diff(self):
"""
Mark up the different lines into buffer
"""
f1 = open(self.file1)
f2 = open(self.file2)
if self.file1_line_num > self.file2_line_num:
line1_num_counter = 0
line2_num_counter = 0
for line1 in f1.readlines():
line2 = f2.readline()
line1_num_counter += 1
line2_num_counter += 1
if line1 == line2:
continue
else:
if line1 == '':
line1 = line1 + '\n'
if line2 == '':
line2 = line2 + '\n'
line1 = str(line1_num_counter) + '-' + line1
line2 = str(line2_num_counter) + '-' + line2
self.buffer.append(line1)
self.buffer.append(line2)
else:
line1_num_counter = 0
line2_num_counter = 0
for line2 in f2.readlines():
line1 = f1.readline()
line1_num_counter += 1
line2_num_counter += 1
if line1 == line2:
continue
else:
if line1 == '':
line1 = line1 + '\n'
if line2 == '':
line2 = line2 + '\n'
line1 = str(line1_num_counter) + '+' + line1
line2 = str(line2_num_counter) + '+' + line2
self.buffer.append(line1)
self.buffer.append(line2)
def write_file(self):
"""
Write the buffer into buffer file `tmp` in current direction
"""
file_write = open('tmp','w')
for line in self.buffer:
file_write.write(line)
if __name__ == '__main__':
test_file_buf = FileBuf('f2.txt', 'f1.txt')
test_file_buf.mark_diff()
test_file_buf.write_file()
| [
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] | 1.832883 | 2,220 |
import re
from wtforms import validators
from solr_admin import keycloak
from solr_admin import models
from solr_admin import solr
from solr_admin.models import synonym_audit
# The customized ModelView that is used for working with the synonyms.
from solr_admin.services.get_stems import get_stems
from solr_admin.services.get_multi_word_synonyms import get_multi_word_synonyms
from solr_admin.views.secured_view import SecuredView
# Validate the Synonyms Text and ensure it meets our standards.
# Check for multi-word synonyms
# Only a-z, 0-9, and space are allowed in the synonyms.
# Multiple spaces are not allowed.
# Duplicate values are not allowed.
# Ensure that there is more than one value.
# Put a CSV string into alphabetical order, and format nicely.
# Do the audit logging - we will write the complete record, not the delta (although the latter is possible).
| [
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532,
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1844,
1700,
11,
407,
262,
25979,
357,
16670,
262,
6846,
318,
1744,
737,
198
] | 3.525692 | 253 |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torch.optim import Optimizer
| [
2,
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'todo list controller'
import json
from flask import request
from flask import jsonify
from flask import current_app
import data.database as database
def list_items():
'GET todo list'
current_app.logger.info('todo controller called, func: list')
db = database.Database(current_app.config['CONN_STRING'])
items = db.get_items()
return jsonify({
'todoList': items
})
def add():
'POST add item into todo list'
current_app.logger.info('todo controller called, func: add')
data = json.loads(request.data.decode("utf-8"))
item = data['newItem']
db = database.Database(current_app.config['CONN_STRING'])
db.insert_item(item)
items = db.get_items()
return jsonify({
'todoList': items
})
def delete():
'POST delete item from list'
current_app.logger.info('todo controller called, func: delete')
data = json.loads(request.data.decode("utf-8"))
item = data['itemToDelete']
db = database.Database(current_app.config['CONN_STRING'])
db.delete_item(item)
items = db.get_items()
return jsonify({
'todoList': items
})
def item_update():
'POST update item in list'
current_app.logger.info('todo controller called, func: item_update')
data = json.loads(request.data.decode('utf-8'))
item = data['itemToUpdate']
db = database.Database(current_app.config['CONN_STRING'])
db.update_item(item)
items = db.get_items()
return jsonify({
'todoList': items
})
| [
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7,
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13,
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6,
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220,
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13,
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62,
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7,
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8,
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220,
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13,
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62,
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3419,
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220,
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33918,
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220,
220,
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220,
220,
705,
83,
24313,
8053,
10354,
3709,
198,
220,
220,
220,
32092,
198
] | 2.615917 | 578 |
"""
The model package
"""
from models.gcn import GCN_2Layers
from models.mlp import MLP_1h, MLP_2h
from models.wgcn import WGCN, WGCN_embedding_classifier, WGCN_VocabEmbedding
__all__ = [
"MLP_1h",
"MLP_2h",
"GCN_2Layers",
"WGCN",
"WGCN_embedding_classifier",
"WGCN_VocabEmbedding",
]
| [
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] | 2.006452 | 155 |
# Advent of Code 2021, Day 14
#
# Apply character insertion rules to a sequence of characters,
# runs out of memory if you try to build up character strings,
# so had to build dictionary of pairs of characters.
#
# AK, 14/12/2021
import time
t0 = time.time()
# Input file name
f = 'sample.txt'
f = 'input.txt'
# Read data, pattern on line 1, rules thereafter
lines = [l.strip() for l in open(f)]
patt = None
rules = {}
for l in lines:
if not patt:
patt = l
elif len(l) > 0: # "AB -> C" to dictionary
rules[l[:2]] = l[6]
# Parse starting pattern, and get frequency counts of letters,
# and number of transitions
trans = {} # "AB" -> count
counts = {} # 'A' -> count
prevC = None
for c in patt:
counts[c] = counts.get(c,0) + 1
if prevC:
t = prevC + c
trans[t] = trans.get(t,0) + 1
prevC = c
# Show starting data
print('Transitions:', trans)
print('Chars:', counts)
print('Rules:', rules)
# Do one iteration
# Do iterations (10 for Part 1, 40 for Part 2)
for i in range(40):
print('\nIteration', i+1)
iter()
print('Counts:', counts)
# Show final results
print('\nFinal character counts:', counts)
print('\nMax - min counts:', max(counts.values()) - min(counts.values()))
print(time.time() - t0, 'secs')
| [
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3419,
532,
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2363,
82,
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198
] | 2.632231 | 484 |
'''
This script illustrates training of an inflammation classifier for patches along SI joints
'''
import argparse
import os
import shutil
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from neuralnets.util.io import print_frm
from neuralnets.util.tools import set_seed
from neuralnets.util.augmentation import *
from pytorch_lightning.callbacks import ModelCheckpoint
from data.datasets import SPARCCDataset
from models.sparcc_cnn import Inflammation_CNN
from util.constants import *
factor = {INFLAMMATION_MODULE: 64, DEEP_INFLAMMATION_MODULE: 12, SPARCC_MODULE: 1, JOINT: 1}
if __name__ == '__main__':
# parse all the arguments
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", help="Path to the directory that contains a preprocessed dataset", type=str,
required=True)
parser.add_argument("--si-joint-model", help="Path to the SI joint detection checkpoint", type=str, required=True)
parser.add_argument("--model-checkpoint-illium", help="Path to the illium U-Net checkpoint", type=str,
required=True)
parser.add_argument("--model-checkpoint-sacrum", help="Path to the sacrum U-Net checkpoint", type=str,
required=True)
parser.add_argument("--repetitions", help="Number of repetitions", type=int, default=1)
parser.add_argument("--folds", help="Number of folds (overrides repetitions parameter if provided)", type=int,
default=None)
# network parameters
parser.add_argument("--train_val_test_split", help="Train/validation/test split", type=str, default="0.50,0.75")
parser.add_argument("--backbone", help="Backbone feature extractor of the model", type=str, default='ResNet18')
parser.add_argument("--omit_t1_input", help="Boolean flag that omits usage of T1 slices", action='store_true',
default=False)
parser.add_argument("--omit_t2_input", help="Boolean flag that omits usage of T1 slices", action='store_true',
default=False)
parser.add_argument("--omit_weighting", help="Boolean flag that specifies ROI masking", action='store_true',
default=False)
# optimization parameters
parser.add_argument("--epochs", help="Number of training epochs", type=int, default=400)
parser.add_argument("--lr", help="Learning rate for the optimization", type=float, default=1e-3)
# compute parameters
parser.add_argument("--train_batch_size", help="Batch size during training", type=int, default=1)
parser.add_argument("--test_batch_size", help="Batch size during testing", type=int, default=1)
parser.add_argument("--num_workers", help="Amount of workers", type=int, default=12)
parser.add_argument("--gpus", help="Devices available for computing", type=str, default='0')
parser.add_argument("--accelerator", help="Acceleration engine for computations", type=str, default='dp')
# logging parameters
parser.add_argument("--log_dir", help="Logging directory", type=str, default='logs')
parser.add_argument("--log_freq", help="Frequency to log results", type=int, default=50)
parser.add_argument("--log_refresh_rate", help="Refresh rate for logging", type=int, default=1)
parser.add_argument("--seed", help="Seed for reproducibility", type=int, default=0)
parser.add_argument("--clean-up", help="Boolean flag that specifies ROI masking", action='store_true', default=False)
args = parser.parse_args()
args.train_val_test_split = [float(item) for item in args.train_val_test_split.split(',')]
metrics = []
if args.folds is not None:
reps = args.folds
range_split = ((0, 1), (0, 1))
else:
reps = args.repetitions
f = None
split = args.train_val_test_split
range_split = ((0, split[1]), (0, split[1]), (split[1], 1))
for i in range(reps):
rep_str = 'fold' if args.folds is not None else 'repetition'
print_frm('')
print_frm('Start processing %s %d/%d ...' % (rep_str, i+1, reps))
print_frm('')
"""
Fix seed (in case of cross validation), or increment if repetitive training
"""
if args.folds is not None:
set_seed(args.seed)
else:
args.seed = args.seed + 1
set_seed(args.seed)
"""
Load the data
"""
print_frm('Loading data')
transform = Compose([Rotate90(), Flip(prob=0.5, dim=0), Flip(prob=0.5, dim=1), RandomDeformation(),
AddNoise(sigma_max=0.05)])
train = SPARCCDataset(args.data_dir, args.si_joint_model, args.model_checkpoint_illium,
args.model_checkpoint_sacrum, range_split=range_split[0], folds=args.folds, f=i,
train=True, transform=transform, seed=args.seed, mode=INFLAMMATION_MODULE,
use_t1_input=not args.omit_t1_input, use_t2_input=not args.omit_t2_input,
apply_weighting=not args.omit_weighting)
val = SPARCCDataset(args.data_dir, args.si_joint_model, args.model_checkpoint_illium,
args.model_checkpoint_sacrum, range_split=range_split[1], folds=args.folds, f=i,
train=False, seed=args.seed, mode=INFLAMMATION_MODULE, use_t1_input=not args.omit_t1_input,
use_t2_input=not args.omit_t2_input, apply_weighting=not args.omit_weighting)
print_frm('Train data distribution: Infl: %.2f - Non-infl: %.2f' % (100*np.mean(train.q_scores),
100*np.mean(1-train.q_scores)))
print_frm('Val data distribution: Infl: %.2f - Non-infl: %.2f' % (100*np.mean(val.q_scores),
100*np.mean(1-val.q_scores)))
if args.folds is None:
test = SPARCCDataset(args.data_dir, args.si_joint_model, args.model_checkpoint_illium,
args.model_checkpoint_sacrum, range_split=range_split[2], seed=args.seed,
mode=INFLAMMATION_MODULE, use_t1_input=not args.omit_t1_input,
use_t2_input=not args.omit_t2_input, apply_weighting=not args.omit_weighting)
print_frm('Test data distribution: Infl: %.2f - Non-infl: %.2f' % (100*np.mean(test.q_scores),
100*np.mean(1-test.q_scores)))
"""
Build the network
"""
print_frm('Building the network')
weights = train.score_weights[0]
net = Inflammation_CNN(backbone=args.backbone, lr=args.lr, use_t1_input=not args.omit_t1_input,
use_t2_input=not args.omit_t2_input, weights=weights)
print_frm('Balancing weights for loss function: %s' % (weights))
"""
Train the inflammation network
"""
print_frm('Starting training of the inflammation network')
trainer = _train_module(net, train, val, args)
print_frm('Testing network')
_test_module(trainer, net, val if args.folds is not None else test, args)
metrics.append([float(trainer.logged_metrics['test/' + m].cpu()) for m in METRICS])
"""
Save the final model
"""
print_frm('Saving final model')
shutil.copyfile(trainer.checkpoint_callback.best_model_path, os.path.join(trainer.log_dir, OPTIMAL_CKPT))
"""
Clean up
"""
print_frm('Cleaning up')
if args.clean_up:
os.system('rm -r ' + os.path.join(trainer.log_dir, 'checkpoints'))
"""
Report final performance results
"""
metrics = np.asarray(metrics)
metrics_avg = np.mean(metrics, axis=0)
print_frm('Final performance report:')
print_frm('=========================')
for i, m in enumerate(METRICS):
print_frm(' %s: %f' % (m, metrics_avg[i]))
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11,
26498,
8,
198,
220,
220,
220,
220,
220,
220,
220,
20731,
13,
33295,
26933,
22468,
7,
2213,
10613,
13,
6404,
2004,
62,
4164,
10466,
17816,
9288,
14,
6,
1343,
285,
4083,
36166,
28955,
329,
285,
287,
31243,
49,
19505,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12793,
262,
2457,
2746,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
62,
8310,
76,
10786,
50,
2703,
2457,
2746,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
4423,
346,
13,
30073,
7753,
7,
2213,
10613,
13,
9122,
4122,
62,
47423,
13,
13466,
62,
19849,
62,
6978,
11,
28686,
13,
6978,
13,
22179,
7,
2213,
10613,
13,
6404,
62,
15908,
11,
39852,
3955,
1847,
62,
34,
42,
11571,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5985,
510,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
62,
8310,
76,
10786,
34,
25909,
510,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
611,
26498,
13,
27773,
62,
929,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
28686,
13,
10057,
10786,
26224,
532,
81,
705,
1343,
28686,
13,
6978,
13,
22179,
7,
2213,
10613,
13,
6404,
62,
15908,
11,
705,
9122,
13033,
6,
4008,
628,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
6358,
2457,
2854,
2482,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
20731,
796,
45941,
13,
292,
18747,
7,
4164,
10466,
8,
198,
220,
220,
220,
20731,
62,
615,
70,
796,
45941,
13,
32604,
7,
4164,
10466,
11,
16488,
28,
15,
8,
198,
220,
220,
220,
3601,
62,
8310,
76,
10786,
19006,
2854,
989,
25,
11537,
198,
220,
220,
220,
3601,
62,
8310,
76,
10786,
4770,
2559,
28,
11537,
198,
220,
220,
220,
329,
1312,
11,
285,
287,
27056,
378,
7,
47123,
49,
19505,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
62,
8310,
76,
10786,
220,
220,
220,
4064,
82,
25,
4064,
69,
6,
4064,
357,
76,
11,
20731,
62,
615,
70,
58,
72,
60,
4008,
198
] | 2.225868 | 3,657 |
print("-----------------rule_1------------------")
| [
4798,
7203,
1783,
12,
25135,
62,
16,
1783,
438,
4943,
198
] | 4.636364 | 11 |
from flask import Flask
from flask import render_template
app = Flask(__name__)
@app.route('/hello/<name>')
@app.route('/user/<username>', methods=['POST','GET'])
@app.route('/test/<num>')
if __name__ == '__main__':
app.run()
| [
6738,
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1330,
8543,
62,
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198,
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7,
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3672,
834,
8,
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198,
31,
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13,
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14,
27,
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31,
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13,
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14,
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14,
27,
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705,
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12417,
834,
10354,
198,
220,
220,
220,
598,
13,
5143,
3419,
198
] | 2.565217 | 92 |
# (c) Copyright IBM Corp. 2021
# (c) Copyright Instana Inc. 2021
from instana.propagators.binary_propagator import BinaryPropagator
from instana.span_context import SpanContext
import unittest
| [
2,
357,
66,
8,
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19764,
11421,
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66,
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] | 3.403509 | 57 |
'''
实验名称：RGB灯带
版本：v1.0
日期：2019.7
作者：01Studio
说明：RGB灯带控制。
'''
from ws2812 import WS2812
from colors import *
from machine import Pin
import pyb
#定义灯带连接引脚，Y11接口
LED = Pin('Y11',Pin.OUT,value=0)
#构建RGB灯带对象,定义控制引脚和灯珠数量
strip = WS2812(spi_bus=LED, led_count=30)
#灯带填色函数,灯珠数量为led_count
#清空RGB灯带颜色
strip.show(fill_color(EMPTY))
while True:
strip.show(fill_color(RED))
pyb.delay(1000)
strip.show(fill_color(GREEN))
pyb.delay(1000)
strip.show(fill_color(BLUE))
pyb.delay(1000)
| [
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163,
223,
107,
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99,
165,
95,
250,
164,
231,
110,
198,
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13,
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7,
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62,
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7,
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9936,
4008,
198,
198,
4514,
6407,
25,
198,
220,
220,
220,
10283,
13,
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7,
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62,
8043,
7,
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4008,
198,
220,
220,
220,
12972,
65,
13,
40850,
7,
12825,
8,
628,
220,
220,
220,
10283,
13,
12860,
7,
20797,
62,
8043,
7,
43016,
4008,
198,
220,
220,
220,
12972,
65,
13,
40850,
7,
12825,
8,
628,
220,
220,
220,
10283,
13,
12860,
7,
20797,
62,
8043,
7,
9148,
8924,
4008,
198,
220,
220,
220,
12972,
65,
13,
40850,
7,
12825,
8,
628
] | 1.371585 | 366 |
from __future__ import with_statement
import unittest
from cStringIO import StringIO
from format import format
from prettyprinter import *
from bindings import bindings
import printervars
if __name__ == "__main__":
unittest.main()
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] | 3.575758 | 66 |
#!/usr/bin/python3
# requires system Python and the python3-apt package
import textwrap
from collections import OrderedDict # Starting with Python 3.7, we could just use vanilla dicts
import apt # ImportError? apt install python3-apt
HELP_INFO = """Top-level package manager
This tool lists all packages that aren't marked auto, and have updates
available. Press Q at any time to exit without touching your system;
if you have no need to make changes, this script can be run without
root privileges.
Press Space to select or deselect a package for upgrade.
Press 'I' on any package to see more info about it.
Press 'A' to mark a package as automatically installed.
Press 'R' to remove a package.
Press 'Q' to go back, or to quit the program.
"""
def find_ultimate_dependency(cache, deps):
"""Find any one manually-installed package that ultimately caused at
least one of the given deps to be installed. Returns "" if none found.
"""
depchain = {dep: dep for dep in deps}
while depchain:
newchain = {}
for dep, path in depchain.items():
for parent in cache[dep]._pkg.rev_depends_list:
if parent.dep_type_untranslated != "Depends": continue
n = parent.parent_pkg.name
if not cache[n].installed: continue
if not cache[n].is_auto_installed:
# Found one!
return path + " --> " + n
newchain[n] = path + " - " + n
depchain = newchain
return ""
def show_packages(scr, cache, upgrades, auto):
"""Returns True after making cache changes, or False to ignore and do nothing"""
desc = [describe(pkg) for pkg in upgrades]
widths = OrderedDict((x, len(x)) for x in desc[0]) # Start with header widths
for d in desc:
for col in d:
widths[col] = max(widths[col], len(d[col]))
fmt = "[%s] " + " ".join("%%-%ds" % col for col in widths.values())
# print(fmt % ("*", *widths), curses.A_BOLD) # Python 3.5+
print(fmt % (("*",) + tuple(widths)), curses.A_BOLD)
print("--- " + " ".join("-" * col for col in widths.values()))
# TODO: Also adjust for insufficient width? Currently will quietly
# truncate lines at the available width, which isn't bad if it's
# just a character or two, but could be wasteful with long pkgnames.
pkg = 0
actions = [" "] * len(upgrades)
lastheight = None
popup = None
nonautodeps = []
while True:
height, width = scr.getmaxyx() # Also used by make_popup()
if height != lastheight:
# Note that a resize event is sent through as a pseudo-key, so
# this will trigger immediately, without waiting for the next
# actual key.
lastheight, lastpage = height, None
scr.setscrreg(0, height - 1)
perpage = min(height - 8, len(upgrades))
scr.move(perpage + 2, 0)
scr.clrtobot()
print()
if auto: print("Plus %d auto-installed packages." % auto)
print("Select packages to upgrade, then Enter to apply.")
print("Press ? for help, or Q to quit without making any changes")
pagestart = pkg - pkg % perpage
if pagestart != lastpage:
lastpage = pagestart
# Update (only if the page has changed)
for i, d in enumerate(desc[pagestart : pagestart + perpage]):
scr.addstr(i + 2, 0, fmt % ((actions[pagestart + i],) + tuple(d.values())))
# Erase any spare space, including the mandatory blank at the end
for i in range(i + 1, perpage + 1):
# Is this the best way to clear a line??
scr.move(i + 2, 0)
scr.clrtoeol()
scr.setscrreg(2, perpage + 4)
scr.move((pkg % perpage) + 2, 1)
key = scr.getkey()
if popup:
# Restricted key handling when a popup is open
if key in "Aa" and nonautodeps:
for i, p in enumerate(upgrades):
if p in nonautodeps:
toggle(i, "A")
if key in "?QqIiAa":
popup = None
nonautodeps = []
scr.touchwin()
scr.refresh()
curses.curs_set(2)
continue
if key == "Q" or key == "q": return False
if key == "\n": break
if key == "KEY_UP": pkg = (pkg - 1) % len(upgrades)
if key == "KEY_DOWN": pkg = (pkg + 1) % len(upgrades)
if key == "KEY_PPAGE": pkg = 0 if pkg < perpage else pkg - perpage
if key == "KEY_NPAGE": pkg = len(upgrades) - 1 if pkg >= len(upgrades) - perpage else pkg + perpage
if key == "KEY_MOUSE": TODO = curses.getmouse()
if key == " ": toggle(pkg, "I")
if key in "Aa": toggle(pkg, "A")
if key in "Rr": toggle(pkg, "R")
if key == "?":
make_popup(HELP_INFO.split("\n"))
if key == "I" or key == "i":
# TODO: Show a new window with package info
# Show the from and to versions, optionally the changelog,
# and ideally, the list of other packages that would be
# upgraded along with this one (its out-of-date deps).
# Note: get_changelog() appears to be broken. No idea why.
# Neither the default URI nor the hand-checked one below
# work; not sure if it's failing to download or failing to
# parse afterwards, but it gets no useful info.
# http://packages.debian.org/changelogs/pool/%(src_section)s/%(prefix)s/%(src_pkg)s/%(src_pkg)s_%(src_ver)s/changelog
# http://metadata.ftp-master.debian.org/changelogs/%(src_section)s/%(prefix)s/%(src_pkg)s/%(src_pkg)s_%(src_ver)s_changelog
sel = upgrades[pkg]
info = ["Upgrading %s from %s to %s" % (sel.fullname, sel.installed.version, sel.candidate.version), ""]
for line in sel.candidate.description.split("\n"):
info.extend(textwrap.fill(line, width - 6).split("\n"))
try: sel.mark_upgrade()
except apt.package.apt_pkg.Error as e:
info.append("Unable to upgrade this package:")
info.append(e.args[0])
# Should I recognize packages by equality, identity, or name?
changes = [p for p in cache.get_changes() if p != sel]
if changes:
info.append("")
info.append("Additional packages to upgrade:")
nonautodeps = []
for p in changes:
if p.installed == p.candidate: continue # For some reason, it sometimes marks "changes" that aren't changes at all.
info.append("* %s from %s to %s" % (
p.fullname,
p.installed.version if p.installed else "(none)",
p.candidate.version,
))
if not p.is_auto_installed:
info[-1] = (info[-1], curses.A_BOLD)
nonautodeps.append(p)
if nonautodeps:
info.append("")
info.append(("%d dependencies were not auto-installed." % len(nonautodeps), curses.A_BOLD))
info.append(("Press 'A' to mark those deps as auto.", curses.A_BOLD))
# TODO: Disambiguate "A to mark my deps auto" from "A to mark me auto"?
cache.clear()
make_popup(info)
if key in "Ww":
# Similar info to "aptitude why".
# Mark this package auto, mark it for deletion. See what needs to be
# deleted. Filter to only those which are not auto. List those as the
# deps of this package.
# 1) Find out why this package was installed
# 2) If this is a hard dep of a non-auto package (or of an auto package
# that is a hard dep of a non-auto package), this can be marked auto.
# 3) If this is a Recommends/Suggests only, say which package.
p = upgrades[pkg]._pkg # Is there a non-private way to find the underlying package?
deps, recs, sugs = {}, {}, {}
for dep in p.rev_depends_list:
# Note: Using get_fullname() would be better than name, but it doesn't work on older apts
n = dep.parent_pkg.name
inst = cache[n]
if not inst.installed: continue
type = dep.dep_type_untranslated
if type == "Depends":
# Hard dependency. Definite reason to install something
# TODO: Keep the most interesting, not the last seen, version?
deps[n] = dep.parent_ver
elif type == "Recommends":
# Soft dependency. If there are no hard deps, then this would be
# why the package was installed, but it shouldn't be marked auto.
recs[n] = dep.parent_ver
elif type == "Suggests":
# Even softer dependency. As with Recommends but even more so.
# A "Suggests" dep won't be shown unless there are no Deps *or*
# Recs.
sugs[n] = dep.parent_ver
info = ["Why was %s installed?" % upgrades[pkg].name, ""]
if deps: info.append("Depended on by:")
elif recs: info.append("Recommended by:")
elif sugs: info.append("Suggested by:")
else: info.append("Presumably manual installation") # No deps.
got_nonauto = False
for dep in deps or recs or sugs: # Pick the highest-priority category only
if not cache[dep].is_auto_installed:
info.append(("* " + dep, curses.A_BOLD))
got_nonauto = True
else: info.append("* " + dep)
if deps and not got_nonauto:
# Trace the chain of deps and find something, anything, that
# was manually installed. Keep going till we get somewhere or
# run out of dependencies to look at.
cause = find_ultimate_dependency(cache, deps)
if cause: info.extend(["", "Installed because:", cause])
else: info.extend(["", "No ultimate installation cause found - everything's autoinstalled."])
make_popup(info)
# scr.addstr(height - 2, 0, repr(key)); scr.clrtoeol()
changes = False
if "R" in actions:
# Don't bother running through the packages (slow) if we aren't removing any
already_auto_removable = {pkg.fullname for pkg in cache if pkg.is_auto_removable}
for pkg, ac in zip(upgrades, actions):
if ac != " ": changes = True
if ac == "I": pkg.mark_upgrade()
elif ac == "A": pkg.mark_auto()
elif ac == "R": pkg.mark_delete(purge=True)
if "R" in actions:
# Remove should be equiv of "apt --purge autoremove pkgname" but
# doesn't remove anything that was already autoremovable
for pkg in cache:
if pkg.is_auto_removable and pkg.fullname not in already_auto_removable:
pkg.mark_delete(purge=True)
return changes
if __name__ == "__main__":
main()
| [
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779,
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8633,
82,
198,
11748,
15409,
1303,
17267,
12331,
30,
15409,
2721,
21015,
18,
12,
2373,
198,
198,
39,
3698,
47,
62,
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796,
37227,
9126,
12,
5715,
5301,
4706,
198,
198,
1212,
2891,
8341,
477,
10392,
326,
3588,
470,
7498,
8295,
11,
290,
423,
5992,
198,
15182,
13,
4332,
1195,
379,
597,
640,
284,
8420,
1231,
15241,
534,
1080,
26,
198,
361,
345,
423,
645,
761,
284,
787,
2458,
11,
428,
4226,
460,
307,
1057,
1231,
198,
15763,
18850,
13,
198,
198,
13800,
4687,
284,
2922,
393,
748,
9509,
257,
5301,
329,
8515,
13,
198,
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705,
40,
6,
319,
597,
5301,
284,
766,
517,
7508,
546,
340,
13,
198,
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705,
32,
6,
284,
1317,
257,
5301,
355,
6338,
6589,
13,
198,
13800,
705,
49,
6,
284,
4781,
257,
5301,
13,
198,
13800,
705,
48,
6,
284,
467,
736,
11,
393,
284,
11238,
262,
1430,
13,
198,
37811,
198,
198,
4299,
1064,
62,
44818,
62,
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1387,
7,
23870,
11,
390,
862,
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198,
197,
37811,
16742,
597,
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12,
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326,
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4073,
379,
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197,
293,
459,
530,
286,
262,
1813,
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13,
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198,
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7983,
796,
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25,
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287,
390,
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92,
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4514,
1207,
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25,
198,
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197,
3605,
7983,
796,
23884,
198,
197,
197,
1640,
1207,
11,
3108,
287,
1207,
7983,
13,
23814,
33529,
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197,
1640,
2560,
287,
12940,
58,
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4083,
62,
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13,
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62,
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2412,
62,
4868,
25,
198,
197,
197,
197,
197,
361,
2560,
13,
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62,
4906,
62,
403,
7645,
17249,
14512,
366,
12156,
2412,
1298,
2555,
198,
197,
197,
197,
197,
77,
796,
2560,
13,
8000,
62,
35339,
13,
3672,
198,
197,
197,
197,
197,
361,
407,
12940,
58,
77,
4083,
37050,
25,
2555,
198,
197,
197,
197,
197,
361,
407,
12940,
58,
77,
4083,
271,
62,
23736,
62,
37050,
25,
198,
197,
197,
197,
197,
197,
2,
4062,
530,
0,
198,
197,
197,
197,
197,
197,
7783,
3108,
1343,
366,
14610,
366,
1343,
299,
198,
197,
197,
197,
197,
3605,
7983,
58,
77,
60,
796,
3108,
1343,
366,
532,
366,
1343,
299,
198,
197,
197,
10378,
7983,
796,
649,
7983,
198,
197,
7783,
13538,
198,
198,
4299,
905,
62,
43789,
7,
1416,
81,
11,
12940,
11,
16608,
11,
8295,
2599,
198,
197,
37811,
35561,
6407,
706,
1642,
12940,
2458,
11,
393,
10352,
284,
8856,
290,
466,
2147,
37811,
198,
197,
20147,
796,
685,
20147,
4892,
7,
35339,
8,
329,
279,
10025,
287,
16608,
60,
198,
197,
10394,
82,
796,
14230,
1068,
35,
713,
19510,
87,
11,
18896,
7,
87,
4008,
329,
2124,
287,
1715,
58,
15,
12962,
1303,
7253,
351,
13639,
9647,
82,
198,
197,
1640,
288,
287,
1715,
25,
198,
197,
197,
1640,
951,
287,
288,
25,
198,
197,
197,
197,
10394,
82,
58,
4033,
60,
796,
3509,
7,
10394,
82,
58,
4033,
4357,
18896,
7,
67,
58,
4033,
60,
4008,
198,
197,
69,
16762,
796,
12878,
4,
82,
60,
366,
1343,
366,
220,
27071,
22179,
7203,
16626,
12,
4,
9310,
1,
4064,
951,
329,
951,
287,
9647,
82,
13,
27160,
28955,
198,
197,
2,
3601,
7,
69,
16762,
4064,
5855,
9,
1600,
1635,
10394,
82,
828,
43878,
13,
32,
62,
33,
15173,
8,
1303,
11361,
513,
13,
20,
10,
198,
197,
4798,
7,
69,
16762,
4064,
357,
7203,
9,
1600,
8,
1343,
46545,
7,
10394,
82,
36911,
43878,
13,
32,
62,
33,
15173,
8,
198,
197,
4798,
7203,
6329,
366,
1343,
366,
220,
27071,
22179,
7203,
21215,
1635,
951,
329,
951,
287,
9647,
82,
13,
27160,
3419,
4008,
198,
197,
2,
16926,
46,
25,
4418,
4532,
329,
19022,
9647,
30,
16888,
481,
12703,
198,
197,
2,
40122,
378,
3951,
379,
262,
1695,
9647,
11,
543,
2125,
470,
2089,
611,
340,
338,
198,
197,
2,
655,
257,
2095,
393,
734,
11,
475,
714,
307,
45393,
351,
890,
279,
74,
4593,
1047,
13,
198,
197,
35339,
796,
657,
198,
197,
4658,
796,
14631,
366,
60,
1635,
18896,
7,
929,
31177,
8,
198,
197,
12957,
17015,
796,
6045,
198,
197,
12924,
929,
796,
6045,
198,
197,
13159,
2306,
375,
25386,
796,
17635,
198,
197,
4514,
6407,
25,
198,
197,
197,
17015,
11,
9647,
796,
6040,
13,
1136,
76,
6969,
87,
3419,
1303,
4418,
973,
416,
787,
62,
12924,
929,
3419,
198,
197,
197,
361,
6001,
14512,
938,
17015,
25,
198,
197,
197,
197,
2,
5740,
326,
257,
47558,
1785,
318,
1908,
832,
355,
257,
24543,
12,
2539,
11,
523,
198,
197,
197,
197,
2,
428,
481,
7616,
3393,
11,
1231,
4953,
329,
262,
1306,
198,
197,
197,
197,
2,
4036,
1994,
13,
198,
197,
197,
197,
12957,
17015,
11,
938,
7700,
796,
6001,
11,
6045,
198,
197,
197,
197,
1416,
81,
13,
28709,
6098,
2301,
7,
15,
11,
6001,
532,
352,
8,
198,
197,
197,
197,
525,
7700,
796,
949,
7,
17015,
532,
807,
11,
18896,
7,
929,
31177,
4008,
198,
197,
197,
197,
1416,
81,
13,
21084,
7,
525,
7700,
1343,
362,
11,
657,
8,
198,
197,
197,
197,
1416,
81,
13,
565,
17034,
672,
313,
3419,
198,
197,
197,
197,
4798,
3419,
198,
197,
197,
197,
361,
8295,
25,
3601,
7203,
17860,
4064,
67,
8295,
12,
37050,
10392,
526,
4064,
8295,
8,
198,
197,
197,
197,
4798,
7203,
17563,
10392,
284,
8515,
11,
788,
6062,
284,
4174,
19570,
198,
197,
197,
197,
4798,
7203,
13800,
5633,
329,
1037,
11,
393,
1195,
284,
11238,
1231,
1642,
597,
2458,
4943,
198,
197,
197,
79,
363,
395,
433,
796,
279,
10025,
532,
279,
10025,
4064,
583,
7700,
198,
197,
197,
361,
42208,
395,
433,
14512,
938,
7700,
25,
198,
197,
197,
197,
12957,
7700,
796,
42208,
395,
433,
198,
197,
197,
197,
2,
10133,
357,
8807,
611,
262,
2443,
468,
3421,
8,
198,
197,
197,
197,
1640,
1312,
11,
288,
287,
27056,
378,
7,
20147,
58,
79,
363,
395,
433,
1058,
42208,
395,
433,
1343,
583,
7700,
60,
2599,
198,
197,
197,
197,
197,
1416,
81,
13,
2860,
2536,
7,
72,
1343,
362,
11,
657,
11,
46996,
4064,
14808,
4658,
58,
79,
363,
395,
433,
1343,
1312,
4357,
8,
1343,
46545,
7,
67,
13,
27160,
3419,
22305,
198,
197,
197,
197,
2,
5256,
589,
597,
13952,
2272,
11,
1390,
262,
13677,
9178,
379,
262,
886,
198,
197,
197,
197,
1640,
1312,
287,
2837,
7,
72,
1343,
352,
11,
583,
7700,
1343,
352,
2599,
198,
197,
197,
197,
197,
2,
1148,
428,
262,
1266,
835,
284,
1598,
257,
1627,
3548,
198,
197,
197,
197,
197,
1416,
81,
13,
21084,
7,
72,
1343,
362,
11,
657,
8,
198,
197,
197,
197,
197,
1416,
81,
13,
565,
81,
44579,
349,
3419,
198,
197,
197,
197,
1416,
81,
13,
28709,
6098,
2301,
7,
17,
11,
583,
7700,
1343,
604,
8,
628,
197,
197,
1416,
81,
13,
21084,
19510,
35339,
4064,
583,
7700,
8,
1343,
362,
11,
352,
8,
198,
197,
197,
2539,
796,
6040,
13,
1136,
2539,
3419,
198,
197,
197,
361,
46207,
25,
198,
197,
197,
197,
2,
8324,
20941,
1994,
9041,
618,
257,
46207,
318,
1280,
198,
197,
197,
197,
361,
1994,
287,
366,
32,
64,
1,
290,
1729,
2306,
375,
25386,
25,
198,
197,
197,
197,
197,
1640,
1312,
11,
279,
287,
27056,
378,
7,
929,
31177,
2599,
198,
197,
197,
197,
197,
197,
361,
279,
287,
1729,
2306,
375,
25386,
25,
198,
197,
197,
197,
197,
197,
197,
44256,
7,
72,
11,
366,
32,
4943,
198,
197,
197,
197,
361,
1994,
287,
366,
30,
48,
80,
40,
72,
32,
64,
1298,
198,
197,
197,
197,
197,
12924,
929,
796,
6045,
198,
197,
197,
197,
197,
13159,
2306,
375,
25386,
796,
17635,
198,
197,
197,
197,
197,
1416,
81,
13,
29332,
5404,
3419,
198,
197,
197,
197,
197,
1416,
81,
13,
5420,
3447,
3419,
198,
197,
197,
197,
197,
66,
46998,
13,
66,
1834,
62,
2617,
7,
17,
8,
198,
197,
197,
197,
43043,
198,
197,
197,
361,
1994,
6624,
366,
48,
1,
393,
1994,
6624,
366,
80,
1298,
1441,
10352,
198,
197,
197,
361,
1994,
6624,
37082,
77,
1298,
2270,
198,
197,
197,
361,
1994,
6624,
366,
20373,
62,
8577,
1298,
220,
220,
279,
10025,
796,
357,
35339,
532,
352,
8,
4064,
18896,
7,
929,
31177,
8,
198,
197,
197,
361,
1994,
6624,
366,
20373,
62,
41925,
1298,
279,
10025,
796,
357,
35339,
1343,
352,
8,
4064,
18896,
7,
929,
31177,
8,
198,
197,
197,
361,
1994,
6624,
366,
20373,
62,
47,
4537,
8264,
1298,
279,
10025,
796,
657,
611,
279,
10025,
1279,
583,
7700,
2073,
279,
10025,
532,
583,
7700,
198,
197,
197,
361,
1994,
6624,
366,
20373,
62,
45,
4537,
8264,
1298,
279,
10025,
796,
18896,
7,
929,
31177,
8,
532,
352,
611,
279,
10025,
18189,
18896,
7,
929,
31177,
8,
532,
583,
7700,
2073,
279,
10025,
1343,
583,
7700,
198,
197,
197,
361,
1994,
6624,
366,
20373,
62,
44,
2606,
5188,
1298,
16926,
46,
796,
43878,
13,
1136,
35888,
3419,
198,
197,
197,
361,
1994,
6624,
366,
366,
25,
19846,
7,
35339,
11,
366,
40,
4943,
198,
197,
197,
361,
1994,
287,
366,
32,
64,
1298,
19846,
7,
35339,
11,
366,
32,
4943,
198,
197,
197,
361,
1994,
287,
366,
49,
81,
1298,
19846,
7,
35339,
11,
366,
49,
4943,
198,
197,
197,
361,
1994,
6624,
366,
30,
1298,
198,
197,
197,
197,
15883,
62,
12924,
929,
7,
39,
3698,
47,
62,
10778,
13,
35312,
7203,
59,
77,
48774,
198,
197,
197,
361,
1994,
6624,
366,
40,
1,
393,
1994,
6624,
366,
72,
1298,
198,
197,
197,
197,
2,
16926,
46,
25,
5438,
257,
649,
4324,
351,
5301,
7508,
198,
197,
197,
197,
2,
5438,
262,
422,
290,
284,
6300,
11,
42976,
262,
1488,
417,
519,
11,
198,
197,
197,
197,
2,
290,
30274,
11,
262,
1351,
286,
584,
10392,
326,
561,
307,
198,
197,
197,
197,
2,
17955,
1863,
351,
428,
530,
357,
896,
503,
12,
1659,
12,
4475,
390,
862,
737,
628,
197,
197,
197,
2,
5740,
25,
651,
62,
354,
8368,
519,
3419,
3568,
284,
307,
5445,
13,
1400,
2126,
1521,
13,
198,
197,
197,
197,
2,
16126,
262,
4277,
43975,
4249,
262,
1021,
12,
26752,
530,
2174,
198,
197,
197,
197,
2,
670,
26,
407,
1654,
611,
340,
338,
9894,
284,
4321,
393,
9894,
284,
198,
197,
197,
197,
2,
21136,
12979,
11,
475,
340,
3011,
645,
4465,
7508,
13,
198,
197,
197,
197,
2,
2638,
1378,
43789,
13,
24689,
13,
2398,
14,
354,
8368,
18463,
14,
7742,
14,
4,
7,
10677,
62,
5458,
8,
82,
14,
4,
7,
40290,
8,
82,
14,
4,
7,
10677,
62,
35339,
8,
82,
14,
4,
7,
10677,
62,
35339,
8,
82,
62,
4,
7,
10677,
62,
332,
8,
82,
14,
354,
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519,
198,
197,
197,
197,
2,
2638,
1378,
38993,
13,
701,
79,
12,
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13,
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13,
2398,
14,
354,
8368,
18463,
14,
4,
7,
10677,
62,
5458,
8,
82,
14,
4,
7,
40290,
8,
82,
14,
4,
7,
10677,
62,
35339,
8,
82,
14,
4,
7,
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62,
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8,
82,
62,
4,
7,
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62,
332,
8,
82,
62,
354,
8368,
519,
628,
197,
197,
197,
741,
796,
16608,
58,
35339,
60,
198,
197,
197,
197,
10951,
796,
14631,
4933,
29247,
4064,
82,
422,
4064,
82,
284,
4064,
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] | 2.644082 | 3,616 |
import csv
import cv2
import numpy as np
from keras.models import Sequential, load_model
from keras.layers import Flatten, Dense, Lambda, Cropping2D, Dropout
from keras.layers.convolutional import Conv2D
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
lines = []
path = 'C:/Users/609600403/Documents/ML/project/CarND-Behavioral-Cloning-P3-master/data/'
# loading the image paths from csv
lines = get_data(path)
print(len(lines))
# Splitting train and validation ,used 20% of data for validation
train_samples, validation_samples = train_test_split(lines, test_size=0.2)
# Getting training and validation using generator function, used batch of 32
train_generator = generator(train_samples, path, batch_size=32)
validation_generator = generator(validation_samples, path, batch_size=32)
# getting model
model = get_model()
# when you are loading the model
#model = load_model('model-4.h5')
# training the model using generator
model.fit_generator(train_generator, steps_per_epoch=4*len(train_samples),validation_data=validation_generator, validation_steps=len(validation_samples),epochs=1, verbose=1)
# Saving the model
model.save('model-5.h5') | [
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] | 3.101299 | 385 |
# -*- coding:utf-8 -*-
"""
flskapp/helper.py
~~~~~~~~~~~~~~
Flask框架帮助方法
"""
import os
from random import randint
import traceback
import urllib2
from sharper.util.string import random_number
from flask import get_flashed_messages, request, jsonify, current_app, logging, session
import sys
from ..lib.error import ErrorCode, AppError
from ..util.helper import get_utf8, get_unicode
from .logger import logger
import time
__authors__ = ['"linnchord gao" <[email protected]>']
def get_flash_msg(_type=None, joiner=' '):
"""
获取指定类别所有flash消息拼接文本
@_type: ('ok', 'info', 'warn', 'alert')
"""
if _type:
return joiner.join(get_flashed_messages(category_filter=[_type]))
else:
return joiner.join(get_flashed_messages())
def need_json_response():
"""
判断是否需要返回json
"""
return 'application/json' in request.headers.get('Accept')
def print_redirect(url="/", text=None, duration=5, title=u'正在跳转', templ=None):
"""
打印内容并在指定时间（秒）跳转到指定url
@param text:
@param url:
@param duration:
@return:
"""
if not templ:
templ = u'<html>' \
u'<title>{title}</title>' \
u'<meta http-equiv="refresh" content="{duration}; url={url}" />' \
u'<body>' \
u'<h1>{text}</h1>' \
u'<span>{duration}秒后将跳转，请稍候</span>' \
u'</body>' \
u'</html>'
return templ.format(duration=duration, url=url, text=text, title=title)
def clear_cookie(resp, name_or_list):
"""
清除指定cookie
@resp: response
@name_or_list: cookie name or name list
"""
resp = current_app.make_response(resp)
if isinstance(name_or_list, basestring):
name_or_list = [name_or_list]
for n in name_or_list:
resp.set_cookie(n, '', expires=0)
return resp
def set_cookie(resp, name, value, expires,max_age=1800):
"""
设置cookie
"""
resp = current_app.make_response(resp)
resp.set_cookie(name, value,expires=expires,max_age=max_age)
return resp
def simple_times_limit_validate(category, key, limit=5, expire=300, _kvdb=None, more_paras=None, amount=1):
"""
针对指定类型+关键字参数+更多其他参数（dict类型拼接）在指定过期时间内仅允许n次(limit)访问
例如：
* 用户登录（类型）指定ip（关键字参数）在5分钟（expire）内只允许访问5次（limit）
* 某api指定ip或客户端在1分钟内只允许访问1000次
@category: 类型（例如 reg | login ）
@key: 关键参数 （例如 203.12.213.30 ）
@limit: 限制访问次数
@expire: 过期时间 单位：秒 通过redis key过期时间控制
@kvdb: redis库 默认kvdb.common
@more_paras: 用于较多参数变量控制，拼接为缓存键
"""
# redis缓存键构造
key = 'STLV:%s:%s' % (category, key)
if more_paras:
for k, v in more_paras.items():
key += ':%s:%s' % (k, v)
if not _kvdb:
from .kvdb import kvdb
_kvdb = kvdb.common
now = _kvdb.incr(key, amount=amount)
ttl = _kvdb.ttl(key)
if not ttl:
_kvdb.expire(key, expire)
return int(now) <= limit
def simple_vcode_validate(category, key, vcode=None, expire=300, _kvdb=None, more_paras=None):
"""
针对指定类型+关键字参数+更多其他参数（dict类型拼接）在指定过期时间设置验证码验证
例如：
* 用户手机绑定（类型）在5分钟（expire）内验证手机验证码
@category: 类型（例如 reg | login ）
@key: 关键参数 （例如 手机号 18621111111 ）
@vcode: 验证码 （若无则生成并返回验证码，若有则验证 ）
@expire: 过期时间 单位：秒 通过redis key过期时间控制
@kvdb: redis库 默认kvdb.common
@more_paras: 用于较多参数变量控制，拼接为缓存键
"""
# redis缓存键构造
key = 'SPV:%s:%s' % (category, key)
if more_paras:
for k, v in more_paras.items():
key += ':%s:%s' % (k, v)
if not _kvdb:
from .kvdb import kvdb
_kvdb = kvdb.common
if vcode:
if vcode == _kvdb.get(key):
_kvdb.delete(key)
return True
else:
return False
else:
vcode = random_number(6)
_kvdb.setex(key, vcode, expire)
return vcode
def is_internal_ip():
"""
check internal ip
"""
ip = get_client_ip()
return (ip in current_app.config.get('INTERNAL_IP_LIST', [])
or ip in ('127.0.0.1', '0.0.0.0')
or ip.startswith('192.168.'))
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] | 1.522062 | 2,697 |
"""
Here timestamps are updated in order to have elapsed times following a particular pattern/rule
Author: Mattia Fraccaro
"""
import csv
import time
from datetime import datetime, timedelta
from random import *
| [
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] | 3.890909 | 55 |
#
# Copyright 2015-2019, Institute for Systems Biology
#
# 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 standard_library
standard_library.install_aliases()
from builtins import object
import json
import base64
import logging
import urllib.request, urllib.parse, urllib.error
import traceback
import requests
from django.conf import settings
from bq_data_access.v1.data_access import get_feature_vector
from bq_data_access.v1.feature_value_types import ValueType
from bq_data_access.v1.utils import VectorMergeSupport
logger = logging.getLogger('main_logger')
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12417,
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628
] | 3.591362 | 301 |
from core.himesis import Himesis
import cPickle as pickle
from uuid import UUID
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] | 2.964286 | 28 |
from easyprocess import EasyProcess
from pyvirtualdisplay.smartdisplay import SmartDisplay
from discogui.hover import active_rectangles
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] | 4.375 | 32 |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2008-2013 Richard Liao <[email protected]>
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution.
#
from __future__ import with_statement
import inspect
import textwrap
import time
import urllib
from pkg_resources import resource_exists, resource_filename
from trac.admin.api import IAdminCommandProvider, IAdminPanelProvider
from trac.core import *
from trac.config import BoolOption, ListOption, Option
from trac.db import DatabaseManager
from trac.env import IEnvironmentSetupParticipant
from trac.perm import IPermissionRequestor
from trac.ticket import Ticket, Type as TicketType
from trac.util.translation import domain_functions
from trac.web.api import IRequestHandler, ITemplateStreamFilter, RequestDone
from trac.web.chrome import Chrome, ITemplateProvider, add_script, \
add_script_data
try:
import json
except ImportError:
import simplejson as json
from default_templates import DEFAULT_TEMPLATES
from tickettemplate.model import TT_Template, schema, schema_version
from utils import *
gettext, _, tag_, N_, add_domain = \
domain_functions('tickettemplate', 'gettext', '_', 'tag_', 'N_',
'add_domain')
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62,
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11537,
628
] | 3.076566 | 431 |
#!/usr/bin/env python
import functools
import numpy
import hypothesis
import hypothesis.extra.numpy
import hypothesis.strategies
import libnu.sum
from test import eq
arrays = functools.partial(
hypothesis.extra.numpy.arrays,
dtype=numpy.float32,
unique=True,
)
floats = hypothesis.strategies.floats(-1.0, 1.0)
numpy.zeros = functools.partial(numpy.zeros, dtype=numpy.float32)
@hypothesis.given(arrays(shape=10, elements=floats))
@hypothesis.given(arrays(shape=10, elements=floats), floats)
@hypothesis.given(arrays(shape=100, elements=floats))
@hypothesis.given(arrays(shape=100, elements=floats))
if __name__ == '__main__':
test_sum()
test_meanvar()
test_mean()
test_var()
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198
] | 2.573477 | 279 |
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