content
stringlengths 1
1.04M
| input_ids
listlengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
import os
from discord.ext import tasks
from ..constants import C3_ADDRESS, FRAX_DECIMALS, \
C3_DECIMALS, FRAX_C3_POOL
from ..contract_info import token_supply, uni_v2_pool_price
from ..utils import get_discord_client, get_eth_web3, \
get_polygon_web3, load_abi, \
update_nickname, update_presence, \
prettify_number
BOT_TOKEN = os.environ["DISCORD_BOT_TOKEN"]
# Initialized Discord client
client = get_discord_client()
# Initialize web3
web3 = get_polygon_web3()
web3_eth = get_eth_web3()
# Load ABI
c3_abi = load_abi('erc20_token.json')
@client.event
@tasks.loop(seconds=300)
client.run(BOT_TOKEN)
|
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] | 2.121951 | 328 |
from setuptools import setup
setup(
name='hx711',
version='0.1',
description='HX711 Python Library for Jetson Nano',
py_modules=['hx711'],
install_requires=['logzero']
)
|
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from models import User, Post
# delete user from post, user where post.user_id = user.id
query = (Post & User).delete(User) # mysql supports; sqlite3 dosenot support
|
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FacultyNameChoices = [
('FAH','Arts And Humanities'),('FBA','Business Admin'),
('FEd','Education'),('FRS','Religous Studies'),('FOS','Science'),
('FIT','Information Technology'),('FON','Nursing')]
CourseCategory = [
('Core Course','Core Course'),('General Education Course','General Education Course'),
('Major Required Course','Major Required Course'),('Major Elective Course','Major Elective Course')
]
HoursChoices = [
(1,'1'),(2,'2'),(3,'3'),(4,'4')
]
Credits = [
('1','1'),('2','2'),('3','3'),('4','4')
]
ResourcesTypes = [
('Text Book','Text Book'),('Internet Resources','Internet Resources'),
('Research Paper','Reserach Paper')
]
EvaluationTypes = [
('Assignments','Assignments'),('Quizzes','Quizzes'),('Attendance','Attendance'),
('Midterm Exam','Midterm Exam'),('Final Exam','Final Exam'),('Projects','Projects')
]
|
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] | 2.982818 | 291 |
import xlrd
from datetime import date
from datetime import datetime
import random
workbook = xlrd.open_workbook('diagnose.xlsx')
worksheet = workbook.sheet_by_name('Sheet1')
file = open("diagnoseInsert.txt","w")
empNum = 2
for x in range(0, 30884):
year = random.choice(range(1990, 2018))
month = random.choice(range(1, 13))
day = random.choice(range(1, 29))
disease=worksheet.cell(x, 0).value
pid=worksheet.cell(x, 1).value
pid = int(pid)
pidStr = str(pid)
disease=disease.encode('utf-8')
pidStr=pidStr.encode('utf-8')
date_diagnosed = date(year,month, day)
eid = "Doc" + str(empNum)
patient = "P-" + str(pidStr)
file.write("Insert into diagnose" +" " + "values ('"+(eid)+"', '"+(disease)+"','"+(patient)+"','"+str(date_diagnosed)+"');\n")
if empNum <30:
empNum += 2
else: empNum = 2
file.close()
|
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# -*- coding: utf-8 -*-
"""
Created on Thu May 16 11:50:16 2019
@author: DiPu
"""
import mysql.connector
con=mysql.connector.connect(user="diksha",password='diksha1702',
host='db4free.net',database='diksha')
c=con.cursor()
c.execute("""CREATE TABLE student(
name text,
age int,
roll_no int,
branch text
)""")
c.execute("INSERT INTO student VALUES ('nEHA',21, 18, 'cs')")
c.execute("INSERT INTO student VALUES ('PRATIK',21, 18, 'IT')")
c.execute("INSERT INTO student VALUES ('pooja',21, 18, 'ec')")
c.execute("INSERT INTO student VALUES ('smita',21, 18, 'IT')")
c.execute("INSERT INTO student VALUES ('saurav',21, 18, 'ec')")
c.execute("INSERT INTO student VALUES ('gaurav',21, 18, 'ee')")
c.execute("INSERT INTO student VALUES ('Ria',21, 18, 'ee')")
c.execute("SELECT * FROM student")
print ( c.fetchall() )
|
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1267,
628,
198
] | 2.343085 | 376 |
from typing import List
|
[
6738,
19720,
1330,
7343
] | 5.75 | 4 |
##########################################################################
#
# MRC FGU Computational Genomics Group
#
# $Id$
#
# Copyright (C) 2009 Andreas Heger
#
# 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., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
##########################################################################
'''
WrapperBl2Seq.py -
======================================================
:Author: Andreas Heger
:Release: $Id$
:Date: |today|
:Tags: Python
Code
----
'''
import os
import sys
import string
import tempfile
import subprocess
from CGAT import Experiment as Experiment
from CGAT import FastaIterator as FastaIterator
if __name__ == "__main__":
parser = E.OptionParser(
version="%prog version: $Id: WrapperBl2Seq.py 2781 2009-09-10 11:33:14Z andreas $")
parser.add_option("-f", "--input-file", dest="input_filename", type="string",
help="input filename. If '-', stdin is used [default=%default].",
metavar="FILE")
parser.add_option("-o", "--output-file", dest="output_filename", type="string",
help="output filename for codon usage. If '-', output is stdout [default=%default].",
metavar="FILE")
parser.add_option("-e", "--error-file", dest="error_filename", type="string",
help="output filename for error messages. If '-', output is stderr [default=%default].",
metavar="FILE")
parser.set_defaults(
input_filename="-",
output_filename="-",
error_filename="/dev/null",
)
(options, args) = Experiment.Start(parser)
wrapper = Bl2Seq()
if options.input_filename == "-":
file_stdin = sys.stdin
else:
file_stdin = open(options.input_filename, "r")
if options.output_filename:
if options.output_filename == "-":
file_stdout = sys.stdout
else:
file_stdout = open(options.output_filename, "w")
if options.error_filename:
if options.error_filename == "-":
file_stderr = sys.stderr
else:
file_stderr = open(options.error_filename, "w")
wrapper.RunOnFile(file_stdin, file_stdout, file_stderr)
if file_stdin and file_stdin != sys.stdin:
file_stdin.close()
if file_stdout and file_stdout != sys.stdout:
file_stdout.close()
if file_stderr and file_stderr != sys.stderr:
file_stderr.close()
Experiment.Stop()
|
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] | 2.660729 | 1,179 |
#!/usr/bin/env python
import click
import json
import logging
import os
import requests
import sys
import prefect
@click.group()
def cli():
"""
The Prefect CLI
"""
pass
@cli.command()
def make_user_config():
"""
Generates a user configuration file
"""
user_config_path = prefect.config.get("user_config_path")
if not user_config_path:
raise ValueError("No user config path set!")
elif os.path.isfile(user_config_path):
raise ValueError("A file already exists at {}".format(user_config_path))
os.makedirs(os.path.dirname(user_config_path), exist_ok=True)
with open(user_config_path, "w") as user_config:
user_config.write(
"# This is a user configuration file.\n"
"# Settings placed here will overwrite Prefect's defaults."
)
click.secho("Config created at {}".format(user_config_path), fg="green")
@cli.command()
@click.argument("environment_file", type=click.Path(exists=True))
@click.option("--runner_kwargs", default={})
def run(environment_file, runner_kwargs):
"""
Run a flow from an environment file.
"""
schema = prefect.serialization.environment.EnvironmentSchema()
with open(environment_file, "r") as f:
environment = schema.load(json.load(f))
click.echo(environment.run(runner_kwargs=runner_kwargs))
@cli.command()
@click.argument("environment_metadata")
def create_environment(environment_metadata):
"""
Call the setup and execute functions for a given environment.
"""
schema = prefect.serialization.environment.EnvironmentSchema()
environment = schema.load(json.loads(environment_metadata))
environment.setup()
environment.execute()
|
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7,
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1023,
28,
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4008,
198,
31,
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13,
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438,
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62,
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22046,
1600,
4277,
34758,
30072,
198,
4299,
1057,
7,
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62,
7753,
11,
17490,
62,
46265,
22046,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
5660,
257,
5202,
422,
281,
2858,
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13,
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220,
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198,
220,
220,
220,
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662,
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220,
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11,
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81,
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25,
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220,
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7,
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2599,
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220,
220,
37227,
198,
220,
220,
220,
4889,
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290,
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5499,
329,
257,
1813,
2858,
13,
198,
220,
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37227,
198,
220,
220,
220,
32815,
796,
662,
2309,
13,
46911,
1634,
13,
38986,
13,
31441,
27054,
2611,
3419,
198,
220,
220,
220,
2858,
796,
32815,
13,
2220,
7,
17752,
13,
46030,
7,
38986,
62,
38993,
4008,
628,
220,
220,
220,
2858,
13,
40406,
3419,
198,
220,
220,
220,
2858,
13,
41049,
3419,
198
] | 2.846787 | 607 |
from day1_func import get_fuel
lines = open('../../data/day1_data.txt').read().strip().split('\n')
fuels = [get_fuel(int(line)) for line in lines]
print(f'Part 1: {sum(fuels)}')
total_sum = 0
for line in lines:
last_value = int(line)
while True:
last_value = get_fuel(last_value)
if last_value <= 0:
break
total_sum += last_value
print(f"Part 2: {total_sum}")
|
[
6738,
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62,
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62,
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7,
69,
1,
7841,
362,
25,
1391,
23350,
62,
16345,
92,
4943,
628
] | 2.258242 | 182 |
from .PgSQL import PostgreSQL
from .config_helpers import make_config_file, read_config_file, configurations
from .geopandas_helpers import spatialize_point_dataframe
from .raw_data import DataSource
from .console import _console
__VERSION__ = "0.2.2"
# _console.print(":globe_showing_americas:", justify="left")
# _console.print(":globe_showing_europe-africa:", justify="center")
# _console.print(":globe_showing_asia-australia:", justify="right")
# _console.print(f"-> postGIS-helpers version {__VERSION__}\n\n")
|
[
6738,
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62,
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25,
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2625,
9464,
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198,
2,
4808,
41947,
13,
4798,
7,
1298,
4743,
5910,
62,
1477,
7855,
62,
44252,
431,
12,
1878,
30997,
25,
1600,
12051,
2625,
16159,
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2,
4808,
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13,
4798,
7,
1298,
4743,
5910,
62,
1477,
7855,
62,
23218,
12,
64,
436,
1373,
544,
25,
1600,
12051,
2625,
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198,
2,
4808,
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13,
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7,
69,
1,
3784,
1281,
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12,
16794,
364,
2196,
1391,
834,
43717,
834,
32239,
77,
59,
77,
4943,
198
] | 2.926554 | 177 |
from django.urls import path
from .views import CreateDeliveryInfo, DeliveryInfoApi, ListDeliveryInfo
app_name = 'delivery'
urlpatterns = [
path('delivery-information/add', CreateDeliveryInfo.as_view(), name='add_delivery_info'),
path('delivery-information/<int:pk>', DeliveryInfoApi.as_view(), name='delivery_info'),
path('delivery-information/list', ListDeliveryInfo.as_view(), name='list_delivery_info')
]
|
[
6738,
42625,
14208,
13,
6371,
82,
1330,
3108,
198,
198,
6738,
764,
33571,
1330,
13610,
33129,
12360,
11,
28682,
12360,
32,
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11,
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198,
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220,
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6315,
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27,
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29,
3256,
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12360,
32,
14415,
13,
292,
62,
1177,
22784,
1438,
11639,
12381,
6315,
62,
10951,
33809,
198,
220,
220,
220,
3108,
10786,
12381,
6315,
12,
17018,
14,
4868,
3256,
7343,
33129,
12360,
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292,
62,
1177,
22784,
1438,
11639,
4868,
62,
12381,
6315,
62,
10951,
11537,
198,
60,
198
] | 3.028571 | 140 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#=======================================================================
#
# day11.py
# -------
# Solutions to Advent of Code 2020, day 11.
# https://adventofcode.com/2020/day/11
#
#=======================================================================
import sys
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
# Implement the seating update logic.
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
#-------------------------------------------------------------------
if __name__=="__main__":
print("Advent of Code 2020, day 11")
print("==========================")
problem1("day11_example.txt")
problem2("day11_input.txt")
sys.exit(0)
#=======================================================================
#=======================================================================
|
[
2,
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220,
220,
220,
1917,
16,
7203,
820,
1157,
62,
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13,
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4943,
628,
220,
220,
220,
1917,
17,
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820,
1157,
62,
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4943,
628,
220,
220,
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8,
198,
198,
2,
23926,
1421,
18604,
198,
2,
23926,
1421,
18604,
198
] | 7.102128 | 235 |
import unittest
import gzip
from edgestats import EdgeStats
if __name__ == '__main__':
unittest.main()
|
[
11748,
555,
715,
395,
198,
11748,
308,
13344,
198,
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1225,
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220,
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555,
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198
] | 2.769231 | 39 |
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from enum import Enum
from six import with_metaclass
from azure.core import CaseInsensitiveEnumMeta
class Access(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The network traffic is allowed or denied.
"""
ALLOW = "allow"
DENY = "deny"
class ClusterState(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The current state of the cluster.
"""
#: Indicates that the cluster resource is created and the resource provider is waiting for Service
#: Fabric VM extension to boot up and report to it.
WAITING_FOR_NODES = "WaitingForNodes"
#: Indicates that the Service Fabric runtime is being installed on the VMs. Cluster resource will
#: be in this state until the cluster boots up and system services are up.
DEPLOYING = "Deploying"
#: Indicates that the cluster is upgrading to establishes the cluster version. This upgrade is
#: automatically initiated when the cluster boots up for the first time.
BASELINE_UPGRADE = "BaselineUpgrade"
#: Indicates that the cluster is being upgraded with the user provided configuration.
UPGRADING = "Upgrading"
#: Indicates that the last upgrade for the cluster has failed.
UPGRADE_FAILED = "UpgradeFailed"
#: Indicates that the cluster is in a stable state.
READY = "Ready"
class ClusterUpgradeCadence(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Indicates when new cluster runtime version upgrades will be applied after they are released. By
default is Wave0.
"""
#: Cluster upgrade starts immediately after a new version is rolled out. Recommended for Test/Dev
#: clusters.
WAVE0 = "Wave0"
#: Cluster upgrade starts 7 days after a new version is rolled out. Recommended for Pre-prod
#: clusters.
WAVE1 = "Wave1"
#: Cluster upgrade starts 14 days after a new version is rolled out. Recommended for Production
#: clusters.
WAVE2 = "Wave2"
class ClusterUpgradeMode(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The upgrade mode of the cluster when new Service Fabric runtime version is available.
"""
#: The cluster will be automatically upgraded to the latest Service Fabric runtime version,
#: **clusterUpgradeCadence** will determine when the upgrade starts after the new version becomes
#: available.
AUTOMATIC = "Automatic"
#: The cluster will not be automatically upgraded to the latest Service Fabric runtime version.
#: The cluster is upgraded by setting the **clusterCodeVersion** property in the cluster resource.
MANUAL = "Manual"
class Direction(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Network security rule direction.
"""
INBOUND = "inbound"
OUTBOUND = "outbound"
class DiskType(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Managed data disk type. IOPS and throughput are given by the disk size, to see more information
go to https://docs.microsoft.com/en-us/azure/virtual-machines/disks-types.
"""
#: Standard HDD locally redundant storage. Best for backup, non-critical, and infrequent access.
STANDARD_LRS = "Standard_LRS"
#: Standard SSD locally redundant storage. Best for web servers, lightly used enterprise
#: applications and dev/test.
STANDARD_SSD_LRS = "StandardSSD_LRS"
#: Premium SSD locally redundant storage. Best for production and performance sensitive workloads.
PREMIUM_LRS = "Premium_LRS"
class FailureAction(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The compensating action to perform when a Monitored upgrade encounters monitoring policy or
health policy violations. Invalid indicates the failure action is invalid. Rollback specifies
that the upgrade will start rolling back automatically. Manual indicates that the upgrade will
switch to UnmonitoredManual upgrade mode.
"""
#: Indicates that a rollback of the upgrade will be performed by Service Fabric if the upgrade
#: fails.
ROLLBACK = "Rollback"
#: Indicates that a manual repair will need to be performed by the administrator if the upgrade
#: fails. Service Fabric will not proceed to the next upgrade domain automatically.
MANUAL = "Manual"
class IPAddressType(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The IP address type.
"""
#: IPv4 address type.
I_PV4 = "IPv4"
#: IPv6 address type.
I_PV6 = "IPv6"
class ManagedClusterAddOnFeature(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Available cluster add-on features
"""
#: Dns service.
DNS_SERVICE = "DnsService"
#: Backup and restore service.
BACKUP_RESTORE_SERVICE = "BackupRestoreService"
#: Resource monitor service.
RESOURCE_MONITOR_SERVICE = "ResourceMonitorService"
class ManagedIdentityType(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The type of managed identity for the resource.
"""
#: Indicates that no identity is associated with the resource.
NONE = "None"
#: Indicates that system assigned identity is associated with the resource.
SYSTEM_ASSIGNED = "SystemAssigned"
#: Indicates that user assigned identity is associated with the resource.
USER_ASSIGNED = "UserAssigned"
#: Indicates that both system assigned and user assigned identity are associated with the
#: resource.
SYSTEM_ASSIGNED_USER_ASSIGNED = "SystemAssigned, UserAssigned"
class ManagedResourceProvisioningState(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The provisioning state of the managed resource.
"""
NONE = "None"
CREATING = "Creating"
CREATED = "Created"
UPDATING = "Updating"
SUCCEEDED = "Succeeded"
FAILED = "Failed"
CANCELED = "Canceled"
DELETING = "Deleting"
DELETED = "Deleted"
OTHER = "Other"
class MoveCost(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Specifies the move cost for the service.
"""
#: Zero move cost. This value is zero.
ZERO = "Zero"
#: Specifies the move cost of the service as Low. The value is 1.
LOW = "Low"
#: Specifies the move cost of the service as Medium. The value is 2.
MEDIUM = "Medium"
#: Specifies the move cost of the service as High. The value is 3.
HIGH = "High"
class NodeTypeSkuScaleType(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Node type capacity scale type.
"""
#: Node count is not adjustable in any way (e.g. it is fixed).
NONE = "None"
#: The user must manually scale out/in.
MANUAL = "Manual"
#: Automatic scale is allowed.
AUTOMATIC = "Automatic"
class NsgProtocol(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Network protocol this rule applies to.
"""
HTTP = "http"
HTTPS = "https"
TCP = "tcp"
UDP = "udp"
ICMP = "icmp"
AH = "ah"
ESP = "esp"
class OsType(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Cluster operating system, the default will be Windows
"""
#: Indicates os is Windows.
WINDOWS = "Windows"
class PartitionScheme(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Enumerates the ways that a service can be partitioned.
"""
#: Indicates that the partition is based on string names, and is a SingletonPartitionScheme
#: object, The value is 0.
SINGLETON = "Singleton"
#: Indicates that the partition is based on Int64 key ranges, and is a
#: UniformInt64RangePartitionScheme object. The value is 1.
UNIFORM_INT64_RANGE = "UniformInt64Range"
#: Indicates that the partition is based on string names, and is a NamedPartitionScheme object.
#: The value is 2.
NAMED = "Named"
class PrivateEndpointNetworkPolicies(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Enable or Disable apply network policies on private end point in the subnet.
"""
ENABLED = "enabled"
DISABLED = "disabled"
class PrivateLinkServiceNetworkPolicies(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Enable or Disable apply network policies on private link service in the subnet.
"""
ENABLED = "enabled"
DISABLED = "disabled"
class ProbeProtocol(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""the reference to the load balancer probe used by the load balancing rule.
"""
TCP = "tcp"
HTTP = "http"
HTTPS = "https"
class Protocol(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The reference to the transport protocol used by the load balancing rule.
"""
TCP = "tcp"
UDP = "udp"
class RollingUpgradeMode(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The mode used to monitor health during a rolling upgrade. The values are Monitored, and
UnmonitoredAuto.
"""
#: The upgrade will stop after completing each upgrade domain and automatically monitor health
#: before proceeding. The value is 0.
MONITORED = "Monitored"
#: The upgrade will proceed automatically without performing any health monitoring. The value is
#: 1.
UNMONITORED_AUTO = "UnmonitoredAuto"
class ServiceCorrelationScheme(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The service correlation scheme.
"""
#: Aligned affinity ensures that the primaries of the partitions of the affinitized services are
#: collocated on the same nodes. This is the default and is the same as selecting the Affinity
#: scheme. The value is 0.
ALIGNED_AFFINITY = "AlignedAffinity"
#: Non-Aligned affinity guarantees that all replicas of each service will be placed on the same
#: nodes. Unlike Aligned Affinity, this does not guarantee that replicas of particular role will
#: be collocated. The value is 1.
NON_ALIGNED_AFFINITY = "NonAlignedAffinity"
class ServiceKind(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The kind of service (Stateless or Stateful).
"""
#: Does not use Service Fabric to make its state highly available or reliable. The value is 0.
STATELESS = "Stateless"
#: Uses Service Fabric to make its state or part of its state highly available and reliable. The
#: value is 1.
STATEFUL = "Stateful"
class ServiceLoadMetricWeight(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Determines the metric weight relative to the other metrics that are configured for this
service. During runtime, if two metrics end up in conflict, the Cluster Resource Manager
prefers the metric with the higher weight.
"""
#: Disables resource balancing for this metric. This value is zero.
ZERO = "Zero"
#: Specifies the metric weight of the service load as Low. The value is 1.
LOW = "Low"
#: Specifies the metric weight of the service load as Medium. The value is 2.
MEDIUM = "Medium"
#: Specifies the metric weight of the service load as High. The value is 3.
HIGH = "High"
class ServicePackageActivationMode(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The activation Mode of the service package
"""
#: Indicates the application package activation mode will use shared process.
SHARED_PROCESS = "SharedProcess"
#: Indicates the application package activation mode will use exclusive process.
EXCLUSIVE_PROCESS = "ExclusiveProcess"
class ServicePlacementPolicyType(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""The type of placement policy for a service fabric service. Following are the possible values.
"""
#: Indicates that the ServicePlacementPolicyDescription is of type
#: ServicePlacementInvalidDomainPolicyDescription, which indicates that a particular fault or
#: upgrade domain cannot be used for placement of this service. The value is 0.
INVALID_DOMAIN = "InvalidDomain"
#: Indicates that the ServicePlacementPolicyDescription is of type
#: ServicePlacementRequireDomainDistributionPolicyDescription indicating that the replicas of the
#: service must be placed in a specific domain. The value is 1.
REQUIRED_DOMAIN = "RequiredDomain"
#: Indicates that the ServicePlacementPolicyDescription is of type
#: ServicePlacementPreferPrimaryDomainPolicyDescription, which indicates that if possible the
#: Primary replica for the partitions of the service should be located in a particular domain as
#: an optimization. The value is 2.
PREFERRED_PRIMARY_DOMAIN = "PreferredPrimaryDomain"
#: Indicates that the ServicePlacementPolicyDescription is of type
#: ServicePlacementRequireDomainDistributionPolicyDescription, indicating that the system will
#: disallow placement of any two replicas from the same partition in the same domain at any time.
#: The value is 3.
REQUIRED_DOMAIN_DISTRIBUTION = "RequiredDomainDistribution"
#: Indicates that the ServicePlacementPolicyDescription is of type
#: ServicePlacementNonPartiallyPlaceServicePolicyDescription, which indicates that if possible all
#: replicas of a particular partition of the service should be placed atomically. The value is 4.
NON_PARTIALLY_PLACE_SERVICE = "NonPartiallyPlaceService"
class ServiceScalingMechanismKind(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Enumerates the ways that a service can be partitioned.
"""
#: Represents a scaling mechanism for adding or removing instances of stateless service partition.
#: The value is 0.
SCALE_PARTITION_INSTANCE_COUNT = "ScalePartitionInstanceCount"
#: Represents a scaling mechanism for adding or removing named partitions of a stateless service.
#: The value is 1.
ADD_REMOVE_INCREMENTAL_NAMED_PARTITION = "AddRemoveIncrementalNamedPartition"
class ServiceScalingTriggerKind(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Enumerates the ways that a service can be partitioned.
"""
#: Represents a scaling trigger related to an average load of a metric/resource of a partition.
#: The value is 0.
AVERAGE_PARTITION_LOAD_TRIGGER = "AveragePartitionLoadTrigger"
#: Represents a scaling policy related to an average load of a metric/resource of a service. The
#: value is 1.
AVERAGE_SERVICE_LOAD_TRIGGER = "AverageServiceLoadTrigger"
class SkuName(with_metaclass(CaseInsensitiveEnumMeta, str, Enum)):
"""Sku Name.
"""
#: Basic requires a minimum of 3 nodes and allows only 1 node type.
BASIC = "Basic"
#: Requires a minimum of 5 nodes and allows 1 or more node type.
STANDARD = "Standard"
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] | 3.207967 | 4,669 |
import logging
import json
from urllib import error
from pkg_resources import resource_filename, Requirement
import pandas as pd
from pvlib import iotools
from requests.exceptions import HTTPError
from solarforecastarbiter.datamodel import Observation, SolarPowerPlant
from solarforecastarbiter.io.reference_observations import (
common, default_forecasts)
DEFAULT_SITEFILE = resource_filename(
Requirement.parse('solarforecastarbiter'),
'solarforecastarbiter/io/reference_observations/'
'srml_reference_sites.json')
# maps the desired variable names to those returned by pvlib.iotools
srml_variable_map = {
'ghi_': 'ghi',
'dni_': 'dni',
'dhi_': 'dhi',
'wind_speed_': 'wind_speed',
'temp_air_': 'air_temperature',
}
# maps SolarForecastArbiter interval_label to the SRML infix which
# designates the time resolution of each file. The list of file types
# is tried in order, so file types starting with 'P' designating
# processed data are listed first, such that if processed data exists
# we retrieve that first.
FILE_TYPE_MAP = {
1: ['PO', 'RO'],
5: ['PF', 'RF'],
15: ['PQ', 'RQ'],
60: ['PH', 'RH'],
}
logger = logging.getLogger('reference_data')
def adjust_site_parameters(site):
"""Inserts modeling parameters for sites with pv measurments
Parameters
----------
site: dict
Returns
-------
dict
Copy of inputs plus a new key 'modeling_parameters'.
"""
return common.apply_json_site_parameters(DEFAULT_SITEFILE, site)
def request_data(site, year, month):
"""Makes a request for each file type until successful or we
run out of filetypes.
Parameters
----------
site: :py:class:`solarforecastarbiter.datamodel.Site`
year: int
The year of the data to request.
month: int
The month of the data to request.
Returns
-------
DataFrame
A month of SRML data.
"""
extra_params = common.decode_extra_parameters(site)
station_code = extra_params['network_api_abbreviation']
interval_length = extra_params['observation_interval_length']
file_types = FILE_TYPE_MAP[interval_length]
for file_type in file_types:
# The list file_types are listed with processed data
# file types first. On a successful retrieval we return
# the month of data, otherwise we log info and continue
# until we've exhausted the list.
try:
srml_month = iotools.read_srml_month_from_solardat(
station_code, year, month, file_type)
except error.URLError:
logger.warning(f'Could not retrieve {file_type} for SRML data '
f'for site {site.name} on {year}/{month} .')
logger.debug(f'Site abbreviation: {station_code}')
continue
except pd.errors.EmptyDataError:
logger.warning(f'SRML returned an empty file for station '
f'{site.name} on {year}/{month}.')
continue
else:
return srml_month
logger.warning(f'Could not retrieve data for site {site.name} on '
f'{year}/{month}.')
def fetch(api, site, start, end):
"""Retrieve observation data for a srml site between start and end.
Parameters
----------
api : :py:class:`solarforecastarbiter.io.api.APISession`
An APISession with a valid JWT for accessing the Reference Data
user.
site : :py:class:`solarforecastarbiter.datamodel.Site`
Site object with the appropriate metadata.
start : datetime
The beginning of the period to request data for. Must include timezone.
end : datetime
The end of the period to request data for. Must include timezone.
Returns
-------
data : pandas.DataFrame
All of the requested data concatenated into a single DataFrame.
Raises
------
TypeError
If start and end have different timezones, or if they do not include a
timezone.
"""
month_dfs = []
start_year = start.year
start_month = start.month
# Retrieve each month file necessary
if start.tzinfo != end.tzinfo:
raise TypeError('start and end cannot have different timezones')
while start_year * 100 + start_month <= end.year * 100 + end.month:
logger.info(f'Requesting data for SRML site {site.name}'
f' for {start_year}-{start_month}')
srml_month = request_data(site, start_year, start_month)
if srml_month is not None:
month_dfs.append(srml_month)
start_month += 1
if start_month > 12:
start_month = 1
start_year += 1
try:
all_period_data = pd.concat(month_dfs)
except ValueError:
logger.warning(f'No data available for site {site.name} '
f'from {start} to {end}.')
return pd.DataFrame()
var_columns = [col for col in all_period_data.columns
if '_flag' not in col]
power_columns = [col for col in var_columns
if col.startswith('5')]
# adjust power from watts to megawatts
for column in power_columns:
all_period_data[column] = all_period_data[column] / 1000000
all_period_data = all_period_data.loc[start:end, var_columns]
# remove possible trailing NaNs, it is necessary to do this after slicing
# because SRML data has nighttime data prefilled with 0s through the end of
# the month. This may not be effective if a given site has more than a 24
# hour lag, which will cause last_valid_index to return the latest
# timestamp just before sunrise, but will suffice for the typical lag on
# the order of hours.
all_period_data = all_period_data[:all_period_data.last_valid_index()]
return all_period_data
def initialize_site_observations(api, site):
"""Creates an observation at the site for each variable in
an SRML site's file.
Parameters
----------
api: :py:class:`solarforecastarbiter.io.api.APISession`
site : :py:class:`solarforecastarbiter.datamodel.Site
The site object for which to create Observations.
Notes
-----
Since variables are labelled with an integer instrument
number, Observations are named with their variable and
instrument number found in the source files.
e.g. A SRML file contains two columns labelled, 1001, and
1002. These columns represent GHI at instrument 1 and
instrument 2 respectively. The `pvlib.iotools` package
converts these to 'ghi_1' and 'ghi_2' for us. We use these
labels to differentiate between measurements recorded by
different instruments.
"""
# Request ~month old data at initialization to ensure we get a response.
start = pd.Timestamp.utcnow() - pd.Timedelta('30 days')
end = start
try:
extra_params = common.decode_extra_parameters(site)
except ValueError:
logger.warning('Cannot create reference observations at MIDC site '
f'{site.name}, missing required parameters.')
return
# use site name without network here to build
# a name with the original column label rather than
# the SFA variable
site_name = common.site_name_no_network(site)
try:
site_df = fetch(api, site, start, end)
except error.HTTPError:
logger.error('Could not find data to create observations '
f'for SRML site {site_name}.')
return
else:
if site_df is None:
logger.error('Could not find data to create observations '
f'for SRML site {site_name}.')
return
for variable in srml_variable_map.keys():
matches = [col for col in site_df.columns
if col.startswith(variable)]
for match in matches:
observation_extra_parameters = extra_params.copy()
observation_extra_parameters.update({
'network_data_label': match})
try:
# Here, we pass a name with match instead of variable
# to differentiate between multiple observations of
# the same variable
common.create_observation(
api, site, srml_variable_map[variable],
name=f'{site_name} {match}',
interval_label='beginning',
extra_params=observation_extra_parameters)
except HTTPError as e:
logger.error(
f'Failed to create {variable} observation at Site '
f'{site.name}. Error: {e.response.text}')
with open(DEFAULT_SITEFILE) as fp:
obs_metadata = json.load(fp)['observations']
for obs in obs_metadata:
obs_site_extra_params = json.loads(obs['site']['extra_parameters'])
if obs_site_extra_params['network_api_id'] == extra_params[
'network_api_id']:
obs['site'] = site
observation = Observation.from_dict(obs)
common.check_and_post_observation(api, observation)
def initialize_site_forecasts(api, site):
"""
Create a forecasts for each variable measured at the site
Parameters
----------
api : :py:class:`solarforecastarbiter.io.api.APISession`
An active Reference user session.
site : :py:class:`solarforecastarbiter.datamodel.Site`
The site object for which to create Forecasts.
"""
variables = list(srml_variable_map.values())
if isinstance(site, SolarPowerPlant):
variables += ['ac_power', 'dc_power']
common.create_forecasts(
api, site, variables,
default_forecasts.TEMPLATE_FORECASTS)
def update_observation_data(api, sites, observations, start, end):
"""Post new observation data to a list of SRML Observations
from start to end.
api : :py:class:`solarforecastarbiter.io.api.APISession`
An active Reference user session.
sites: list of :py:class:`solarforecastarbiter.datamodel.Site`
List of all reference sites as Objects
observations: list of :py:class:`solarforecastarbiter.datamodel.Observation`
List of all reference observations as Objects
start : datetime
The beginning of the period to request data for.
end : datetime
The end of the period to request data for.
""" # noqa
srml_sites = common.filter_by_networks(sites, 'UO SRML')
for site in srml_sites:
common.update_site_observations(api, fetch, site, observations,
start, end)
|
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220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49706,
13,
43917,
7,
69,
6,
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407,
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1391,
7753,
62,
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92,
329,
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5805,
1366,
705,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
277,
6,
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2524,
1391,
15654,
13,
3672,
92,
319,
1391,
1941,
92,
14,
90,
8424,
92,
764,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49706,
13,
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6,
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25,
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62,
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92,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
2845,
279,
67,
13,
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13,
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220,
220,
220,
220,
220,
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92,
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8,
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21207,
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11,
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11,
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220,
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220,
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220,
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220,
220,
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220,
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286,
262,
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1366,
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257,
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6060,
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220,
220,
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220,
220,
220,
40103,
198,
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220,
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220,
220,
220,
220,
220,
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62,
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220,
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] | 2.454566 | 4,380 |
import os
import sys
# sys.path.insert(0, os.path.abspath('/home/jrodrigues/Documents/PhD/fulmar'))
from fulmar.estimators import (
estimate_planet_mass,
estimate_semi_amplitude
)
from fulmar.utils import (
FulmarWarning
)
import astropy.units as u
import numpy as np
import numpy.testing as npt
from astropy.units import UnitConversionError
import pytest
def test_estimate_planet_mass():
"""test if estimate_planet_mass behaves as expected"""
npt.assert_equal(estimate_planet_mass(
1, 'Earth').value, 1) # * u.earthMass)
npt.assert_almost_equal(estimate_planet_mass(
1, 'Neptune').value, 0.29706202) # * u.earthMass)
npt.assert_almost_equal(estimate_planet_mass(
1, 5514).value, 1) # * u.earthMass)
with pytest.raises(TypeError, match='`astropy.units.Quantity` or float'):
estimate_planet_mass('string', 'Earth')
with pytest.raises(ValueError, match="Accepted str values for rho_p"):
estimate_planet_mass(1, 'Uranus')
with pytest.raises(UnitConversionError):
estimate_planet_mass(1 * u.s, 'neptune')
def test_estimate_semi_amplitude():
"""test if estime_semi_amplitude behaves as exected"""
npt.assert_almost_equal(estimate_semi_amplitude(
365, 1, 1).value, 0.08948015) # * u.m / u.s)
npt.assert_almost_equal(estimate_semi_amplitude(
365, 1, R_planet=1, rho_planet='earth').value, 0.08948015) # * u.m / u.s)
npt.assert_equal(estimate_semi_amplitude(
365, 1, 1, inc=0).value, 0)
npt.assert_almost_equal(estimate_semi_amplitude(
365, 1, 1, ecc=0.5).value, 0.12654404) # * u.m / u.s)
with pytest.raises(TypeError, match='`astropy.units.Quantity` or float'):
estimate_planet_mass('1 earthRad', 'earth')
estimate_semi_amplitude('1 year', 1, 1)
estimate_semi_amplitude(365, '1 solMass', 1)
estimate_semi_amplitude(365, 1, M_planet='1 earthMass')
estimate_semi_amplitude(365, 1, M_planet=1 * u.earthMass, inc='90 deg')
with pytest.raises(ValueError, match='required when M_planet is not'):
estimate_semi_amplitude(365, 1)
estimate_semi_amplitude(365, 1, R_planet=1)
estimate_semi_amplitude(365, 1, rho_planet=1)
with pytest.warns(FulmarWarning, match='overrides'):
estimate_semi_amplitude(365, 1, 1, R_planet=1, rho_planet='earth')
|
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'''
This data was automatically generated by script img2data.py.
These images are part of the FAMFAMFAM silk icons set which is
provided under a creative commons license. For more information see
http://www.famfamfam.com/lab/icons/silk/
'''
data = {
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19,
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86,
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48,
53,
14636,
43,
73,
19,
80,
201,
198,
220,
220,
220,
220,
220,
220,
220,
289,
77,
49,
44526,
54,
87,
75,
42,
86,
57,
86,
33191,
23,
76,
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48,
82,
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33,
31522,
44,
14,
5840,
19,
74,
39,
38162,
55,
48,
71,
33,
89,
33,
87,
12740,
66,
70,
74,
24187,
4914,
36,
65,
15739,
1671,
81,
33,
89,
66,
16,
24723,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1395,
38162,
39,
45,
18,
46,
26502,
44,
2943,
48,
32,
1219,
33,
66,
4579,
6581,
4579,
33,
48,
4339,
33,
7336,
47920,
4743,
85,
5211,
5760,
70,
15,
10008,
48,
88,
36,
5760,
56,
20034,
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8874,
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13909,
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220,
220,
220,
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220,
220,
289,
9273,
24,
6998,
24,
73,
18,
84,
2390,
34,
312,
993,
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1050,
56,
72,
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3838,
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20,
66,
22296,
41374,
66,
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3398,
41275,
8924,
2943,
37,
88,
48802,
67,
528,
20,
32,
3824,
11682,
4106,
24334,
38,
86,
49,
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220,
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220,
220,
220,
220,
220,
308,
54,
56,
8264,
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67,
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33901,
33484,
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3856,
37,
48,
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10,
57,
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35,
80,
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29697,
22,
201,
198,
220,
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10148,
3256,
201,
198,
201,
198,
92,
201,
198
] | 1.422896 | 18,021 |
"""
.. module:: create
:synopsis: Import a Word `docx` document, define its metadata, cover and rights, and publish it as an EPUB3.
.. moduleauthor:: Gavin Chait <github.com/turukawa>
CreateWork
==========
Publish a standards compliant EPUB3 creative work from a source Microsoft Word `docx` document, and define its
metadata, cover and publishing rights. Currently does not support `odt` since `Pandoc` seems to lose any embedded
graphics.
.. note:: This process will overwrite any existing EPUB3 file of the same name, if it already exists.
Workflow
--------
There are two main publication approaches, stateless and non-stateless. A non-stateless approach assumes you may be
starting each step discretely (perhaps via a set of one-time network calls). The second maintains state, so you can
complete the process in one step.
The *stateless* publication process runs as follows:
* Set the working directory on creation,
* Define and validate the metadata required for the creative work,
* Copy the `docx` file to import into the working directory,
* Copy the cover image to import into the working directory,
* Define and add any contributors, such as cover artist,
* Update the creative work's publication rights,
* Add in an optional dedication,
* Build the creative work,
* Validate the work is EPUB3 standards compliant.
The objective of this workflow is to support what may be a stateless process i.e. the individual steps first bring all
the data required to produce the creative work into a project directory, and then produces it. State does not need
to be maintained between steps.
The *non-stateless* process runs as follows:
* Define and validate the metadata required for the creative work,
* Supply the `docx` file as a base64 string,
* Copy the cover image as a base64 string,
* Add in an optional dedication,
* Build the creative work,
* Validate the work is EPUB3 standards compliant.
The objective in a non-stateless workflow is to minimise disruption, and store the minimum amount of information. Only
the epub itself will be saved, and then only because Pandoc does not support a memory-only epub build.
Build your work
---------------
Import **Chapisha** and create a work:
.. code-block:: python
from chapisha.create import CreateWork
work = CreateWork(directory)
Where `directory` is the complete path to where you would like the EPUB created. If you want a stateless workflow,
set the `stateless` boolean to `True`. If you already have the `metadata` (perhaps via a web form), you can skip
several steps and pick up again for setting the files and images.
.. code-block:: python
from chapisha.create import CreateWork
work = CreateWork(directory, metadata=metadata, stateless=True)
Set metadata
^^^^^^^^^^^^
`Dublin Core <https://www.dublincore.org/specifications/dublin-core/dces/>`_ is a vocabulary of fifteen properties for
use in resource description. Four of them - `title`, `identifier`, `language` and `rights` - are required. The
`language` code is defined by the `ISO 679-1 <https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes>`_ standard
(e.g. `en` for English, or `fr` for French).
Metadata properties:
* `identifier`: UUID, DOI or ISBN of the creative work. A UUID will be generated if not included.
* `title`: Name given to the creative work.
* `language`: Specify the language of the creative work. Two letter code defined by ISO 639-1.
* `creator`: Name of a person, organisation, etc. responsible for the creation of the work. May be more than one.
* `work_uri`: The URI for your creative work.
* `contributor`: Name of a person, organisation, etc. that played a secondary role - such as an editor - in the creation of the work. May be more than one.
* `date`: The publication date of the creative work. Provide in ISO format, YYYY-MM-DD.
* `subject`: The subject, or tag, of the creative work. May be more than one.
* `publisher`: Name of a person, organisation, etc. responsible for making the creative work available.
* `publisher_uri`: The URI for the publisher of your creative work.
* `rights`: A short, single-sentence statement of copyright and publication terms for the creative work, e.g. 'All rights reserved.' or 'Attribution-NonCommercial-ShareAlike 4.0 International.'
* `long_rights`: Lengthier description and information about copyright held in and over the creative work. Formatted as you wish it to appear.
* `description`: A short, single-sentence summary of the creative work.
* `long_description`: The pitch, or jacket-cover, description of the creative work.
Create a paired dictionary of these properties. As example:
.. code-block:: python
METADATA = {
"identifier": "isbn:9780993191459",
"title": "Usan Abasi's Lament",
"description": "Years after the events of \"Lament for the Fallen\", Isaiah tells of the myth of Usan Abasi, who was punished by the Sky God to spend eternity in the form of a brass bowl and imprisoned within a vast termite mountain. Now the ceremony which ensures that Usan Abasi remains dormant has failed, and his ancient evil awakes. A free, stand-alone short-story set in the city of Ewuru and linking \"Lament for the Fallen\" to a forthcoming novel.",
"language": "en",
"creator": ["Gavin Chait"],
"rights": "All rights reserved.",
"long_rights": ["The right of the creator to be identified as the author of the Work has been asserted by them in accordance with the Copyright, Designs and Patents Act 1988. This creator supports copyright. Copyright gives creators space to explore and provides for their long-term ability to sustain themselves from their work. Thank you for buying this work and for complying with copyright laws by not reproducing, scanning, or distributing any part of it without permission. Your support will contribute to future works by the creator."],
"publisher": "Qwyre Publishing",
"publisher_uri": "https://qwyre.com",
"work-uri": "https://gavinchait.com",
"date": "2017-07-23",
"subject": ["science fiction", "african mythology"]
}
Set the metadata:
.. code-block:: python
work.set_metadata(METADATA)
Set document
^^^^^^^^^^^^
Most writers still use `Microsoft Word <https://www.microsoft.com/en-us/microsoft-365/word>`_ as their default work tool.
There are certainly other word processors, but this is the one most people will work with if they intend to be
professionally published as publishers still expect Word `docx` files for editing and markup.
**Chapisha** will create your cover, rights and dedication pages, as well as the table of contents. Your `docx` file
must contain **only** the creative content you wish included in that table of contents. Your document must also be
correctly marked up to ensure proper chapter creation.
EPUB documents will be read on multiple and diverse electronic devices. Don't have any expectations for page
number-dependant formatting. Instead:
* Each chapter must have a title, formatted as `Heading 1`, with lower-level headings formatted for each heading type.
* There must be no title page, contents, or anything else. Chapter 1 starts at the top of the first line of the document.
* Page numbers and other page-specific information will be lost.
* Fonts or typographic formats and alignment will be lost, although `bold` and `italics` will be maintained.
* Images will be maintained.
Once the work is built you can enhance its styling. However, there are still limits in the EPUB3 standard in comparison
to a printed work.
.. code-block:: python
work.set_document(source)
Where `source` is any of the complete path to the source `docx` file, a `bytes` file import, or a `base64` string.
Set cover
^^^^^^^^^
There is, unfortunately, no standardisation on the image size, dimensions or resolution required for an EPUB. However,
a recommendation is an image (`.jpeg`, `.jpg` or `.png`) of 1,600 by 2,400 pixels, and less than 5Mb is size. You will
need to create your image (or have someone create it for you) exactly as you wish it to appear on the cover. Nothing
will be added, removed, or changed.
Please also ensure you have the appropriate rights to use the image on your cover. There are more than sufficient
services providing openly-licenced, or even public domain, work for you to use.
.. note:: You can optionally add the image contributor details here, or on the next step. Do not do it in both or the contributor information will be repeated.
Example code:
.. code-block:: python
CONTRIBUTOR = {
"role": "artist",
"name": "Rodd Halstead",
"terms": "Cover image 'Red Maple Fruit (Samara)' photograph. All rights reserved. Used under licence.",
"year": "2006"
}
work.set_cover(source, contributor=CONTRIBUTOR)
Where `source` is the complete path to the image file, a `bytes` file import, or a `base64` string.
Add contributors
^^^^^^^^^^^^^^^^
You may have numerous contributors you wish to acknowledge. Fields are:
* `role`: Contributor identity, based on a specified list of `artist`, `editor` or `translator`.
* `name`: Name of a person, organisation, etc. that played a secondary role - such as an editor - in the creation of the work.
* `terms`: Information about copyright held by the rights-holder in and over their contribution to the creative work. Formatted as you wish it to appear.
* `year`: The year of the contribution or publication of the contributor's work.
Example code:
.. code-block:: python
CONTRIBUTOR = {
"role": "artist",
"name": "Rodd Halstead",
"terms": "Cover image 'Red Maple Fruit (Samara)' photograph. All rights reserved. Used under licence.",
"year": "2006"
}
work.add_contributor(CONTRIBUTOR)
`add_contributor` as many times as you have people or organisations to acknowledge.
Set rights
^^^^^^^^^^
This refers to the `long_rights` you can set, and which you may wish to adjust for presentation on the colophon page.
There are obviously a broad range of rights with which you can release your creative work. Here are two examples which
you can modify as you require.
* Commercial copyright with all rights reserved:
The right of the creator to be identified as the author of the Work has been asserted by them in
accordance with the Copyright, Designs and Patents Act 1988. This creator supports copyright. Copyright
gives creators space to explore and provides for their long-term ability to sustain themselves from
their work. Thank you for buying this work and for complying with copyright laws by not reproducing,
scanning, or distributing any part of it without permission. Your support will contribute to future
works by the creator.
* Commercial copyright but licenced for distribution under Attribution-NonCommercial-ShareAlike 4.0 International (`CC BY-NC-SA 4.0 <https://creativecommons.org/licenses/by-nc-sa/4.0/>`_):
You are free to copy and redistribute the Work in any medium or format, and remix, transform, and build
upon the Work. The creator cannot revoke these freedoms as long as you follow the license terms.
In return: You may not use the material for commercial purposes. You must give appropriate credit, provide
a link to this license, and indicate if changes were made. You may do so in any reasonable manner, but not
in any way that suggests the creator endorses you or your use. If you remix, transform, or build upon the
material, you must distribute your contributions under the same license as the original. You may not apply
legal terms or technological measures that legally restrict others from doing anything the license
permits.
Example code:
.. code-block:: python
RIGHTS = [
"You are free to copy and redistribute the Work in any medium or format, and remix, transform, and build upon the Work. The creator cannot revoke these freedoms as long as you follow the license terms.",
"In return: You may not use the material for commercial purposes. You must give appropriate credit, provide a link to this license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the creator endorses you or your use. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits."
]
work.set_rights(RIGHTS)
Rights terms can be one line of text, or several. If several, each line must be provided as a separate term in a `list`.
Set dedication
^^^^^^^^^^^^^^
Most creators have a dedication for their work in mind - usually to apologise for all the late nights and impoverishing
returns on their creative efforts.
This is optional, but you can include a dedication page. Each item in the list will be set on a different paragraph.
.. code-block:: python
dedication = [
"For those who leave.",
"For those who remain.",
"For the wings and tail.",
"But most, for her"
]
work.set_dedication(dedication)
The dedication can be one line of text, or several. If several, each line must be provided as a separate term in a `list`.
Build
^^^^^
The build function is straightforward. Once everything is in place:
.. code-block:: python
work.build()
You will find your EPUB in the directory you specified.
Validate
^^^^^^^^
If you have any doubts as to whether your EPUB is standards compliant, run the validation. This tests the `epub` file
against the standards maintained by the `DAISY Consortium <http://validator.idpf.org/>`_. You can check the file online
at that link. It's the same test.
.. code-block:: python
work.validate()
Output will be `True` or `False`.
"""
import pypandoc
from bs4 import BeautifulSoup
from epubcheck import EpubCheck
from typing import Optional, Literal, List
from urllib.parse import urlparse
from pathlib import Path
import os
import re
import base64
import filetype
from ..models.metadata import WorkMetadata, Contributor
from ..models.matter import Matter, MatterPartition
from ..helpers import pages, formats, coreio as _c
from ..helpers.updatezipfile import UpdateZipFile
class CreateWork:
"""
Publish a standards compliant EPUB3 creative work from a source Microsoft Word `docx` document, and
define its metadata, cover and publishing rights.
If the EPUB file already exists, then publishing this work will overwrite it.
On instantiation, checks `directory` to see if `DEFAULT_METADATA_SETTINGS` is present, loading the required data,
or replacing with specified defaults.
"""
def __init__(self,
directory: Optional[str] = None,
metadata: Optional[WorkMetadata] = None,
stateless: bool = False):
"""
Initialise the CreateWork class.
Parameters
----------
directory: str
A directory path where you would like to save your work.
metadata: WorkMetadata
A model defined by a dictionary of terms.
stateless: bool
Whether your workflow is stateless (default False).
"""
self.stateless = stateless
self.directory = Path(directory)
if self.stateless:
_c.check_path(self.directory)
# Load metadata settings, if exists
try:
_c.check_source(self.directory / _c.DEFAULT_METADATA_SETTINGS)
self.metadata = WorkMetadata(_c.load_json(self.directory / _c.DEFAULT_METADATA_SETTINGS))
self.work_name = self.directory.name # Since will be `.../work-name/`
except FileNotFoundError:
self.metadata = None
self.work_name = None
# Construct the metadata, if it is provided
if metadata:
if isinstance(metadata, WorkMetadata):
metadata = metadata.dict()
self.set_metadata(metadata)
self.source_path = _c.get_helper_path() / "data"
# Set default cover and work bytes
self.work = None
self.cover = None
self.dedication = None
############################################################################
# GATHER WORKING DATA
############################################################################
def get_metadata_schema(self) -> dict:
"""
Return the standard Dublin Core schema permitted for the EPUB3 standard.
Returns
-------
dict
"""
return self.metadata.schema()
def set_metadata(self, metadata: WorkMetadata) -> bool:
"""
Validate metadata values for the permitted Dublin Core schema terms, along with additional metadata. The full
schema, with descriptions, and requirements, is listed by `get_metadata_schema`.
.. note:: The terms `identifier`, `title`, `creator`, `rights` and `language` are required. A random UUID will be assigned if none is provided.
Parameters
----------
metadata: WorkMetadata
A model defined by a dictionary of terms.
Returns
-------
bool
"""
# Dict snake_case fields need to be hyphenated for import
# This as a result of alias names in model
if isinstance(metadata, dict):
for k in [k for k in metadata.keys()]:
hyphenated = "-".join(k.split("_"))
metadata[hyphenated] = metadata.pop(k)
# Rename 'isodate' if it exists
if "isodate" in metadata:
metadata["date"] = metadata.pop("isodate")
# Fix "long-rights" if needed
if "long-rights" in metadata:
metadata["long-rights"] = formats.get_text_paragraphs(metadata["long-rights"])
# Create a temporary WorkMetadata model to hold updated metadata
updated_metadata = WorkMetadata(**metadata)
# And update the original data
# https://fastapi.tiangolo.com/tutorial/body-updates/#partial-updates-with-patch
if self.metadata:
self.metadata = self.metadata.copy(update=updated_metadata.dict(exclude_unset=True))
else:
self.metadata = updated_metadata
work_name = "-".join(["".join([e for e in w if e.isalnum()])
for w in self.metadata.title.lower().split(" ")])
# Set the working directory, if it isn't already, and save metadata there
if not self.work_name:
self.work_name = work_name
self.directory = self.directory / work_name
# If stateless, save the metadata to the working folder
if self.stateless:
_c.check_path(self.directory)
_c.save_json(self.metadata.dict(by_alias=True), self.directory / _c.DEFAULT_METADATA_SETTINGS, overwrite=True)
return True
def _get_validated_bytes(self,
source: [Path, bytes],
base_type: Optional[List[Literal["cover", "work"]]] = None) -> bytes:
"""
Validate a source file, and return a bytes version.
Parameters
----------
source: Path, bytes or base64 string
Filename to open, base64 string, or bytes from an opened file
base_type: Optional, str
Must be one of "cover" or "work" for interpreting base64 mime type
Raises
------
PermissionError: if metadata not yet validated.
FileNotFoundError: if the source is not valid.
Returns
-------
bytes
"""
if not self.metadata:
e = "`set_metadata` before setting source document."
raise PermissionError(e)
if isinstance(source, Path):
try:
_c.check_source(source)
with open(source, "rb") as f:
source = f.read()
except FileNotFoundError:
e = F"`{source}` is not a valid file source."
raise FileNotFoundError(e)
if isinstance(source, str) and base_type:
# Base64 string, remove any provided mime type
source_type = re.search(_c.DEFAULT_BASE64_TYPES[base_type], source)
if source_type:
source = source.replace(source_type.group(0), "")
source = base64.b64decode(source)
if not isinstance(source, bytes):
e = F"File is not valid."
raise FileNotFoundError(e)
return source
def set_document(self, source: [Path, bytes, str]):
"""
Import source `docx` document and, if stateless, save to the working directory. If you're finding errors in
the build step, it could be you need to convert your base64 string to "utf-8" (`source.decode("utf-8")`).
Parameters
----------
source: Path, bytes, or str
Filename to open, bytes from an opened file, or a base64 string
Raises
------
PermissionError: if metadata not yet validated.
FileNotFoundError: if the source is not valid.
"""
if not self.work_name or not self.metadata:
e = "`set_metadata` before setting source document."
raise PermissionError(e)
source = self._get_validated_bytes(source, base_type = "work")
if self.stateless:
with open(self.directory / F"{self.work_name}.docx", "wb") as w:
w.write(source)
else:
self.work = source
def set_cover(self,
source: [Path, bytes],
contributor: Optional[Contributor] = None):
"""
Import cover image and, if stateless, save to the working directory, along with any rights and contributor
information. If you're finding errors in the build step, it could be you need to convert your base64 string to
"utf-8" (`source.decode("utf-8")`).
Parameters
----------
source: Path or bytes
Filename to open, including path, or bytes for file
contributor: Contributor
Optional, string indicating contributor name for cover image.
Raises
------
PermissionError: if metadata not yet validated.
FileNotFoundError: if the source is not valid.
"""
if not self.work_name or not self.metadata:
e = "`set_metadata` before setting cover."
raise PermissionError(e)
# Cover contributor
if contributor:
if self.metadata.contributor is None:
self.metadata.contributor = []
self.metadata.contributor.append(Contributor(**contributor))
# Cover image
source = self._get_validated_bytes(source, base_type = "cover")
if self.stateless:
kind = filetype.guess(source).extension
with open(self.directory / F"cover.{kind}", "wb") as w:
w.write(source)
_c.save_json(self.metadata.dict(by_alias=True),
self.directory / _c.DEFAULT_METADATA_SETTINGS,
overwrite=True)
else:
self.cover = source
def add_contributor(self, contributor: Contributor):
"""
Add a contributor to the list of those supporting the creation of the work. `contributor` is defined as a dict:
.. code-block:: python
contributor = {
"role": "artist",
"name": "Great Artist",
"year": "2021",
"terms": "Public Domain."
}
Parameters
----------
contributor: Contributor
Include the types of contributor who supported the creation of the work. `role`: `artist`, `editor`, `translator`.
Raises
------
PermissionError: if metadata not yet validated.
"""
if not self.work_name or not self.metadata:
e = "`set_metadata` before adding contributors, or add the contributors when you set the metadata."
raise PermissionError(e)
if self.metadata.contributor is None:
self.metadata.contributor = []
self.metadata.contributor.append(Contributor(**contributor))
_c.save_json(self.metadata.dict(by_alias=True), self.directory / _c.DEFAULT_METADATA_SETTINGS, overwrite=True)
def set_dedication(self, dedication: [str, list[str]]):
"""
Set dedication page for creative work. Provide as a string, unless it is on multiple paragraphs.
Parameters
----------
dedication: str or list of str
Provide as a string, or list of strings for multiple paragraphs.
"""
if not self.work_name or not self.metadata:
e = "`set_metadata` before setting dedication."
raise PermissionError(e)
self.dedication = pages.create_dedication_xhtml(dedication)
if self.stateless:
with open(self.directory / F"dedication.xhtml", "w") as w:
w.write(self.dedication)
def set_rights(self, rights: [str, list[str]]):
"""
Set publication `long_rights` for creative work. Provide as a string, or list of strings if it is on multiple
paragraphs.
There are multiple appropriate rights, and two examples are below. Modify as you require.
* Commercial copyright with all rights reserved:
The right of the creator to be identified as the author of the Work has been asserted by them in
accordance with the Copyright, Designs and Patents Act 1988. This creator supports copyright. Copyright
gives creators space to explore and provides for their long-term ability to sustain themselves from
their work. Thank you for buying this work and for complying with copyright laws by not reproducing,
scanning, or distributing any part of it without permission. Your support will contribute to future
works by the creator.
* Commercial copyright but licenced for distribution under Attribution-NonCommercial-ShareAlike 4.0 International (`CC BY-NC-SA 4.0 <https://creativecommons.org/licenses/by-nc-sa/4.0/>`_):
You are free to copy and redistribute the Work in any medium or format, and remix, transform, and build
upon the Work. The creator cannot revoke these freedoms as long as you follow the license terms.
In return: You may not use the material for commercial purposes. You must give appropriate credit, provide
a link to this license, and indicate if changes were made. You may do so in any reasonable manner, but not
in any way that suggests the creator endorses you or your use. If you remix, transform, or build upon the
material, you must distribute your contributions under the same license as the original. You may not apply
legal terms or technological measures that legally restrict others from doing anything the license
permits.
Parameters
----------
rights: str or list of str
Provide as a string, or list of strings for multiple paragraphs.
"""
if not self.work_name or not self.metadata:
e = "`set_metadata` before setting rights."
raise PermissionError(e)
if isinstance(rights, str):
rights = [rights]
self.metadata.long_rights = rights
_c.save_json(self.metadata.dict(by_alias=True), self.directory / _c.DEFAULT_METADATA_SETTINGS, overwrite=True)
############################################################################
# BUILD CREATIVE WORK
############################################################################
def build(self):
"""
Automatically build the creative work as a standards compliant EPUB3. Save to the root directory.
"""
if not self.work_name or not self.metadata:
e = "`set_metadata` before building creative work."
raise PermissionError(e)
epub_path = self.directory.parent / F"{self.work_name}.epub"
# Generate the initial creative content using Pandoc
# pypandoc can't handle PosixPaths ...
if self.stateless:
pypandoc.convert_file(str(self.directory / F"{self.work_name}.docx"),
format="docx",
to="epub3",
outputfile=str(epub_path))
else:
# Maybe one day Pandoc can return an epub object and we won't save the interim file
pypandoc.convert_text(self.work,
format="docx",
to="epub3",
outputfile=str(epub_path))
# Generate the epub version
with UpdateZipFile(epub_path, "a") as w:
# REMOVES
REMOVES = ["EPUB/styles/stylesheet1.css", "EPUB/text/title_page.xhtml", "EPUB/nav.xhtml"]
# DEFAULT COMPONENTS
DEFAULT = [(self.source_path / "css" / "core.css", "EPUB/css/core.css"),
(self.source_path / "images" / "logo.svg", "EPUB/images/logo.svg"),
(self.source_path / "xhtml" / "onix.xml", "EPUB/onix.xml"),
(self.source_path / "xhtml" / "container.xml", "META-INF/container.xml")]
for default_file, write_file in DEFAULT:
w.write(default_file, write_file)
# DEFAULT FONTS
for f in os.listdir(self.source_path / "fonts"):
w.write(self.source_path / "fonts" / f, F"EPUB/fonts/{f}")
# ADD titlepage.xhtml
w.writestr("EPUB/text/titlepage.xhtml", pages.create_titlepage_xhtml(self.metadata))
# ADD colophon.xhtml
w.writestr("EPUB/text/colophon.xhtml", pages.create_colophon_xhtml(self.metadata))
# ADD cover.img
if self.stateless:
for image_path in [self.directory / F"cover.{t}" for t in ["jpg", "jpeg", "png", "gif", "svg"]]:
if image_path.exists():
w.write(image_path, F"EPUB/images/{image_path.name}")
elif self.cover:
t = filetype.guess(self.cover).extension
w.writestr(F"EPUB/images/cover.{t}", self.cover)
# GET DEDICATION and CHAPTERS
spine = []
# check if the path to dedication exists, if it does, add it to the work and spine
if (self.directory / "dedication.xhtml").exists() or self.dedication:
if self.dedication:
w.writestr("EPUB/text/dedication.xhtml", self.dedication)
else:
w.write(self.directory / "dedication.xhtml", "EPUB/text/dedication.xhtml")
spine = [Matter(partition="frontmatter", content="dedication", title="Dedication")]
CHAPTERS = [f for f in w.namelist() if f.startswith("EPUB/text/ch")]
CHAPTERS.sort()
self.metadata.word_count = 0
for chapter in CHAPTERS:
file_as = F"EPUB/text/chapter-{chapter.split('.')[0][-1]}.xhtml"
try:
chapter_xml = w.read(chapter)
except KeyError:
continue
if file_as != chapter:
# If delete and then re-add same file, causes ZipFile confusion
REMOVES.append(chapter)
# Restructure chapter xml into standard format
chapter_xml = pages.restructure_chapter(chapter_xml)
chapter_title = chapter_xml.title.string
# Count the words (XHTML and HTML treated differently by BeautifulSoup, so first extract `section`)
words = BeautifulSoup(str(chapter_xml.section), "lxml").get_text()
self.metadata.word_count += len(words.replace("\n", " ").replace(" ", " ").strip().split())
w.writestr(file_as, str(chapter_xml))
spine.append(Matter(partition=MatterPartition.body, title=chapter_title))
# PANDOC MAY STILL ADD IMAGES FOUND IN THE WORK WHICH WE NEED TO DISCOVER AND ADD TO THE MANIFEST
# NOTE, these are not only to be added to the manifest, but the folder renamed as well
image_manifest = [f.replace("EPUB/", "") for f in w.namelist() if f.startswith("EPUB/images/")]
for img in [f for f in w.namelist() if f.startswith("EPUB/media/")]:
REMOVES.append(img)
new_img = img.replace("/media/", "/images/")
try:
old_img = w.read(img)
w.writestr(new_img, old_img)
except KeyError:
continue
image_manifest.append(new_img.replace("EPUB/", ""))
# ADD content.opf
w.writestr("EPUB/content.opf", pages.create_content_opf(self.metadata, image_manifest, spine))
# ADD toc.ncx
w.writestr("EPUB/toc.ncx", pages.create_toc_ncx(self.metadata, spine))
# ADD toc.xhtml
w.writestr("EPUB/toc.xhtml", pages.create_toc_xhtml(self.metadata, spine))
# PERFORM REMOVES
for remove in REMOVES:
try:
w.remove_file(remove)
except KeyError:
continue
def validate(self) -> bool:
"""
Validate the creative work as a standards compliant EPUB3.
"""
epub_path = self.directory.parent / F"{self.work_name}.epub"
_c.check_source(epub_path)
result = EpubCheck(epub_path)
return result.valid
|
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] | 2.706205 | 12,587 |
from __future__ import unicode_literals
import glob
import pdb
from builtins import range
from imp import reload
from io import open
import numpy as np
from dlinputs import tarrecords
reload(tarrecords)
# get_ipython().system(u'tar -ztvf testdata/imagenet-000000.tgz | sed 7q')
# get_ipython().system(u'tar xvf testdata/imagenet-000000.tgz 10.png')
# get_ipython().system(u'file 10.png')
|
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] | 2.783217 | 143 |
#!/usr/bin/python
#
# Copyright 2016 Canonical Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import pcmk
import json
import os
import re
import subprocess
import socket
import fcntl
import struct
import time
import xml.etree.ElementTree as ET
import itertools
from base64 import b64decode
from charmhelpers.core.strutils import (
bool_from_string,
)
from charmhelpers.core.hookenv import (
local_unit,
log,
TRACE,
DEBUG,
ERROR,
INFO,
WARNING,
leader_get,
leader_set,
relation_get,
relation_set,
related_units,
relation_ids,
config,
unit_get,
status_set,
)
from charmhelpers.core import unitdata
from charmhelpers.contrib.openstack.utils import (
get_host_ip,
set_unit_paused,
clear_unit_paused,
is_unit_paused_set,
is_unit_upgrading_set,
)
from charmhelpers.contrib.openstack.ha.utils import (
assert_charm_supports_dns_ha
)
from charmhelpers.core.host import (
mkdir,
rsync,
service_start,
service_stop,
service_running,
write_file,
file_hash,
lsb_release,
init_is_systemd,
CompareHostReleases,
)
from charmhelpers.fetch import (
apt_install,
add_source,
apt_update,
)
from charmhelpers.contrib.hahelpers.cluster import (
peer_ips,
)
from charmhelpers.contrib.network import ip as utils
import netifaces
from netaddr import IPNetwork
import jinja2
TEMPLATES_DIR = 'templates'
COROSYNC_CONF = '/etc/corosync/corosync.conf'
COROSYNC_DEFAULT = '/etc/default/corosync'
COROSYNC_AUTHKEY = '/etc/corosync/authkey'
COROSYNC_HACLUSTER_ACL_DIR = '/etc/corosync/uidgid.d'
COROSYNC_HACLUSTER_ACL = COROSYNC_HACLUSTER_ACL_DIR + '/hacluster'
COROSYNC_CONF_FILES = [
COROSYNC_DEFAULT,
COROSYNC_AUTHKEY,
COROSYNC_CONF,
COROSYNC_HACLUSTER_ACL,
]
SUPPORTED_TRANSPORTS = ['udp', 'udpu', 'multicast', 'unicast']
PCMKR_CONFIG_DIR = '/etc/pacemaker'
PCMKR_AUTHKEY = PCMKR_CONFIG_DIR + '/authkey'
PCMKR_MAX_RETRIES = 3
PCMKR_SLEEP_SECS = 5
SYSTEMD_OVERRIDES_DIR = '/etc/systemd/system/{}.service.d'
SYSTEMD_OVERRIDES_FILE = '{}/overrides.conf'
MAAS_DNS_CONF_DIR = '/etc/maas_dns'
STONITH_CONFIGURED = 'stonith-configured'
def nulls(data):
"""Returns keys of values that are null (but not bool)"""
return [k for k in data.keys()
if not isinstance(data[k], bool) and not data[k]]
def emit_systemd_overrides_file():
"""Generate the systemd overrides file
With Start and Stop timeout values
Note: (David Ames) Bug#1654403 Work around
May be removed if bug is resolved
If timeout value is set to -1 pass infinity
"""
if not init_is_systemd():
return
stop_timeout = int(config('service_stop_timeout'))
if stop_timeout < 0:
stop_timeout = 'infinity'
start_timeout = int(config('service_start_timeout'))
if start_timeout < 0:
start_timeout = 'infinity'
systemd_overrides_context = {'service_stop_timeout': stop_timeout,
'service_start_timeout': start_timeout,
}
for service in ['corosync', 'pacemaker']:
overrides_dir = SYSTEMD_OVERRIDES_DIR.format(service)
overrides_file = SYSTEMD_OVERRIDES_FILE.format(overrides_dir)
if not os.path.isdir(overrides_dir):
os.mkdir(overrides_dir)
write_file(path=overrides_file,
content=render_template('systemd-overrides.conf',
systemd_overrides_context))
# Update systemd with the new information
subprocess.check_call(['systemctl', 'daemon-reload'])
def get_pcmkr_key():
"""Return the pacemaker auth key"""
return config('pacemaker_key') or config('corosync_key')
def assert_charm_supports_ipv6():
"""Check whether we are able to support charms ipv6."""
_release = lsb_release()['DISTRIB_CODENAME'].lower()
if CompareHostReleases(_release) < "trusty":
msg = "IPv6 is not supported in the charms for Ubuntu " \
"versions less than Trusty 14.04"
status_set('blocked', msg)
raise Exception(msg)
def get_ipv6_addr():
"""Exclude any ip addresses configured or managed by corosync."""
excludes = []
for rid in relation_ids('ha'):
for unit in related_units(rid):
resources = parse_data(rid, unit, 'resources')
for res in resources.values():
if 'ocf:heartbeat:IPv6addr' in res:
res_params = parse_data(rid, unit, 'resource_params')
res_p = res_params.get(res)
if res_p:
for k, v in res_p.values():
if utils.is_ipv6(v):
log("Excluding '%s' from address list" % v,
level=DEBUG)
excludes.append(v)
return utils.get_ipv6_addr(exc_list=excludes)[0]
def get_node_flags(flag):
"""Nodes which have advertised the given flag.
:param flag: Flag to check peers relation data for.
:type flag: str
:returns: List of IPs of nodes that are ready to join the cluster
:rtype: List
"""
hosts = []
if config('prefer-ipv6'):
hosts.append(get_ipv6_addr())
else:
hosts.append(unit_get('private-address'))
for relid in relation_ids('hanode'):
for unit in related_units(relid):
if relation_get(flag, rid=relid, unit=unit):
hosts.append(relation_get('private-address',
rid=relid,
unit=unit))
hosts.sort()
return hosts
def get_cluster_nodes():
"""Nodes which have advertised that they are ready to join the cluster.
:returns: List of IPs of nodes that are ready to join the cluster
:rtype: List
"""
return get_node_flags('ready')
def get_member_ready_nodes():
"""List of nodes which have advertised that they have joined the cluster.
:returns: List of IPs of nodes that have joined thcluster.
:rtype: List
"""
return get_node_flags('member_ready')
def parse_data(relid, unit, key):
"""Helper to detect and parse json or ast based relation data"""
_key = 'json_{}'.format(key)
data = relation_get(_key, unit, relid) or relation_get(key, unit, relid)
if data:
try:
return json.loads(data)
except (TypeError, ValueError):
return ast.literal_eval(data)
return {}
def configure_monitor_host():
"""Configure extra monitor host for better network failure detection"""
log('Checking monitor host configuration', level=DEBUG)
monitor_host = config('monitor_host')
if monitor_host:
if not pcmk.crm_opt_exists('ping'):
log('Implementing monitor host configuration (host: %s)' %
monitor_host, level=DEBUG)
monitor_interval = config('monitor_interval')
cmd = ('crm -w -F configure primitive ping '
'ocf:pacemaker:ping params host_list="%s" '
'multiplier="100" op monitor interval="%s" ' %
(monitor_host, monitor_interval))
pcmk.commit(cmd)
cmd = ('crm -w -F configure clone cl_ping ping '
'meta interleave="true"')
pcmk.commit(cmd)
else:
log('Reconfiguring monitor host configuration (host: %s)' %
monitor_host, level=DEBUG)
cmd = ('crm -w -F resource param ping set host_list="%s"' %
monitor_host)
else:
if pcmk.crm_opt_exists('ping'):
log('Disabling monitor host configuration', level=DEBUG)
pcmk.commit('crm -w -F resource stop ping')
pcmk.commit('crm -w -F configure delete ping')
def configure_cluster_global(failure_timeout, cluster_recheck_interval=60):
"""Configure global cluster options
:param failure_timeout: Duration in seconds (measured from the most recent
failure) to wait before resetting failcount to 0.
:type failure_timeout: int
:param cluster_recheck_interval: Duration in seconds for the polling
interval at which the cluster checks for
changes in the resource parameters,
constraints or other cluster options.
:type cluster_recheck_interval: int
"""
log('Applying global cluster configuration', level=DEBUG)
# NOTE(lathiat) quorum in a two-node scenario is handled by
# corosync two_node=1. In this case quorum is required for
# initial cluster startup but not if a node was previously in
# contact with the full cluster.
log('Configuring no-quorum-policy to stop', level=DEBUG)
cmd = "crm configure property no-quorum-policy=stop"
pcmk.commit(cmd)
cmd = ('crm configure rsc_defaults $id="rsc-options" '
'resource-stickiness="100" '
'failure-timeout={}'.format(failure_timeout))
pcmk.commit(cmd)
log('Configuring cluster-recheck-interval to {} seconds'.format(
cluster_recheck_interval), level=DEBUG)
cmd = "crm configure property cluster-recheck-interval={}".format(
cluster_recheck_interval)
pcmk.commit(cmd)
def remove_legacy_maas_stonith_resources():
"""Remove maas stoniths resources using the old name."""
stonith_resources = pcmk.crm_maas_stonith_resource_list()
for resource_name in stonith_resources:
pcmk.commit(
'crm -w -F resource stop {}'.format(resource_name))
pcmk.commit(
'crm -w -F configure delete {}'.format(resource_name))
def configure_null_stonith_resource(stonith_hostnames):
"""Create null stonith resource for the given hostname.
:param stonith_hostnames: The hostnames that the stonith management system
refers to the remote node as.
:type stonith_hostname: List
"""
ctxt = {
'stonith_plugin': 'stonith:null',
'stonith_hostnames': stonith_hostnames,
'stonith_resource_name': 'st-null',
'resource_params': (
"params hostlist='{hostnames}' "
"op monitor interval=25 start-delay=25 "
"timeout=25")}
_configure_stonith_resource(ctxt)
# NOTE (gnuoy): Not enabling the global stonith-enabled setting as it
# does not make sense to have stonith-enabled when the only resources
# are null resources, so defer enabling stonith-enabled to the 'real'
# stonith resources.
return {ctxt['stonith_resource_name']: ctxt['stonith_plugin']}
def configure_maas_stonith_resource(stonith_hostnames):
"""Create maas stonith resource for the given hostname.
:param stonith_hostnames: The hostnames that the stonith management system
refers to the remote node as.
:type stonith_hostname: List
"""
ctxt = {
'stonith_plugin': 'stonith:external/maas',
'stonith_hostnames': stonith_hostnames,
'stonith_resource_name': 'st-maas',
'url': config('maas_url'),
'apikey': config('maas_credentials'),
'resource_params': (
"params url='{url}' apikey='{apikey}' hostnames='{hostnames}' "
"op monitor interval=25 start-delay=25 "
"timeout=25")}
_configure_stonith_resource(ctxt)
return {ctxt['stonith_resource_name']: ctxt['stonith_plugin']}
def enable_stonith():
"""Enable stonith via the global property stonith-enabled.
:raises: EnableStonithFailed
"""
log('Enabling STONITH', level=INFO)
try:
pcmk.commit(
"crm configure property stonith-enabled=true",
failure_is_fatal=True)
except subprocess.CalledProcessError as e:
raise EnableStonithFailed(e)
def disable_stonith(failure_is_fatal=True):
"""Disable stonith via the global property stonith-enabled.
:param failure_is_fatal: Whether to raise exception if command fails.
:type failure_is_fatal: bool
:raises: DisableStonithFailed
"""
log('Disabling STONITH', level=INFO)
try:
pcmk.commit(
"crm configure property stonith-enabled=false",
failure_is_fatal=failure_is_fatal)
except subprocess.CalledProcessError as e:
raise DisableStonithFailed(e)
def get_ip_addr_from_resource_params(params):
"""Returns the IP address in the resource params provided
:return: the IP address in the params or None if not found
"""
reg_ex = r'.* ip_address="([a-fA-F\d\:\.]+)".*'
res = re.search(reg_ex, params)
return res.group(1) if res else None
def need_resources_on_remotes():
"""Whether to run resources on remote nodes.
Check the 'enable-resources' setting across the remote units. If it is
absent or inconsistent then raise a ValueError.
:returns: Whether to run resources on remote nodes
:rtype: bool
:raises: ValueError
"""
responses = []
for relid in relation_ids('pacemaker-remote'):
for unit in related_units(relid):
data = parse_data(relid, unit, 'enable-resources')
# parse_data returns {} if key is absent.
if type(data) is bool:
responses.append(data)
if len(set(responses)) == 1:
run_resources_on_remotes = responses[0]
else:
msg = "Inconsistent or absent enable-resources setting {}".format(
responses)
log(msg, level=WARNING)
raise ValueError(msg)
return run_resources_on_remotes
def set_cluster_symmetry():
"""Set the cluster symmetry.
By default the cluster is an Opt-out cluster (equivalent to
symmetric-cluster=true) this means that any resource can run anywhere
unless a node explicitly Opts-out. When using pacemaker-remotes there may
be hundreds of nodes and if they are not prepared to run resources the
cluster should be switched to an Opt-in cluster.
"""
try:
symmetric = need_resources_on_remotes()
except ValueError:
msg = 'Unable to calculated desired symmetric-cluster setting'
log(msg, level=WARNING)
return
log('Configuring symmetric-cluster: {}'.format(symmetric), level=DEBUG)
cmd = "crm configure property symmetric-cluster={}".format(
str(symmetric).lower())
pcmk.commit(cmd, failure_is_fatal=True)
def add_score_location_rule(res_name, node, location_score):
"""Add or update a location rule that uses a score.
:param res_name: Resource that this location rule controls.
:type res_name: str
:param node: Node that this location rule relates to.
:type node: str
:param location_score: The score to give this location.
:type location_score: int
"""
loc_constraint_name = 'loc-{}-{}'.format(res_name, node)
pcmk.crm_update_location(
loc_constraint_name,
res_name,
location_score,
node)
def add_location_rules_for_local_nodes(res_name):
"""Add location rules for running resource on local nodes.
Add location rules allowing the given resource to run on local nodes (eg
not remote nodes).
:param res_name: Resource name to create location rules for.
:type res_name: str
"""
for node in pcmk.list_nodes():
loc_constraint_name = 'loc-{}-{}'.format(res_name, node)
if not pcmk.crm_opt_exists(loc_constraint_name):
cmd = 'crm -w -F configure location {} {} 0: {}'.format(
loc_constraint_name,
res_name,
node)
pcmk.commit(cmd, failure_is_fatal=True)
log('%s' % cmd, level=DEBUG)
def add_location_rules_for_pacemaker_remotes(res_names):
"""Add location rules for pacemaker remote resources on local nodes.
Add location rules allowing the pacemaker remote resource to run on a local
node. Use location score rules to spread resources out.
:param res_names: Pacemaker remote resource names.
:type res_names: List[str]
"""
res_names = sorted(res_names)
nodes = sorted(pcmk.list_nodes())
prefered_nodes = list(zip(res_names, itertools.cycle(nodes)))
for res_name in res_names:
for node in nodes:
location_score = 0
if (res_name, node) in prefered_nodes:
location_score = 200
add_score_location_rule(
res_name,
node,
location_score)
def configure_pacemaker_remote(remote_hostname, remote_ip):
"""Create a resource corresponding to the pacemaker remote node.
:param remote_hostname: Remote hostname used for registering remote node.
:type remote_hostname: str
:param remote_ip: Remote IP used for registering remote node.
:type remote_ip: str
:returns: Name of resource for pacemaker remote node.
:rtype: str
"""
resource_name = remote_hostname
if not pcmk.is_resource_present(resource_name):
cmd = (
"crm configure primitive {} ocf:pacemaker:remote "
"params server={} reconnect_interval=60 "
"op monitor interval=30s").format(resource_name,
remote_ip)
pcmk.commit(cmd, failure_is_fatal=True)
return resource_name
def cleanup_remote_nodes(remote_nodes):
"""Cleanup pacemaker remote resources
Remove all status records of the resource and
probe the node afterwards.
:param remote_nodes: List of resource names associated with remote nodes
:type remote_nodes: list
"""
for res_name in remote_nodes:
cmd = 'crm resource cleanup {}'.format(res_name)
# Resource cleanups seem to fail occasionally even on healthy nodes
# Bug #1822962. Given this cleanup task is just housekeeping log
# the message if a failure occurs and move on.
if pcmk.commit(cmd, failure_is_fatal=False) == 0:
log(
'Cleanup of resource {} succeeded'.format(res_name),
level=DEBUG)
else:
log(
'Cleanup of resource {} failed'.format(res_name),
level=WARNING)
def configure_pacemaker_remote_stonith_resource():
"""Create a maas stonith resource for the pacemaker-remotes.
:returns: Stonith resource dict {res_name: res_type}
:rtype: dict
"""
hostnames = []
stonith_resource = {}
for relid in relation_ids('pacemaker-remote'):
for unit in related_units(relid):
stonith_hostname = parse_data(relid, unit, 'stonith-hostname')
if stonith_hostname:
hostnames.append(stonith_hostname)
if hostnames:
stonith_resource = configure_maas_stonith_resource(hostnames)
return stonith_resource
def configure_peer_stonith_resource():
"""Create a null stonith resource for lxd containers.
:returns: Stonith resource dict {res_name: res_type}
:rtype: dict
"""
hostnames = [get_hostname()]
stonith_resource = {}
for relid in relation_ids('hanode'):
for unit in related_units(relid):
stonith_hostname = relation_get('hostname', unit, relid)
if stonith_hostname:
hostnames.append(stonith_hostname)
stonith_resource = configure_null_stonith_resource(hostnames)
return stonith_resource
def configure_pacemaker_remote_resources():
"""Create resources corresponding to the pacemaker remote nodes.
Create resources, location constraints and stonith resources for pacemaker
remote node.
:returns: resource dict {res_name: res_type, ...}
:rtype: dict
"""
log('Checking for pacemaker-remote nodes', level=DEBUG)
resources = []
for relid in relation_ids('pacemaker-remote'):
for unit in related_units(relid):
remote_hostname = parse_data(relid, unit, 'remote-hostname')
remote_ip = parse_data(relid, unit, 'remote-ip')
if remote_hostname:
resource_name = configure_pacemaker_remote(
remote_hostname,
remote_ip)
resources.append(resource_name)
cleanup_remote_nodes(resources)
return {name: 'ocf:pacemaker:remote' for name in resources}
def configure_resources_on_remotes(resources=None, clones=None, groups=None):
"""Add location rules as needed for resources, clones and groups
If remote nodes should not run resources then add location rules then add
location rules to enable them on local nodes.
:param resources: Resource definitions
:type resources: dict
:param clones: Clone definitions
:type clones: dict
:param groups: Group definitions
:type groups: dict
"""
clones = clones or {}
groups = groups or {}
try:
resources_on_remote = need_resources_on_remotes()
except ValueError:
msg = 'Unable to calculate whether resources should run on remotes'
log(msg, level=WARNING)
return
if resources_on_remote:
msg = ('Resources are permitted to run on remotes, no need to create '
'location constraints')
log(msg, level=WARNING)
return
pacemaker_remotes = []
for res_name, res_type in resources.items():
if res_name not in list(clones.values()) + list(groups.values()):
if res_type == 'ocf:pacemaker:remote':
pacemaker_remotes.append(res_name)
else:
add_location_rules_for_local_nodes(res_name)
add_location_rules_for_pacemaker_remotes(pacemaker_remotes)
for cl_name in clones:
add_location_rules_for_local_nodes(cl_name)
# Limit clone resources to only running on X number of nodes where X
# is the number of local nodes. Otherwise they will show as offline
# on the remote nodes.
node_count = len(pcmk.list_nodes())
cmd = ('crm_resource --resource {} --set-parameter clone-max '
'--meta --parameter-value {}').format(cl_name, node_count)
pcmk.commit(cmd, failure_is_fatal=True)
log('%s' % cmd, level=DEBUG)
for grp_name in groups:
add_location_rules_for_local_nodes(grp_name)
def restart_corosync_on_change():
"""Simple decorator to restart corosync if any of its config changes"""
return wrap
def try_pcmk_wait():
"""Try pcmk.wait_for_pcmk()
Log results and set status message
"""
try:
pcmk.wait_for_pcmk()
log("Pacemaker is ready", level=TRACE)
except pcmk.ServicesNotUp as e:
status_msg = "Pacemaker is down. Please manually start it."
status_set('blocked', status_msg)
full_msg = "{} {}".format(status_msg, e)
log(full_msg, ERROR)
raise pcmk.ServicesNotUp(full_msg)
@restart_corosync_on_change()
def services_running():
"""Determine if both Corosync and Pacemaker are running
Both from the operating system perspective and with a functional test
@returns boolean
"""
pacemaker_status = service_running("pacemaker")
corosync_status = service_running("corosync")
log("Pacemaker status: {}, Corosync status: {}"
"".format(pacemaker_status, corosync_status),
level=DEBUG)
if not (pacemaker_status and corosync_status):
# OS perspective
return False
# Functional test of pacemaker. This will raise if pacemaker doesn't get
# fully ready in time:
pcmk.wait_for_pcmk()
return True
def validated_restart_corosync(retries=10):
"""Restart and validate Corosync and Pacemaker are in fact up and running.
@param retries: number of attempts to restart the services before giving up
@raises pcmk.ServicesNotUp if after retries services are still not up
"""
for restart in range(retries):
try:
if restart_corosync():
log("Corosync and Pacemaker are validated as up and running.",
INFO)
return
else:
log("Corosync or Pacemaker not validated as up yet, retrying",
WARNING)
except pcmk.ServicesNotUp:
log("Pacemaker failed to start, retrying", WARNING)
continue
msg = ("Corosync and/or Pacemaker failed to restart after {} retries"
"".format(retries))
log(msg, ERROR)
status_set('blocked', msg)
raise pcmk.ServicesNotUp(msg)
def validate_dns_ha():
"""Validate the DNS HA
Assert the charm will support DNS HA
Check MAAS related configuration options are properly set
:raises MAASConfigIncomplete: if maas_url and maas_credentials are not set
"""
# Will raise an exception if unable to continue
assert_charm_supports_dns_ha()
if config('maas_url') and config('maas_credentials'):
return True
else:
msg = ("DNS HA is requested but the maas_url or maas_credentials "
"settings are not set")
raise MAASConfigIncomplete(msg)
def setup_maas_api():
"""Install MAAS PPA and packages for accessing the MAAS API.
"""
add_source(config('maas_source'), config('maas_source_key'))
apt_update(fatal=True)
apt_install('python3-maas-client', fatal=True)
def setup_ocf_files():
"""Setup OCF resrouce agent files
"""
# TODO (thedac) Eventually we want to package the OCF files.
# Bundle with the charm until then.
mkdir('/usr/lib/ocf/resource.d/ceph')
mkdir('/usr/lib/ocf/resource.d/maas')
# Xenial corosync is not creating this directory
mkdir('/etc/corosync/uidgid.d')
rsync('files/ocf/ceph/rbd', '/usr/lib/ocf/resource.d/ceph/rbd')
rsync('files/ocf/maas/dns', '/usr/lib/ocf/resource.d/maas/dns')
rsync('files/ocf/maas/maas_dns.py', '/usr/lib/heartbeat/maas_dns.py')
rsync('files/ocf/maas/maasclient/', '/usr/lib/heartbeat/maasclient/')
rsync(
'files/ocf/maas/maas_stonith_plugin.py',
'/usr/lib/stonith/plugins/external/maas')
def write_maas_dns_address(resource_name, resource_addr):
"""Writes the specified IP address to the resource file for MAAS dns.
:param resource_name: the name of the resource the address belongs to.
This is the name of the file that will be written in /etc/maas_dns.
:param resource_addr: the IP address for the resource. This will be
written to the resource_name file.
"""
mkdir(MAAS_DNS_CONF_DIR)
write_file(os.path.join(MAAS_DNS_CONF_DIR, resource_name),
content=resource_addr)
def needs_maas_dns_migration():
"""Determines if the MAAS DNS ocf resources need migration.
:return: True if migration is necessary, False otherwise.
"""
try:
subprocess.check_call(['grep', 'OCF_RESOURCE_INSTANCE',
'/usr/lib/ocf/resource.d/maas/dns'])
return True
except subprocess.CalledProcessError:
# check_call will raise an exception if grep doesn't find the string
return False
def is_in_standby_mode(node_name):
"""Check if node is in standby mode in pacemaker
@param node_name: The name of the node to check
@returns boolean - True if node_name is in standby mode
"""
out = (subprocess
.check_output(['crm', 'node', 'status', node_name])
.decode('utf-8'))
root = ET.fromstring(out)
standby_mode = False
for nvpair in root.iter('nvpair'):
if (nvpair.attrib.get('name') == 'standby' and
nvpair.attrib.get('value') == 'on'):
standby_mode = True
return standby_mode
def get_hostname():
"""Return the hostname of this unit
@returns hostname
"""
return socket.gethostname()
def enter_standby_mode(node_name, duration='forever'):
"""Put this node into standby mode in pacemaker
@returns None
"""
subprocess.check_call(['crm', 'node', 'standby', node_name, duration])
def leave_standby_mode(node_name):
"""Take this node out of standby mode in pacemaker
@returns None
"""
subprocess.check_call(['crm', 'node', 'online', node_name])
def node_has_resources(node_name):
"""Check if this node is running resources
@param node_name: The name of the node to check
@returns boolean - True if node_name has resources
"""
out = subprocess.check_output(['crm_mon', '-X']).decode('utf-8')
root = ET.fromstring(out)
has_resources = False
for resource in root.iter('resource'):
for child in resource:
if child.tag == 'node' and child.attrib.get('name') == node_name:
has_resources = True
return has_resources
def node_is_dc(node_name):
"""Check if this node is the designated controller.
@param node_name: The name of the node to check
@returns boolean - True if node_name is the DC
"""
out = subprocess.check_output(['crm_mon', '-X']).decode('utf-8')
root = ET.fromstring(out)
for current_dc in root.iter("current_dc"):
if current_dc.attrib.get('name') == node_name:
return True
return False
def set_unit_status():
"""Set the workload status for this unit
@returns None
"""
status_set(*assess_status_helper())
def resume_unit():
"""Resume services on this unit and update the units status
@returns None
"""
node_name = get_hostname()
messages = []
leave_standby_mode(node_name)
if is_in_standby_mode(node_name):
messages.append("Node still in standby mode")
if messages:
raise Exception("Couldn't resume: {}".format("; ".join(messages)))
else:
clear_unit_paused()
set_unit_status()
def pause_unit():
"""Pause services on this unit and update the units status
@returns None
"""
node_name = get_hostname()
messages = []
enter_standby_mode(node_name)
if not is_in_standby_mode(node_name):
messages.append("Node not in standby mode")
# some resources may take some time to be migrated out from the node. So 3
# retries are made with a 5 seconds wait between each one.
i = 0
ready = False
has_resources = False
while i < PCMKR_MAX_RETRIES and not ready:
if node_has_resources(node_name):
has_resources = True
i += 1
time.sleep(PCMKR_SLEEP_SECS)
else:
ready = True
has_resources = False
if has_resources:
messages.append("Resources still running on unit")
status, message = assess_status_helper()
# New status message will indicate the resource is not running
if status != 'active' and 'not running' not in message:
messages.append(message)
if messages and not is_unit_upgrading_set():
raise Exception("Couldn't pause: {}".format("; ".join(messages)))
else:
set_unit_paused()
status_set("maintenance",
"Paused. Use 'resume' action to resume normal service.")
def assess_status_helper():
"""Assess status of unit
@returns status, message - status is workload status and message is any
corresponding messages
"""
if config('stonith_enabled') in ['true', 'True', True]:
return(
'blocked',
'stonith_enabled config option is no longer supported')
if config('no_quorum_policy'):
if config('no_quorum_policy').lower() not in ['ignore', 'freeze',
'stop', 'suicide']:
return(
'blocked',
'Invalid no_quorum_policy specified')
if is_unit_upgrading_set():
return ("blocked",
"Ready for do-release-upgrade. Set complete when finished")
if is_waiting_unit_series_upgrade_set():
return ("blocked",
"HA services shutdown, peers are ready for series upgrade")
if is_unit_paused_set():
return ("maintenance",
"Paused. Use 'resume' action to resume normal service.")
node_count = int(config('cluster_count'))
status = 'active'
message = 'Unit is ready and clustered'
try:
try_pcmk_wait()
except pcmk.ServicesNotUp:
message = 'Pacemaker is down'
status = 'blocked'
for relid in relation_ids('hanode'):
if len(related_units(relid)) + 1 < node_count:
status = 'blocked'
message = ("Insufficient peer units for ha cluster "
"(require {})".format(node_count))
# if the status was not changed earlier, we verify the maintenance status
try:
if status == 'active':
prop = pcmk.get_property('maintenance-mode').strip()
except pcmk.PropertyNotFound:
# the property is not the output of 'crm configure show xml', so we use
# the default value for this property. For crmsh>=2.2.0 the default
# value is automatically provided by show-property or get-property.
prop = 'false'
if (status == 'active' and prop == 'true'):
# maintenance mode enabled in pacemaker
status = 'maintenance'
message = 'Pacemaker in maintenance mode'
for resource in get_resources().keys():
if not pcmk.is_resource_present(resource):
return ("waiting",
"Resource: {} not yet configured".format(resource))
if not pcmk.crm_res_running_on_node(resource, get_hostname()):
return ("blocked",
"Resource: {} not running".format(resource))
return status, message
def ocf_file_exists(res_name, resources,
RES_ROOT='/usr/lib/ocf/resource.d'):
"""To determine whether the ocf file exists, allow multiple ocf
files with the same name in different directories
@param res_name: The name of the ocf resource to check
@param resources: ocf resources
@return: boolean - True if the ocf resource exists
"""
res_type = None
for key, val in resources.items():
if res_name == key:
if len(val.split(':')) > 2:
res_type = val.split(':')[1]
ocf_name = res_name.replace('res_', '').replace('_', '-')
ocf_file = os.path.join(RES_ROOT, res_type, ocf_name)
if os.path.isfile(ocf_file):
return True
return False
def kill_legacy_ocf_daemon_process(res_name):
"""Kill legacy ocf daemon process
@param res_name: The name of the ocf process to kill
"""
ocf_name = res_name.replace('res_', '').replace('_', '-')
reg_expr = r'([0-9]+)\s+[^0-9]+{}'.format(ocf_name)
cmd = ['ps', '-eo', 'pid,cmd']
ps = subprocess.check_output(cmd).decode('utf-8')
res = re.search(reg_expr, ps, re.MULTILINE)
if res:
pid = res.group(1)
subprocess.call(['sudo', 'kill', '-9', pid])
def maintenance_mode(enable):
"""Enable/disable pacemaker's maintenance mode"""
log('Setting maintenance-mode to %s' % enable, level=INFO)
try:
current_state = pcmk.get_property('maintenance-mode').strip().lower()
except pcmk.PropertyNotFound:
current_state = 'false'
current_state = True if current_state == 'true' else False
log('Is maintenance-mode currently enabled? %s' % current_state,
level=DEBUG)
if current_state != enable:
pcmk.set_property('maintenance-mode', str(enable).lower())
else:
log('Desired value for maintenance-mode is already set', level=DEBUG)
def get_resources():
"""Get resources from the HA relation
:returns: dict of resources
"""
resources = {}
for rid in relation_ids("ha"):
for unit in related_units(rid):
resources = parse_data(rid, unit, 'resources')
return resources
def set_waiting_unit_series_upgrade():
"""Set the unit to a waiting upgrade state in the local kv() store.
"""
log("Setting waiting-unit-series-upgrade=true in local kv", DEBUG)
with unitdata.HookData()() as t:
kv = t[0]
kv.set('waiting-unit-series-upgrade', True)
def clear_waiting_unit_series_upgrade():
"""Clear the unit from a waiting upgrade state in the local kv() store.
"""
log("Setting waiting-unit-series-upgrade=false in local kv", DEBUG)
with unitdata.HookData()() as t:
kv = t[0]
kv.set('waiting-unit-series-upgrade', False)
def is_waiting_unit_series_upgrade_set():
"""Return the state of the kv().get('waiting-unit-series-upgrade').
To help with units that don't have HookData() (testing)
if it excepts, return False
"""
with unitdata.HookData()() as t:
kv = t[0]
if not kv.get('waiting-unit-series-upgrade'):
return False
return kv.get('waiting-unit-series-upgrade')
def get_series_upgrade_notifications(relid):
"""Check peers for notifications that they are upgrading their series.
Returns a dict of the form {unit_name: target_series, ...}
:param relid: Relation id to check for notifications.
:type relid: str
:returns: dict
"""
notifications = {}
for unit in related_units(relid):
relation_data = relation_get(rid=relid, unit=unit)
for key, value in relation_data.items():
if key.startswith('series_upgrade_of_'):
notifications[unit] = value
log("Found series upgrade notifications: {}".format(notifications), DEBUG)
return notifications
def disable_ha_services():
"""Shutdown and disable HA services."""
log("Disabling HA services", INFO)
for svc in ['corosync', 'pacemaker']:
disable_lsb_services(svc)
if service_running(svc):
service_stop(svc)
def enable_ha_services():
"""Startup and enable HA services."""
log("Enabling HA services", INFO)
for svc in ['pacemaker', 'corosync']:
enable_lsb_services(svc)
if not service_running(svc):
service_start(svc)
def notify_peers_of_series_upgrade():
"""Notify peers which release this unit is upgrading from."""
ubuntu_rel = lsb_release()['DISTRIB_CODENAME'].lower()
series_upgrade_key = get_series_upgrade_key()
relation_data = {
series_upgrade_key: ubuntu_rel}
for rel_id in relation_ids('hanode'):
relation_set(
relation_id=rel_id,
relation_settings=relation_data)
def clear_series_upgrade_notification():
"""Remove from series upgrade notification from peers."""
log("Removing upgrade notification from peers")
series_upgrade_key = get_series_upgrade_key()
relation_data = {
series_upgrade_key: None}
for rel_id in relation_ids('hanode'):
relation_set(
relation_id=rel_id,
relation_settings=relation_data)
def set_stonith_configured(is_configured):
"""Set the STONITH_CONFIGURED state.
:param is_configured: Flag to check peers relation data for.
:type is_configured: bool
:returns: List of IPs of nodes that are ready to join the cluster
:rtype: List
"""
leader_set({STONITH_CONFIGURED: is_configured})
def is_stonith_configured():
"""Get the STONITH_CONFIGURED state.
:returns: State of STONITH_CONFIGURED state.
:rtype: bool
"""
configured = leader_get(STONITH_CONFIGURED) or 'False'
return bool_from_string(configured)
def get_hanode_hostnames():
"""Hostnames of nodes in the hanode relation.
:returns: List of hostnames of nodes in the hanode relation.
:rtype: List
"""
hanode_hostnames = [get_hostname()]
for relid in relation_ids('hanode'):
for unit in related_units(relid):
hostname = relation_get('hostname', rid=relid, unit=unit)
if hostname:
hanode_hostnames.append(hostname)
hanode_hostnames.sort()
return hanode_hostnames
def update_node_list():
"""Determine and delete unexpected nodes from the corosync ring.
:returns: Set of pcmk nodes not part of Juju hanode relation
:rtype: Set[str]
:raises: RemoveCorosyncNodeFailed
"""
pcmk_nodes = set(pcmk.list_nodes())
juju_nodes = set(get_hanode_hostnames())
diff_nodes = pcmk_nodes.difference(juju_nodes)
log("pcmk_nodes[{}], juju_nodes[{}], diff[{}]"
"".format(pcmk_nodes, juju_nodes, diff_nodes),
DEBUG)
for old_node in diff_nodes:
try:
pcmk.set_node_status_to_maintenance(old_node)
pcmk.delete_node(old_node)
except subprocess.CalledProcessError as e:
raise RemoveCorosyncNodeFailed(old_node, e)
return diff_nodes
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62,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
11338,
3256,
705,
2385,
5285,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
1441,
7,
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220,
220,
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220,
220,
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220,
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198,
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220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
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44651,
645,
62,
421,
19220,
62,
30586,
7368,
11537,
628,
220,
220,
220,
611,
318,
62,
20850,
62,
929,
29247,
62,
2617,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
5855,
2436,
3543,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
35474,
329,
466,
12,
20979,
12,
929,
9526,
13,
5345,
1844,
618,
5201,
4943,
198,
220,
220,
220,
611,
318,
62,
10247,
1780,
62,
20850,
62,
25076,
62,
929,
9526,
62,
2617,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
5855,
2436,
3543,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
7801,
2594,
18325,
11,
14495,
389,
3492,
329,
2168,
8515,
4943,
198,
220,
220,
220,
611,
318,
62,
20850,
62,
8957,
1484,
62,
2617,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
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12417,
8219,
1600,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
28875,
1484,
13,
5765,
705,
411,
2454,
6,
2223,
284,
15294,
3487,
2139,
19570,
628,
220,
220,
220,
10139,
62,
9127,
796,
493,
7,
11250,
10786,
565,
5819,
62,
9127,
6,
4008,
198,
220,
220,
220,
3722,
796,
705,
5275,
6,
198,
220,
220,
220,
3275,
796,
705,
26453,
318,
3492,
290,
49480,
6,
198,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
62,
79,
11215,
74,
62,
17077,
3419,
198,
220,
220,
220,
2845,
279,
11215,
74,
13,
31007,
3673,
4933,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3275,
796,
705,
18844,
32174,
318,
866,
6,
198,
220,
220,
220,
220,
220,
220,
220,
3722,
796,
705,
2436,
3543,
6,
198,
220,
220,
220,
329,
823,
312,
287,
8695,
62,
2340,
10786,
7637,
1098,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
5363,
62,
41667,
7,
2411,
312,
4008,
1343,
352,
1279,
10139,
62,
9127,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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3722,
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6,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
3275,
796,
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15267,
12720,
4991,
329,
387,
13946,
366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
30629,
46115,
23884,
8,
1911,
18982,
7,
17440,
62,
9127,
4008,
628,
220,
220,
220,
1303,
611,
262,
3722,
373,
407,
3421,
2961,
11,
356,
11767,
262,
9262,
3722,
198,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3722,
6624,
705,
5275,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2632,
796,
279,
11215,
74,
13,
1136,
62,
26745,
10786,
12417,
8219,
12,
14171,
27691,
36311,
3419,
198,
220,
220,
220,
2845,
279,
11215,
74,
13,
21746,
3673,
21077,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
3119,
318,
407,
262,
5072,
286,
705,
6098,
76,
17425,
905,
35555,
3256,
523,
356,
779,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
4277,
1988,
329,
428,
3119,
13,
1114,
1067,
907,
71,
29,
28,
17,
13,
17,
13,
15,
262,
4277,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1988,
318,
6338,
2810,
416,
905,
12,
26745,
393,
651,
12,
26745,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2632,
796,
705,
9562,
6,
628,
220,
220,
220,
611,
357,
13376,
6624,
705,
5275,
6,
290,
2632,
6624,
705,
7942,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
9262,
4235,
9343,
287,
23503,
32174,
198,
220,
220,
220,
220,
220,
220,
220,
3722,
796,
705,
12417,
8219,
6,
198,
220,
220,
220,
220,
220,
220,
220,
3275,
796,
705,
18844,
32174,
287,
9262,
4235,
6,
628,
220,
220,
220,
329,
8271,
287,
651,
62,
37540,
22446,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
279,
11215,
74,
13,
271,
62,
31092,
62,
25579,
7,
31092,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
5855,
10247,
1780,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
26198,
25,
23884,
407,
1865,
17839,
1911,
18982,
7,
31092,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
279,
11215,
74,
13,
6098,
76,
62,
411,
62,
20270,
62,
261,
62,
17440,
7,
31092,
11,
651,
62,
4774,
3672,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
5855,
2436,
3543,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
26198,
25,
23884,
407,
2491,
1911,
18982,
7,
31092,
4008,
628,
220,
220,
220,
1441,
3722,
11,
3275,
628,
198,
4299,
267,
12993,
62,
7753,
62,
1069,
1023,
7,
411,
62,
3672,
11,
4133,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15731,
62,
13252,
2394,
11639,
14,
14629,
14,
8019,
14,
420,
69,
14,
31092,
13,
67,
6,
2599,
198,
220,
220,
220,
37227,
2514,
5004,
1771,
262,
267,
12993,
2393,
7160,
11,
1249,
3294,
267,
12993,
198,
220,
220,
220,
220,
220,
220,
3696,
351,
262,
976,
1438,
287,
1180,
29196,
628,
220,
220,
220,
2488,
17143,
581,
62,
3672,
25,
383,
1438,
286,
262,
267,
12993,
8271,
284,
2198,
198,
220,
220,
220,
2488,
17143,
4133,
25,
267,
12993,
4133,
198,
220,
220,
220,
2488,
7783,
25,
25131,
532,
6407,
611,
262,
267,
12993,
8271,
7160,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
581,
62,
4906,
796,
6045,
198,
220,
220,
220,
329,
1994,
11,
1188,
287,
4133,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
611,
581,
62,
3672,
6624,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
2100,
13,
35312,
7,
10354,
6,
4008,
1875,
362,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
581,
62,
4906,
796,
1188,
13,
35312,
7,
10354,
11537,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
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62,
3672,
796,
581,
62,
3672,
13,
33491,
10786,
411,
62,
3256,
10148,
737,
33491,
10786,
62,
3256,
705,
12,
11537,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
267,
12993,
62,
7753,
796,
28686,
13,
6978,
13,
22179,
7,
19535,
62,
13252,
2394,
11,
581,
62,
4906,
11,
267,
12993,
62,
3672,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
28686,
13,
6978,
13,
4468,
576,
7,
420,
69,
62,
7753,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6407,
198,
220,
220,
220,
1441,
10352,
628,
198,
4299,
1494,
62,
1455,
1590,
62,
420,
69,
62,
6814,
7966,
62,
14681,
7,
411,
62,
3672,
2599,
198,
220,
220,
220,
37227,
27100,
10655,
267,
12993,
33386,
1429,
628,
220,
220,
220,
2488,
17143,
581,
62,
3672,
25,
383,
1438,
286,
262,
267,
12993,
1429,
284,
1494,
198,
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220,
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220,
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14,
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338,
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220,
220,
2604,
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34149,
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12,
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284,
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82,
6,
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11,
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28,
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8,
628,
220,
220,
220,
1949,
25,
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220,
220,
220,
220,
220,
220,
220,
1459,
62,
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74,
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30,
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2073,
25,
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2604,
10786,
5960,
1202,
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12,
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318,
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329,
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611,
1994,
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342,
10786,
25076,
62,
929,
9526,
62,
1659,
62,
6,
2599,
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220,
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220,
220,
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60,
796,
1988,
198,
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2604,
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21077,
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8515,
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25,
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1911,
18982,
7,
1662,
6637,
828,
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24890,
8,
198,
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220,
329,
264,
28435,
287,
37250,
10215,
418,
13361,
3256,
705,
33587,
32174,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
15560,
62,
7278,
65,
62,
30416,
7,
21370,
66,
8,
198,
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220,
220,
220,
220,
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2139,
62,
20270,
7,
21370,
66,
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220,
220,
220,
220,
220,
220,
220,
220,
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2139,
62,
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66,
8,
628,
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62,
3099,
62,
30416,
33529,
198,
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220,
220,
37227,
10434,
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290,
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526,
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198,
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220,
2604,
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4834,
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8,
198,
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220,
220,
329,
264,
28435,
287,
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705,
10215,
418,
13361,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
7139,
62,
7278,
65,
62,
30416,
7,
21370,
66,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
2139,
62,
20270,
7,
21370,
66,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2139,
62,
9688,
7,
21370,
66,
8,
628,
198,
198,
4299,
19361,
62,
431,
364,
62,
1659,
62,
25076,
62,
929,
9526,
33529,
198,
220,
220,
220,
37227,
3673,
1958,
14495,
543,
2650,
428,
4326,
318,
25485,
422,
526,
15931,
198,
220,
220,
220,
20967,
11157,
62,
2411,
796,
300,
36299,
62,
20979,
3419,
17816,
26288,
5446,
9865,
62,
34,
3727,
1677,
10067,
6,
4083,
21037,
3419,
198,
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220,
2168,
62,
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62,
2539,
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62,
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62,
2539,
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62,
7890,
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198,
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220,
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62,
2539,
25,
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62,
2411,
92,
198,
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220,
329,
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62,
312,
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62,
2340,
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7637,
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6,
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37227,
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318,
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19762,
284,
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3554,
62,
2617,
15090,
41924,
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10943,
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1961,
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1522,
30072,
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37227,
3855,
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1961,
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1058,
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3563,
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16254,
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1961,
1181,
13,
198,
220,
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] | 2.430063 | 16,958 |
"""Mid-level helper functions for AutoTS."""
import numpy as np
import pandas as pd
import datetime
import json
from hashlib import md5
from autots.evaluator.metrics import PredictionEval
from autots.tools.transform import RandomTransform
def seasonal_int(include_one: bool = False):
"""Generate a random integer of typical seasonalities."""
if include_one:
lag = np.random.choice(
a=[
'random_int',
1,
2,
4,
7,
10,
12,
24,
28,
60,
96,
168,
364,
1440,
420,
52,
84,
],
size=1,
p=[
0.10,
0.05,
0.05,
0.05,
0.15,
0.01,
0.1,
0.1,
0.1,
0.1,
0.04,
0.01,
0.1,
0.01,
0.01,
0.01,
0.01,
],
).item()
else:
lag = np.random.choice(
a=[
'random_int',
2,
4,
7,
10,
12,
24,
28,
60,
96,
168,
364,
1440,
420,
52,
84,
],
size=1,
p=[
0.15,
0.05,
0.05,
0.15,
0.01,
0.1,
0.1,
0.1,
0.1,
0.04,
0.01,
0.1,
0.01,
0.01,
0.01,
0.01,
],
).item()
if lag == 'random_int':
lag = np.random.randint(2, 100, size=1).item()
return int(lag)
def create_model_id(
model_str: str, parameter_dict: dict = {}, transformation_dict: dict = {}
):
"""Create a hash ID which should be unique to the model parameters."""
str_repr = (
str(model_str) + json.dumps(parameter_dict) + json.dumps(transformation_dict)
)
str_repr = ''.join(str_repr.split())
hashed = md5(str_repr.encode('utf-8')).hexdigest()
return hashed
class ModelObject(object):
"""Generic class for holding forecasting models.
Models should all have methods:
.fit(df, future_regressor = []) (taking a DataFrame with DatetimeIndex and n columns of n timeseries)
.predict(forecast_length = int, future_regressor = [], just_point_forecast = False)
.get_new_params() - return a dictionary of weighted random selected parameters
Args:
name (str): Model Name
frequency (str): String alias of datetime index frequency or else 'infer'
prediction_interval (float): Confidence interval for probabilistic forecast
n_jobs (int): used by some models that parallelize to multiple cores
"""
def __repr__(self):
"""Print."""
return 'ModelObject of ' + self.name + ' uses standard .fit/.predict'
def basic_profile(self, df):
"""Capture basic training details."""
self.startTime = datetime.datetime.now()
self.train_shape = df.shape
self.column_names = df.columns
self.train_last_date = df.index[-1]
if self.frequency == 'infer':
self.frequency = pd.infer_freq(df.index, warn=False)
return df
def create_forecast_index(self, forecast_length: int):
"""Generate a pd.DatetimeIndex appropriate for a new forecast.
Warnings:
Requires ModelObject.basic_profile() being called as part of .fit()
"""
forecast_index = pd.date_range(
freq=self.frequency, start=self.train_last_date, periods=forecast_length + 1
)
forecast_index = forecast_index[1:]
self.forecast_index = forecast_index
return forecast_index
def get_params(self):
"""Return dict of current parameters."""
return {}
def get_new_params(self, method: str = 'random'):
"""Return dict of new parameters for parameter tuning."""
return {}
class PredictionObject(object):
"""Generic class for holding forecast information."""
def __repr__(self):
"""Print."""
if isinstance(self.forecast, pd.DataFrame):
return "Prediction object: \nReturn .forecast, \n .upper_forecast, \n .lower_forecast \n .model_parameters \n .transformation_parameters"
else:
return "Empty prediction object."
def __bool__(self):
"""bool version of class."""
if isinstance(self.forecast, pd.DataFrame):
return True
else:
return False
def total_runtime(self):
"""Combine runtimes."""
return self.fit_runtime + self.predict_runtime + self.transformation_runtime
def ModelMonster(
model: str,
parameters: dict = {},
frequency: str = 'infer',
prediction_interval: float = 0.9,
holiday_country: str = 'US',
startTimeStamps=None,
forecast_length: int = 14,
random_seed: int = 2020,
verbose: int = 0,
n_jobs: int = None,
):
"""Directs strings and parameters to appropriate model objects.
Args:
model (str): Name of Model Function
parameters (dict): Dictionary of parameters to pass through to model
"""
model = str(model)
if model == 'ZeroesNaive':
from autots.models.basics import ZeroesNaive
return ZeroesNaive(frequency=frequency, prediction_interval=prediction_interval)
if model == 'LastValueNaive':
from autots.models.basics import LastValueNaive
return LastValueNaive(
frequency=frequency, prediction_interval=prediction_interval
)
if model == 'AverageValueNaive':
from autots.models.basics import AverageValueNaive
if parameters == {}:
return AverageValueNaive(
frequency=frequency, prediction_interval=prediction_interval
)
else:
return AverageValueNaive(
frequency=frequency,
prediction_interval=prediction_interval,
method=parameters['method'],
)
if model == 'SeasonalNaive':
from autots.models.basics import SeasonalNaive
if parameters == {}:
return SeasonalNaive(
frequency=frequency, prediction_interval=prediction_interval
)
else:
return SeasonalNaive(
frequency=frequency,
prediction_interval=prediction_interval,
method=parameters['method'],
lag_1=parameters['lag_1'],
lag_2=parameters['lag_2'],
)
if model == 'GLS':
from autots.models.statsmodels import GLS
return GLS(frequency=frequency, prediction_interval=prediction_interval)
if model == 'GLM':
from autots.models.statsmodels import GLM
if parameters == {}:
model = GLM(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
else:
model = GLM(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
family=parameters['family'],
constant=parameters['constant'],
regression_type=parameters['regression_type'],
)
return model
if model == 'ETS':
from autots.models.statsmodels import ETS
if parameters == {}:
model = ETS(
frequency=frequency,
prediction_interval=prediction_interval,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
else:
model = ETS(
frequency=frequency,
prediction_interval=prediction_interval,
damped=parameters['damped'],
trend=parameters['trend'],
seasonal=parameters['seasonal'],
seasonal_periods=parameters['seasonal_periods'],
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
return model
if model == 'ARIMA':
from autots.models.statsmodels import ARIMA
if parameters == {}:
model = ARIMA(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
else:
model = ARIMA(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
p=parameters['p'],
d=parameters['d'],
q=parameters['q'],
regression_type=parameters['regression_type'],
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
return model
if model == 'FBProphet':
from autots.models.prophet import FBProphet
if parameters == {}:
model = FBProphet(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
else:
model = FBProphet(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
holiday=parameters['holiday'],
regression_type=parameters['regression_type'],
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
return model
if model == 'RollingRegression':
from autots.models.sklearn import RollingRegression
if parameters == {}:
model = RollingRegression(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
)
else:
model = RollingRegression(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
holiday=parameters['holiday'],
regression_type=parameters['regression_type'],
random_seed=random_seed,
verbose=verbose,
regression_model=parameters['regression_model'],
mean_rolling_periods=parameters['mean_rolling_periods'],
std_rolling_periods=parameters['std_rolling_periods'],
macd_periods=parameters['macd_periods'],
max_rolling_periods=parameters['max_rolling_periods'],
min_rolling_periods=parameters['min_rolling_periods'],
ewm_alpha=parameters['ewm_alpha'],
additional_lag_periods=parameters['additional_lag_periods'],
x_transform=parameters['x_transform'],
rolling_autocorr_periods=parameters['rolling_autocorr_periods'],
abs_energy=parameters['abs_energy'],
add_date_part=parameters['add_date_part'],
polynomial_degree=parameters['polynomial_degree'],
n_jobs=n_jobs,
)
return model
if model == 'UnobservedComponents':
from autots.models.statsmodels import UnobservedComponents
if parameters == {}:
model = UnobservedComponents(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = UnobservedComponents(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
regression_type=parameters['regression_type'],
random_seed=random_seed,
verbose=verbose,
level=parameters['level'],
trend=parameters['trend'],
cycle=parameters['cycle'],
damped_cycle=parameters['damped_cycle'],
irregular=parameters['irregular'],
stochastic_trend=parameters['stochastic_trend'],
stochastic_level=parameters['stochastic_level'],
stochastic_cycle=parameters['stochastic_cycle'],
)
return model
if model == 'DynamicFactor':
from autots.models.statsmodels import DynamicFactor
if parameters == {}:
model = DynamicFactor(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = DynamicFactor(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
regression_type=parameters['regression_type'],
random_seed=random_seed,
verbose=verbose,
k_factors=parameters['k_factors'],
factor_order=parameters['factor_order'],
)
return model
if model == 'VAR':
from autots.models.statsmodels import VAR
if parameters == {}:
model = VAR(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = VAR(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
regression_type=parameters['regression_type'],
maxlags=parameters['maxlags'],
ic=parameters['ic'],
random_seed=random_seed,
verbose=verbose,
)
return model
if model == 'VECM':
from autots.models.statsmodels import VECM
if parameters == {}:
model = VECM(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = VECM(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
regression_type=parameters['regression_type'],
random_seed=random_seed,
verbose=verbose,
deterministic=parameters['deterministic'],
k_ar_diff=parameters['k_ar_diff'],
)
return model
if model == 'VARMAX':
from autots.models.statsmodels import VARMAX
if parameters == {}:
model = VARMAX(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = VARMAX(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
order=parameters['order'],
trend=parameters['trend'],
)
return model
if model == 'GluonTS':
from autots.models.gluonts import GluonTS
if parameters == {}:
model = GluonTS(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
forecast_length=forecast_length,
)
else:
model = GluonTS(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
gluon_model=parameters['gluon_model'],
epochs=parameters['epochs'],
learning_rate=parameters['learning_rate'],
forecast_length=forecast_length,
)
return model
if model == 'TSFreshRegressor':
from autots.models.tsfresh import TSFreshRegressor
if parameters == {}:
model = TSFreshRegressor(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = TSFreshRegressor(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
max_timeshift=parameters['max_timeshift'],
regression_model=parameters['regression_model'],
feature_selection=parameters['feature_selection'],
)
return model
if model == 'MotifSimulation':
from autots.models.basics import MotifSimulation
if parameters == {}:
model = MotifSimulation(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = MotifSimulation(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
phrase_len=parameters['phrase_len'],
comparison=parameters['comparison'],
shared=parameters['shared'],
distance_metric=parameters['distance_metric'],
max_motifs=parameters['max_motifs'],
recency_weighting=parameters['recency_weighting'],
cutoff_threshold=parameters['cutoff_threshold'],
cutoff_minimum=parameters['cutoff_minimum'],
point_method=parameters['point_method'],
)
return model
if model == 'WindowRegression':
from autots.models.sklearn import WindowRegression
if parameters == {}:
model = WindowRegression(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
forecast_length=forecast_length,
n_jobs=n_jobs,
)
else:
model = WindowRegression(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
window_size=parameters['window_size'],
regression_model=parameters['regression_model'],
input_dim=parameters['input_dim'],
output_dim=parameters['output_dim'],
normalize_window=parameters['normalize_window'],
shuffle=parameters['shuffle'],
max_windows=parameters['max_windows'],
forecast_length=forecast_length,
n_jobs=n_jobs,
)
return model
if model == 'TensorflowSTS':
from autots.models.tfp import TensorflowSTS
if parameters == {}:
model = TensorflowSTS(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = TensorflowSTS(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
seasonal_periods=parameters['seasonal_periods'],
ar_order=parameters['ar_order'],
trend=parameters['trend'],
fit_method=parameters['fit_method'],
num_steps=parameters['num_steps'],
)
return model
if model == 'TFPRegression':
from autots.models.tfp import TFPRegression
if parameters == {}:
model = TFPRegression(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
)
else:
model = TFPRegression(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
kernel_initializer=parameters['kernel_initializer'],
epochs=parameters['epochs'],
batch_size=parameters['batch_size'],
optimizer=parameters['optimizer'],
loss=parameters['loss'],
dist=parameters['dist'],
regression_type=parameters['regression_type'],
)
return model
if model == 'ComponentAnalysis':
from autots.models.sklearn import ComponentAnalysis
if parameters == {}:
model = ComponentAnalysis(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
forecast_length=forecast_length,
)
else:
model = ComponentAnalysis(
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
model=parameters['model'],
model_parameters=parameters['model_parameters'],
decomposition=parameters['decomposition'],
n_components=parameters['n_components'],
forecast_length=forecast_length,
)
return model
else:
raise AttributeError(
("Model String '{}' not a recognized model type").format(model)
)
def ModelPrediction(
df_train,
forecast_length: int,
transformation_dict: dict,
model_str: str,
parameter_dict: dict,
frequency: str = 'infer',
prediction_interval: float = 0.9,
no_negatives: bool = False,
constraint: float = None,
future_regressor_train=[],
future_regressor_forecast=[],
holiday_country: str = 'US',
startTimeStamps=None,
grouping_ids=None,
random_seed: int = 2020,
verbose: int = 0,
n_jobs: int = None,
):
"""Feed parameters into modeling pipeline
Args:
df_train (pandas.DataFrame): numeric training dataset of DatetimeIndex and series as cols
forecast_length (int): number of periods to forecast
transformation_dict (dict): a dictionary of outlier, fillNA, and transformation methods to be used
model_str (str): a string to be direct to the appropriate model, used in ModelMonster
frequency (str): str representing frequency alias of time series
prediction_interval (float): width of errors (note: rarely do the intervals accurately match the % asked for...)
no_negatives (bool): whether to force all forecasts to be > 0
constraint (float): when not None, use this value * data st dev above max or below min for constraining forecast values.
future_regressor_train (pd.Series): with datetime index, of known in advance data, section matching train data
future_regressor_forecast (pd.Series): with datetime index, of known in advance data, section matching test data
holiday_country (str): passed through to holiday package, used by a few models as 0/1 regressor.
startTimeStamps (pd.Series): index (series_ids), columns (Datetime of First start of series)
n_jobs (int): number of processes
Returns:
PredictionObject (autots.PredictionObject): Prediction from AutoTS model object
"""
transformationStartTime = datetime.datetime.now()
from autots.tools.transform import GeneralTransformer
try:
coerce_integer = transformation_dict['coerce_integer']
grouping = transformation_dict['grouping']
if grouping == 'user' and grouping_ids is None:
grouping = 'kmeans5'
transformation_dict['grouping'] = 'kmeans5'
reconciliation = transformation_dict['reconciliation']
except Exception:
coerce_integer = False
grouping = None
grouping_ids = None
reconciliation = None
transformer_object = GeneralTransformer(
outlier_method=transformation_dict['outlier_method'],
outlier_threshold=transformation_dict['outlier_threshold'],
outlier_position=transformation_dict['outlier_position'],
fillna=transformation_dict['fillna'],
transformation=transformation_dict['transformation'],
detrend=transformation_dict['detrend'],
second_transformation=transformation_dict['second_transformation'],
transformation_param=transformation_dict['transformation_param'],
third_transformation=transformation_dict['third_transformation'],
transformation_param2=transformation_dict['transformation_param2'],
fourth_transformation=transformation_dict['fourth_transformation'],
discretization=transformation_dict['discretization'],
n_bins=transformation_dict['n_bins'],
grouping=grouping,
grouping_ids=grouping_ids,
reconciliation=reconciliation,
coerce_integer=coerce_integer,
).fit(df_train)
df_train_transformed = transformer_object.transform(df_train)
# slice the context, ie shorten the amount of data available.
if transformation_dict['context_slicer'] not in [None, 'None']:
from autots.tools.transform import simple_context_slicer
df_train_transformed = simple_context_slicer(
df_train_transformed,
method=transformation_dict['context_slicer'],
forecast_length=forecast_length,
)
# make sure regressor has same length. This could be a problem if wrong size regressor is passed.
if len(future_regressor_train) > 0:
future_regressor_train = future_regressor_train.tail(
df_train_transformed.shape[0]
)
transformation_runtime = datetime.datetime.now() - transformationStartTime
# from autots.evaluator.auto_model import ModelMonster
model = ModelMonster(
model_str,
parameters=parameter_dict,
frequency=frequency,
prediction_interval=prediction_interval,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
forecast_length=forecast_length,
n_jobs=n_jobs,
)
model = model.fit(df_train_transformed, future_regressor=future_regressor_train)
df_forecast = model.predict(
forecast_length=forecast_length, future_regressor=future_regressor_forecast
)
if df_forecast.forecast.isnull().all(axis=0).astype(int).sum() > 0:
raise ValueError(
"Model {} returned NaN for one or more series".format(model_str)
)
transformationStartTime = datetime.datetime.now()
# Inverse the transformations
df_forecast.forecast = pd.DataFrame(
transformer_object.inverse_transform(df_forecast.forecast)
) # , index = df_forecast.forecast_index, columns = df_forecast.forecast_columns)
df_forecast.lower_forecast = pd.DataFrame(
transformer_object.inverse_transform(df_forecast.lower_forecast)
) # , index = df_forecast.forecast_index, columns = df_forecast.forecast_columns)
df_forecast.upper_forecast = pd.DataFrame(
transformer_object.inverse_transform(df_forecast.upper_forecast)
) # , index = df_forecast.forecast_index, columns = df_forecast.forecast_columns)
df_forecast.transformation_parameters = transformation_dict
# Remove negatives if desired
# There's df.where(df_forecast.forecast > 0, 0) or df.clip(lower = 0), not sure which faster
if no_negatives:
df_forecast.lower_forecast = df_forecast.lower_forecast.clip(lower=0)
df_forecast.forecast = df_forecast.forecast.clip(lower=0)
df_forecast.upper_forecast = df_forecast.upper_forecast.clip(lower=0)
if constraint is not None:
if verbose > 2:
print("Using constraint.")
constraint = float(constraint)
train_std = df_train.std(axis=0)
train_min = df_train.min(axis=0) - (constraint * train_std)
train_max = df_train.max(axis=0) + (constraint * train_std)
df_forecast.forecast = df_forecast.forecast.clip(lower=train_min, axis=1)
df_forecast.forecast = df_forecast.forecast.clip(upper=train_max, axis=1)
transformation_runtime = transformation_runtime + (
datetime.datetime.now() - transformationStartTime
)
df_forecast.transformation_runtime = transformation_runtime
return df_forecast
class TemplateEvalObject(object):
"""Object to contain all your failures!."""
def __repr__(self):
"""Print."""
return 'Results objects, result table at self.model_results (pd.df)'
def concat(self, another_eval):
"""Merge another TemplateEvalObject onto this one."""
self.model_results = pd.concat(
[self.model_results, another_eval.model_results],
axis=0,
ignore_index=True,
sort=False,
).reset_index(drop=True)
self.per_series_mae = pd.concat(
[self.per_series_mae, another_eval.per_series_mae], axis=0, sort=False
)
self.per_series_spl = pd.concat(
[self.per_series_spl, another_eval.per_series_spl], axis=0, sort=False
)
self.per_series_rmse1 = pd.concat(
[self.per_series_rmse1, another_eval.per_series_rmse1], axis=0, sort=False
)
self.per_series_rmse2 = pd.concat(
[self.per_series_rmse2, another_eval.per_series_rmse2], axis=0, sort=False
)
self.per_timestamp_smape = pd.concat(
[self.per_timestamp_smape, another_eval.per_timestamp_smape],
axis=0,
sort=False,
)
self.model_count = self.model_count + another_eval.model_count
return self
def save(self, filename):
"""Save results to a file."""
if '.csv' in filename:
self.model_results.to_csv(filename, index=False)
elif '.pickle' in filename:
import pickle
with open(filename, "wb") as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
else:
raise ValueError("filename not .csv or .pickle")
def unpack_ensemble_models(
template,
template_cols: list = [
'Model',
'ModelParameters',
'TransformationParameters',
'Ensemble',
],
keep_ensemble: bool = True,
recursive: bool = False,
):
"""Take ensemble models from template and add as new rows."""
ensemble_template = pd.DataFrame()
template['Ensemble'] = np.where(
((template['Model'] == 'Ensemble') & (template['Ensemble'] < 1)),
1,
template['Ensemble'],
)
for index, value in template[template['Ensemble'] != 0][
'ModelParameters'
].iteritems():
model_dict = json.loads(value)['models']
model_df = pd.DataFrame.from_dict(model_dict, orient='index')
model_df = model_df.rename_axis('ID').reset_index(drop=False)
model_df['Ensemble'] = 0
# unpack nested ensembles, if recursive specified
if recursive and 'Ensemble' in model_df['Model'].tolist():
model_df = pd.concat(
[
unpack_ensemble_models(
model_df, recursive=True, template_cols=template_cols
),
model_df,
],
axis=0,
ignore_index=True,
sort=False,
).reset_index(drop=True)
ensemble_template = pd.concat(
[ensemble_template, model_df], axis=0, ignore_index=True, sort=False
).reset_index(drop=True)
if not keep_ensemble:
template = template[template['Ensemble'] == 0]
template = pd.concat(
[template, ensemble_template], axis=0, ignore_index=True, sort=False
).reset_index(drop=True)
template = template.drop_duplicates(subset=template_cols)
return template
def PredictWitch(
template,
df_train,
forecast_length: int,
frequency: str = 'infer',
prediction_interval: float = 0.9,
no_negatives: bool = False,
constraint: float = None,
future_regressor_train=[],
future_regressor_forecast=[],
holiday_country: str = 'US',
startTimeStamps=None,
grouping_ids=None,
random_seed: int = 2020,
verbose: int = 0,
n_jobs: int = None,
template_cols: list = [
'Model',
'ModelParameters',
'TransformationParameters',
'Ensemble',
],
):
"""Takes numeric data, returns numeric forecasts.
Only one model (albeit potentially an ensemble)!
Well, she turned me into a newt.
A newt?
I got better. -Python
Args:
df_train (pandas.DataFrame): numeric training dataset of DatetimeIndex and series as cols
forecast_length (int): number of periods to forecast
transformation_dict (dict): a dictionary of outlier, fillNA, and transformation methods to be used
model_str (str): a string to be direct to the appropriate model, used in ModelMonster
frequency (str): str representing frequency alias of time series
prediction_interval (float): width of errors (note: rarely do the intervals accurately match the % asked for...)
no_negatives (bool): whether to force all forecasts to be > 0
constraint (float): when not None, use this value * data st dev above max or below min for constraining forecast values.
future_regressor_train (pd.Series): with datetime index, of known in advance data, section matching train data
future_regressor_forecast (pd.Series): with datetime index, of known in advance data, section matching test data
holiday_country (str): passed through to holiday package, used by a few models as 0/1 regressor.
startTimeStamps (pd.Series): index (series_ids), columns (Datetime of First start of series)
template_cols (list): column names of columns used as model template
Returns:
PredictionObject (autots.PredictionObject): Prediction from AutoTS model object):
"""
if isinstance(template, pd.Series):
template = pd.DataFrame(template).transpose()
template = template.head(1)
for index_upper, row_upper in template.iterrows():
# if an ensemble
if row_upper['Model'] == 'Ensemble':
from autots.models.ensemble import EnsembleForecast
forecasts_list = []
forecasts_runtime = []
forecasts = []
upper_forecasts = []
lower_forecasts = []
ens_model_str = row_upper['Model']
ens_params = json.loads(row_upper['ModelParameters'])
ens_template = unpack_ensemble_models(
template, template_cols, keep_ensemble=False
)
for index, row in ens_template.iterrows():
# recursive recursion!
if verbose > 2:
total_ens = ens_template.shape[0]
print(
"Ensemble component {} of {} ".format(
model_str, str(index), str(total_ens)
)
)
df_forecast = PredictWitch(
row,
df_train=df_train,
forecast_length=forecast_length,
frequency=frequency,
prediction_interval=prediction_interval,
no_negatives=no_negatives,
constraint=constraint,
future_regressor_train=future_regressor_train,
future_regressor_forecast=future_regressor_forecast,
holiday_country=holiday_country,
startTimeStamps=startTimeStamps,
grouping_ids=grouping_ids,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
template_cols=template_cols,
)
model_id = create_model_id(
df_forecast.model_name,
df_forecast.model_parameters,
df_forecast.transformation_parameters,
)
total_runtime = (
df_forecast.fit_runtime
+ df_forecast.predict_runtime
+ df_forecast.transformation_runtime
)
forecasts_list.extend([model_id])
forecasts_runtime.extend([total_runtime])
forecasts.extend([df_forecast.forecast])
upper_forecasts.extend([df_forecast.upper_forecast])
lower_forecasts.extend([df_forecast.lower_forecast])
ens_forecast = EnsembleForecast(
ens_model_str,
ens_params,
forecasts_list=forecasts_list,
forecasts=forecasts,
lower_forecasts=lower_forecasts,
upper_forecasts=upper_forecasts,
forecasts_runtime=forecasts_runtime,
prediction_interval=prediction_interval,
)
return ens_forecast
# if not an ensemble
else:
model_str = row_upper['Model']
parameter_dict = json.loads(row_upper['ModelParameters'])
transformation_dict = json.loads(row_upper['TransformationParameters'])
df_forecast = ModelPrediction(
df_train,
forecast_length,
transformation_dict,
model_str,
parameter_dict,
frequency=frequency,
prediction_interval=prediction_interval,
no_negatives=no_negatives,
constraint=constraint,
future_regressor_train=future_regressor_train,
future_regressor_forecast=future_regressor_forecast,
grouping_ids=grouping_ids,
holiday_country=holiday_country,
random_seed=random_seed,
verbose=verbose,
startTimeStamps=startTimeStamps,
n_jobs=n_jobs,
)
return df_forecast
def TemplateWizard(
template,
df_train,
df_test,
weights,
model_count: int = 0,
ensemble: str = True,
forecast_length: int = 14,
frequency: str = 'infer',
prediction_interval: float = 0.9,
no_negatives: bool = False,
constraint: float = None,
future_regressor_train=[],
future_regressor_forecast=[],
holiday_country: str = 'US',
startTimeStamps=None,
random_seed: int = 2020,
verbose: int = 0,
n_jobs: int = None,
validation_round: int = 0,
model_interrupt: bool = False,
grouping_ids=None,
template_cols: list = [
'Model',
'ModelParameters',
'TransformationParameters',
'Ensemble',
],
):
"""
Take Template, returns Results.
There are some who call me... Tim. - Python
Args:
template (pandas.DataFrame): containing model str, and json of transformations and hyperparamters
df_train (pandas.DataFrame): numeric training dataset of DatetimeIndex and series as cols
df_test (pandas.DataFrame): dataframe of actual values of (forecast length * n series)
weights (dict): key = column/series_id, value = weight
ensemble (str): desc of ensemble types to prepare metric collection
forecast_length (int): number of periods to forecast
transformation_dict (dict): a dictionary of outlier, fillNA, and transformation methods to be used
model_str (str): a string to be direct to the appropriate model, used in ModelMonster
frequency (str): str representing frequency alias of time series
prediction_interval (float): width of errors (note: rarely do the intervals accurately match the % asked for...)
no_negatives (bool): whether to force all forecasts to be > 0
constraint (float): when not None, use this value * data st dev above max or below min for constraining forecast values.
future_regressor_train (pd.Series): with datetime index, of known in advance data, section matching train data
future_regressor_forecast (pd.Series): with datetime index, of known in advance data, section matching test data
holiday_country (str): passed through to holiday package, used by a few models as 0/1 regressor.
startTimeStamps (pd.Series): index (series_ids), columns (Datetime of First start of series)
validation_round (int): int passed to record current validation.
model_interrupt (bool): if True, keyboard interrupts are caught and only break current model eval.
template_cols (list): column names of columns used as model template
Returns:
TemplateEvalObject
"""
ensemble = str(ensemble)
template_result = TemplateEvalObject()
template_result.model_count = model_count
if isinstance(template, pd.Series):
template = pd.DataFrame(template).transpose()
# template = unpack_ensemble_models(template, template_cols, keep_ensemble = False)
for index, row in template.iterrows():
try:
model_str = row['Model']
parameter_dict = json.loads(row['ModelParameters'])
transformation_dict = json.loads(row['TransformationParameters'])
ensemble_input = row['Ensemble']
current_template = pd.DataFrame(row).transpose()
template_result.model_count += 1
if verbose > 0:
if verbose > 1:
print(
"Model Number: {} with model {} in Validation {} with params {} and transformations {}".format(
str(template_result.model_count),
model_str,
str(validation_round),
json.dumps(parameter_dict),
json.dumps(transformation_dict),
)
)
else:
print(
"Model Number: {} with model {} in Validation {} ".format(
str(template_result.model_count),
model_str,
str(validation_round),
)
)
df_forecast = PredictWitch(
current_template,
df_train=df_train,
forecast_length=forecast_length,
frequency=frequency,
prediction_interval=prediction_interval,
no_negatives=no_negatives,
constraint=constraint,
future_regressor_train=future_regressor_train,
future_regressor_forecast=future_regressor_forecast,
holiday_country=holiday_country,
startTimeStamps=startTimeStamps,
grouping_ids=grouping_ids,
random_seed=random_seed,
verbose=verbose,
n_jobs=n_jobs,
template_cols=template_cols,
)
per_ts = True if 'distance' in ensemble else False
if 'hdist' in ensemble:
dist_n = int(np.ceil(0.3 * forecast_length))
else:
dist_n = None
model_error = PredictionEval(
df_forecast,
df_test,
series_weights=weights,
df_train=df_train,
per_timestamp_errors=per_ts,
dist_n=dist_n,
)
model_id = create_model_id(
df_forecast.model_name,
df_forecast.model_parameters,
df_forecast.transformation_parameters,
)
total_runtime = (
df_forecast.fit_runtime
+ df_forecast.predict_runtime
+ df_forecast.transformation_runtime
)
result = pd.DataFrame(
{
'ID': model_id,
'Model': df_forecast.model_name,
'ModelParameters': json.dumps(df_forecast.model_parameters),
'TransformationParameters': json.dumps(
df_forecast.transformation_parameters
),
'TransformationRuntime': df_forecast.transformation_runtime,
'FitRuntime': df_forecast.fit_runtime,
'PredictRuntime': df_forecast.predict_runtime,
'TotalRuntime': total_runtime,
'Ensemble': ensemble_input,
'Exceptions': np.nan,
'Runs': 1,
'ValidationRound': validation_round,
},
index=[0],
)
a = pd.DataFrame(
model_error.avg_metrics_weighted.rename(lambda x: x + '_weighted')
).transpose()
result = pd.concat(
[result, pd.DataFrame(model_error.avg_metrics).transpose(), a], axis=1
)
template_result.model_results = pd.concat(
[template_result.model_results, result],
axis=0,
ignore_index=True,
sort=False,
).reset_index(drop=True)
if 'horizontal' in ensemble:
cur_mae = model_error.per_series_metrics.loc['mae']
cur_mae = pd.DataFrame(cur_mae).transpose()
cur_mae.index = [model_id]
template_result.per_series_mae = pd.concat(
[template_result.per_series_mae, cur_mae], axis=0
)
if 'probabilistic' in ensemble:
cur_spl = model_error.per_series_metrics.loc['spl']
cur_spl = pd.DataFrame(cur_spl).transpose()
cur_spl.index = [model_id]
template_result.per_series_spl = pd.concat(
[template_result.per_series_spl, cur_spl], axis=0
)
if 'distance' in ensemble:
cur_smape = model_error.per_timestamp.loc['weighted_smape']
cur_smape = pd.DataFrame(cur_smape).transpose()
cur_smape.index = [model_id]
template_result.per_timestamp_smape = pd.concat(
[template_result.per_timestamp_smape, cur_smape], axis=0
)
if 'hdist' in ensemble:
cur_rmse1 = model_error.per_series_metrics.loc['rmse1']
cur_rmse2 = model_error.per_series_metrics.loc['rmse2']
cur_rmse1 = pd.DataFrame(cur_rmse1).transpose()
cur_rmse2 = pd.DataFrame(cur_rmse2).transpose()
cur_rmse1.index = [model_id]
cur_rmse2.index = [model_id]
template_result.per_series_rmse1 = pd.concat(
[template_result.per_series_rmse1, cur_rmse1], axis=0
)
template_result.per_series_rmse2 = pd.concat(
[template_result.per_series_rmse2, cur_rmse2], axis=0
)
except KeyboardInterrupt:
if model_interrupt:
result = pd.DataFrame(
{
'ID': create_model_id(
model_str, parameter_dict, transformation_dict
),
'Model': model_str,
'ModelParameters': json.dumps(parameter_dict),
'TransformationParameters': json.dumps(transformation_dict),
'Ensemble': ensemble_input,
'TransformationRuntime': datetime.timedelta(0),
'FitRuntime': datetime.timedelta(0),
'PredictRuntime': datetime.timedelta(0),
'TotalRuntime': datetime.timedelta(0),
'Exceptions': "KeyboardInterrupt by user",
'Runs': 1,
'ValidationRound': validation_round,
},
index=[0],
)
template_result.model_results = pd.concat(
[template_result.model_results, result],
axis=0,
ignore_index=True,
sort=False,
).reset_index(drop=True)
else:
raise KeyboardInterrupt
except Exception as e:
if verbose >= 0:
print(
'Template Eval Error: {} in model {}: {}'.format(
(repr(e)), template_result.model_count, model_str
)
)
result = pd.DataFrame(
{
'ID': create_model_id(
model_str, parameter_dict, transformation_dict
),
'Model': model_str,
'ModelParameters': json.dumps(parameter_dict),
'TransformationParameters': json.dumps(transformation_dict),
'Ensemble': ensemble_input,
'TransformationRuntime': datetime.timedelta(0),
'FitRuntime': datetime.timedelta(0),
'PredictRuntime': datetime.timedelta(0),
'TotalRuntime': datetime.timedelta(0),
'Exceptions': repr(e),
'Runs': 1,
'ValidationRound': validation_round,
},
index=[0],
)
template_result.model_results = pd.concat(
[template_result.model_results, result],
axis=0,
ignore_index=True,
sort=False,
).reset_index(drop=True)
return template_result
def RandomTemplate(
n: int = 10,
model_list: list = [
'ZeroesNaive',
'LastValueNaive',
'AverageValueNaive',
'GLS',
'GLM',
'ETS',
'ARIMA',
'FBProphet',
'RollingRegression',
'GluonTS',
'UnobservedComponents',
'VARMAX',
'VECM',
'DynamicFactor',
],
):
"""
Returns a template dataframe of randomly generated transformations, models, and hyperparameters.
Args:
n (int): number of random models to return
"""
n = abs(int(n))
template = pd.DataFrame()
counter = 0
while len(template.index) < n:
model_str = np.random.choice(model_list)
param_dict = ModelMonster(model_str).get_new_params()
trans_dict = RandomTransform()
row = pd.DataFrame(
{
'Model': model_str,
'ModelParameters': json.dumps(param_dict),
'TransformationParameters': json.dumps(trans_dict),
'Ensemble': 0,
},
index=[0],
)
template = pd.concat([template, row], axis=0, ignore_index=True)
template.drop_duplicates(inplace=True)
counter += 1
if counter > (n * 3):
break
return template
def UniqueTemplates(
existing_templates,
new_possibilities,
selection_cols: list = [
'Model',
'ModelParameters',
'TransformationParameters',
'Ensemble',
],
):
"""
Returns unique dataframe rows from new_possiblities not in existing_templates.
Args:
selection_cols (list): list of column namess to use to judge uniqueness/match on
"""
keys = list(new_possibilities[selection_cols].columns.values)
idx1 = existing_templates.copy().set_index(keys).index
idx2 = new_possibilities.set_index(keys).index
new_template = new_possibilities[~idx2.isin(idx1)]
return new_template
def dict_recombination(a: dict, b: dict):
"""Recombine two dictionaries with identical keys. Return new dict."""
b_keys = [*b]
key_size = int(len(b_keys) / 2) if len(b_keys) > 1 else 1
bs_keys = np.random.choice(b_keys, size=key_size)
b_prime = {k: b[k] for k in bs_keys}
c = {**a, **b_prime} # overwrites with B
return c
def trans_dict_recomb(dict_array):
"""Recombine two transformation param dictionaries from array of dicts."""
r_sel = np.random.choice(dict_array, size=2, replace=False)
a = r_sel[0]
b = r_sel[1]
c = dict_recombination(a, b)
out_keys = ['outlier_method', 'outlier_threshold', 'outlier_position']
current_dict = np.random.choice([a, b], size=1).item()
c = {**c, **{k: current_dict[k] for k in out_keys}}
mid_trans_keys = ['second_transformation', 'transformation_param']
current_dict = np.random.choice([a, b], size=1).item()
c = {**c, **{k: current_dict[k] for k in mid_trans_keys}}
mid_trans_keys = ['third_transformation', 'transformation_param2']
current_dict = np.random.choice([a, b], size=1).item()
c = {**c, **{k: current_dict[k] for k in mid_trans_keys}}
disc_keys = ['discretization', 'n_bins']
current_dict = np.random.choice([a, b], size=1).item()
c = {**c, **{k: current_dict[k] for k in disc_keys}}
disc_keys = ['grouping', 'reconciliation']
current_dict = np.random.choice([a, b], size=1).item()
if all([x in current_dict.keys() for x in disc_keys]):
c = {**c, **{k: current_dict[k] for k in disc_keys}}
return c
def NewGeneticTemplate(
model_results,
submitted_parameters,
sort_column: str = "smape_weighted",
sort_ascending: bool = True,
max_results: int = 50,
max_per_model_class: int = 5,
top_n: int = 50,
template_cols: list = [
'Model',
'ModelParameters',
'TransformationParameters',
'Ensemble',
],
):
"""
Return new template given old template with model accuracies.
Args:
model_results (pandas.DataFrame): models that have actually been run
submitted_paramters (pandas.DataFrame): models tried (may have returned different parameters to results)
"""
new_template = pd.DataFrame()
# filter existing templates
sorted_results = model_results[model_results['Ensemble'] == 0].copy()
sorted_results = sorted_results.sort_values(
by=sort_column, ascending=sort_ascending, na_position='last'
)
sorted_results = sorted_results.drop_duplicates(subset=template_cols, keep='first')
if str(max_per_model_class).isdigit():
sorted_results = (
sorted_results.sort_values(sort_column, ascending=sort_ascending)
.groupby('Model')
.head(max_per_model_class)
.reset_index()
)
sorted_results = sorted_results.sort_values(
by=sort_column, ascending=sort_ascending, na_position='last'
).head(top_n)
no_params = ['ZeroesNaive', 'LastValueNaive', 'GLS']
recombination_approved = [
'SeasonalNaive',
'MotifSimulation',
"ETS",
'DynamicFactor',
'VECM',
'VARMAX',
'GLM',
'ARIMA',
'FBProphet',
'GluonTS',
'RollingRegression',
'VAR',
'WindowRegression',
'TensorflowSTS',
'TFPRegression',
]
borrow = ['ComponentAnalysis']
best = json.loads(sorted_results.iloc[0, :]['TransformationParameters'])
for model_type in sorted_results['Model'].unique():
if model_type in no_params:
current_ops = sorted_results[sorted_results['Model'] == model_type]
n = 3
trans_dicts = _trans_dicts(current_ops, best=best, n=n)
model_param = current_ops.iloc[0, :]['ModelParameters']
new_row = pd.DataFrame(
{
'Model': model_type,
'ModelParameters': model_param,
'TransformationParameters': trans_dicts,
'Ensemble': 0,
},
index=list(range(n)),
)
elif model_type in recombination_approved:
current_ops = sorted_results[sorted_results['Model'] == model_type]
n = 4
trans_dicts = _trans_dicts(current_ops, best=best, n=n)
# select the best model of this type
fir = json.loads(current_ops.iloc[0, :]['ModelParameters'])
cur_len = current_ops.shape[0]
if cur_len > 1:
# select randomly from best of data, doesn't handle lengths < 2
top_r = np.floor((cur_len / 5) + 2)
r_id = np.random.randint(1, top_r)
sec = json.loads(current_ops.iloc[r_id, :]['ModelParameters'])
else:
sec = ModelMonster(model_type).get_new_params()
# generate new random parameters ('mutations')
r = ModelMonster(model_type).get_new_params()
r2 = ModelMonster(model_type).get_new_params()
arr = [fir, sec, r2, r]
model_dicts = list()
# recombine best and random to create new generation
for _ in range(n):
r_sel = np.random.choice(arr, size=2, replace=False)
a = r_sel[0]
b = r_sel[1]
c = dict_recombination(a, b)
model_dicts.append(json.dumps(c))
new_row = pd.DataFrame(
{
'Model': model_type,
'ModelParameters': model_dicts,
'TransformationParameters': trans_dicts,
'Ensemble': 0,
},
index=list(range(n)),
)
else:
current_ops = sorted_results[sorted_results['Model'] == model_type]
n = 3
trans_dicts = _trans_dicts(current_ops, best=best, n=n)
model_dicts = list()
for _ in range(n):
c = ModelMonster(model_type).get_new_params()
model_dicts.append(json.dumps(c))
new_row = pd.DataFrame(
{
'Model': model_type,
'ModelParameters': model_dicts,
'TransformationParameters': trans_dicts,
'Ensemble': 0,
},
index=list(range(n)),
)
new_template = pd.concat(
[new_template, new_row], axis=0, ignore_index=True, sort=False
)
"""
# recombination of transforms across models by shifting transforms
recombination = sorted_results.tail(len(sorted_results.index) - 1).copy()
recombination['TransformationParameters'] = sorted_results['TransformationParameters'].shift(1).tail(len(sorted_results.index) - 1)
new_template = pd.concat([new_template,
recombination.head(top_n)[template_cols]],
axis=0, ignore_index=True, sort=False)
"""
# remove generated models which have already been tried
sorted_results = pd.concat(
[submitted_parameters, sorted_results], axis=0, ignore_index=True, sort=False
).reset_index(drop=True)
new_template = UniqueTemplates(
sorted_results, new_template, selection_cols=template_cols
).head(max_results)
return new_template
def validation_aggregation(validation_results):
"""Aggregate a TemplateEvalObject."""
groupby_cols = [
'ID',
'Model',
'ModelParameters',
'TransformationParameters',
'Ensemble',
]
col_aggs = {
'Runs': 'sum',
'smape': 'mean',
'mae': 'mean',
'rmse': 'mean',
'containment': 'mean',
'spl': 'mean',
'contour': 'mean',
'smape_weighted': 'mean',
'mae_weighted': 'mean',
'rmse_weighted': 'mean',
'containment_weighted': 'mean',
'contour_weighted': 'mean',
'spl_weighted': 'mean',
'containment_weighted': 'mean',
'TotalRuntimeSeconds': 'mean',
'Score': 'mean',
}
validation_results.model_results['TotalRuntimeSeconds'] = (
validation_results.model_results['TotalRuntime'].dt.seconds + 1
)
validation_results.model_results = validation_results.model_results[
pd.isnull(validation_results.model_results['Exceptions'])
]
validation_results.model_results = validation_results.model_results.replace(
[np.inf, -np.inf], np.nan
)
validation_results.model_results = validation_results.model_results.groupby(
groupby_cols
).agg(col_aggs)
validation_results.model_results = validation_results.model_results.reset_index(
drop=False
)
return validation_results
def generate_score(
model_results, metric_weighting: dict = {}, prediction_interval: float = 0.9
):
"""Generate score based on relative accuracies."""
try:
smape_weighting = metric_weighting['smape_weighting']
except KeyError:
smape_weighting = 1
try:
mae_weighting = metric_weighting['mae_weighting']
except KeyError:
mae_weighting = 0
try:
rmse_weighting = metric_weighting['rmse_weighting']
except KeyError:
rmse_weighting = 0
try:
containment_weighting = metric_weighting['containment_weighting']
except KeyError:
containment_weighting = 0
try:
runtime_weighting = metric_weighting['runtime_weighting'] * 0.1
except KeyError:
runtime_weighting = 0
try:
spl_weighting = metric_weighting['spl_weighting']
except KeyError:
spl_weighting = 0
try:
contour_weighting = metric_weighting['contour_weighting']
except KeyError:
contour_weighting = 0
# handle various runtime information records
if 'TotalRuntimeSeconds' in model_results.columns:
if 'TotalRuntime' in model_results.columns:
model_results['TotalRuntimeSeconds'] = np.where(
model_results['TotalRuntimeSeconds'].isna(),
model_results['TotalRuntime'].dt.seconds,
model_results['TotalRuntimeSeconds'],
)
else:
model_results['TotalRuntimeSeconds'] = np.where(
model_results['TotalRuntimeSeconds'].isna(),
model_results['TotalRuntimeSeconds'].max(),
model_results['TotalRuntimeSeconds'],
)
else:
model_results['TotalRuntimeSeconds'] = model_results['TotalRuntime'].dt.seconds
# generate minimizing scores, where smaller = better accuracy
try:
model_results = model_results.replace([np.inf, -np.inf], np.nan)
# model_results = model_results.fillna(value=model_results.max(axis=0))
smape_score = model_results['smape_weighted'] / (
model_results['smape_weighted'].min(skipna=True) + 1
) # smaller better
rmse_scaler = model_results['rmse_weighted'].median(skipna=True)
rmse_scaler = 1 if rmse_scaler == 0 else rmse_scaler
rmse_score = model_results['rmse_weighted'] / rmse_scaler
mae_scaler = model_results['mae_weighted'].median(skipna=True)
mae_scaler = 1 if mae_scaler == 0 else mae_scaler
mae_score = model_results['mae_weighted'] / mae_scaler
containment_score = (
abs(prediction_interval - model_results['containment'])
) + 1 # from 1 to 2, smaller better
runtime = model_results['TotalRuntimeSeconds'] + 120
runtime_score = runtime / (runtime.min(skipna=True)) # smaller better
spl_score = model_results['spl_weighted'] / (
model_results['spl_weighted'].min(skipna=True) + 1
) # smaller better
contour_score = (
(1 / (model_results['contour_weighted']))
.replace([np.inf, -np.inf, np.nan], 10)
.clip(upper=10)
)
except KeyError:
raise KeyError(
"Inconceivable! Evaluation Metrics are missing and all models have failed, by an error in TemplateWizard or metrics. A new template may help, or an adjusted model_list."
)
return (
(smape_score * smape_weighting)
+ (mae_score * mae_weighting)
+ (rmse_score * rmse_weighting)
+ (containment_score * containment_weighting)
+ (runtime_score * runtime_weighting)
+ (spl_score * spl_weighting)
+ (contour_score * contour_weighting)
)
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220,
220,
220,
220,
220,
220,
220,
1267,
628,
198,
4299,
9104,
39156,
2867,
7,
198,
220,
220,
220,
47764,
62,
27432,
11,
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220,
220,
220,
11092,
62,
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25,
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13389,
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25,
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220,
2746,
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25,
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220,
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25,
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11,
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220,
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25,
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259,
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3256,
198,
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220,
17724,
62,
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2100,
25,
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796,
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13,
24,
11,
198,
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220,
220,
645,
62,
12480,
2929,
25,
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796,
10352,
11,
198,
220,
220,
220,
32315,
25,
12178,
796,
6045,
11,
198,
220,
220,
220,
2003,
62,
2301,
44292,
62,
27432,
41888,
4357,
198,
220,
220,
220,
2003,
62,
2301,
44292,
62,
754,
2701,
41888,
4357,
198,
220,
220,
220,
9912,
62,
19315,
25,
965,
796,
705,
2937,
3256,
198,
220,
220,
220,
923,
7575,
1273,
9430,
28,
14202,
11,
198,
220,
220,
220,
36115,
62,
2340,
28,
14202,
11,
198,
220,
220,
220,
4738,
62,
28826,
25,
493,
796,
12131,
11,
198,
220,
220,
220,
15942,
577,
25,
493,
796,
657,
11,
198,
220,
220,
220,
299,
62,
43863,
25,
493,
796,
6045,
11,
198,
2599,
198,
220,
220,
220,
37227,
18332,
10007,
656,
21128,
11523,
628,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
27432,
357,
79,
392,
292,
13,
6601,
19778,
2599,
35575,
3047,
27039,
286,
16092,
8079,
15732,
290,
2168,
355,
951,
82,
198,
220,
220,
220,
220,
220,
220,
220,
11092,
62,
13664,
357,
600,
2599,
1271,
286,
9574,
284,
11092,
198,
220,
220,
220,
220,
220,
220,
220,
13389,
62,
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357,
11600,
2599,
257,
22155,
286,
503,
2505,
11,
6070,
4535,
11,
290,
13389,
5050,
284,
307,
973,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
62,
2536,
357,
2536,
2599,
257,
4731,
284,
307,
1277,
284,
262,
5035,
2746,
11,
973,
287,
9104,
40872,
198,
220,
220,
220,
220,
220,
220,
220,
8373,
357,
2536,
2599,
965,
10200,
8373,
16144,
286,
640,
2168,
198,
220,
220,
220,
220,
220,
220,
220,
17724,
62,
3849,
2100,
357,
22468,
2599,
9647,
286,
8563,
357,
11295,
25,
8365,
466,
262,
20016,
14351,
2872,
262,
4064,
1965,
329,
23029,
198,
220,
220,
220,
220,
220,
220,
220,
645,
62,
12480,
2929,
357,
30388,
2599,
1771,
284,
2700,
477,
26119,
284,
307,
1875,
657,
198,
220,
220,
220,
220,
220,
220,
220,
32315,
357,
22468,
2599,
618,
407,
6045,
11,
779,
428,
1988,
1635,
1366,
336,
1614,
2029,
3509,
393,
2174,
949,
329,
1500,
24674,
11092,
3815,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
27432,
357,
30094,
13,
27996,
2599,
351,
4818,
8079,
6376,
11,
286,
1900,
287,
5963,
1366,
11,
2665,
12336,
4512,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
754,
2701,
357,
30094,
13,
27996,
2599,
351,
4818,
8079,
6376,
11,
286,
1900,
287,
5963,
1366,
11,
2665,
12336,
1332,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
9912,
62,
19315,
357,
2536,
2599,
3804,
832,
284,
9912,
5301,
11,
973,
416,
257,
1178,
4981,
355,
657,
14,
16,
842,
44292,
13,
198,
220,
220,
220,
220,
220,
220,
220,
923,
7575,
1273,
9430,
357,
30094,
13,
27996,
2599,
6376,
357,
25076,
62,
2340,
828,
15180,
357,
27354,
8079,
286,
3274,
923,
286,
2168,
8,
198,
220,
220,
220,
220,
220,
220,
220,
299,
62,
43863,
357,
600,
2599,
1271,
286,
7767,
628,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
46690,
10267,
357,
2306,
1747,
13,
39156,
2867,
10267,
2599,
46690,
422,
11160,
4694,
2746,
2134,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
13389,
10434,
7575,
796,
4818,
8079,
13,
19608,
8079,
13,
2197,
3419,
198,
220,
220,
220,
422,
1960,
1747,
13,
31391,
13,
35636,
1330,
3611,
8291,
16354,
628,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
31255,
344,
62,
41433,
796,
13389,
62,
11600,
17816,
1073,
263,
344,
62,
41433,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
36115,
796,
13389,
62,
11600,
17816,
8094,
278,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
611,
36115,
6624,
705,
7220,
6,
290,
36115,
62,
2340,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36115,
796,
705,
74,
1326,
504,
20,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13389,
62,
11600,
17816,
8094,
278,
20520,
796,
705,
74,
1326,
504,
20,
6,
198,
220,
220,
220,
220,
220,
220,
220,
27515,
796,
13389,
62,
11600,
17816,
260,
1102,
66,
15547,
20520,
198,
220,
220,
220,
2845,
35528,
25,
198,
220,
220,
220,
220,
220,
220,
220,
31255,
344,
62,
41433,
796,
10352,
198,
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220,
220,
220,
220,
220,
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36115,
796,
6045,
198,
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198,
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220,
220,
220,
220,
220,
27515,
796,
6045,
198,
220,
220,
220,
47385,
62,
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796,
3611,
8291,
16354,
7,
198,
220,
220,
220,
220,
220,
220,
220,
503,
2505,
62,
24396,
28,
7645,
1161,
62,
11600,
17816,
448,
2505,
62,
24396,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
503,
2505,
62,
400,
10126,
28,
7645,
1161,
62,
11600,
17816,
448,
2505,
62,
400,
10126,
6,
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198,
220,
220,
220,
220,
220,
220,
220,
503,
2505,
62,
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28,
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1161,
62,
11600,
17816,
448,
2505,
62,
9150,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
6070,
2616,
28,
7645,
1161,
62,
11600,
17816,
20797,
2616,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
13389,
28,
7645,
1161,
62,
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17816,
7645,
1161,
6,
4357,
198,
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220,
220,
220,
220,
220,
220,
1062,
10920,
28,
7645,
1161,
62,
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17816,
15255,
10920,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
1218,
62,
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1161,
28,
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62,
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62,
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6,
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220,
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220,
220,
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13389,
62,
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28,
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62,
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17816,
7645,
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62,
17143,
6,
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198,
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220,
220,
220,
220,
220,
220,
2368,
62,
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1161,
28,
7645,
1161,
62,
11600,
17816,
17089,
62,
7645,
1161,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
13389,
62,
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17,
28,
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1161,
62,
11600,
17816,
7645,
1161,
62,
17143,
17,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
5544,
62,
7645,
1161,
28,
7645,
1161,
62,
11600,
17816,
49393,
62,
7645,
1161,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
1221,
1186,
1634,
28,
7645,
1161,
62,
11600,
17816,
15410,
1186,
1634,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
299,
62,
65,
1040,
28,
7645,
1161,
62,
11600,
17816,
77,
62,
65,
1040,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
36115,
28,
8094,
278,
11,
198,
220,
220,
220,
220,
220,
220,
220,
36115,
62,
2340,
28,
8094,
278,
62,
2340,
11,
198,
220,
220,
220,
220,
220,
220,
220,
27515,
28,
260,
1102,
66,
15547,
11,
198,
220,
220,
220,
220,
220,
220,
220,
31255,
344,
62,
41433,
28,
1073,
263,
344,
62,
41433,
11,
198,
220,
220,
220,
6739,
11147,
7,
7568,
62,
27432,
8,
198,
220,
220,
220,
47764,
62,
27432,
62,
7645,
12214,
796,
47385,
62,
15252,
13,
35636,
7,
7568,
62,
27432,
8,
628,
220,
220,
220,
1303,
16416,
262,
4732,
11,
37941,
45381,
262,
2033,
286,
1366,
1695,
13,
198,
220,
220,
220,
611,
13389,
62,
11600,
17816,
22866,
62,
82,
677,
263,
20520,
407,
287,
685,
14202,
11,
705,
14202,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
422,
1960,
1747,
13,
31391,
13,
35636,
1330,
2829,
62,
22866,
62,
82,
677,
263,
628,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
27432,
62,
7645,
12214,
796,
2829,
62,
22866,
62,
82,
677,
263,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
27432,
62,
7645,
12214,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2446,
28,
7645,
1161,
62,
11600,
17816,
22866,
62,
82,
677,
263,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11092,
62,
13664,
28,
754,
2701,
62,
13664,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
1303,
787,
1654,
842,
44292,
468,
976,
4129,
13,
770,
714,
307,
257,
1917,
611,
2642,
2546,
842,
44292,
318,
3804,
13,
198,
220,
220,
220,
611,
18896,
7,
37443,
62,
2301,
44292,
62,
27432,
8,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
27432,
796,
2003,
62,
2301,
44292,
62,
27432,
13,
13199,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
27432,
62,
7645,
12214,
13,
43358,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
13389,
62,
43282,
796,
4818,
8079,
13,
19608,
8079,
13,
2197,
3419,
532,
13389,
10434,
7575,
198,
220,
220,
220,
1303,
422,
1960,
1747,
13,
18206,
84,
1352,
13,
23736,
62,
19849,
1330,
9104,
40872,
198,
220,
220,
220,
2746,
796,
9104,
40872,
7,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
62,
2536,
11,
198,
220,
220,
220,
220,
220,
220,
220,
10007,
28,
17143,
2357,
62,
11600,
11,
198,
220,
220,
220,
220,
220,
220,
220,
8373,
28,
35324,
11,
198,
220,
220,
220,
220,
220,
220,
220,
17724,
62,
3849,
2100,
28,
28764,
2867,
62,
3849,
2100,
11,
198,
220,
220,
220,
220,
220,
220,
220,
9912,
62,
19315,
28,
37689,
62,
19315,
11,
198,
220,
220,
220,
220,
220,
220,
220,
4738,
62,
28826,
28,
25120,
62,
28826,
11,
198,
220,
220,
220,
220,
220,
220,
220,
15942,
577,
28,
19011,
577,
11,
198,
220,
220,
220,
220,
220,
220,
220,
11092,
62,
13664,
28,
754,
2701,
62,
13664,
11,
198,
220,
220,
220,
220,
220,
220,
220,
299,
62,
43863,
28,
77,
62,
43863,
11,
198,
220,
220,
220,
1267,
198,
220,
220,
220,
2746,
796,
2746,
13,
11147,
7,
7568,
62,
27432,
62,
7645,
12214,
11,
2003,
62,
2301,
44292,
28,
37443,
62,
2301,
44292,
62,
27432,
8,
198,
220,
220,
220,
47764,
62,
754,
2701,
796,
2746,
13,
79,
17407,
7,
198,
220,
220,
220,
220,
220,
220,
220,
11092,
62,
13664,
28,
754,
2701,
62,
13664,
11,
2003,
62,
2301,
44292,
28,
37443,
62,
2301,
44292,
62,
754,
2701,
198,
220,
220,
220,
1267,
628,
220,
220,
220,
611,
47764,
62,
754,
2701,
13,
754,
2701,
13,
271,
8423,
22446,
439,
7,
22704,
28,
15,
737,
459,
2981,
7,
600,
737,
16345,
3419,
1875,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
11052,
12331,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
17633,
23884,
4504,
11013,
45,
329,
530,
393,
517,
2168,
1911,
18982,
7,
19849,
62,
2536,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
13389,
10434,
7575,
796,
4818,
8079,
13,
19608,
8079,
13,
2197,
3419,
198,
220,
220,
220,
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329,
1500,
24674,
11092,
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13,
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27432,
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13,
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351,
4818,
8079,
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2003,
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2701,
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30094,
13,
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4818,
8079,
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220,
220,
220,
220,
9912,
62,
19315,
357,
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2599,
3804,
832,
284,
9912,
5301,
11,
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416,
257,
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355,
657,
14,
16,
842,
44292,
13,
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1273,
9430,
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13,
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357,
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62,
2340,
828,
15180,
357,
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8079,
286,
3274,
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286,
2168,
8,
198,
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220,
220,
220,
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11055,
62,
4033,
82,
357,
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286,
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355,
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11055,
628,
220,
220,
220,
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25,
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10267,
357,
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13,
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2867,
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422,
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198,
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220,
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611,
318,
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11,
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1960,
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13,
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2100,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
645,
62,
12480,
2929,
28,
3919,
62,
12480,
2929,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32315,
28,
1102,
2536,
2913,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
27432,
28,
37443,
62,
2301,
44292,
62,
27432,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
754,
2701,
28,
37443,
62,
2301,
44292,
62,
754,
2701,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9912,
62,
19315,
28,
37689,
62,
19315,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
923,
7575,
1273,
9430,
28,
9688,
7575,
1273,
9430,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36115,
62,
2340,
28,
8094,
278,
62,
2340,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4738,
62,
28826,
28,
25120,
62,
28826,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15942,
577,
28,
19011,
577,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
299,
62,
43863,
28,
77,
62,
43863,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11055,
62,
4033,
82,
28,
28243,
62,
4033,
82,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2746,
62,
312,
796,
2251,
62,
19849,
62,
312,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
754,
2701,
13,
19849,
62,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
754,
2701,
13,
19849,
62,
17143,
7307,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
754,
2701,
13,
7645,
1161,
62,
17143,
7307,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2472,
62,
43282,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
754,
2701,
13,
11147,
62,
43282,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1343,
47764,
62,
754,
2701,
13,
79,
17407,
62,
43282,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1343,
47764,
62,
754,
2701,
13,
7645,
1161,
62,
43282,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26119,
62,
4868,
13,
2302,
437,
26933,
19849,
62,
312,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26119,
62,
43282,
13,
2302,
437,
26933,
23350,
62,
43282,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26119,
13,
2302,
437,
26933,
7568,
62,
754,
2701,
13,
754,
2701,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6727,
62,
754,
40924,
13,
2302,
437,
26933,
7568,
62,
754,
2701,
13,
45828,
62,
754,
2701,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2793,
62,
754,
40924,
13,
2302,
437,
26933,
7568,
62,
754,
2701,
13,
21037,
62,
754,
2701,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3140,
62,
754,
2701,
796,
2039,
15140,
16351,
2701,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3140,
62,
19849,
62,
2536,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3140,
62,
37266,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26119,
62,
4868,
28,
754,
40924,
62,
4868,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26119,
28,
754,
40924,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2793,
62,
754,
40924,
28,
21037,
62,
754,
40924,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6727,
62,
754,
40924,
28,
45828,
62,
754,
40924,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26119,
62,
43282,
28,
754,
40924,
62,
43282,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17724,
62,
3849,
2100,
28,
28764,
2867,
62,
3849,
2100,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
3140,
62,
754,
2701,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
407,
281,
34549,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2746,
62,
2536,
796,
5752,
62,
45828,
17816,
17633,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11507,
62,
11600,
796,
33918,
13,
46030,
7,
808,
62,
45828,
17816,
17633,
48944,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13389,
62,
11600,
796,
33918,
13,
46030,
7,
808,
62,
45828,
17816,
8291,
1161,
48944,
6,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
754,
2701,
796,
9104,
39156,
2867,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
27432,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11092,
62,
13664,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13389,
62,
11600,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2746,
62,
2536,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11507,
62,
11600,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8373,
28,
35324,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17724,
62,
3849,
2100,
28,
28764,
2867,
62,
3849,
2100,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
645,
62,
12480,
2929,
28,
3919,
62,
12480,
2929,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
32315,
28,
1102,
2536,
2913,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
27432,
28,
37443,
62,
2301,
44292,
62,
27432,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
754,
2701,
28,
37443,
62,
2301,
44292,
62,
754,
2701,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36115,
62,
2340,
28,
8094,
278,
62,
2340,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9912,
62,
19315,
28,
37689,
62,
19315,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4738,
62,
28826,
28,
25120,
62,
28826,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15942,
577,
28,
19011,
577,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
923,
7575,
1273,
9430,
28,
9688,
7575,
1273,
9430,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
299,
62,
43863,
28,
77,
62,
43863,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
47764,
62,
754,
2701,
628,
198,
4299,
37350,
54,
8669,
7,
198,
220,
220,
220,
11055,
11,
198,
220,
220,
220,
47764,
62,
27432,
11,
198,
220,
220,
220,
47764,
62,
9288,
11,
198,
220,
220,
220,
19590,
11,
198,
220,
220,
220,
2746,
62,
9127,
25,
493,
796,
657,
11,
198,
220,
220,
220,
34549,
25,
965,
796,
6407,
11,
198,
220,
220,
220,
11092,
62,
13664,
25,
493,
796,
1478,
11,
198,
220,
220,
220,
8373,
25,
965,
796,
705,
259,
2232,
3256,
198,
220,
220,
220,
17724,
62,
3849,
2100,
25,
12178,
796,
657,
13,
24,
11,
198,
220,
220,
220,
645,
62,
12480,
2929,
25,
20512,
796,
10352,
11,
198,
220,
220,
220,
32315,
25,
12178,
796,
6045,
11,
198,
220,
220,
220,
2003,
62,
2301,
44292,
62,
27432,
41888,
4357,
198,
220,
220,
220,
2003,
62,
2301,
44292,
62,
754,
2701,
41888,
4357,
198,
220,
220,
220,
9912,
62,
19315,
25,
965,
796,
705,
2937,
3256,
198,
220,
220,
220,
923,
7575,
1273,
9430,
28,
14202,
11,
198,
220,
220,
220,
4738,
62,
28826,
25,
493,
796,
12131,
11,
198,
220,
220,
220,
15942,
577,
25,
493,
796,
657,
11,
198,
220,
220,
220,
299,
62,
43863,
25,
493,
796,
6045,
11,
198,
220,
220,
220,
21201,
62,
744,
25,
493,
796,
657,
11,
198,
220,
220,
220,
2746,
62,
3849,
3622,
25,
20512,
796,
10352,
11,
198,
220,
220,
220,
36115,
62,
2340,
28,
14202,
11,
198,
220,
220,
220,
11055,
62,
4033,
82,
25,
1351,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
705,
17633,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
705,
17633,
48944,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
705,
8291,
1161,
48944,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
705,
4834,
15140,
3256,
198,
220,
220,
220,
16589,
198,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
7214,
37350,
11,
5860,
15691,
13,
628,
220,
220,
220,
1318,
389,
617,
508,
869,
502,
986,
5045,
13,
532,
11361,
628,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
11055,
357,
79,
392,
292,
13,
6601,
19778,
2599,
7268,
2746,
965,
11,
290,
33918,
286,
38226,
290,
8718,
17143,
1010,
198,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
27432,
357,
79,
392,
292,
13,
6601,
19778,
2599,
35575,
3047,
27039,
286,
16092,
8079,
15732,
290,
2168,
355,
951,
82,
198,
220,
220,
220,
220,
220,
220,
220,
47764,
62,
9288,
357,
79,
392,
292,
13,
6601,
19778,
2599,
1366,
14535,
286,
4036,
3815,
286,
357,
754,
2701,
4129,
1635,
299,
2168,
8,
198,
220,
220,
220,
220,
220,
220,
220,
19590,
357,
11600,
2599,
1994,
796,
5721,
14,
25076,
62,
312,
11,
1988,
796,
3463,
198,
220,
220,
220,
220,
220,
220,
220,
34549,
357,
2536,
2599,
1715,
286,
34549,
3858,
284,
8335,
18663,
4947,
198,
220,
220,
220,
220,
220,
220,
220,
11092,
62,
13664,
357,
600,
2599,
1271,
286,
9574,
284,
11092,
198,
220,
220,
220,
220,
220,
220,
220,
13389,
62,
11600,
357,
11600,
2599,
257,
22155,
286,
503,
2505,
11,
6070,
4535,
11,
290,
13389,
5050,
284,
307,
973,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
62,
2536,
357,
2536,
2599,
257,
4731,
284,
307,
1277,
284,
262,
5035,
2746,
11,
973,
287,
9104,
40872,
198,
220,
220,
220,
220,
220,
220,
220,
8373,
357,
2536,
2599,
965,
10200,
8373,
16144,
286,
640,
2168,
198,
220,
220,
220,
220,
220,
220,
220,
17724,
62,
3849,
2100,
357,
22468,
2599,
9647,
286,
8563,
357,
11295,
25,
8365,
466,
262,
20016,
14351,
2872,
262,
4064,
1965,
329,
23029,
198,
220,
220,
220,
220,
220,
220,
220,
645,
62,
12480,
2929,
357,
30388,
2599,
1771,
284,
2700,
477,
26119,
284,
307,
1875,
657,
198,
220,
220,
220,
220,
220,
220,
220,
32315,
357,
22468,
2599,
618,
407,
6045,
11,
779,
428,
1988,
1635,
1366,
336,
1614,
2029,
3509,
393,
2174,
949,
329,
1500,
24674,
11092,
3815,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
27432,
357,
30094,
13,
27996,
2599,
351,
4818,
8079,
6376,
11,
286,
1900,
287,
5963,
1366,
11,
2665,
12336,
4512,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
754,
2701,
357,
30094,
13,
27996,
2599,
351,
4818,
8079,
6376,
11,
286,
1900,
287,
5963,
1366,
11,
2665,
12336,
1332,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
9912,
62,
19315,
357,
2536,
2599,
3804,
832,
284,
9912,
5301,
11,
973,
416,
257,
1178,
4981,
355,
657,
14,
16,
842,
44292,
13,
198,
220,
220,
220,
220,
220,
220,
220,
923,
7575,
1273,
9430,
357,
30094,
13,
27996,
2599,
6376,
357,
25076,
62,
2340,
828,
15180,
357,
27354,
8079,
286,
3274,
923,
286,
2168,
8,
198,
220,
220,
220,
220,
220,
220,
220,
21201,
62,
744,
357,
600,
2599,
493,
3804,
284,
1700,
1459,
21201,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
62,
3849,
3622,
357,
30388,
2599,
611,
6407,
11,
10586,
48237,
389,
4978,
290,
691,
2270,
1459,
2746,
5418,
13,
198,
220,
220,
220,
220,
220,
220,
220,
11055,
62,
4033,
82,
357,
4868,
2599,
5721,
3891,
286,
15180,
973,
355,
2746,
11055,
628,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
37350,
36,
2100,
10267,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
34549,
796,
965,
7,
1072,
11306,
8,
198,
220,
220,
220,
11055,
62,
20274,
796,
37350,
36,
2100,
10267,
3419,
198,
220,
220,
220,
11055,
62,
20274,
13,
19849,
62,
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796,
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62,
9127,
198,
220,
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220,
611,
318,
39098,
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11,
279,
67,
13,
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198,
220,
220,
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279,
67,
13,
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19778,
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28243,
737,
7645,
3455,
3419,
198,
220,
220,
220,
1303,
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796,
555,
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82,
11,
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1072,
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796,
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628,
220,
220,
220,
329,
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11,
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287,
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13,
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198,
220,
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220,
220,
220,
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11507,
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11600,
796,
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13,
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808,
17816,
17633,
48944,
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12962,
198,
220,
220,
220,
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220,
220,
220,
220,
220,
13389,
62,
11600,
796,
33918,
13,
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808,
17816,
8291,
1161,
48944,
6,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
34549,
62,
15414,
796,
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15140,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1459,
62,
28243,
796,
279,
67,
13,
6601,
19778,
7,
808,
737,
7645,
3455,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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11055,
62,
20274,
13,
19849,
62,
9127,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
15942,
577,
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657,
25,
198,
220,
220,
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352,
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198,
220,
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3601,
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198,
220,
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220,
220,
220,
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220,
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220,
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220,
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366,
17633,
7913,
25,
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351,
2746,
23884,
287,
3254,
24765,
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351,
42287,
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290,
38226,
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1911,
18982,
7,
198,
220,
220,
220,
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965,
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28243,
62,
20274,
13,
19849,
62,
9127,
828,
198,
220,
220,
220,
220,
220,
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2746,
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11,
198,
220,
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220,
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965,
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12102,
341,
62,
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198,
220,
220,
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33918,
13,
67,
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2357,
62,
11600,
828,
198,
220,
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33918,
13,
67,
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1161,
62,
11600,
828,
198,
220,
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220,
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1267,
198,
220,
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198,
220,
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2073,
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3601,
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198,
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965,
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13,
19849,
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198,
220,
220,
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11,
198,
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198,
220,
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1267,
198,
220,
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220,
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220,
47764,
62,
754,
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796,
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54,
2007,
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198,
220,
220,
220,
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220,
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220,
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1459,
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198,
220,
220,
220,
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220,
220,
220,
220,
220,
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220,
220,
220,
220,
47764,
62,
27432,
28,
7568,
62,
27432,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11092,
62,
13664,
28,
754,
2701,
62,
13664,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8373,
28,
35324,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17724,
62,
3849,
2100,
28,
28764,
2867,
62,
3849,
2100,
11,
198,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
220,
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645,
62,
12480,
2929,
28,
3919,
62,
12480,
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11,
198,
220,
220,
220,
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220,
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220,
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32315,
28,
1102,
2536,
2913,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
27432,
28,
37443,
62,
2301,
44292,
62,
27432,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2003,
62,
2301,
44292,
62,
754,
2701,
28,
37443,
62,
2301,
44292,
62,
754,
2701,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9912,
62,
19315,
28,
37689,
62,
19315,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
923,
7575,
1273,
9430,
28,
9688,
7575,
1273,
9430,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36115,
62,
2340,
28,
8094,
278,
62,
2340,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4738,
62,
28826,
28,
25120,
62,
28826,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15942,
577,
28,
19011,
577,
11,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
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299,
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28,
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11055,
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198,
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583,
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47764,
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198,
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220,
220,
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198
] | 2.050615 | 32,777 |
from utils import get_input_lines
pos_x = 0
pos_y = 0
waypoint_x = 10
waypoint_y = 1
for line in get_input_lines(__file__):
action = line[0]
n = int(line[1:])
# Handle actions
if action == "N":
waypoint_y += n
elif action == "S":
waypoint_y -= n
elif action == "E":
waypoint_x += n
elif action == "W":
waypoint_x -= n
elif action == "L":
# Rotate (n//90) times CCW: (new_x, new_y) = (-old_y, old_x)
for i in range(n // 90):
tmp_x = waypoint_x
waypoint_x = -waypoint_y
waypoint_y = tmp_x
elif action == "R":
# Rotate (n//90) times CW: (new_x, new_y) = (old_y, -old_x)
for i in range(n // 90):
tmp_x = waypoint_x
waypoint_x = waypoint_y
waypoint_y = -tmp_x
elif action == "F":
pos_x += n * waypoint_x
pos_y += n * waypoint_y
else:
raise NotImplementedError(f"Unknown action '{action}'")
print(abs(pos_x) + abs(pos_y))
|
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] | 1.915888 | 535 |
"""2019 - Day 13 Part 1: Care Package."""
from src.year2019.intcode import Computer
def solve(task: str) -> int:
"""Count the number of blocks."""
computer = Computer()
computer.load_program(task)
computer.execute()
return list(computer.stdout)[2::3].count(2)
|
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] | 2.9375 | 96 |
#!/usr/bin/env python
'FontForge: Demo script to add menu items to FF tools menu'
__url__ = 'http://github.com/silnrsi/pysilfont'
__copyright__ = 'Copyright (c) 2014 SIL International (http://www.sil.org)'
__license__ = 'Released under the MIT License (http://opensource.org/licenses/MIT)'
__author__ = 'David Raymond'
import sys, os, fontforge
sys.path.append(os.path.join(os.environ['HOME'], 'src/pysilfont/scripts'))
import samples.demoFunctions
from samples.demoFunctions import functionList, callFunctions
#from samples.demoCallFunctions import callFunctions
funcList=functionList()
for functionGroup in funcList :
menuType = funcList[functionGroup][0]
fontforge.registerMenuItem(toolMenuFunction,None,functionGroup,menuType,None,functionGroup);
print functionGroup, " registered"
''' This script needs to be called from one of the folders that FontForge looks in for scripts to
run when it is started. With current versions of FontForge, one is Home/.config/fontforge/python.
You may need to turn on showing hidden files (ctrl-H in Nautilus) before you can see the .config
folder. Within there create a one-line python script, say call sampledemo.py containing a call
to this script, eg:
execfile("/home/david/src/pysilfont/scripts/samples/demoAddToMenu.py")
Due to the reload(samples.demoFunctions) line above, changes functions defined in demoFunctions.py
are dynamic, ie FontForge does not have to be restarted (as would be the case if the functions were
called directly from the tools menu. Functions can even be added dynamically to the function groups.
If new function groups are defined, FontForge does have to be restarted to add them to the tools menu.
'''
|
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7,
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220,
220,
220,
3601,
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11,
366,
6823,
1,
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198,
7061,
6,
770,
4226,
2476,
284,
307,
1444,
422,
530,
286,
262,
24512,
326,
24060,
19857,
3073,
287,
329,
14750,
284,
198,
5143,
618,
340,
318,
2067,
13,
2080,
1459,
6300,
286,
24060,
19857,
11,
530,
318,
5995,
11757,
11250,
14,
10331,
30293,
14,
29412,
13,
198,
1639,
743,
761,
284,
1210,
319,
4478,
7104,
3696,
357,
44755,
12,
39,
287,
399,
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35815,
8,
878,
345,
460,
766,
262,
764,
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220,
198,
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13,
220,
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612,
2251,
257,
530,
12,
1370,
21015,
4226,
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41903,
13,
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7268,
257,
869,
220,
198,
1462,
428,
4226,
11,
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25,
198,
198,
18558,
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14,
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14,
67,
8490,
14,
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14,
79,
893,
346,
10331,
14,
46521,
14,
82,
12629,
14,
9536,
78,
4550,
2514,
23381,
13,
9078,
4943,
198,
198,
22229,
284,
262,
18126,
7,
82,
12629,
13,
9536,
78,
24629,
2733,
8,
1627,
2029,
11,
2458,
5499,
5447,
287,
13605,
24629,
2733,
13,
9078,
220,
198,
533,
8925,
11,
37941,
24060,
19857,
857,
407,
423,
284,
307,
15765,
276,
357,
292,
561,
307,
262,
1339,
611,
262,
5499,
547,
198,
7174,
3264,
422,
262,
4899,
6859,
13,
40480,
460,
772,
307,
2087,
32366,
284,
262,
2163,
2628,
13,
198,
198,
1532,
649,
2163,
2628,
389,
5447,
11,
24060,
19857,
857,
423,
284,
307,
15765,
276,
284,
751,
606,
284,
262,
4899,
6859,
13,
198,
7061,
6,
198
] | 3.453252 | 492 |
import math
from copy import deepcopy
from tensor2struct.utils import tree
# Common routines for kernel functions
####
# An extremely simple cache
|
[
11748,
10688,
198,
6738,
4866,
1330,
2769,
30073,
198,
198,
6738,
11192,
273,
17,
7249,
13,
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1330,
5509,
628,
220,
220,
220,
1303,
8070,
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628,
628,
628,
628,
198,
4242,
628,
220,
220,
220,
1303,
1052,
4457,
2829,
12940,
198
] | 3.688889 | 45 |
import json
from django.conf import settings
from django.http import Http404
from django.utils import timezone
from PIL import Image, ImageFilter
from board.models import TelegramSync
from board.modules.response import StatusDone, StatusError
from modules.subtask import sub_task_manager
from modules.telegram import TelegramBot
from modules.randomness import randstr
|
[
11748,
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367,
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1330,
50203,
20630,
198,
6738,
13103,
13,
25120,
1108,
1330,
43720,
2536
] | 4.054945 | 91 |
_chars = "あいうおえかきくけこさしすせそたちつてとなにぬねのはひふへほまみむめもやゆよらりるれろわをんがぎぐげござじずぜぞだぢづでどばびぶべぼぱぴぷぺぽぁぃぅぇぉゃゅょっー１２３４５６７８９０！？、。"
chars = [c for c in _chars]
print(data_load())
|
[
62,
354,
945,
796,
366,
40948,
18566,
29557,
2515,
232,
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230,
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120,
240,
171,
120,
241,
171,
120,
242,
171,
120,
243,
171,
120,
244,
171,
120,
245,
171,
120,
246,
171,
120,
247,
171,
120,
238,
171,
120,
223,
171,
120,
253,
23513,
16764,
1,
198,
354,
945,
796,
685,
66,
329,
269,
287,
4808,
354,
945,
60,
198,
198,
4798,
7,
7890,
62,
2220,
28955,
198
] | 0.782828 | 198 |
# coding=utf-8
# Author: Jianghan LI
# Question: 593.Valid_Square
# Date: 2017-05-22
|
[
2,
19617,
28,
40477,
12,
23,
198,
2,
6434,
25,
40922,
6064,
24653,
198,
2,
18233,
25,
642,
6052,
13,
47139,
62,
48011,
198,
2,
7536,
25,
2177,
12,
2713,
12,
1828,
628
] | 2.606061 | 33 |
lines_count = int(input())
lines = [input() for _ in range(lines_count)]
solve(lines)
|
[
198,
198,
6615,
62,
9127,
796,
493,
7,
15414,
28955,
198,
198,
6615,
796,
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7,
6615,
62,
9127,
15437,
198,
198,
82,
6442,
7,
6615,
8
] | 2.69697 | 33 |
# Copyright 2021 Touca, Inc. Subject to Apache-2.0 License.
import math
from pathlib import Path
from colorama import Style, Fore, Back, init
init()
|
[
2,
15069,
33448,
23359,
6888,
11,
3457,
13,
15540,
284,
24843,
12,
17,
13,
15,
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13,
198,
198,
11748,
10688,
198,
6738,
3108,
8019,
1330,
10644,
198,
6738,
3124,
1689,
1330,
17738,
11,
4558,
11,
5157,
11,
2315,
198,
198,
15003,
3419,
628
] | 3.454545 | 44 |
# Copyright (c) 2013-2015 Siphon Contributors.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Tools for accessing atmospheric and oceanic science data on remote servers."""
# Version import needs to come first so everyone else can pull on import
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
|
[
2,
15069,
357,
66,
8,
2211,
12,
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311,
13323,
261,
25767,
669,
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651,
62,
47178,
3419,
17816,
9641,
20520,
198,
12381,
651,
62,
47178,
198
] | 3.716981 | 106 |
# B2025-输出字符菱形
print(" *")
print(" ***")
print("*****")
print(" ***")
print(" *")
|
[
2,
347,
1238,
1495,
12,
164,
122,
241,
49035,
118,
27764,
245,
163,
105,
99,
164,
237,
109,
37605,
95,
201,
198,
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201,
198,
4798,
7203,
17202,
4943,
201,
198,
4798,
7203,
220,
1635,
4943
] | 1.692308 | 52 |
import copy
from .util import is_terminator, is_label, mklabel
|
[
11748,
4866,
198,
198,
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764,
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318,
62,
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1352,
11,
318,
62,
18242,
11,
33480,
18242,
628,
628,
198
] | 3.090909 | 22 |
from config import config
from tests.pages import SignInPage
from tests.test_utils import do_verify, do_email_auth_verify
|
[
6738,
4566,
1330,
4566,
198,
6738,
5254,
13,
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62,
332,
1958,
11,
466,
62,
12888,
62,
18439,
62,
332,
1958,
628,
628,
198
] | 3.342105 | 38 |
# rave's virtual file system.
import os
import io
import re
import threading
import collections
import rave.common
import rave.log
_log = rave.log.get(__name__)
# Canonical path separator.
PATH_SEPARATOR = '/'
# Root.
ROOT = '/'
# Unnormalized path pattern.
BAD_PATH_PATTERN = re.compile(r'(?:{0}{{2,}}|(?:{0}|^)\.+(?:{0}|$))'.format(PATH_SEPARATOR))
# Various standard mount points.
ENGINE_MOUNT = '/.rave'
MODULE_MOUNT = '/.modules'
GAME_MOUNT = '/'
COMMON_MOUNT = '/.common'
class File(io.IOBase):
"""
An open file in the virtual file system.
Subclasses are expected to at least override the following:
- opened()
- readable() (if readable, returns False by default)
- writable() (if writable, returns False by default)
- seekable() (if seekable, returns False by default)
- close()
- read(amount=None) (if readable, raises FileNotReadable by default)
- write(data) (if writable, raises FileNotWritable by default)
- seek(position, mode) (if seekable, raises FileNotSeekable by default)
- tell() (if seekable, raises FileNotSeekable by default)
"""
def close(self):
""" Close file. Any operation on the file after calling this method will fail with `FileClosed` raised. """
raise NotImplementedError
def opened(self):
""" Return whether this file is open. """
raise NotImplementedError
def readable(self):
""" Return whether this file is readable. """
return False
def writable(self):
""" Return whether this file is writable. """
return False
def seekable(self):
""" Return whether this file is seeekable. """
return False
def read(self, amount=None):
""" Read `amount` bytes from file. Will read full contents if `amount` is not given. """
raise FileNotReadable(self)
def write(self, data):
""" Write `data` to file. """
raise FileNotWritable(self)
def seek(self, position, mode=os.SEEK_CUR):
""" Seek in file. May raise `FileNotSeekable` if this file can't be seeked in. """
raise FileNotSeekable(self)
def tell(self):
""" Tell current file position. May raise `FileNotSeekable` if this file can't be seeked in. """
raise FileNotSeekable(self)
class FileSystemProvider:
""" A provider to mount a filesystem within another filesystem. """
## Stateful API.
|
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7,
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220,
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220,
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276,
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13,
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220,
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220,
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220,
220,
220,
5298,
9220,
3673,
4653,
988,
540,
7,
944,
8,
628,
198,
4871,
9220,
11964,
29495,
25,
198,
220,
220,
220,
37227,
317,
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3817,
257,
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1626,
1194,
29905,
13,
37227,
628,
198,
2235,
1812,
913,
7824,
13,
198
] | 2.776888 | 874 |
#from django.http import HttpResponse
#from django.http import response_redirect
from django.shortcuts import render_to_response
from django.views.generic import DetailView, CreateView, TemplateView
from geogebra_applet.models import GeogebraApplet
|
[
2,
6738,
42625,
14208,
13,
4023,
1330,
367,
29281,
31077,
198,
2,
6738,
42625,
14208,
13,
4023,
1330,
2882,
62,
445,
1060,
198,
6738,
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1324,
1616,
13,
27530,
1330,
32960,
29230,
4677,
1616,
628,
198
] | 3.652174 | 69 |
import os
import sys
import json
import shutil
import tempfile
from os.path import isfile, join
import pathlib
import pytest
import conda_mirror.diff_tar as dt
EMPTY_MD5 = "d41d8cd98f00b204e9800998ecf8427e"
@pytest.fixture
|
[
11748,
28686,
198,
11748,
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198
] | 2.540816 | 98 |
# Generated by Django 3.0.2 on 2020-02-05 10:54
from django.db import migrations, models
|
[
2,
2980,
515,
416,
37770,
513,
13,
15,
13,
17,
319,
12131,
12,
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13,
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1330,
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11,
4981,
628
] | 2.84375 | 32 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import os
from views.CmdView.tokenify import Tokenify
from views.CmdView.command import Command
|
[
2,
48443,
14629,
14,
8800,
14,
29412,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
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9,
12,
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1958,
198,
6738,
5009,
13,
40109,
7680,
13,
21812,
1330,
9455,
628
] | 2.916667 | 48 |
import numpy as np
from scipy.misc import imread
import torch
import random
#这里跳帧
#增加跳帧功能
#跳帧且加载gt
|
[
11748,
299,
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] | 1.544118 | 68 |
'''
Created on 21 Feb 2017
@author: jkiesele
'''
from TrainDataDeepJet import TrainData_Flavour, TrainData_simpleTruth, TrainData_fullTruth, fileTimeOut
import numpy as np
class TrainData_deepCSV(TrainData_Flavour, TrainData_simpleTruth):
'''
same as TrainData_deepCSV but with 4 truth labels: B BB C UDSG
'''
def __init__(self):
'''
Constructor
'''
TrainData_Flavour.__init__(self)
self.addBranches(['jet_pt', 'jet_eta',
'TagVarCSV_jetNSecondaryVertices',
'TagVarCSV_trackSumJetEtRatio', 'TagVarCSV_trackSumJetDeltaR',
'TagVarCSV_vertexCategory', 'TagVarCSV_trackSip2dValAboveCharm',
'TagVarCSV_trackSip2dSigAboveCharm', 'TagVarCSV_trackSip3dValAboveCharm',
'TagVarCSV_trackSip3dSigAboveCharm', 'TagVarCSV_jetNSelectedTracks',
'TagVarCSV_jetNTracksEtaRel'])
self.addBranches(['TagVarCSVTrk_trackJetDistVal',
'TagVarCSVTrk_trackPtRel',
'TagVarCSVTrk_trackDeltaR',
'TagVarCSVTrk_trackPtRatio',
'TagVarCSVTrk_trackSip3dSig',
'TagVarCSVTrk_trackSip2dSig',
'TagVarCSVTrk_trackDecayLenVal'],
6)
self.addBranches(['TagVarCSV_trackEtaRel'],4)
self.addBranches(['TagVarCSV_vertexMass',
'TagVarCSV_vertexNTracks',
'TagVarCSV_vertexEnergyRatio',
'TagVarCSV_vertexJetDeltaR',
'TagVarCSV_flightDistance2dVal',
'TagVarCSV_flightDistance2dSig',
'TagVarCSV_flightDistance3dVal',
'TagVarCSV_flightDistance3dSig'],
1)
class TrainData_deepCSV_RNN(TrainData_fullTruth):
'''
same as TrainData_deepCSV but with 4 truth labels: B BB C UDSG
'''
def __init__(self):
'''
Constructor
'''
super(TrainData_deepCSV_RNN, self).__init__()
self.addBranches([
'jet_pt', 'jet_eta',
'TagVarCSV_jetNSecondaryVertices',
'TagVarCSV_trackSumJetEtRatio', 'TagVarCSV_trackSumJetDeltaR',
'TagVarCSV_vertexCategory', 'TagVarCSV_trackSip2dValAboveCharm',
'TagVarCSV_trackSip2dSigAboveCharm', 'TagVarCSV_trackSip3dValAboveCharm',
'TagVarCSV_trackSip3dSigAboveCharm', 'TagVarCSV_jetNSelectedTracks',
'TagVarCSV_jetNTracksEtaRel'])
self.addBranches([
'TagVarCSVTrk_trackJetDistVal',
'TagVarCSVTrk_trackPtRel',
'TagVarCSVTrk_trackDeltaR',
'TagVarCSVTrk_trackPtRatio',
'TagVarCSVTrk_trackSip3dSig',
'TagVarCSVTrk_trackSip2dSig',
'TagVarCSVTrk_trackDecayLenVal'
], 6)
self.addBranches(['TagVarCSV_trackEtaRel'],4)
self.addBranches([
'TagVarCSV_vertexMass',
'TagVarCSV_vertexNTracks',
'TagVarCSV_vertexEnergyRatio',
'TagVarCSV_vertexJetDeltaR',
'TagVarCSV_flightDistance2dVal',
'TagVarCSV_flightDistance2dSig',
'TagVarCSV_flightDistance3dVal',
'TagVarCSV_flightDistance3dSig'
], 1)
self.addBranches(['jet_corr_pt'])
self.registerBranches(['gen_pt_WithNu'])
self.regressiontargetclasses=['uncPt','Pt']
class TrainData_deepCSV_RNN_Deeper(TrainData_deepCSV_RNN):
'''
same as TrainData_deepCSV but with 4 truth labels: B BB C UDSG
'''
def __init__(self):
'''
Constructor
'''
super(TrainData_deepCSV_RNN_Deeper, self).__init__()
self.branchcutoffs = [1, 20, 13, 4, 1]
|
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] | 1.711765 | 2,380 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from clover.gui import CloverApp, Clock
# may want to consider bounding speed
app = CloverApp(title="leo-demo")
|
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2,
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] | 2.688525 | 61 |
import docker
from docker.errors import ContainerError
import logging
import pytest
LOGGER = logging.getLogger(__name__)
PYTHON_VERSION='3.9.5'
NOTEBOOK_IMAGE_TAG=f'python-{PYTHON_VERSION}'
@pytest.mark.parametrize(
'language,version_output',
[
('python', ['Python', '3.9.5\n']),
],
)
def test_languages(language, version_output):
"""Ensure that the language is available in the container's PATH and that
it has the correct version
"""
LOGGER.info(f'Test that language {language} {PYTHON_VERSION} is correctly installed ...')
client = docker.from_env()
output = client.containers.run(f'illumidesk/flatiron-notebook:{NOTEBOOK_IMAGE_TAG}', f'{language} --version')
output_decoded = output.decode('utf-8').split(' ')
assert output_decoded[0:3] == version_output
LOGGER.info(f'Output from command: {output_decoded[0:3]}')
def test_invalid_cmd():
"""Ensure that an invalid command returns a docker.errors.ContainerError
"""
with pytest.raises(ContainerError):
LOGGER.info('Test an invalid command ...')
client = docker.from_env()
client.containers.run('illumidesk/flatiron-notebook', 'foo --version')
|
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220,
220,
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220,
220,
220,
5456,
796,
36253,
13,
6738,
62,
24330,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
5456,
13,
3642,
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13,
5143,
10786,
359,
388,
1460,
74,
14,
38568,
1934,
12,
11295,
2070,
3256,
705,
21943,
1377,
9641,
11537,
198
] | 2.702032 | 443 |
"""
Orka Discord Bot
Copyright (c) 2017 William Bennett
"""
###########
# IMPORTS #
###########
import discord
import random
import markovify
from os import path, makedirs
from scripts import *
###################
# OTHER FUNCTIONS #
###################
def add_msg(channel, text, mode='a+'):
"""
Appends a message to the end of a file.
"""
with open('channels/{0}.txt'.format(channel), '{0}'.format(mode), encoding="utf_8") as file:
file.write('{0}\n'.format(text))
#######
# BOT #
#######
#######
# RUN #
#######
client = Orka()
read = []
if __name__ == '__main__':
if not path.exists('channels\\'):
makedirs('channels\\')
client.run()
|
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198,
361,
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3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
611,
407,
3108,
13,
1069,
1023,
10786,
354,
8961,
6852,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
285,
4335,
17062,
10786,
354,
8961,
6852,
11537,
628,
220,
220,
220,
5456,
13,
5143,
3419,
198
] | 2.693798 | 258 |
# Copyright 2021 MosaicML. All Rights Reserved.
"""Callback to save checkpoints during training."""
from __future__ import annotations
import logging
import os
import pathlib
import textwrap
from typing import Callable, List, Optional, Tuple, Union
from composer.core import Event, State
from composer.core.callback import Callback
from composer.core.time import Time, Timestamp, TimeUnit
from composer.loggers import Logger
from composer.loggers.logger import LogLevel
from composer.utils import checkpoint, dist
from composer.utils.file_helpers import (FORMAT_NAME_WITH_DIST_AND_TIME_TABLE, FORMAT_NAME_WITH_DIST_TABLE,
ensure_folder_is_empty, format_name_with_dist, format_name_with_dist_and_time,
is_tar)
log = logging.getLogger(__name__)
__all__ = ["CheckpointSaver", "checkpoint_periodically"]
def checkpoint_periodically(interval: Union[str, int, Time]) -> Callable[[State, Event], bool]:
"""Helper function to create a checkpoint scheduler according to a specified interval.
Args:
interval (Union[str, int, Time]): The interval describing how often checkpoints should be
saved. If an integer, it will be assumed to be in :attr:`~TimeUnit.EPOCH`\\s.
Otherwise, the unit must be either :attr:`TimeUnit.EPOCH` or :attr:`TimeUnit.BATCH`.
Checkpoints will be saved every ``n`` batches or epochs (depending on the unit),
and at the end of training.
Returns:
Callable[[State, Event], bool]: A function that can be passed as the ``save_interval``
argument into the :class:`CheckpointSaver`.
"""
if isinstance(interval, str):
interval = Time.from_timestring(interval)
if isinstance(interval, int):
interval = Time(interval, TimeUnit.EPOCH)
if interval.unit == TimeUnit.EPOCH:
save_event = Event.EPOCH_CHECKPOINT
elif interval.unit == TimeUnit.BATCH:
save_event = Event.BATCH_CHECKPOINT
else:
raise NotImplementedError(
f"Unknown checkpointing interval: {interval.unit}. Must be TimeUnit.EPOCH or TimeUnit.BATCH.")
last_checkpoint_batch = None
return save_interval
|
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33,
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220,
220,
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25,
198,
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220,
220,
220,
220,
220,
220,
5298,
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220,
220,
220,
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220,
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1,
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628,
220,
220,
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938,
62,
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62,
43501,
796,
6045,
628,
220,
220,
220,
1441,
3613,
62,
3849,
2100,
628
] | 2.728606 | 818 |
import torch
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
import models
# Return triplet of predictions, ground-truth and error
|
[
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] | 3.886364 | 44 |
# Faça um programa que tenha um função notas() que pode receber várias
# notas de alunos e vai retornar um dicionário com as seguintes informações:
# - Quantidade de notas;
# - A maior nota;
# - A menor nota;
# - A média da turma;
# - A situação(opcional);
# Adicione também as docstrings da função.
def notas(show=False):
"""
:param show: mostra a situação da turma de acordo com o escolhido: True ou False
:return: sem retorno
"""
somanotas = 0
d = dict()
lista = list()
qtdvalores = 0
while True:
n1 = float(input(f'Nota do aluno {qtdvalores}: '))
somanotas += n1
lista.append(n1)
qtdvalores += 1
d['Qtd notas'] = qtdvalores
resp = str(input('Quer continuar: [S/N] ')).upper().strip()[0]
while resp != 'S' and resp != 'N':
resp = str(input('Quer continuar? [S/N] ')).upper().strip()[0]
if resp == 'N':
break
d['Maior nota'] = max(lista)
d['Menor nota'] = min(lista)
d['Média da turma'] = somanotas / qtdvalores
if show:
if d['Média da turma'] < 5:
d['Situação'] = 'Ruim'
elif 5 <= d['Média da turma'] < 7:
d['Situação'] = 'Razoável'
else:
d['Situação'] = 'Boa'
print(d)
else:
print(d)
notas()
notas(show=True)
|
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#!/usr/bin/env python3.6
# -*- coding: utf-8 -*-
#
# Copyright 2016 Alec Goliboda
#
# group.py
from __future__ import print_function
import logging
import json
import zlib
# import markdown
from datetime import datetime
import tornado.options
# import pymysql
import hashlib
import bcrypt
import base64
# from _overlapped import NULL
##############
import config
from . import Model, CipherWrapper
from .. import WikiException
from core.models.author import Author
from ..constants.data_base import *
# from core.models.template import Template
from ..constants.data_base import *
class Group(Model):
"""
модель - для Группы
внутри будут:
- список участников
- библиотека
Просмотр:
- список всех групп
- одну группу Описание
- список участников группы
- список статей (библиотека)
- создавать группы
- "удалять группы" - о... нужен флаг - "группа удалена"???
- добавлять (удаять) участников в группу
- по приглашению - нужен список приглашений - соотв, у каждого автора может быть список приглашений "вступить в группу"
- нужен список заявок на вступление - это инструмент админа группы "список заявок на вступление"
- добавлять (удалять) статьи в библиотеку
- статья моет ажодится в библиотеке и иметь флаг
"pbl" - для всеобщего доступа
"grp" - только для группы, такие стаьи будут ЗАКРЫТЫМИ!!!!
Видимость групп (group_status)
- публичная - 'pbl' - любой посетитель может читать публичные материалы группы
- закрытая - 'shut' ??? - что - то я пока не знаю.... может, в закрытых группах не может быть "публичных статей"??
Процедура создания новой группы:
При создании новой группы, Создатель группы становится ее Администратором.
Запись о создании группы размещается в таблицах "dt_headers" и "groups"
Запись о вступлении в группу Администратора добавляется в таблицу "members";
Процедура работы с Ключами:
Создается уникальная пара RSA-ключей,
Публичный ключ помещается в заголовок группы,
персональный - размещается в списке "members",
Приватный ключ группы закрывается Публичным ключем Создателя группы,
и добавляется в соответствующее поле таблицы "members"
Когда Участник Группы открывает страницу группы (переходит на рабочий стол группы)
в профиль Участника добавляется значение его копии приватного ключа группы;
После этого пользователь сможет читать и редактировать все статьи из групповой библиотеки, имеющие флаг "grp"
"""
def get(self, groupId):
"""
загрузить ОДНО значение - по ИД группы
"""
resList = self.select(
'dt_headers.dt_header_id, group_title, group_annotation ' , # строка - чего хотим получить из селекта
'dt_headers', #'authors', # строка - список таблиц
{
'whereStr': " groups.actual_flag = 'A' AND groups.dt_header_id = dt_headers.dt_header_id AND dt_headers.dt_header_id = " + str(groupId)
} # все остальные секции селекта
)
# for item in resList:
# logging.info('Author:: get:: resList = ' + str(item))
if len(resList) == 1:
# return resList[0]
objValuesNameList = list(resList[0].__dict__.keys())
for objValue in objValuesNameList:
if objValue.find('_') != 0:
self.__setattr__(objValue,resList[0].__getattribute__(objValue) )
return self
else:
raise WikiException(LOAD_ONE_VALUE_ERROR)
def list(self):
"""
загрузить список всех групп
"""
resList = self.select(
'dt_headers.dt_header_id, group_title, group_annotation, group_status ' , # строка - чего хотим получить из селекта
'dt_headers', #'authors', # строка - список таблиц
{
'whereStr': " groups.actual_flag = 'A' AND groups.dt_header_id = dt_headers.dt_header_id "
} # все остальные секции селекта
)
# logging.info('Author:: get:: resList = ')
# logging.info(resList)
return resList
def grouplistForAutor(self, authorId):
"""
Получить список групп для одного автора - все руппы, которые АВТОР создал,
и в которых АВТОР является участником
вот тут возможно, надо будет все поправить -
и показывать только ПАБЛИК группы, и/или приватные группы,
в которых участвуют оба - и зритель, и автор
"""
try:
resList = self.select(
' DISTINCT dt_headers.dt_header_id, groups.group_title, groups.group_annotation, groups.group_status, ' +
' members.member_role_type ' , # строка - чего хотим получить из селекта
' members, dt_headers ', #'authors', # строка - список таблиц
{
'whereStr': " groups.actual_flag = 'A' AND groups.dt_header_id = dt_headers.dt_header_id AND " +
" members.author_id = " + str(authorId) +
" AND members.group_id = groups.dt_header_id ",
'orderStr': ' groups.group_title '
} # все остальные секции селекта
)
# logging.info( 'grouplistForAutor:: resList = ' + str(resList))
return resList
except Exception as e:
# except WikiException as e:
# WikiException( ARTICLE_NOT_FOUND )
logging.info( 'grouplistForAutor::Have ERROR!!! ' + str(e))
if not article: raise tornado.web.HTTPError(404)
else: return (article, [])
def getGroupArticleList(self, groupId):
"""
Получить список всех статей одной группы
"""
libControl = self.Library ()
return libControl.getGroupArticleList( groupId)
def getGroupMembersleList(self, groupId):
"""
Получить список всех Участников одной группы
"""
memberControl = self.Member ()
return memberControl.getGroupMembersleList( groupId)
def save(self, authorId ):
"""
сохранить группу,
пользователя, который создал группу надо воткнуть не только в авторы группы,
но, и в "members" да еще и АДМИНОМ!!!
"""
bbsalt = config.options.salt.encode()
cip = CipherWrapper()
logging.info(' save:: before SAVE = ' + str(self))
if self.dt_header_id == 0:
# self.group_create_date = datetime.now()
operationFlag = 'I'
autotControl = Author()
creator = autotControl.get(authorId)
cip.rsaInit() # сделать пару ключей
self.public_key = cip.rsaPubSerialiation(cip.getPublicKey())
pKey = cip.getPrivateKey() # поучить незакрытый приватный ключ
# self.private_key_hash = bcrypt.hashpw(cip.rsaPrivateSerialiation(pKey), bbsalt).decode('utf-8') # получим ХЕш приватного ключа - для последуюей проверки при восстановлении пароля
# logging.info(' save:: before SAVE creator.publicKey() = ' + str(creator.publicKey()))
pkTmp = cip.rsaEncrypt(creator.publicKey(), cip.rsaPrivateSerialiation(pKey))
# logging.info(' save:: before SAVE pkTmp = ' + str(pkTmp))
self.private_key = pkTmp
else:
operationFlag = 'U'
self.begin()
revisions_sha_hash_sou = str(self.group_title) + str(self.group_annotation) + str(self.group_status)
# self.dt_header_id =
Model.save(self, authorId, operationFlag, revisions_sha_hash_sou )
# теперь сохранить автора группы как ее админа.
# logging.info(' SAVE:: GROUPPPPP authorId = ' + str(authorId))
# logging.info(' SAVE:: GROUPPPPP 2 = ' + str(self))
if operationFlag == 'I':
memberControl = self.Member()
memberControl.author_id = authorId
memberControl.group_id = self.dt_header_id
memberControl.member_role_type = 'A'
memberControl.private_key = self.private_key
# bbWrk = (bytePass+bbsalt)[0:32]
# cipher_aes = AES.new(bbWrk, AES.MODE_EAX) # закроем приватный ключ на пароль пользователя.
# ciphertext = cipher_aes.encrypt(pKey)
# self.private_key = pickle.dumps({'cipherKey': ciphertext, 'nonce': cipher_aes.nonce})
memberControl.save(authorId)
self.commit()
return True
def librarySave(self, authorId = 0, groupId = 0, article_id=0, library_permission_type = 'W'):
"""
Добавить статью к группе
"""
libControl = self.Library(groupId, authorId, library_permission_type)
libControl.save(authorId)
|
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] | 1.400899 | 6,897 |
# Copyright: (c) OpenSpug Organization. https://github.com/openspug/spug
# Copyright: (c) <[email protected]>
# Released under the AGPL-3.0 License.
import json
from .utils import AttrDict
# 自定义的解析异常
# 需要校验的参数对象
class Argument(object):
"""
:param name: name of option
:param default: default value if the argument if absent
:param bool required: is required
"""
# 解析器基类
# Json解析器
|
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import re;
from .cBugTranslation import cBugTranslation;
aoBugTranslations = [
# ASan build related -> Ignored
cBugTranslation(
azs0rbAdditionalIrrelevantStackFrameSymbols = [
rb".*!`anonymous namespace'::Create", # Part of skia
rb".*!base::debug::BreakDebugger",
rb".*!base::debug::CollectGDIUsageAndDie",
rb".*!blink::ReportFatalErrorInMainThread",
rb".*!blink::V8ScriptRunner::CallExtraOrCrash(<.+>)?",
rb".*!crash_reporter::internal::CrashForExceptionInNonABICompliantCodeRange",
rb".*!CrashForException_ExportThunk",
rb".*!crashpad::`anonymous namespace'::UnhandledExceptionHandler",
rb".*!crashpad::CrashpadClient::DumpAndCrash",
rb".*!raise",
rb".*!sk_abort_no_print",
rb".*!SkMallocPixelRef::MakeUsing",
rb".*!v8::Utils::ApiCheck",
rb".*!WTF::Deque<.+>::ExpandCapacity(IfNeeded)",
rb".*!WTF::Deque<.+>::push_back",
],
),
# Breakpoint -> Ignored
cBugTranslation(
srzOriginalBugTypeId = r"Breakpoint",
azs0rbAppliesOnlyToTopStackFrame = [
rb".*!__sanitizer_cov",
],
s0zTranslatedBugTypeId = None, # This is apparently triggered by ASAN builds to determine EIP/RIP.
s0zTranslatedBugDescription = None,
s0zTranslatedSecurityImpact = None,
),
# Breakpoint -> OOM
cBugTranslation(
srzOriginalBugTypeId = r"Breakpoint",
azs0rbAppliesOnlyToTopStackFrame = [
rb".*!base::`anonymous namespace'::OnNoMemory",
rb".*!base::internal::SchedulerWorkerPoolImpl::Start", # CHECK() on thread start
rb".*!base::PartitionRecommitSystemPages",
rb".*!blink::MemoryRegion::Commit",
rb".*!content::`anonymous namespace'::CrashOnMapFailure",
rb".*!skia::CreateHBitmap",
rb".*!ui::ClientGpuMemoryBufferManager::ClientGpuMemoryBufferManager", # std::vector throws breakpoint
],
s0zTranslatedBugTypeId = "OOM",
s0zTranslatedBugDescription = "The application triggered a breakpoint to indicate it was unable to allocate enough memory.",
s0zTranslatedSecurityImpact = None,
),
# Breakpoint -> Assert
cBugTranslation(
srzOriginalBugTypeId = r"Breakpoint",
azs0rbAppliesOnlyToTopStackFrame = [
rb".*!blink::reportFatalErrorInMainThread",
rb".*!v8::Utils::ReportApiFailure",
rb".*!logging::LogMessage::~LogMessage",
],
s0zTranslatedBugTypeId = "Assert",
s0zTranslatedBugDescription = "The application triggered an exception to indicate an assertion failed.",
s0zTranslatedSecurityImpact = None,
),
# AVW@NULL -> Assert
cBugTranslation(
srzOriginalBugTypeId = r"AVW@NULL",
azs0rbAppliesOnlyToTopStackFrame = [
rb".*!base::win::`anonymous namespace'::ForceCrashOnSigAbort",
],
s0zTranslatedBugTypeId = "Assert",
s0zTranslatedBugDescription = "The application triggered a NULL pointer access violation to indicate an assertion failed.",
s0zTranslatedSecurityImpact = None,
),
# Various -> OOM
cBugTranslation(
srzOriginalBugTypeId = r"0xE0000008|Assert|AVW@NULL", # 0xE0000008 (win::kOomExceptionCode) -> OOM
azs0rbAppliesOnlyToTopStackFrame = [
rb".*!base::`anonymous namespace'::OnNoMemory",
rb".*!(?:base|WTF)::[Pp]artitions?(?:ExcessiveAllocationSize|OutOfMemory(Using\w+)?)",
rb".*!blink::(?:BlinkGCOutOfMemory|ReportOOMErrorInMainThread)",
rb".*!FX_OutOfMemoryTerminate",
rb".*!SkBitmap::allocPixels",
],
s0zTranslatedBugTypeId = "OOM",
s0zTranslatedBugDescription = "The application caused an access violation by writing to NULL to indicate it was unable to allocate enough memory.",
s0zTranslatedSecurityImpact = None,
),
# OOM -> hide irrelevant frames
cBugTranslation(
srzOriginalBugTypeId = r"OOM",
azs0rbAdditionalIrrelevantStackFrameSymbols = [
rb".+!(.+::)?(Win)?CallNewHandler",
rb".+!(.+::)?\w+_malloc(_\w+)?",
rb".+!(.+::)?\w*(Alloc|alloc|OutOfMemory)\w*(<.+>)?",
],
),
];
|
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] | 2.491493 | 1,587 |
from django.db import models
# Create your models here.
|
[
6738,
42625,
14208,
13,
9945,
1330,
4981,
198,
198,
2,
13610,
534,
4981,
994,
13,
628,
628,
220,
220,
220,
220,
628,
198,
220,
220,
220
] | 2.692308 | 26 |
from typing import Callable, Hashable, TypeVar, Iterable, Union
from ._symbol import Symbol
from .head import Head
_T = TypeVar("_T", bound=Hashable)
_A = TypeVar("_A")
class Validator:
"""A validator class that will be used for Expr argument validation."""
def register(self, value: _T):
"""Register value for validation."""
return wrapper
def __call__(self, arg: _T, *args: _A) -> Union[_A, Iterable[_A]]:
"""Run validation."""
try:
func = self._map[arg]
except KeyError:
return args
try:
out = func(*args)
except ValidationError as e:
e.args = (f"{args} is incompatible with {arg}",)
raise e
return out
class ValidationError(ValueError):
"""Raised when validation failed."""
validator = Validator()
@validator.register(Head.empty)
@validator.register(Head.del_)
@validator.register(Head.raise_)
@validator.register(Head.comment)
@validator.register(Head.assert_)
@validator.register(Head.getitem)
@validator.register(Head.unop)
@validator.register(Head.getattr)
@validator.register(Head.assign)
@validator.register(Head.kw)
@validator.register(Head.annotate)
@validator.register(Head.binop)
@validator.register(Head.aug)
@validator.register(Head.function)
@validator.register(Head.for_)
@validator.register(Head.while_)
@validator.register(Head.if_)
@validator.register(Head.elif_)
|
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] | 2.544658 | 571 |
"""``cubi-tk archive prepare``: Prepare a project for archival"""
import errno
import os
import re
import shutil
import sys
import tempfile
from cookiecutter.main import cookiecutter
from logzero import logger
from ..common import execute_shell_commands
from ..isa_tpl import IsaTabTemplate
from ..isa_tpl import load_variables
_BASE_DIR = os.path.dirname(__file__)
TEMPLATE = IsaTabTemplate(
name="archive",
path=os.path.join(os.path.dirname(_BASE_DIR), "isa_tpl", "archive"),
description="Prepare project for archival",
configuration=load_variables("archive"),
)
DU = re.compile("^ *([0-9]+)[ \t]+[^ \t]+.*$")
DATE = re.compile("^(20[0-9][0-9]-[01][0-9]-[0-3][0-9])[_-].+$")
MAIL = (
"(?:[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*"
'|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]'
'|\\\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")'
"@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?"
"|\\[(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}"
"(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?|[a-z0-9-]*[a-z0-9]:"
"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]"
"|\\\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)"
"\\])"
)
PATTERNS = {
"project_name": re.compile("^ *- *Project name: *.+$"),
"date": re.compile("^ *- *Start date: *20[0-9]{2}-[01][0-9]-[0-3][0-9].*$"),
"status": re.compile("^ *- *Current status: *(Active|Inactive|Finished|Archived) *$"),
"PI": re.compile("^ *- P.I.: \\[([A-z '-]+)\\]\\(mailto:(" + MAIL + ")\\) *$"),
"client": re.compile("^ *- *Client contact: \\[([A-z '-]+)\\]\\(mailto:(" + MAIL + ")\\) *$"),
"archiver": re.compile("^ *- *CUBI contact: \\[([A-z '-]+)\\]\\(mailto:(" + MAIL + ")\\) *$"),
"CUBI": re.compile("^ *- *CUBI project leader: ([A-z '-]+) *$"),
}
COMMANDS = {
"size": ["du", "--bytes", "--max-depth=0"],
"inodes": ["du", "--inodes", "--max-depth=0"],
"size_follow": ["du", "--dereference", "--bytes", "--max-depth=0"],
"inodes_follow": ["du", "--dereference", "--inodes", "--max-depth=0"],
}
MSG = "**Contents of original `README.md` file**"
|
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198
] | 1.887411 | 1,128 |
import numpy as np
import argparse
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
parser = argparse.ArgumentParser()
parser.add_argument('-x', '--datapath', type=str, required=True)
parser.add_argument('-y', '--labels', type=str, required=True)
parser.add_argument('-v', '--verbose', type=bool, default=False)
parser.add_argument('-o', '--outputpath', type=str, required=True)
args = parser.parse_args()
X = np.load(args.datapath, allow_pickle=True)
# https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline
# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
# https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA
pca = make_pipeline(StandardScaler(), PCA(n_components=10,svd_solver="full")).fit(X)
np.savetxt(args.outputpath, pca.steps[1][1].components_, delimiter=",")
|
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] | 2.833811 | 349 |
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 13 09:14:32 2020
@author: Pante
"""
import os, features, time
import numpy as np
from sklearn.preprocessing import StandardScaler
from array_helper import find_szr_idx, match_szrs, merge_close
from build_feature_data import get_data, get_features_allch
from sklearn.metrics import log_loss,recall_score
import matplotlib.pyplot as plt
####### consider isolation forest for outlier detection!!!!!!
def user_cost(y_true, y_pred):
"""
user_cost(y_true, y_pred)
Parameters
----------
y_true : 1ndarray bool, ground truth values
y_pred : 1ndarray bool, predicted values
Returns
-------
cost : float
"""
detected = 0 # number of detected seizures
# get bounds of sezures
bounds_true = find_szr_idx(y_true, np.array([0,1])) # total predicted
bounds_pred = find_szr_idx(y_pred, np.array([0,1])) # total predicted
bounds_pred = merge_close(bounds_pred, merge_margin = 5) # merge seizures close together
if bounds_pred.shape[0]>0: # find matching seizures
detected = match_szrs(bounds_true, bounds_pred, err_margin = 10)
# calculate cost
a = 1 - (detected/bounds_true.shape[0]) # get detected ratio
b = (bounds_pred.shape[0] - detected) # get false positives
cost = a + np.log10(b+1) # cost function
return cost
def create_cost(bounds_true, bounds_pred):
"""
create_cost(bounds_true, bounds_pred)
Parameters
----------
bounds_true : 2d ndarray (rows = seizrs, columns = start,stop), ground truth
bounds_pred : 2d ndarray (rows = seizrs, columns = start,stop), predicted
Returns
-------
cost : Float,
"""
# find matching seizurs
detected = 0
a = 100
if bounds_pred.shape[0]>0:
detected = match_szrs(bounds_true, bounds_pred, err_margin = 10)
if bounds_true.shape[0]>0:
# get detected ratio
a = (1 - (detected/bounds_true.shape[0]))*20
# get false positives
b = (bounds_pred.shape[0] - detected)
# cost function
# L = 1 # learning rate
cost = a + np.log10(b+1)
return cost
def szr_cost(bounds_true, bounds_pred):
"""
create_cost(bounds_true, bounds_pred)
Parameters
----------
bounds_true : 2d ndarray (rows = seizrs, columns = start,stop), ground truth
bounds_pred : 2d ndarray (rows = seizrs, columns = start,stop), predicted
Returns
-------
cost : Float,
"""
# find matching seizurs
detected = 0
if bounds_pred.shape[0]>0:
detected = match_szrs(bounds_true, bounds_pred, err_margin = 10)
if bounds_true.shape[0]>0:
# get detected ratio
a = 1 - (detected/bounds_true.shape[0])
if (a > 0 and a <= 1):
a = 20
# get false positives
b = (bounds_pred.shape[0] - detected)
# cost function
cost = a + np.log10(b+1)
return cost
def get_min_cost(feature, y_true):
"""
get_min_cost(feature, y_true)
Parameters
----------
feature : 1D ndarray, extracted feature
y_true : 1D ndarray, bool grund truth labels
Returns
-------
TYPE: Float, threshold value that gves minimum cost
"""
n_loop = 100 # loop number and separation
thresh_array = np.linspace(1, 20, n_loop) # thresholds to test
cost_array = np.zeros(n_loop)
for i in range(n_loop):
# thresh_array[i] = thresh
y_pred = feature> (np.mean(feature) + thresh_array[i]*np.std(feature))
# get number of seizures
bounds_true = find_szr_idx(y_true, np.array([0,1])) # true
bounds_pred = find_szr_idx(y_pred, np.array([0,1])) # predicted
# merge seizures close together
if bounds_pred.shape[0]>1:
bounds_pred = merge_close(bounds_pred, merge_margin = 5)
cost = szr_cost(bounds_true, bounds_pred) # get cost
# pass to array
cost_array[i] = cost
return thresh_array[np.argmin(cost_array)]
# define parameter list
param_list = (features.autocorr, features.line_length, features.rms, features.mad, features.var, features.std, features.psd, features.energy,
features.get_envelope_max_diff,)
cross_ch_param_list = (features.cross_corr, features.signal_covar, features.signal_abs_covar,)
# get data and true labels
exp_path = r'C:\Users\Pante\Desktop\seizure_data_tb\Train_data\3642_3641_3560_3514'
# 071919_3514 071719_3560
data, y_true = get_data(exp_path, '072519_3642',ch_num = [0,1],
inner_path={'data_path':'filt_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True)
# # get file list
# main_path = r'C:\Users\Pante\Desktop\seizure_data_tb\Train_data'
# folder_path = '3514_3553_3639_3640'
# ver_path = os.path.join(main_path,folder_path, 'verified_predictions_pantelis')
# filelist = list(filter(lambda k: '.csv' in k, os.listdir(ver_path))) # get only files with predictions
# filelist = [os.path.splitext(x)[0] for x in filelist] # remove csv ending
# # data, y_true = get_data(r'W:\Maguire Lab\Trina\2019\07-July\3514_3553_3639_3640, '071819_3553a',ch_num = [0,1],
# # inner_path={'data_path':'reorganized_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True)
# for i in range(1):
# # 071919_3514 071719_3560
# data, y_true = get_data(os.path.join(main_path, folder_path), filelist[i],ch_num = [0,1],
# inner_path={'data_path':'filt_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True)
# if sum(y_true) == 0:
# continue
# get features
x_data, labels = get_features_allch(data,param_list,cross_ch_param_list)
# Normalize data
x_data = StandardScaler().fit_transform(x_data)
# get cost plot
cost_array,thresh_array = find_threshold(x_data, y_true)
|
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] | 2.288813 | 2,628 |
# -*- coding: utf-8 -*-
from qcloudsdkcore.request import Request
|
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from cartography.intel.aws import ecr
from tests.data.aws.ecr import GET_ECR_REPOSITORY_IMAGE_VULNS
|
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] | 2.657895 | 38 |
from time import sleep, gmtime, mktime
from datetime import datetime
import logging
import scrapy
import redis
import msgpack
import xxhash
import pymongo as pm
import feedparser as fp
from colorama import Back, Fore, Style
from ..settings import MONGODB_URI, REDIS_HOST, REDIS_PORT, REDIS_PWD, REDIS_PENDING_QUEUE
def hs(s):
"""
hash function to convert url to fixed length hash code
"""
return xxhash.xxh32(s).hexdigest()
def time2ts(time_struct):
"""
convert time_struct to epoch
"""
return mktime(time_struct)
|
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] | 2.840206 | 194 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 8 15:36:15 2020
dataset transform
@author: minjie
"""
import albumentations as A
from albumentations.pytorch import ToTensor as ToTensor_albu
import cv2
import torch
from multiprocessing import Pool
from utils.parse_meta import parse_kpds
import numpy as np
# imgs = []
# pts = []
#
# hh,ww,_ = img.shape
# for _ in range(self.n_aug):
# #points = [ww/2.0,hh/2.0,1.0]
# points = [[0.0,0.0,1.0], [0.0,hh,1.0], [ww,0.0,1.0],[ww,hh,1.0]]
# augmented = self.augment(image = img,keypoints=points,category_id = ['0'])
# imgs.append(augmented['image'])
# pts.append(augmented['keypoints'])
# # NOTE: use bbox will have prob that box is outside crop region.
# bboxes= [[0.45, 0.45, 0.55, 0.55]]
#
# augmented = self.T_aug(image = img,bboxes = bboxes,category_id = ['0'])
# hh,ww,_ = img.shape
# points = [[ww/2.0,hh/2.0,1.0]]
# augmented = self.augment(image = img,keypoints=points,category_id = ['0'])
#return augmented['image']
|
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] | 1.896923 | 650 |
'''
Created on 1.12.2016
@author: Darren
''''''
Follow up for H-Index: What if the citations array is sorted in ascending order? Could you optimize your algorithm?
Expected runtime complexity is in O(log n) and the input is sorted.
"
'''
|
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import numpy as np
import threading
import queue
import imageio
import os,time
import math
import visual_words
import multiprocessing as mp
def build_recognition_system(num_workers=2):
'''
Creates a trained recognition system by generating training features from all training images.
[input]
* num_workers: number of workers to process in parallel
[saved]
* features: numpy.ndarray of shape (N,M)
* labels: numpy.ndarray of shape (N)
* dictionary: numpy.ndarray of shape (K,3F)
* SPM_layer_num: number of spatial pyramid layers
'''
train_data = np.load("../data/train_data.npz")
dictionary = np.load("../outputs/dictionary.npy")
data = train_data['image_names']
SPM_layer_num = 2
K = 100
size_Feature = int(K*(4**(SPM_layer_num+1) -1)/3)
pool = mp.Pool(num_workers)
results = []
for i in range(0, len(data)):
print (i)
args = [data[i][0], dictionary, SPM_layer_num, K]
results.append(pool.apply_async(get_image_feature, args))
features = []
for result in results:
features.append(result.get())
final_features = np.reshape(features, (len(data), size_Feature))
labels = np.asarray(train_data['labels'])
np.savez('../outputs/trained_system.npz', features = final_features, labels = labels, SPM_layer_num = SPM_layer_num, dictionary = dictionary)
def evaluate_recognition_system(num_workers=2):
'''
Evaluates the recognition system for all test images and returns the confusion matrix.
[input]
* num_workers: number of workers to process in parallel
[output]
* conf: numpy.ndarray of shape (8,8)
* accuracy: accuracy of the evaluated system
'''
test_data = np.load("../data/test_data.npz")
trained_system = np.load("../outputs/trained_system.npz")
features = trained_system['features']
dictionary = trained_system['dictionary']
SPM_layer_num = trained_system['SPM_layer_num']
labels = trained_system['labels']
K = dictionary.shape[0]
data = test_data['image_names']
pool = mp.Pool(num_workers)
features_test = []
for i in range(0, len(data)):
args = [(data[i][0], dictionary, SPM_layer_num, K, features, labels)]
features_test.append(pool.apply_async(test_label, args))
test_labels = []
for feature in features_test:
test_labels.append(feature.get())
testActualLabels = test_data['labels']
size_confusion = len(np.unique(testActualLabels))
C = np.zeros((size_confusion, size_confusion))
for a,p in zip(testActualLabels, test_labels):
C[a][p] += 1
accuracy = np.diag(C).sum()/C.sum()
return C, accuracy
def get_image_feature(file_path,dictionary,layer_num,K):
'''
Extracts the spatial pyramid matching feature.
[input]
* file_path: path of image file to read
* dictionary: numpy.ndarray of shape (K,3F)
* layer_num: number of spatial pyramid layers
* K: number of clusters for the word maps
[output]
* feature: numpy.ndarray of shape (K*(4^layer_num-1)/3)
'''
image = imageio.imread('../data/' + file_path)
wordmap = visual_words.get_visual_words(image, dictionary)
hist_all = get_feature_from_wordmap_SPM(wordmap, layer_num, K)
return hist_all
def distance_to_set(word_hist,histograms):
'''
Compute similarity between a histogram of visual words with all training image histograms.
[input]
* word_hist: numpy.ndarray of shape (K)
* histograms: numpy.ndarray of shape (N,K)
[output]
* sim: numpy.ndarray of shape (N)
'''
min_compare = np.minimum(histograms, word_hist)
return np.sum(min_compare, axis=1)
def get_feature_from_wordmap(wordmap,dict_size):
'''
Compute histogram of visual words.
[input]
* wordmap: numpy.ndarray of shape (H,W)
* dict_size: dictionary size K
[output]
* hist: numpy.ndarray of shape (K)
'''
flatten_wordmap = wordmap.flatten()
hist = np.histogram(flatten_wordmap, bins = dict_size, range = (0,dict_size))
hist = hist[0]/np.linalg.norm(hist[0], ord = 1)
return np.asarray(hist)
def get_feature_from_wordmap_SPM(wordmap,layer_num,dict_size):
'''
Compute histogram of visual words using spatial pyramid matching.
[input]
* wordmap: numpy.ndarray of shape (H,W)
* layer_num: number of spatial pyramid layers
* dict_size: dictionary size K
[output]
* hist_all: numpy.ndarray of shape (K*(4^layer_num-1)/3)
'''
i_h, i_w = wordmap.shape
hist_all = []
for layer in range(0, layer_num+1):
D = 2**layer
if layer == 0 or layer == 1:
weight = 1/(2**(layer_num))
else:
weight = 1/(2**(layer_num+1-layer))
height_indices = np.round(np.arange(0, i_h+1, i_h/D)).astype('int')
width_indices = np.round(np.arange(0, i_w+1, i_w/D)).astype('int')
divisions = height_indices.shape[0]-1
for i in range(0, divisions):
for j in range (0, divisions):
s_h, s_w = height_indices[i], width_indices[j]
e_h, e_w = height_indices[i+1], width_indices[j+1]
imageSection = wordmap[s_h:e_h, s_w:e_w]
imageDictionary = get_feature_from_wordmap(imageSection, dict_size)
imageDictionary = imageDictionary*weight
hist_all.append(imageDictionary)
hist_all = np.asarray(hist_all)
hist_all = hist_all.flatten()
hist_all = hist_all/np.linalg.norm(hist_all, ord = 1)
return hist_all
|
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11,
2760,
796,
352,
8,
201,
198,
197,
7783,
1554,
62,
439
] | 2.444186 | 2,150 |
from flask import Flask
import gensim
import re
from gensim.models import LdaModel
from gensim.test.utils import datapath
from gensim import corpora, models
from gensim.corpora import Dictionary
from re import sub
import os
import string
import codecs
import nltk
#from nltk.tokenize import RegexpTokenizer
#from nltk import stem
#from nltk.stem import WordNetLemmatizer
app = Flask(__name__)
from app import views
from app import model
|
[
6738,
42903,
1330,
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198,
198,
11748,
308,
641,
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8,
198,
6738,
598,
1330,
5009,
198,
6738,
598,
1330,
2746
] | 3.195652 | 138 |
from app.utils.str_utils import str_to_int
|
[
6738,
598,
13,
26791,
13,
2536,
62,
26791,
1330,
965,
62,
1462,
62,
600,
628
] | 2.933333 | 15 |
import psycopg2
from config import config
import datetime
from textblob import TextBlob
import nltk
from nltk.corpus import stopwords
def create_tables():
""" create tables in the PostgreSQL database"""
params = config()
# connect to the PostgreSQL server
conn = psycopg2.connect(**params)
cur = conn.cursor()
cur.execute('''CREATE TABLE IF NOT EXISTS POSTS
(POST_ID INT PRIMARY KEY NOT NULL,
DATA TEXT NOT NULL,
CREATED TIMESTAMP NOT NULL,
COMMENTS INT,
COUNT INT);''')
cur.execute('''CREATE TABLE IF NOT EXISTS COMMENTS
(POST_ID INT NOT NULL,
COMMENT_ID INT PRIMARY KEY NOT NULL,
DATA TEXT NOT NULL,
CREATED TIMESTAMP NOT NULL,
UPVOTES INT,
DOWNVOTES INT);''')
conn.commit()
conn.close()
def create_post(postid, text):
""" insert a new post into the vendors table """
print("inside create post")
# read database configuration
params = config()
# connect to the PostgreSQL database
conn = psycopg2.connect(**params)
cur = conn.cursor()
count = 0
comments = 0
time = datetime.datetime.utcnow();
cur.execute("INSERT INTO POSTS (POST_ID, DATA, CREATED, COMMENTS, COUNT) VALUES (%s, %s, %s, %s, %s)",(postid,text,time,comments,count));
conn.commit()
print("Records created successfully")
conn.close()
def create_comment(postid, commentid, text):
""" insert a new comment into the post table """
print("inside create comments")
# read database configuration
params = config()
# connect to the PostgreSQL database
conn = psycopg2.connect(**params)
cur = conn.cursor()
count = 0
time = datetime.datetime.utcnow();
cur.execute("INSERT INTO COMMENTS (POST_ID, COMMENT_ID, DATA, CREATED, UPVOTES, DOWNVOTES) VALUES (%s, %s, %s, %s, 0, 0)",(postid,commentid,text, time));
# Get Corresponding post
cur.execute("SELECT POST_ID, COMMENTS from POSTS where POST_ID = {0} ORDER BY COUNT DESC".format(postid));
rows = cur.fetchall()
for row in rows:
comments = row[1]
break
comments = comments+1
# Update Comments count of post
cur.execute("UPDATE POSTS set COMMENTS = {0} where POST_ID = {1}".format(comments,postid));
conn.commit()
print("Records created successfully")
conn.close()
def lookup_table(text):
""" insert a new post into the vendors table """
print("inside lookup to tables")
# read database configuration
params = config()
# connect to the PostgreSQL database
conn = psycopg2.connect(**params)
cur = conn.cursor()
#initialize id and count to null values
postid = 0
count = 0
#Select post
cur.execute("SELECT POST_ID, DATA, COUNT from POSTS where DATA = '{0}' ORDER BY COUNT DESC".format(text));
rows = cur.fetchall()
for row in rows:
postid = row[0]
count = row[2]
break
print "Lookup operation done successfully. Id = {0}".format(id);
conn.close()
return postid, count
def update_table_count(postid, count):
""" update post with count """
print("inside lookup to tables")
# read database configuration
params = config()
# connect to the PostgreSQL database
conn = psycopg2.connect(**params)
cur = conn.cursor()
cur.execute("UPDATE POSTS set COUNT = {0} where POST_ID = {1}".format(count,postid));
conn.commit()
print "Update operation done successfully for POST_ID {0} and count {1}".format(postid,count)
conn.close()
def comment_upvote(comment_id):
""" update post with count """
print("inside upvote comment")
# read database configuration
params = config()
# connect to the PostgreSQL database
conn = psycopg2.connect(**params)
cur = conn.cursor()
# Get Corresponding comment
cur.execute("SELECT COMMENT_ID, UPVOTES, POST_ID from COMMENTS where COMMENT_ID = {0} ORDER BY UPVOTES DESC".format(comment_id));
rows = cur.fetchall()
for row in rows:
upvotes = row[1]
break
upvotes = upvotes+1
# Update Comments count of post
cur.execute("UPDATE COMMENTS set UPVOTES = {0} where COMMENT_ID = {1}".format(upvotes,comment_id));
conn.commit()
print ("Comment upvote completed")
conn.close()
#return post ID so that redirect can use it
return (row[2])
def comment_downvote(comment_id):
""" update comment with dwnvote """
print("inside downvote comment")
# read database configuration
params = config()
# connect to the PostgreSQL database
conn = psycopg2.connect(**params)
cur = conn.cursor()
# Get Corresponding comment
cur.execute("SELECT COMMENT_ID, DOWNVOTES, POST_ID from COMMENTS where COMMENT_ID = {0} ORDER BY DOWNVOTES DESC".format(comment_id));
rows = cur.fetchall()
for row in rows:
downvotes = row[1]
break
downvotes = downvotes+1
# Update Comments count of post
cur.execute("UPDATE COMMENTS set DOWNVOTES = {0} where COMMENT_ID = {1}".format(downvotes,comment_id));
conn.commit()
print ("Comment upvote completed")
conn.close()
#return post ID so that redirect can use it
return (row[2])
|
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] | 2.571631 | 2,115 |
"""
Defined code units and physical quantities
The Python parts of Weltgeist use cgs
VH1 uses units defined below
Sam Geen, February 2018
"""
import numpy as np
# Physical quantities (base units in cgs)
pc = 3.086e+18
mH = 1.66e-24
year = 3.154e+7
Myr = 1e6*year
kB = 1.3806485279e-16 # in cgs
G = 6.67428e-8
X = 0.74
mp = mH / X
c = 2.998e+10
eV = 1.60217662e-12 # in ergs
Msun = 1.9891e33 # g
# Code units
# Used by VH1 - the Python parts of Weltgeist use cgs
distance = pc # in cm
density = mH # 1 g/cm^3
time = 1.0 / np.sqrt(G*density) # sets G=1 in VH1 (not super important here, though)
# Derived units
velocity = distance / time
mass = density*distance**3.0
pressure = density * velocity**2.0
energy = mass*velocity**2.0
# Note: this is acceleration! In the code (e.g. forces.f90), grav = v*v/r
# e.g. 2*GM/r = v_esc^2, so g=GM/r^2=0.5*v_esc^2/r
gravity = G*mass/distance**2 # velocity*velocity/distance
|
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] | 2.328431 | 408 |
from rest_framework.response import Response
from rest_framework.filters import SearchFilter
from rest_framework.viewsets import ModelViewSet
from interest.models import Interest
from .serializers import InterestSerializer
|
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] | 4.645833 | 48 |
import re
from django.contrib.gis.gdal import OGRException, OGRGeometry
from django.contrib.gis.geos import GEOSGeometry
from django.forms.widgets import Textarea
from django.template.loader import render_to_string
from django.utils import simplejson
from django.conf import settings
from django import forms
# Default settings for paths and API URLs. These can all be overridden by
# specifying a value in settings.py
api_defaults = {
'GOOGLE_API_KEY': "",
'YAHOO_APP_ID': "",
'OLWIDGET_MEDIA_URL': url_join(settings.MEDIA_URL, "olwidget"),
'GOOGLE_API': "http://maps.google.com/maps?file=api&v=2",
'YAHOO_API': "http://api.maps.yahoo.com/ajaxymap?v=3.0",
'OSM_API': "http://openstreetmap.org/openlayers/OpenStreetMap.js",
'OL_API': "http://openlayers.org/api/2.8/OpenLayers.js",
'MS_VE_API' : "http://dev.virtualearth.net/mapcontrol/mapcontrol.ashx?v=6.1",
}
for key, default in api_defaults.iteritems():
if not hasattr(settings, key):
setattr(settings, key, default)
OLWIDGET_JS = url_join(settings.OLWIDGET_MEDIA_URL, "js/olwidget.js")
OLWIDGET_CSS = url_join(settings.OLWIDGET_MEDIA_URL, "css/olwidget.css")
DEFAULT_PROJ = "4326"
class EditableMap(forms.Textarea, MapMixin):
"""
An OpenLayers mapping widget for geographic data.
Example::
from django import forms
from olwidget.widgets import OLWidget
class MyForm(forms.Form):
location = forms.CharField(widget=EditableMap(
options={'geometry': 'point'}))
"""
default_template = 'olwidget/editable_map.html'
class MapDisplay(EditableMap):
"""
Object for display of geometries on an OpenLayers map. Arguments (all are
optional):
* ``fields`` - a list of geometric fields or WKT strings to display on the
map. If none are given, the map will have no overlay.
* ``name`` - a name to use for display of the field data layer.
* ``options`` - a dict of options for map display. A complete list of
options is in the documentation for olwidget.js.
Example::
from olwidget.widgets import MapDisplay
map = MapDisplay(fields=[my_model.start_point, my_model.destination])
To use in a template, first display the media (URLs for javascript and CSS
needed for map display) and then print the MapDisplay object, as in the
following::
<html>
<head>
{{ map.media }}
</head>
<body>
{{ map }}
</body>
</html>
By default, maps rendered by MapDisplay objects are not editable, but this
can be overriden by setting "options['editable'] = True".
"""
class InfoMap(forms.Widget, MapMixin):
"""
Widget for displaying maps with pop-up info boxes over geometries.
Arguments:
* ``info``: an array of [geometry, HTML] pairs that specify geometries, and
the popup contents associated with them. Geometries can be expressed as
geometry fields, or as WKT strings. Example::
[
[geomodel1.geofield, "<p>Model One</p>"],
[geomodel2.geofield, "<p>Model Two</p>"],
...
]
* ``options``: an optional dict of options for map display.
In templates, InfoMap.media must be displayed in addition to InfoMap for
the map to function properly.
"""
default_template = 'olwidget/info_map.html'
ewkt_re = re.compile("^SRID=(?P<srid>\d+);(?P<wkt>.+)$", re.I)
def get_wkt(value, srid=DEFAULT_PROJ):
"""
`value` is either a WKT string or a geometry field. Returns WKT in the
projection for the given SRID.
"""
ogr = None
if value:
if isinstance(value, OGRGeometry):
ogr = value
elif isinstance(value, GEOSGeometry):
ogr = value.ogr
elif isinstance(value, basestring):
match = ewkt_re.match(value)
if match:
ogr = OGRGeometry(match.group('wkt'), match.group('srid'))
else:
ogr = OGRGeometry(value)
wkt = ''
if ogr:
# Workaround for Django bug #12312. GEOSGeometry types don't support 3D wkt;
# OGRGeometry types output 3D for linestrings even if they should do 2D, causing
# IntegrityError's.
if ogr.dimension == 2:
geos = ogr.geos
geos.transform(srid)
wkt = geos.wkt
else:
ogr.transform(srid)
wkt = ogr.wkt
return wkt
def collection_wkt(fields):
""" Returns WKT for the given list of geometry fields. """
if not fields:
return ""
if len(fields) == 1:
return get_wkt(fields[0])
return "GEOMETRYCOLLECTION(%s)" % \
",".join(get_wkt(field) for field in fields)
def add_srid(wkt, srid=DEFAULT_PROJ):
"""
Returns EWKT (WKT with a specified SRID) for the given wkt and SRID
(default 4326).
"""
if wkt:
return "SRID=%s;%s" % (srid, wkt)
return ""
|
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] | 2.359661 | 2,127 |
#!/usr/local/bin/python3
def part1(data):
"""
>>> part1([199, 200, 208, 210, 200, 207, 240, 269, 260, 263])
7
>>> part1(read_input())
1581
"""
previous = data[0]
count = 0
for value in data[1:]:
if value > previous:
count += 1
previous = value
return count
def part2(data):
"""
>>> part2([199, 200, 208, 210, 200, 207, 240, 269, 260, 263])
5
>>> part2(read_input())
1618
"""
count = 0
for i in range(1, len(data) - 2):
previous = sum(data[i - 1:i + 2])
value = sum(data[i:i + 3])
if value > previous:
count += 1
return count
if __name__ == "__main__":
main()
|
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3419,
198
] | 2.084302 | 344 |
from SloppyCell.ReactionNetworks import *
net = Network('Lee2003')
net.add_compartment('extract')
net.add_parameter('Dsh0', 100, name = r'Dsh^0')
net.add_parameter('APC0', 100, name = r'APC^0')
net.add_parameter('TCF0', 15, name = r'TCF^0')
net.add_parameter('GSK0', 50, name = r'GSK^0')
net.add_species('X2', 'extract', 0)#, name=r'Dsh_a')
net.add_species('X3', 'extract', 0)#, name=r'APC^*/axin^*/GSK3')
net.add_species('X4', 'extract', 0)#, name=r'APC/axin/GSK3')
net.add_species('X9', 'extract', 0)#, name=r'\beta-catenin^*/APC^*/axin^*/GSK3')
net.add_species('X10', 'extract', 0)#, name=r'\beta-catenin^*')
net.add_species('X11', 'extract', 0)#, name=r'\beta-catenin')
net.add_species('X12', 'extract', 0)#, name=r'Axin')
#net.add_species('X5', 'extract', 'GSK0', is_constant=True)#, name=r'GSK3')
net.add_species('X5', 'extract', 'GSK0', is_constant=True)#, name=r'GSK3')
net.add_species('X1', 'extract')#, name=r'Dsh_i')
net.add_species('X6', 'extract')#, name=r'APC/axin')
net.add_species('X7', 'extract')#, name=r'APC')
net.add_species('X8', 'extract')#, name=r'\beta-catenin/APC^*/axin^*/GSK3')
net.add_species('X13', 'extract')#, name=r'TCF')
net.add_species('X14', 'extract')#, name=r'\beta-catenin/TCF')
net.add_species('X15', 'extract')#, name=r'\beta-catenin/APC')
net.add_parameter('K7', 50, name = r'K_7')
net.add_parameter('K8', 120, name = r'K_8')
net.add_parameter('K16', 30, name = r'K_16')
net.add_parameter('K17', 1200, name = r'K_17')
net.add_parameter('k1', 0.182, name = r'k_{1}')
net.add_parameter('k2', 1.82e-2, name = r'k_{2}')
net.add_parameter('k3', 5e-2, name = r'k_{3}')
net.add_parameter('k4', 0.267, name = r'k_{4}')
net.add_parameter('k5', 0.133, name = r'k_{5}')
net.add_parameter('k6', 9.09e-2, name = r'k_{6}')
net.add_parameter('km6', 0.909, name = 'k_{-6}')
net.add_parameter('k9', 206, name = r'k_{9}')
net.add_parameter('k10', 206, name = r'k_{10}')
net.add_parameter('k11', 0.417, name = r'k_{11}')
net.add_parameter('k13', 2.57e-4, name = r'k_{13}')
net.add_parameter('k15', 0.167, name = r'k_{15}')
net.add_parameter('v12', 0.423, name = r'v_{12}')
net.add_parameter('v14', 8.22e-5, name = r'v_{14}')
#net.add_parameter('k1', 0.18, name = r'k_{1}')
#net.add_parameter('k2', 1.8e-2, name = r'k_{2}')
#net.add_parameter('k3', 5e-2, name = r'k_{3}')
#net.add_parameter('k4', 0.27, name = r'k_{4}')
#net.add_parameter('k5', 0.13, name = r'k_{5}')
#net.add_parameter('k6', 9.1e-2, name = r'k_{6}')
#net.add_parameter('km6', 0.91, name = 'k_{-6}')
#net.add_parameter('k9', 210, name = r'k_{9}')
#net.add_parameter('k10', 210, name = r'k_{10}')
#net.add_parameter('k11', 0.42, name = r'k_{11}')
#net.add_parameter('k13', 2.6e-4, name = r'k_{13}')
#net.add_parameter('k15', 0.17, name = r'k_{15}')
#
#net.add_parameter('v12', 0.42, name = r'v_{12}')
#net.add_parameter('v14', 8.2e-5, name = r'v_{14}')
net.add_parameter('W', 0, is_optimizable=False)
net.add_rate_rule('X2', 'k1*W*(Dsh0-X2)-k2*X2')
net.add_rate_rule('X9', 'k9 * X8 - k10*X9')
net.add_rate_rule('X10', 'k10*X9-k11*X10')
net.add_rate_rule('X4', '-k3*X2*X4 - k4*X4 + k5*X3 + k6*X5*X6 - km6*X4')
net.add_parameter('a')
net.add_assignment_rule('a', '1+APC0*K17/(K7*(K17+X11))')
net.add_parameter('b')
net.add_assignment_rule('b', 'APC0*K17*X12/(K7*(K17+X11)**2)')
net.add_parameter('c')
net.add_assignment_rule('c', 'k3*X2*X4 - k6 * GSK0*APC0*K17*X12/(K7*(K17+X11)) + km6*X4 + v14 - k15*X12')
net.add_parameter('d')
net.add_assignment_rule('d', '1+X11/K8')
net.add_parameter('e')
net.add_assignment_rule('e', 'X3/K8')
net.add_parameter('f')
net.add_assignment_rule('f', 'k4*X4 - k5*X3 - k9*X3*X11/K8 + k10*X9')
net.add_parameter('g')
net.add_assignment_rule('g', '1+X3/K8+TCF0*K16/(K16+X11)**2 + APC0*K17/(K17+X11)**2')
net.add_parameter('h')
net.add_assignment_rule('h', 'X11/K8')
net.add_parameter('i')
net.add_assignment_rule('i', 'v12 - (k9*X3/K8 + k13)*X11')
net.add_parameter('rhsX11', name = 'rhs_{X11}')
net.add_assignment_rule('rhsX11', '(d*i - f*h)/(d*g - e*h)')
net.add_rate_rule('X11', 'rhsX11')
net.add_rate_rule('X12', '(c + rhsX11*b)/a')
net.add_rate_rule('X3', '(e*i - f*g)/(e*h - d*g)')
net.add_assignment_rule('X1', 'Dsh0 - X2')
net.add_assignment_rule('X7', 'K17*APC0/(K17+X11)')
net.add_assignment_rule('X15', 'X11*APC0/(K17+X11)')
net.add_assignment_rule('X13', 'K16*TCF0/(K16+X11)')
net.add_assignment_rule('X14', 'X11*TCF0/(K16+X11)')
net.add_assignment_rule('X8', 'X3*X11/K8')
net.add_assignment_rule('X6', 'K17*X12*APC0/(K7*(K17+X11))')
# These are just for my own monitoring purposes
net.add_parameter('BCatenin', name = r'\beta-catenin')
net.add_assignment_rule('BCatenin', 'X8+X9+X10+X11+X14+X15')
net.add_parameter('Axin', name = r'Axin')
net.add_assignment_rule('Axin', 'X3+X4+X6+X8+X9+X12')
|
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55,
23,
10,
55,
24,
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55,
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11537,
198
] | 1.958661 | 2,419 |
from omegaconf import DictConfig
import pytorch_lightning as pl
import torch
from torch import nn
import torchmetrics
from .model import Model
|
[
6738,
267,
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7807,
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#**************************************************************************
#* Copyright(c) 1998-2014, ALICE Experiment at CERN, All rights reserved. *
#* *
#* Author: The ALICE Off-line Project. *
#* Contributors are mentioned in the code where appropriate. *
#* *
#* Permission to use, copy, modify and distribute this software and its *
#* documentation strictly for non-commercial purposes is hereby granted *
#* without fee, provided that the above copyright notice appears in all *
#* copies and that both the copyright notice and this permission notice *
#* appear in the supporting documentation. The authors make no claims *
#* about the suitability of this software for any purpose. It is *
#* provided "as is" without express or implied warranty. *
#**************************************************************************
"""
Interpolation module
@author: Jacek Otwinowski
@organization: ALICE Collaboration
Translated into PYTHON by Markus Fasel <[email protected]>, Lawrence Berkeley National Laboratory
"""
import math
|
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2,
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"""
This is the root schema definition that combines individual applications'
schemas into one.
Each application that has queries or mutations exports them as either Query
or Mutation from the application's schema module.
No resolvers or type definitions should be included here.
"""
import graphene
from django.conf import settings
import creator.analyses.schema
import creator.buckets.schema
import creator.files.schema
import creator.studies.schema
import creator.projects.schema
import creator.users.schema
import creator.referral_tokens.schema
import creator.status.schema
import creator.jobs.schema
import creator.releases.schema
import creator.data_reviews.schema
import creator.ingest_runs.schema
import creator.organizations.schema
import creator.data_templates.schema
class Query(
creator.analyses.schema.Query,
creator.files.schema.Query,
creator.studies.schema.Query,
creator.users.schema.Query,
creator.events.schema.Query,
creator.projects.schema.Query,
creator.buckets.schema.Query,
creator.referral_tokens.schema.Query,
creator.status.schema.Query,
creator.jobs.schema.Query,
creator.releases.schema.Query,
creator.data_reviews.schema.Query,
creator.ingest_runs.schema.Query,
creator.organizations.schema.Query,
creator.data_templates.schema.Query,
graphene.ObjectType,
):
""" Root query schema combining all apps' schemas """
node = graphene.relay.Node.Field()
if settings.DEBUG:
from graphene_django.debug import DjangoDebug
debug = graphene.Field(DjangoDebug, name="_debug")
class Mutation(
creator.analyses.schema.Mutation,
creator.buckets.schema.Mutation,
creator.projects.schema.Mutation,
creator.studies.schema.Mutation,
creator.files.schema.Mutation,
creator.users.schema.Mutation,
creator.referral_tokens.schema.Mutation,
creator.status.schema.Mutation,
creator.releases.schema.Mutation,
creator.data_reviews.schema.Mutation,
creator.ingest_runs.schema.Mutation,
creator.organizations.schema.Mutation,
creator.data_templates.schema.Mutation,
graphene.ObjectType,
):
""" Root mutation schema combining all apps' schemas """
pass
schema = graphene.Schema(query=Query, mutation=Mutation)
|
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28,
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11,
15148,
28,
44,
7094,
8,
198
] | 2.940568 | 774 |
#
# Copyright 2013 The Regents of The University California
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import logging
import math
import numpy
import random
from util import Job, TaskDistributions
import Queue
MEDIAN_TASK_DURATION = 100
NETWORK_DELAY = 0
TASKS_PER_JOB = 500
SLOTS_PER_WORKER = 4
TOTAL_WORKERS = 10000
PROBE_RATIO = 2
class Event(object):
""" Abstract class representing events. """
def run(self, current_time):
""" Returns any events that should be added to the queue. """
raise NotImplementedError("The run() method must be implemented by "
"each class subclassing Event")
class JobArrival(Event):
""" Event to signify a job arriving at a scheduler. """
class ProbeEvent(Event):
""" Event to signify a probe arriving at a worker. """
class NoopGetTaskResponseEvent(Event):
""" Signifies when a getTask() RPC response arrives at a worker, with a noop response. """
if __name__ == "__main__":
main()
|
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# coding=utf-8
__author__ = 'André Meneghelli'
"""
/*******************************************************************************
* Aluno: André Meneghelli Vale, Núm. USP: 4898948
* Curso: Bacharelado em Ciências da Computação
* Aula 13 - Stone Pile
* MAC0327 -- IME/USP, -- Prof. Cristina Gomes Fernandes
******************************************************************************/
"""
pedras = []
if __name__ == '__main__':
main()
|
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import csv
import matplotlib.image as mpimg
import pickle
import numpy as np
from keras.models import Sequential
from keras.layers.core import Flatten,Lambda,Dense
from keras.layers.convolutional import Cropping2D,Conv2D
from keras import backend as K
from keras.layers.core import Activation
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import cv2
"""
To show the preprocessing for final model
"""
def read_data_gen(batch_size):
"""
Generator function to load driving logs and input images.
"""
while 1:
with open('data/driving_log.csv') as driving_log_file:
reader = csv.DictReader(driving_log_file)
count = 0
inputs, targets = [], []
try:
for row in reader:
center_img = mpimg.imread('data/'+ row['center'].strip())
flipped_center_img = np.fliplr(center_img)
center_steering = float(row['steering'])
if count < batch_size//2:
inputs += [center_img, flipped_center_img]
targets += [center_steering, -center_steering]
count += 1
else:
yield np.array(inputs, dtype=center_img.dtype), np.array(targets)
count = 0
inputs, targets= [], []
except StopIteration:
pass
batch_size = 128
# define model
"""
model = nvidia_model()
model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
model.summary()
# train model
model.fit_generator(read_data_gen(batch_size), samples_per_epoch=8000*2, nb_epoch=5)
model.save('model.h5')
"""
if __name__=="__main__":
train_model()
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198,
361,
11593,
3672,
834,
855,
1,
834,
12417,
834,
1298,
198,
220,
220,
220,
4512,
62,
19849,
3419,
628
] | 2.120958 | 835 |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue739-v5"]
CONFIGS = [
IssueConfig('translate', [], driver_options=['--translate']),
IssueConfig('translate-with-options', ['--translate-options', '--keep-unreachable-facts', '--keep-unimportant-variables', '--full-encoding'], driver_options=['--translate']),
IssueConfig('translate-time-limit', [], driver_options=['--translate-time-limit', '5s', '--translate']),
IssueConfig('translate-memory-limit', [], driver_options=['--translate-memory-limit', '100M', '--translate']),
]
SUITE = common_setup.DEFAULT_OPTIMAL_SUITE
ENVIRONMENT = BaselSlurmEnvironment(email="[email protected]", export=["PATH", "DOWNWARD_BENCHMARKS"])
if common_setup.is_test_run():
SUITE = ['gripper:prob10.pddl','mystery:prob07.pddl']
ENVIRONMENT = LocalEnvironment(processes=4)
exp = IssueExperiment(
revisions=REVISIONS,
configs=CONFIGS,
environment=ENVIRONMENT,
)
exp.add_suite(BENCHMARKS_DIR, SUITE)
exp.add_parser(exp.LAB_STATIC_PROPERTIES_PARSER)
exp.add_parser(exp.LAB_DRIVER_PARSER)
exp.add_parser(exp.EXITCODE_PARSER)
exp.add_parser(exp.TRANSLATOR_PARSER)
exp.add_step('build', exp.build)
exp.add_step('start', exp.start_runs)
exp.add_fetcher(name='fetch')
exp.add_absolute_report_step(attributes=['translator_*', 'error'])
exp.run_steps()
|
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7,
11201,
13,
5446,
1565,
8634,
25633,
62,
27082,
35009,
8,
198,
198,
11201,
13,
2860,
62,
9662,
10786,
11249,
3256,
1033,
13,
11249,
8,
198,
11201,
13,
2860,
62,
9662,
10786,
9688,
3256,
1033,
13,
9688,
62,
48381,
8,
198,
11201,
13,
2860,
62,
34045,
2044,
7,
3672,
11639,
69,
7569,
11537,
198,
198,
11201,
13,
2860,
62,
48546,
62,
13116,
62,
9662,
7,
1078,
7657,
28,
17816,
7645,
41880,
62,
9,
3256,
705,
18224,
6,
12962,
198,
198,
11201,
13,
5143,
62,
20214,
3419,
198
] | 2.577465 | 639 |
"""Matplotlib science style"""
from .core import available_styles, set_style, style_context
# https://github.com/python-poetry/poetry/pull/2366#issuecomment-652418094
# https://github.com/python-poetry/poetry/issues/144#issuecomment-623927302
try:
import importlib.metadata as importlib_metadata
except ModuleNotFoundError:
import importlib_metadata
package_name = "mplscience"
__version__ = importlib_metadata.version(package_name)
__all__ = ["available_styles", "set_style", "style_context"]
|
[
37811,
19044,
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8019,
3783,
3918,
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198,
198,
6738,
764,
7295,
1330,
1695,
62,
47720,
11,
900,
62,
7635,
11,
3918,
62,
22866,
198,
198,
2,
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12567,
13,
785,
14,
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12,
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14,
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11973,
14,
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2,
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21,
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25,
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220,
220,
220,
1330,
1330,
8019,
13,
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355,
1330,
8019,
62,
38993,
198,
16341,
19937,
3673,
21077,
12331,
25,
198,
220,
220,
220,
1330,
1330,
8019,
62,
38993,
198,
26495,
62,
3672,
796,
366,
76,
489,
16801,
1,
198,
834,
9641,
834,
796,
1330,
8019,
62,
38993,
13,
9641,
7,
26495,
62,
3672,
8,
628,
198,
834,
439,
834,
796,
14631,
15182,
62,
47720,
1600,
366,
2617,
62,
7635,
1600,
366,
7635,
62,
22866,
8973,
198
] | 3.123457 | 162 |
from contextlib import suppress
from pathlib import Path
from typing import List
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QColor
from PyQt5.QtWidgets import QFileDialog, QListWidgetItem
with suppress(ImportError):
from appwindow import AppWindow
from tools.settings import FinalCifSettings
class ReportTemplates:
"""
Displays the list of report templates in the options menu.
"""
|
[
6738,
4732,
8019,
1330,
18175,
198,
6738,
3108,
8019,
1330,
10644,
198,
6738,
19720,
1330,
7343,
198,
198,
6738,
9485,
48,
83,
20,
13,
48,
83,
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83,
20,
13,
48,
83,
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20,
13,
48,
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54,
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1330,
1195,
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1195,
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4480,
18175,
7,
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12331,
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198,
220,
220,
220,
422,
598,
17497,
1330,
2034,
27703,
198,
6738,
4899,
13,
33692,
1330,
8125,
34,
361,
26232,
628,
198,
4871,
6358,
12966,
17041,
25,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
3167,
26024,
262,
1351,
286,
989,
24019,
287,
262,
3689,
6859,
13,
198,
220,
220,
220,
37227,
198
] | 3.261905 | 126 |
from guillotina import configure
from guillotina.catalog.utils import parse_query
from guillotina.interfaces import IResource
from guillotina.utils import find_container
from guillotina_cms.utils import get_search_utility
import itertools
from collections import Counter
@configure.service(
context=IResource, method='GET', permission='guillotina.AccessContent', name='@search',
summary='Make search request',
responses={
"200": {
"description": "Search results",
"type": "object",
"schema": {
"$ref": "#/definitions/SearchResults"
}
}
})
@configure.service(
context=IResource, method='GET', permission='guillotina.AccessContent', name='@suggestion',
summary='Make search request',
responses={
"200": {
"description": "Search results",
"type": "object",
"schema": {
"$ref": "#/definitions/SearchResults"
}
}
})
|
[
6738,
915,
359,
313,
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13,
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1330,
21136,
62,
22766,
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1298,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17971,
5420,
1298,
25113,
14,
4299,
50101,
14,
18243,
25468,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
32092,
628,
198,
198,
31,
11250,
495,
13,
15271,
7,
198,
220,
220,
220,
4732,
28,
4663,
274,
1668,
11,
2446,
11639,
18851,
3256,
7170,
11639,
5162,
359,
313,
1437,
13,
15457,
19746,
3256,
1438,
11639,
31,
47811,
295,
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198,
220,
220,
220,
10638,
11639,
12050,
2989,
2581,
3256,
198,
220,
220,
220,
9109,
34758,
198,
220,
220,
220,
220,
220,
220,
220,
366,
2167,
1298,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
11213,
1298,
366,
18243,
2482,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
4906,
1298,
366,
15252,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
15952,
2611,
1298,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17971,
5420,
1298,
25113,
14,
4299,
50101,
14,
18243,
25468,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
32092,
628
] | 2.390995 | 422 |
import os
from PIL import Image
|
[
11748,
28686,
198,
6738,
350,
4146,
1330,
7412,
628
] | 3.666667 | 9 |
import numpy as np
import pytest
from gym import utils
from more_itertools import last
from gym_quickcheck.envs import RandomWalkEnv
from tests.aux import assert_that, follows_contract, assert_obs_eq, unpack_reward, unpack_obs, unpack_done, until_done, \
run_example
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.mark.parametrize('direction', [0, 1])
|
[
11748,
299,
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355,
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62,
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31,
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198,
31,
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9288,
13,
69,
9602,
628,
198,
31,
9078,
9288,
13,
69,
9602,
628,
628,
628,
198,
198,
31,
9078,
9288,
13,
4102,
13,
17143,
316,
380,
2736,
10786,
37295,
3256,
685,
15,
11,
352,
12962,
628,
628,
628,
628,
628,
198
] | 2.680233 | 172 |
from os import environ
TOKEN = environ.get('AMIGOCLOUD_TOKEN')
BROKER_URL = environ.get('BROKER_URL')
PROJECT_URL = 'users/475/projects/13608'
DATASET_ID = 79746
|
[
6738,
28686,
1330,
551,
2268,
198,
198,
10468,
43959,
796,
551,
2268,
13,
1136,
10786,
2390,
3528,
4503,
43,
2606,
35,
62,
10468,
43959,
11537,
198,
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62,
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198,
35,
1404,
1921,
2767,
62,
2389,
796,
767,
5607,
3510,
198
] | 2.295775 | 71 |
#!/usr/bin/env python
import rospy
from geometry_msgs.msg import Twist
pub = rospy.Publisher("/diff_drive_controller/cmd_vel", Twist, queue_size = 10)
if __name__ == '__main__':
rospy.init_node('twist_remapper', anonymous=True)
rospy.Subscriber("/cmd_vel", Twist, callback)
rospy.spin()
|
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2,
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8,
198,
220,
220,
220,
686,
2777,
88,
13,
39706,
3419
] | 2.564103 | 117 |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2018.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
# pylint: disable=invalid-name,missing-docstring,inconsistent-return-statements
"""mpl circuit visualization backend."""
import collections
import fractions
import itertools
import json
import logging
import math
import numpy as np
try:
from matplotlib import get_backend
from matplotlib import patches
from matplotlib import pyplot as plt
HAS_MATPLOTLIB = True
except ImportError:
HAS_MATPLOTLIB = False
from qiskit.circuit import ControlledGate
from qiskit.visualization import exceptions
from qiskit.visualization.qcstyle import DefaultStyle, BWStyle
from qiskit import user_config
from qiskit.circuit.tools.pi_check import pi_check
logger = logging.getLogger(__name__)
WID = 0.65
HIG = 0.65
DEFAULT_SCALE = 4.3
PORDER_GATE = 5
PORDER_LINE = 3
PORDER_REGLINE = 2
PORDER_GRAY = 3
PORDER_TEXT = 6
PORDER_SUBP = 4
|
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12532,
1137,
62,
50,
10526,
47,
796,
604,
628,
198
] | 3.061086 | 442 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import pywikibot
|
[
2,
48443,
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14,
8800,
14,
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198,
2,
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9,
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25,
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69,
12,
23,
532,
9,
12,
198,
198,
11748,
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571,
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198
] | 2.068966 | 29 |
from __future__ import unicode_literals
from inspect import cleandoc
from genshi.filters import Transformer
from flask_genshi import render_template
from flatland.out.genshi import setup as flatland_setup
from flatland import Form, String
def test_applies_method_filters(app):
"""Method filters are applied for generated and rendered templates"""
with app.test_request_context():
genshi = app.extensions["genshi"]
@genshi.filter("html")
rendered = render_template("filter.html")
# Remove leading indentation, for cleaner multi-line string
expected = cleandoc(
"""
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
<html><head><title>Flask-Genshi - Hi!</title></head></html>
"""
)
assert rendered == expected
def test_filters_per_render(app):
"""Filters can be applied per rendering"""
with app.test_request_context():
rendered = render_template("filter.html", filter=prepend_title)
# Remove leading indentation, for cleaner multi-line string
expected = cleandoc(
"""
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
<html><head><title>Hi! - Flask-Genshi</title></head></html>
"""
)
assert rendered == expected
def test_works_with_flatland(app):
"""Filters can take the context and support flatland"""
with app.test_request_context():
genshi = app.extensions["genshi"]
@genshi.template_parsed
context = dict(form=FlatlandForm({"username": "dag"}))
rendered = render_template("flatland.html", context)
# Remove leading indentation, for cleaner multi-line string
expected = cleandoc(
"""
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
<input type="text" name="username" value="dag">
"""
)
assert rendered == expected
|
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15111,
6624,
2938,
198
] | 2.401611 | 869 |
from django.db import models
from django import forms
from django.contrib.auth.models import User
from .form_dynamic import NominationForm
import json
FIELD_TYPES = (
('Short_answer', forms.CharField),
('Paragraph', forms.CharField),
('Integer', forms.IntegerField),
('ChoiceField', forms.ChoiceField),
('MultipleChoiceField', forms.MultipleChoiceField),
# ('Date', forms.DateField),
)
QUES_TYPES = (
('Short_answer', 'One Line Answer'),
('Paragraph', 'Multiple Line Answer'),
('Integer', 'Integer Answer'),
('ChoiceField', 'Choice'),
('MultipleChoiceField', 'Multiple-choice'),
# ('Date', 'date'),
)
|
[
6738,
42625,
14208,
13,
9945,
1330,
4981,
198,
6738,
42625,
14208,
1330,
5107,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
13,
27530,
1330,
11787,
198,
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764,
687,
62,
67,
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1883,
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198,
11748,
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628,
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198,
220,
220,
220,
19203,
10044,
6111,
3256,
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13,
12441,
15878,
828,
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13,
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220,
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62,
41484,
3256,
705,
3198,
6910,
23998,
33809,
198,
220,
220,
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10044,
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3256,
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6910,
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220,
220,
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3256,
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46541,
23998,
33809,
198,
220,
220,
220,
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3256,
705,
46770,
33809,
198,
220,
220,
220,
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46770,
15878,
3256,
705,
31217,
12,
25541,
33809,
198,
220,
220,
220,
1303,
19203,
10430,
3256,
705,
4475,
33809,
198,
8,
628,
628,
628,
628
] | 2.972973 | 222 |
import numpy as np
import torch, torchvision, math
from torch.functional import F
import os
import PIL.Image as Image
from functools import partial
DATADIR = 'dataset/'
load_celeba32 = partial(load_celeba, image_size=32)
load_celeba64 = partial(load_celeba, image_size=64)
load_celeba128 = partial(load_celeba, image_size=128)
|
[
11748,
299,
32152,
355,
45941,
198,
11748,
28034,
11,
28034,
10178,
11,
10688,
198,
6738,
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13,
45124,
1330,
376,
198,
11748,
28686,
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6738,
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198,
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796,
13027,
7,
2220,
62,
49840,
7012,
11,
2939,
62,
7857,
28,
12762,
8
] | 2.928571 | 112 |
"""
Basis Theory API
## Getting Started * Sign-in to [Basis Theory](https://basistheory.com) and go to [Applications](https://portal.basistheory.com/applications) * Create a Basis Theory Server to Server Application * All permissions should be selected * Paste the API Key into the `BT-API-KEY` variable # noqa: E501
The version of the OpenAPI document: v1
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from basistheory.api_client import ApiClient, Endpoint as _Endpoint
from basistheory.model_utils import ( # noqa: F401
check_allowed_values,
check_validations,
date,
datetime,
file_type,
none_type,
validate_and_convert_types,
set_request_options
)
from basistheory.model.create_tenant_invitation_request import CreateTenantInvitationRequest
from basistheory.model.problem_details import ProblemDetails
from basistheory.model.tenant import Tenant
from basistheory.model.tenant_invitation_response import TenantInvitationResponse
from basistheory.model.tenant_invitation_response_paginated_list import TenantInvitationResponsePaginatedList
from basistheory.model.tenant_invitation_status import TenantInvitationStatus
from basistheory.model.tenant_member_response_paginated_list import TenantMemberResponsePaginatedList
from basistheory.model.tenant_usage_report import TenantUsageReport
from basistheory.model.update_tenant_request import UpdateTenantRequest
from basistheory.model.validation_problem_details import ValidationProblemDetails
class TenantsApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def create_invitation(
self,
**kwargs
):
"""create_invitation # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_invitation(async_req=True)
>>> result = thread.get()
Keyword Args:
create_tenant_invitation_request (CreateTenantInvitationRequest): [optional]
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
TenantInvitationResponse
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
return self.create_invitation_endpoint.call_with_http_info(**kwargs)
def delete(
self,
**kwargs
):
"""delete # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete(async_req=True)
>>> result = thread.get()
Keyword Args:
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
return self.delete_endpoint.call_with_http_info(**kwargs)
def delete_invitation(
self,
invitation_id,
**kwargs
):
"""delete_invitation # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_invitation(invitation_id, async_req=True)
>>> result = thread.get()
Args:
invitation_id (str):
Keyword Args:
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
kwargs['invitation_id'] = \
invitation_id
return self.delete_invitation_endpoint.call_with_http_info(**kwargs)
def delete_member(
self,
member_id,
**kwargs
):
"""delete_member # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_member(member_id, async_req=True)
>>> result = thread.get()
Args:
member_id (str):
Keyword Args:
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
kwargs['member_id'] = \
member_id
return self.delete_member_endpoint.call_with_http_info(**kwargs)
def get(
self,
**kwargs
):
"""get # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get(async_req=True)
>>> result = thread.get()
Keyword Args:
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
Tenant
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
return self.get_endpoint.call_with_http_info(**kwargs)
def get_invitations(
self,
**kwargs
):
"""get_invitations # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_invitations(async_req=True)
>>> result = thread.get()
Keyword Args:
status (TenantInvitationStatus): [optional]
page (int): [optional]
size (int): [optional]
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
TenantInvitationResponsePaginatedList
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
return self.get_invitations_endpoint.call_with_http_info(**kwargs)
def get_members(
self,
**kwargs
):
"""get_members # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_members(async_req=True)
>>> result = thread.get()
Keyword Args:
user_id ([str]): [optional]
page (int): [optional]
size (int): [optional]
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
TenantMemberResponsePaginatedList
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
return self.get_members_endpoint.call_with_http_info(**kwargs)
def get_tenant_operation_report(
self,
**kwargs
):
"""get_tenant_operation_report # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_tenant_operation_report(async_req=True)
>>> result = thread.get()
Keyword Args:
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
TenantUsageReport
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
return self.get_tenant_operation_report_endpoint.call_with_http_info(**kwargs)
def get_tenant_usage_report(
self,
**kwargs
):
"""get_tenant_usage_report # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_tenant_usage_report(async_req=True)
>>> result = thread.get()
Keyword Args:
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
TenantUsageReport
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
return self.get_tenant_usage_report_endpoint.call_with_http_info(**kwargs)
def resend_invitation(
self,
invitation_id,
**kwargs
):
"""resend_invitation # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.resend_invitation(invitation_id, async_req=True)
>>> result = thread.get()
Args:
invitation_id (str):
Keyword Args:
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
TenantInvitationResponse
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
kwargs['invitation_id'] = \
invitation_id
return self.resend_invitation_endpoint.call_with_http_info(**kwargs)
def update(
self,
**kwargs
):
"""update # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update(async_req=True)
>>> result = thread.get()
Keyword Args:
update_tenant_request (UpdateTenantRequest): [optional]
request_options(RequestOptions): [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
Tenant
If the method is called asynchronously, returns the request
thread.
"""
if kwargs.get('request_options'):
set_request_options(kwargs.pop('request_options'), self)
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
return self.update_endpoint.call_with_http_info(**kwargs)
|
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] | 2.198209 | 18,652 |
"""Config flow for National Weather Service (NWS) integration."""
import logging
import voluptuous as vol
from nws_radar.nws_radar_mosaic import REGIONS
from homeassistant import config_entries
from . import unique_id
# pylint: disable=unused-import
from .const import (
CONF_LOOP,
CONF_STATION,
CONF_STYLE,
STYLES,
CONF_TYPE,
RADAR_TYPES,
DEFAULT_RADAR_TYPE,
CONF_NAME,
DOMAIN,
)
_LOGGER = logging.getLogger(__name__)
class ConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):
"""Handle a config flow for National Weather Service (NWS)."""
VERSION = 1
CONNECTION_CLASS = config_entries.CONN_CLASS_CLOUD_POLL
async def async_step_user(self, user_input=None):
"""Handle the initial step."""
errors = {}
if user_input is not None:
self._config = user_input # pylint: disable=attribute-defined-outside-init
if user_input[CONF_STYLE] in {"Standard", "Enhanced"}:
return await self.async_step_standard_enhanced()
# Mosaic
return await self.async_step_mosaic()
data_schema = vol.Schema(
{
vol.Required(CONF_STYLE): vol.In(STYLES),
}
)
return self.async_show_form(
step_id="user", data_schema=data_schema, errors=errors
)
async def async_step_standard_enhanced(self, user_input=None):
"""Standard or enhanced step."""
errors = {}
if user_input is not None:
self._config.update(user_input)
self._config[CONF_STATION] = self._config[CONF_STATION].upper()
title = unique_id(self._config)
self._config[CONF_NAME] = None
await self.async_set_unique_id(unique_id(self._config))
self._abort_if_unique_id_configured()
return self.async_create_entry(title=title, data=self._config)
data_schema = vol.Schema(
{
vol.Required(CONF_STATION): str,
vol.Required(CONF_LOOP, default=True): bool,
vol.Required(CONF_TYPE, default=DEFAULT_RADAR_TYPE): vol.In(
RADAR_TYPES.keys()
),
}
)
return self.async_show_form(
step_id="standard_enhanced", data_schema=data_schema, errors=errors
)
async def async_step_mosaic(self, user_input=None):
"""Mosaic step."""
errors = {}
if user_input is not None:
self._config.update(user_input)
self._config[CONF_TYPE] = ""
self._config[CONF_NAME] = None
title = unique_id(self._config)
await self.async_set_unique_id(title)
self._abort_if_unique_id_configured()
return self.async_create_entry(title=title, data=self._config)
data_schema = vol.Schema(
{
vol.Required(CONF_STATION): vol.In(REGIONS),
vol.Required(CONF_LOOP, default=True): bool,
}
)
return self.async_show_form(
step_id="mosaic", data_schema=data_schema, errors=errors
)
async def async_step_import(self, user_input=None):
"""Import an entry from yaml."""
title = unique_id(user_input)
await self.async_set_unique_id(title)
self._abort_if_unique_id_configured()
return self.async_create_entry(title=title, data=user_input)
|
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220,
220,
220,
220,
220,
220,
1366,
62,
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2611,
796,
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13,
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7,
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220,
220,
220,
220,
220,
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220,
220,
220,
220,
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1391,
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220,
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220,
220,
220,
220,
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220,
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220,
220,
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220,
220,
2322,
13,
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7,
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37,
62,
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6234,
2599,
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13,
818,
7,
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828,
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220,
220,
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2322,
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7,
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37,
62,
21982,
3185,
11,
4277,
28,
17821,
2599,
20512,
11,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
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198,
220,
220,
220,
220,
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1441,
2116,
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292,
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62,
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62,
687,
7,
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220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
1267,
628,
220,
220,
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7,
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11,
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62,
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28,
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2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
20939,
281,
5726,
422,
331,
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526,
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198,
220,
220,
220,
220,
220,
220,
220,
3670,
796,
3748,
62,
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7,
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62,
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8,
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220,
220,
220,
220,
220,
220,
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25507,
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13,
292,
13361,
62,
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62,
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62,
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7,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
397,
419,
62,
361,
62,
34642,
62,
312,
62,
11250,
1522,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
292,
13361,
62,
17953,
62,
13000,
7,
7839,
28,
7839,
11,
1366,
28,
7220,
62,
15414,
8,
198
] | 2.074474 | 1,665 |
#!/usr/bin/env python
# coding: utf-8
# @Author: lapis-hong
# @Date : 2018/6/17
"""This module contains the main algorithm for chinese word extraction.
criterion 1:
solid rate
criterion 2:
character entropy
"""
from __future__ import unicode_literals
from __future__ import division
import math
from indexer import CnTextIndexer
from utils import WordCountDict
class EntropyJudger:
"""Use entropy and solid rate to judge whether a candidate is a chinese word or not."""
def __init__(self, document, least_cnt_threshold=5, solid_rate_threshold=0.018, entropy_threshold=1.92):
"""
Args:
least_cnt_threshold: a word least appeared count, can not pass judge if less than this value.
solid_rate_threshold: p(candidate)/p(candidate[0]) * p(candidate)/p(candidate[1]) * ...
entropy_threshold: min(left_char_entropy, right_char_entropy), The smaller this values is,
more new words you will get, but with less accuracy.
"""
self._least_cnt_threshold = least_cnt_threshold
self._solid_rate_threshold = solid_rate_threshold
self._entropy_threshold = entropy_threshold
self._indexer = CnTextIndexer(document)
|
[
2,
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14,
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220,
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2116,
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298,
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198,
220,
220,
220,
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220,
220,
220,
2116,
13557,
9630,
263,
796,
327,
77,
8206,
15732,
263,
7,
22897,
8,
628,
628,
198
] | 2.655172 | 464 |
#! usr/bin/env python
# -*- coding:utf-8 -*-
"""
基础篇
"""
from numpy import *
'''
NumPy的主要对象是同种元素的多维数组。
这是一个所有的元素都是一种类型、通过一个正整数元组索引的元素表格(通常是元素是数字)
'''
'''
NumPy中维度(dimensions)叫做轴(axes)，轴的个数叫做秩(rank)。
'''
#eg:
'''
在3D空间一个点的坐标 [1,2,3]
是一个秩为1的数组，它只有一个轴，轴的长度为3
'''
#注意直接写，他的类型不是一个ndarray，是一个list，此处只是用于举例说明秩
arr2 = [[1,0,0],
[0,1,0]]
'''
arr2 的秩为2 (它有两个维度)
'''
'''
NumPy的数组类被称作 ndarray 。通常被称作数组。
注意numpy.array和标准python库类array.array并不相同，
后者只处理一维数组和提供少量功能
'''
# 创建一个numpy的对象
a = arange(15).reshape(3, 5)
print a
# 数组轴的个数（秩），行数
print "a的秩为", a.ndim
help(ndim)
|
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2,
0,
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2,
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108,
163,
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120,
230,
163,
100,
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118,
1600,
257,
13,
358,
320,
198,
198,
16794,
7,
358,
320,
8
] | 0.875195 | 641 |
# -*- coding: utf-8 -*-
'''
:codeauthor: :email: `Bo Maryniuk <[email protected]>`
'''
# Import Python libs
from __future__ import absolute_import
import errno
# Import Salt Testing libs
from tests.support.mock import patch, Mock
from tests.support.unit import TestCase
# Import Salt libs
from salt.ext.six.moves import range
from salt.fileclient import Client
class FileclientTestCase(TestCase):
'''
Fileclient test
'''
opts = {
'extension_modules': '',
'cachedir': '/__test__',
}
def test_cache_skips_makedirs_on_race_condition(self):
'''
If cache contains already a directory, do not raise an exception.
'''
with patch('os.path.isfile', lambda prm: False):
for exists in range(2):
with patch('os.makedirs', self._fake_makedir()):
with Client(self.opts)._cache_loc('testfile') as c_ref_itr:
assert c_ref_itr == '/__test__/files/base/testfile'
def test_cache_raises_exception_on_non_eexist_ioerror(self):
'''
If makedirs raises other than EEXIST errno, an exception should be raised.
'''
with patch('os.path.isfile', lambda prm: False):
with patch('os.makedirs', self._fake_makedir(num=errno.EROFS)):
with self.assertRaises(OSError):
with Client(self.opts)._cache_loc('testfile') as c_ref_itr:
assert c_ref_itr == '/__test__/files/base/testfile'
|
[
2,
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9,
12,
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25,
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12,
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220,
220,
220,
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220,
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2302,
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705,
3256,
198,
220,
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] | 2.213549 | 679 |
"""Prepare datasets
Script that:
- downloads, pre-processes, and saves base datasets from OpenML
- computes meta-features
- computes meta-targets (combining feature-importance measures and base models)
- saves the meta-datasets
Usage: python -m prepare_datasets --help
"""
import argparse
import multiprocessing
import pathlib
from typing import Collection, Dict, Optional, Sequence, Union
import warnings
import numpy as np
import openml
import pandas as pd
import sklearn.preprocessing
import tqdm
import data_utility
import meta_features
import meta_targets
# Download one base dataset with the given "data_id" from OpenML and store it in X, y format in
# "base_data_dir", all columns made numeric. Note that the method might throw an exception if
# OpenML is not able to retrieve the dataset.
# Download OpenML datasets and store them in "base_data_dir". Either retrieve base datasets by
# "data_ids" or search according to fixed dataset characteristics. The latter was done for the
# paper, but the datasets matching the characteristics can change in future.
# Compute all meta-features for one base dataset with "base_dataset_name", located in
# "base_data_dir", and store the resulting meta-data in "meta_data_dir"
# For each base dataset from "base_data_dir", compute all meta-features. Save the resulting
# meta-data into "meta_data_dir".
# Compute one meta-target, i.e., apply one importance measure and one base model to one base
# dataset. Return the actual meta-target (numeric feature importances) and some information
# identifying it.
# For each base dataset from "base_data_dir", compute all meta-targets, i.e., all
# feature-importance measures for all base models. Save the resulting meta-data into
# "meta_data_dir".
# Parse command-line arguments and prepare base + meta datasets.
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Retrieves base datasets from OpenML, creates meta-datasets ' +
'and stores all these data.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-b', '--base_data_dir', type=pathlib.Path, default='data/base_datasets/',
help='Directory to store base datasets. Will be created if necessary.')
parser.add_argument('-i', '--data_ids', type=int, default=[], nargs='*',
help='Ids of OpenML datasets. If none provided, will search for datasets.')
parser.add_argument('-m', '--meta_data_dir', type=pathlib.Path, default='data/meta_datasets/',
help='Directory to store meta-datasets. Will be created if necessary.')
parser.add_argument('-p', '--n_processes', type=int, default=None,
help='Number of processes for multi-processing (default: all cores).')
args = parser.parse_args()
prepare_base_datasets(base_data_dir=args.base_data_dir, data_ids=args.data_ids)
prepare_meta_features(base_data_dir=args.base_data_dir, meta_data_dir=args.meta_data_dir,
n_processes=args.n_processes)
prepare_meta_targets(base_data_dir=args.base_data_dir, meta_data_dir=args.meta_data_dir,
n_processes=args.n_processes)
|
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] | 2.988827 | 1,074 |
#!/usr/bin/env python
import signal
import sys
import tornado.ioloop
import tornado.web
import tornado.websocket
import tornado.wsgi as myapp_wsgi
from datetime import datetime
import time
import ast
import random
from datetime import date
from models.model import Sites
from models.model import Coordonnates
from models.db_connection import Session,engine,Base
from myclasses.gpsaccess import Gpsaccess as Gps
# Javascript Usage:
# var ws = new WebSocket('ws://localhost:8000/ws');
# ws.onopen = function(event){ console.log('socket open'); }
# ws.onclose = function(event){ console.log('socket closed'); }
# ws.onerror = function(error){ console.log('error:', err); }
# ws.onmessage = function(event){ console.log('message:', event.data); }
# # ... wait for connection to open
# ws.send('hello world')
# Simple Websocket echo handler. This could be extended to
# use Redis PubSub to broadcast updates to clients.
application = tornado.web.Application([
(r'/ws', MyAppWebSocket),
(r'/(.*)', tornado.web.FallbackHandler, dict(
fallback=tornado.wsgi.WSGIContainer(myapp_wsgi)
)),
], debug=True)
if __name__ == '__main__':
application.listen(8001)
instance=tornado.ioloop.IOLoop.instance()
instance.start()
signal.signal(signal.SIGINT, signal_handler)
signal.pause()
|
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7,
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11,
6737,
62,
30281,
8,
198,
220,
220,
220,
6737,
13,
32125,
3419
] | 2.988814 | 447 |
import shutil
import os
import re
from tkinter import *
# Storage
concat = ""
assets = []
startclass = ""
# Static config
config = {
"javapath": "java",
"classpath": "class",
"sourcetxt": "source.txt",
"compilebat": "compile.bat",
"startclass": "Main.class",
"runbat": "run.bat",
"copyassets": "true"
}
# Getting configurations from a file
if os.path.exists("compile_config.txt"):
f = open("compile_config.txt", "r")
for line in f:
line = line.replace(" ", "").split("=");
config[line[0]] = line[1].rstrip();
f.close()
# Entries
entries = {
"javapath": "Java dir: ",
"classpath": "Class dir: ",
"sourcetxt": "Source txt: ",
"compilebat": "Compile bat: ",
"startclass": "Start class: ",
"runbat": "Run bat: ",
"copyassets": "Copy assets: "
}
# Setting configurations
# GUI
# Create field
# Concatenating paths to java files
# Getting the path to the starting class
# Copy assets
# File creation
# Start programm
# Call GUI
tkinter_interface()
|
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1430,
76,
628,
198,
2,
4889,
25757,
198,
30488,
3849,
62,
39994,
3419
] | 2.640541 | 370 |
#! /usr/bin/env python3
# -*- coding:utf-8 -*-
###############################################################
# © kenwaldek MIT-license
#
# Title: multiprocessing 2 Version: 1.0
# Date: 30-12-16 Language: python3
# Description: multiprocessing dus met meerdere cores te samen
#
###############################################################
from multiprocessing import Pool
if __name__ == '__main__':
p = Pool(processes=20)
data = p.map(job, range(20))
p.close()
print(data)
|
[
2,
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7,
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28,
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8,
198,
220,
220,
220,
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796,
279,
13,
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7,
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11,
2837,
7,
1238,
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198,
220,
220,
220,
279,
13,
19836,
3419,
198,
220,
220,
220,
3601,
7,
7890,
8,
628,
198
] | 2.443966 | 232 |
__author__ = 'shuai'
l1 = ListNode(2)
l1.next = ListNode(4)
l1.next.next = ListNode(3)
l2 = ListNode(5)
l2.next = ListNode(6)
l2.next.next = ListNode(4)
sol = Solution()
sol.addTwoNumbers(l1, l2)
|
[
834,
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6,
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198,
34453,
13,
2860,
7571,
49601,
7,
75,
16,
11,
300,
17,
8
] | 2.084211 | 95 |
from module1.Class1 import Class1
|
[
6738,
8265,
16,
13,
9487,
16,
1330,
5016,
16,
628
] | 3.5 | 10 |
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