text stringlengths 0 1.05M | meta dict |
|---|---|
ALERT_RESP = {
"primary_id": 3232,
"alert_type": {
"id": 1793,
"created_at": "2019-05-25T19:40:09.132456Z",
"updated_at": "2019-08-12T18:40:12.132456Z",
"type_id": "8916-1b5d68c0519f",
"category": "Host",
"detail_fields": [
"username"
],
... | {
"repo_name": "demisto/content",
"path": "Packs/FireEyeHelix/Integrations/FireEyeHelix/test_data/response_constants.py",
"copies": "1",
"size": "66486",
"license": "mit",
"hash": 4443847686036751000,
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"autogenerated": false,
... |
"""alert slack with any auditor"""
import json
import requests
from security_monkey import app
from security_monkey.alerters import custom_alerter
class SlackAlerter(object, metaclass=custom_alerter.AlerterType):
def __init__(self, cls, name, bases, attrs):
super().__init__(cls, name, bases, attrs)
... | {
"repo_name": "Netflix/security_monkey",
"path": "security_monkey/alerters/slack_alerter.py",
"copies": "1",
"size": "1467",
"license": "apache-2.0",
"hash": -1053776179268034700,
"line_mean": 34.7804878049,
"line_max": 153,
"alpha_frac": 0.5610088616,
"autogenerated": false,
"ratio": 3.964864864... |
'''Alerts module: Triggers a outbound alert to a 3rd party API when a
known sound has been detected.
'''
import requests
import datetime
import os
import json
class AlertSender(object):
'''Class to handle the sending of alerts from the audio analysis, via HTTP, to the API.'''
def __init__(self, logger, profi... | {
"repo_name": "tanapop/rfcx-worker-analysis",
"path": "modules/domain_modules/alerts.py",
"copies": "1",
"size": "3083",
"license": "apache-2.0",
"hash": 5662013720085624000,
"line_mean": 44.3382352941,
"line_max": 140,
"alpha_frac": 0.5828738242,
"autogenerated": false,
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"""alert table
Revision ID: afe08201dbc8
Revises: b46043443bf2
Create Date: 2017-04-26 18:10:54.562302
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'afe08201dbc8'
down_revision = 'b46043443bf2'
branch_labels = None
depends_on = None
def upgrade():
# ##... | {
"repo_name": "qiubit/luminis",
"path": "backend/alembic/versions/afe08201dbc8_alert_table.py",
"copies": "1",
"size": "1647",
"license": "mit",
"hash": 7039382028349722000,
"line_mean": 39.1707317073,
"line_max": 110,
"alpha_frac": 0.5701275046,
"autogenerated": false,
"ratio": 3.978260869565217... |
"""Alert when resources are made public."""
import json
from policyuniverse.policy import Policy
from streamalert.shared.rule import rule
@rule(logs=['cloudtrail:events'])
def cloudtrail_public_resources(rec):
"""
author: spiper
description: Detect resources being made public.
playbook:... | {
"repo_name": "airbnb/streamalert",
"path": "rules/community/cloudwatch_events/cloudtrail_public_resources.py",
"copies": "1",
"size": "2856",
"license": "apache-2.0",
"hash": 5220971040745723000,
"line_mean": 35.6153846154,
"line_max": 84,
"alpha_frac": 0.6022408964,
"autogenerated": false,
"rat... |
"""Alert when root AWS credentials are used."""
from streamalert.shared.rule import rule
@rule(
logs=['cloudwatch:events'],
req_subkeys={
'detail': ['userIdentity', 'eventType']
})
def cloudtrail_root_account_usage(rec):
"""
author: airbnb_csirt
description: Root AWS cre... | {
"repo_name": "airbnb/streamalert",
"path": "rules/community/cloudwatch_events/cloudtrail_root_account_usage.py",
"copies": "1",
"size": "1052",
"license": "apache-2.0",
"hash": -7604687137036976000,
"line_mean": 42.8333333333,
"line_max": 91,
"alpha_frac": 0.6207224335,
"autogenerated": false,
"... |
"""Alexa capabilities."""
from datetime import datetime
import logging
from homeassistant.const import (
ATTR_SUPPORTED_FEATURES,
ATTR_TEMPERATURE,
ATTR_UNIT_OF_MEASUREMENT,
STATE_LOCKED,
STATE_OFF,
STATE_ON,
STATE_UNAVAILABLE,
STATE_UNLOCKED,
)
import homeassistant.components.climate.c... | {
"repo_name": "fbradyirl/home-assistant",
"path": "homeassistant/components/alexa/capabilities.py",
"copies": "1",
"size": "18509",
"license": "apache-2.0",
"hash": -8064290734625911000,
"line_mean": 30.1599326599,
"line_max": 127,
"alpha_frac": 0.6309363013,
"autogenerated": false,
"ratio": 4.41... |
"""Alexa capabilities."""
import logging
from typing import List, Optional
from homeassistant.components import (
cover,
fan,
image_processing,
input_number,
light,
timer,
vacuum,
)
from homeassistant.components.alarm_control_panel import ATTR_CODE_FORMAT, FORMAT_NUMBER
from homeassistant.c... | {
"repo_name": "sdague/home-assistant",
"path": "homeassistant/components/alexa/capabilities.py",
"copies": "3",
"size": "61105",
"license": "apache-2.0",
"hash": 3597684275242113000,
"line_mean": 30.3358974359,
"line_max": 127,
"alpha_frac": 0.5954831847,
"autogenerated": false,
"ratio": 4.260563... |
"""Alexa capabilities."""
import logging
from homeassistant.const import (
ATTR_SUPPORTED_FEATURES,
ATTR_TEMPERATURE,
ATTR_UNIT_OF_MEASUREMENT,
STATE_ALARM_ARMED_AWAY,
STATE_ALARM_ARMED_CUSTOM_BYPASS,
STATE_ALARM_ARMED_HOME,
STATE_ALARM_ARMED_NIGHT,
STATE_CLOSED,
STATE_LOCKED,
S... | {
"repo_name": "qedi-r/home-assistant",
"path": "homeassistant/components/alexa/capabilities.py",
"copies": "1",
"size": "41019",
"license": "apache-2.0",
"hash": -8649807002126222000,
"line_mean": 31.736632083,
"line_max": 127,
"alpha_frac": 0.5964796801,
"autogenerated": false,
"ratio": 4.559693... |
"""Alexa capabilities."""
import logging
from homeassistant.const import (
ATTR_SUPPORTED_FEATURES,
ATTR_TEMPERATURE,
ATTR_UNIT_OF_MEASUREMENT,
STATE_LOCKED,
STATE_OFF,
STATE_ON,
STATE_UNAVAILABLE,
STATE_UNLOCKED,
STATE_UNKNOWN,
)
import homeassistant.components.climate.const as cli... | {
"repo_name": "Cinntax/home-assistant",
"path": "homeassistant/components/alexa/capabilities.py",
"copies": "1",
"size": "19019",
"license": "apache-2.0",
"hash": 5174968461482526000,
"line_mean": 30.0261011419,
"line_max": 127,
"alpha_frac": 0.6276355224,
"autogenerated": false,
"ratio": 4.40764... |
"""Alexa configuration for Home Assistant Cloud."""
import asyncio
from contextlib import suppress
from datetime import timedelta
import logging
import aiohttp
import async_timeout
from hass_nabucasa import Cloud, cloud_api
from homeassistant.components.alexa import (
config as alexa_config,
entities as alexa... | {
"repo_name": "adrienbrault/home-assistant",
"path": "homeassistant/components/cloud/alexa_config.py",
"copies": "3",
"size": "10583",
"license": "mit",
"hash": 8145811865807830000,
"line_mean": 31.3639143731,
"line_max": 91,
"alpha_frac": 0.5880185203,
"autogenerated": false,
"ratio": 4.20795228... |
"""Alexa configuration for Home Assistant Cloud."""
import asyncio
from datetime import timedelta
import logging
import aiohttp
import async_timeout
from hass_nabucasa import cloud_api
from homeassistant.const import CLOUD_NEVER_EXPOSED_ENTITIES
from homeassistant.helpers import entity_registry
from homeassistant.hel... | {
"repo_name": "fbradyirl/home-assistant",
"path": "homeassistant/components/cloud/alexa_config.py",
"copies": "1",
"size": "8884",
"license": "apache-2.0",
"hash": 2052722925840038000,
"line_mean": 31.1884057971,
"line_max": 88,
"alpha_frac": 0.585434489,
"autogenerated": false,
"ratio": 4.159176... |
"""Alexa entity adapters."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING
from homeassistant.components import (
alarm_control_panel,
alert,
automation,
binary_sensor,
camera,
cover,
fan,
group,
image_processing,
input_boolean,
input_nu... | {
"repo_name": "kennedyshead/home-assistant",
"path": "homeassistant/components/alexa/entities.py",
"copies": "1",
"size": "30471",
"license": "apache-2.0",
"hash": 8125997992793968000,
"line_mean": 32.89432703,
"line_max": 116,
"alpha_frac": 0.6570509665,
"autogenerated": false,
"ratio": 4.214522... |
"""Alexa entity adapters."""
from typing import List
from homeassistant.components import (
alarm_control_panel,
alert,
automation,
binary_sensor,
cover,
fan,
group,
image_processing,
input_boolean,
input_number,
light,
lock,
media_player,
scene,
script,
... | {
"repo_name": "Teagan42/home-assistant",
"path": "homeassistant/components/alexa/entities.py",
"copies": "1",
"size": "25487",
"license": "apache-2.0",
"hash": -5503343197778340000,
"line_mean": 32.6684280053,
"line_max": 115,
"alpha_frac": 0.6640640326,
"autogenerated": false,
"ratio": 4.2274008... |
"""Alexa entity adapters."""
from typing import List
from homeassistant.core import callback
from homeassistant.const import (
ATTR_DEVICE_CLASS,
ATTR_SUPPORTED_FEATURES,
ATTR_UNIT_OF_MEASUREMENT,
CLOUD_NEVER_EXPOSED_ENTITIES,
CONF_NAME,
TEMP_CELSIUS,
TEMP_FAHRENHEIT,
)
from homeassistant.u... | {
"repo_name": "joopert/home-assistant",
"path": "homeassistant/components/alexa/entities.py",
"copies": "1",
"size": "19079",
"license": "apache-2.0",
"hash": 4173983716888928000,
"line_mean": 32.3548951049,
"line_max": 89,
"alpha_frac": 0.6653388542,
"autogenerated": false,
"ratio": 4.2682326621... |
"""Alexa entity adapters."""
import logging
from typing import List
from homeassistant.components import (
alarm_control_panel,
alert,
automation,
binary_sensor,
camera,
cover,
fan,
group,
image_processing,
input_boolean,
input_number,
light,
lock,
media_player,
... | {
"repo_name": "mKeRix/home-assistant",
"path": "homeassistant/components/alexa/entities.py",
"copies": "3",
"size": "27599",
"license": "mit",
"hash": 1779032652107930000,
"line_mean": 32.739608802,
"line_max": 115,
"alpha_frac": 0.6585021196,
"autogenerated": false,
"ratio": 4.251232285890326,
... |
"""Alexa entity adapters."""
import logging
from typing import TYPE_CHECKING, List
from homeassistant.components import (
alarm_control_panel,
alert,
automation,
binary_sensor,
camera,
cover,
fan,
group,
image_processing,
input_boolean,
input_number,
light,
lock,
... | {
"repo_name": "partofthething/home-assistant",
"path": "homeassistant/components/alexa/entities.py",
"copies": "3",
"size": "29781",
"license": "mit",
"hash": -1036778919343009700,
"line_mean": 32.7653061224,
"line_max": 101,
"alpha_frac": 0.6577347974,
"autogenerated": false,
"ratio": 4.21528662... |
"""Alexa HTTP interface."""
import logging
from homeassistant import core
from homeassistant.components.http.view import HomeAssistantView
from .auth import Auth
from .config import AbstractConfig
from .const import (
CONF_CLIENT_ID,
CONF_CLIENT_SECRET,
CONF_ENDPOINT,
CONF_ENTITY_CONFIG,
CONF_FILT... | {
"repo_name": "leppa/home-assistant",
"path": "homeassistant/components/alexa/smart_home_http.py",
"copies": "1",
"size": "3648",
"license": "apache-2.0",
"hash": 5396199520842676000,
"line_mean": 30.1794871795,
"line_max": 88,
"alpha_frac": 0.6559758772,
"autogenerated": false,
"ratio": 4.075977... |
"""Alexa message handlers."""
import logging
import math
from homeassistant import core as ha
from homeassistant.components import (
camera,
cover,
fan,
group,
input_number,
light,
media_player,
timer,
vacuum,
)
from homeassistant.components.climate import const as climate
from home... | {
"repo_name": "turbokongen/home-assistant",
"path": "homeassistant/components/alexa/handlers.py",
"copies": "3",
"size": "50582",
"license": "apache-2.0",
"hash": 4335843287752829400,
"line_mean": 31.3001277139,
"line_max": 101,
"alpha_frac": 0.6453679174,
"autogenerated": false,
"ratio": 3.91289... |
"""Alexa models."""
import logging
from uuid import uuid4
from .const import (
API_CONTEXT,
API_DIRECTIVE,
API_ENDPOINT,
API_EVENT,
API_HEADER,
API_PAYLOAD,
API_SCOPE,
)
from .entities import ENTITY_ADAPTERS
from .errors import AlexaInvalidEndpointError
_LOGGER = logging.getLogger(__name__... | {
"repo_name": "fbradyirl/home-assistant",
"path": "homeassistant/components/alexa/messages.py",
"copies": "2",
"size": "6215",
"license": "apache-2.0",
"hash": 3833510430703904300,
"line_mean": 31.3697916667,
"line_max": 88,
"alpha_frac": 0.6077232502,
"autogenerated": false,
"ratio": 4.461593682... |
## Alexander Farrell, Defne Surujon
## This program takes an input GBK file and writes a list of noralization genes
## Normalization genes for TnSeq are those that have the product annotation
## "Transposase" or "Mobile element"
import os
import csv
from optparse import OptionParser
options = OptionPa... | {
"repo_name": "jsa-aerial/aerobio",
"path": "Scripts/get_norm_genes.py",
"copies": "1",
"size": "2675",
"license": "mit",
"hash": 5873572569557796000,
"line_mean": 26.4574468085,
"line_max": 100,
"alpha_frac": 0.5757009346,
"autogenerated": false,
"ratio": 3.1883194278903457,
"config_test": fal... |
"""alexandria URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.9/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-... | {
"repo_name": "baltzar/alexandria",
"path": "alexandria/urls.py",
"copies": "1",
"size": "1069",
"license": "mit",
"hash": 2155247259961657900,
"line_mean": 38.5925925926,
"line_max": 106,
"alpha_frac": 0.6875584659,
"autogenerated": false,
"ratio": 3.470779220779221,
"config_test": false,
"h... |
# Alexa Personal Assistant Companion Program for Raspberry Pi
# Modified by Simon Beal and Matthew Timmons-Brown for "The Raspberry Pi Guy" YouTube channel
# Built upon the work of Sam Machin, (c)2016
# This is a library that includes all of the web functionality of the Alexa Amazon Echo personal assistant service
# Th... | {
"repo_name": "the-raspberry-pi-guy/Artificial-Intelligence-Pi",
"path": "alexa_helper.py",
"copies": "2",
"size": "3404",
"license": "mit",
"hash": 7310811381330656000,
"line_mean": 32.702970297,
"line_max": 131,
"alpha_frac": 0.6454171563,
"autogenerated": false,
"ratio": 3.196244131455399,
"... |
# Alexa Personal Assitant for Raspberry Pi
# Coded by Simon Beal and Matthew Timmons-Brown for "The Raspberry Pi Guy" YouTube channel
# Built upon the work of Sam Machin, (c)2016
# Feel free to look through the code, try to understand it & modify as you wish!
# The installer MUST be run before this code.
#!/usr/bin/py... | {
"repo_name": "the-raspberry-pi-guy/Artificial-Intelligence-Pi",
"path": "main.py",
"copies": "1",
"size": "4515",
"license": "mit",
"hash": -4175548929271900000,
"line_mean": 36.625,
"line_max": 137,
"alpha_frac": 0.6819490587,
"autogenerated": false,
"ratio": 3.3247422680412373,
"config_test"... |
"""Alexa related errors."""
from homeassistant.exceptions import HomeAssistantError
from .const import API_TEMP_UNITS
class UnsupportedInterface(HomeAssistantError):
"""This entity does not support the requested Smart Home API interface."""
class UnsupportedProperty(HomeAssistantError):
"""This entity does... | {
"repo_name": "Teagan42/home-assistant",
"path": "homeassistant/components/alexa/errors.py",
"copies": "26",
"size": "3482",
"license": "apache-2.0",
"hash": 2922607705129670700,
"line_mean": 28.0166666667,
"line_max": 82,
"alpha_frac": 0.6990235497,
"autogenerated": false,
"ratio": 4.09647058823... |
"""Alexa Resources and Assets."""
class AlexaGlobalCatalog:
"""The Global Alexa catalog.
https://developer.amazon.com/docs/device-apis/resources-and-assets.html#global-alexa-catalog
You can use the global Alexa catalog for pre-defined names of devices, settings, values, and units.
This catalog is lo... | {
"repo_name": "leppa/home-assistant",
"path": "homeassistant/components/alexa/resources.py",
"copies": "1",
"size": "12713",
"license": "apache-2.0",
"hash": -7156532409524071000,
"line_mean": 31.850129199,
"line_max": 116,
"alpha_frac": 0.6503579014,
"autogenerated": false,
"ratio": 3.4583786724... |
APPLICATION_ID = "amzn1.ask.skill.dd677950-cade-4805-b1f1-ce2e3a3569f0"
# Bible API
BIBLE_TRANSLATION = "GNBDC" # Can't use NIV - it's still in copyright
BIBLE_API_URL = "https://bibles.org/v2/eng-{translation}/passages.js".format(
translation=BIBLE_TRANSLATION)
# Sermons
SERMONS_XML_URL = "http://www.christchur... | {
"repo_name": "mauriceyap/ccm-assistant",
"path": "src/config.py",
"copies": "1",
"size": "1051",
"license": "mit",
"hash": 7596480830766024000,
"line_mean": 41.04,
"line_max": 95,
"alpha_frac": 0.7164605138,
"autogenerated": false,
"ratio": 2.6144278606965172,
"config_test": false,
"has_no_k... |
"""Alexa Skills represent the logic used to build JSON responses to
events as specified in the Alexa Skills Kit."""
import functools
from alexa.response import response_to_dict
def intent_callback(intent_name):
"""Makes the decorated method activate on the correct intent.
This crazy black magic works by do... | {
"repo_name": "ianonavy/python-alexa-skills-kit",
"path": "alexa/skill.py",
"copies": "1",
"size": "4286",
"license": "mit",
"hash": -4089579771239716000,
"line_mean": 33.564516129,
"line_max": 75,
"alpha_frac": 0.629724685,
"autogenerated": false,
"ratio": 4.821147356580427,
"config_test": fal... |
"""Alexa Skill to harness the power of the Giant Bomb API."""
import sys
import logging
from flask import Flask, render_template
from flask_ask import Ask, statement, question, session
from gb import api
app = Flask(__name__)
ask = Ask(app, '/')
logging.getLogger('flask_ask').setLevel(logging.DEBUG)
giant_bomb = a... | {
"repo_name": "jaykwon/giantanswers",
"path": "skill.py",
"copies": "1",
"size": "2063",
"license": "mit",
"hash": 1407605121719263000,
"line_mean": 25.1265822785,
"line_max": 94,
"alpha_frac": 0.6621425109,
"autogenerated": false,
"ratio": 3.670818505338078,
"config_test": false,
"has_no_key... |
"""Alexa Skill to look up the flavor forecast for The Diary Godmother."""
import sys
import logging
import datetime
from flask import Flask, render_template
from flask_ask import Ask, statement, question, convert_errors, session
import api
app = Flask(__name__)
ask = Ask(app, '/')
logging.getLogger('flask_ask').se... | {
"repo_name": "pasharkey/flavorforecast",
"path": "src/skill.py",
"copies": "1",
"size": "6859",
"license": "mit",
"hash": 2838073168650510300,
"line_mean": 30.036199095,
"line_max": 91,
"alpha_frac": 0.6662778831,
"autogenerated": false,
"ratio": 3.8105555555555557,
"config_test": false,
"ha... |
"""Alexa state report code."""
import asyncio
import json
import logging
import aiohttp
import async_timeout
from homeassistant.const import MATCH_ALL
from .const import API_CHANGE, Cause
from .entities import ENTITY_ADAPTERS
from .messages import AlexaResponse
_LOGGER = logging.getLogger(__name__)
DEFAULT_TIMEOUT ... | {
"repo_name": "Cinntax/home-assistant",
"path": "homeassistant/components/alexa/state_report.py",
"copies": "1",
"size": "5879",
"license": "apache-2.0",
"hash": 4879994860031726000,
"line_mean": 30.6075268817,
"line_max": 129,
"alpha_frac": 0.6625276408,
"autogenerated": false,
"ratio": 3.961590... |
"""Alexa state report code."""
import asyncio
import json
import logging
import aiohttp
import async_timeout
from homeassistant.const import MATCH_ALL, STATE_ON
import homeassistant.util.dt as dt_util
from .const import API_CHANGE, Cause
from .entities import ENTITY_ADAPTERS
from .messages import AlexaResponse
_LOG... | {
"repo_name": "robbiet480/home-assistant",
"path": "homeassistant/components/alexa/state_report.py",
"copies": "2",
"size": "7965",
"license": "apache-2.0",
"hash": 8588217609352270000,
"line_mean": 30.4822134387,
"line_max": 129,
"alpha_frac": 0.6462021343,
"autogenerated": false,
"ratio": 3.994... |
ALEXA_VERSION_NUMBER = 1
STOP_INTENT = 'AMAZON.StopIntent'
CANCEL_INTENT = 'AMAZON.CancelIntent'
HELP_INTENT = 'AMAZON.HelpIntent'
TYPE_LAUNCH = 'LaunchRequest'
ALEXA_DISCOVERY = 'DiscoverAppliancesRequest'
ALEXA_DISCOVERY_NAMESPACE = 'Alexa.ConnectedHome.Discovery'
ALEXA_CONTROL_NAMESPACE = 'Alexa.ConnectedHome.Contr... | {
"repo_name": "Firefly-Automation/Firefly",
"path": "Firefly/services/alexa/alexa_const.py",
"copies": "1",
"size": "5246",
"license": "apache-2.0",
"hash": 1948325477456009000,
"line_mean": 23.6291079812,
"line_max": 117,
"alpha_frac": 0.5947388486,
"autogenerated": false,
"ratio": 3.21249234537... |
# Alex Ciaramella and Greg Suner
# Abstract Tournament Class
# Tournament is observable while players are observers
import Message
import Observable
import Display
import ScoreKeeper
class Tournament(Observable.Observable):
# set up a list of players when tournament is initialized
def __init__(self):
... | {
"repo_name": "geebzter/game-framework",
"path": "Tournament.py",
"copies": "1",
"size": "3830",
"license": "apache-2.0",
"hash": 5956504372984971000,
"line_mean": 32.3043478261,
"line_max": 100,
"alpha_frac": 0.6433420366,
"autogenerated": false,
"ratio": 4.070138150903294,
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# Alex Ciaramella and Greg Suner
# Abstract Tournament Class
# Tournament is observable while players are observers
import Message
import Observable
import Display
class Tournament(Observable.Observable):
# set up a list of players when tournament is initialized
def __init__(self):
Observable.Observa... | {
"repo_name": "mccler89/RPS_Player",
"path": "Tournament.py",
"copies": "3",
"size": "3118",
"license": "apache-2.0",
"hash": -2208371542740168000,
"line_mean": 29.568627451,
"line_max": 79,
"alpha_frac": 0.6423989737,
"autogenerated": false,
"ratio": 4.075816993464052,
"config_test": false,
... |
# Alex Ciaramella and Greg Suner
# Abstract Tournament Class
# Tournament is observable while players are observers
import Message
import Observable
import Display
class Tournament(Observable.Observable):
# set up a list of players when tournament is initialized
def __init__(self):
Observable.Observ... | {
"repo_name": "PaulieC/sprint1_Council_a",
"path": "Tournament.py",
"copies": "2",
"size": "3437",
"license": "apache-2.0",
"hash": 5243066021880614000,
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"line_max": 100,
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"autogenerated": false,
"ratio": 4.116167664670659,
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# A lexer in Python
# By Anthony Nguyen
# MIT Licensed
#
# Pretty basic; easily extensible
import re
class Token():
def __init__(self, name, rule, data, start, end):
self.name = name
self.rule = rule
self.data = data
self.start = start
self.end = end
def __str__(self):
return "{0}({1}): {2}".format(sel... | {
"repo_name": "anthonynguyen/pylexer",
"path": "pylexer.py",
"copies": "1",
"size": "1796",
"license": "mit",
"hash": 196452366284821380,
"line_mean": 24.6714285714,
"line_max": 120,
"alpha_frac": 0.6542316258,
"autogenerated": false,
"ratio": 2.963696369636964,
"config_test": false,
"has_no_... |
# A lexer used by all the parsers
# A simple lexer for C like expressions. The major difference with C is that consecutive operators have
# to be separated with white spaces.
import re
class Token:
def __init__(self, kind, lexem):
self.kind = kind
self.lexem = lexem
def __repr__(self):
... | {
"repo_name": "bourguet/operator_precedence_parsing",
"path": "lexer.py",
"copies": "1",
"size": "1039",
"license": "bsd-2-clause",
"hash": -4707189783009254000,
"line_mean": 28.6857142857,
"line_max": 104,
"alpha_frac": 0.5043310876,
"autogenerated": false,
"ratio": 3.4177631578947367,
"config... |
'''Alexey is trying to develop a program for a very simple microcontroller. It makes readings from various sensors over time, and these readings must happen at specific regular times. Unfortunately, if two of these readings occur at the same time, the microcontroller freezes and must be reset.
There are N different ... | {
"repo_name": "OMEHA/HELLO-WORLD",
"path": "PYTHON-CODECHEF_ALEXTASK.py",
"copies": "1",
"size": "2580",
"license": "unlicense",
"hash": -8358206587142630000,
"line_mean": 29.8518518519,
"line_max": 319,
"alpha_frac": 0.665503876,
"autogenerated": false,
"ratio": 3.649222065063649,
"config_test... |
# Alex Goudine
# GEOG 490 - Webscraping and Database Design
# Scrapes weather data from forecast.io and returns a dict of the relevant information
# Modified by Taylor Denouden
# Shortened script and made into a simple function in which geom and date data can be passed
# Added more efficient and robust cardinal direct... | {
"repo_name": "SPARLab/BikeMaps",
"path": "mapApp/utils/weather.py",
"copies": "1",
"size": "2587",
"license": "mit",
"hash": -2301689683614506500,
"line_mean": 46.9074074074,
"line_max": 132,
"alpha_frac": 0.6671820642,
"autogenerated": false,
"ratio": 3.500676589986468,
"config_test": false,
... |
# Alex Gould
# 9/26/15
# COMP50CP
# transform_image.py - Uses multithreading to concurrently alter
# sections of an image.
# Since the threads never share resources, there's no real need to
# coordinate them, apart from making sure they're all done
# before continuing with the program
from threading import Thread
impo... | {
"repo_name": "lyra833/Stufts2",
"path": "Concurrent Systems/Homework/HW2/image_transform.py",
"copies": "1",
"size": "2292",
"license": "mit",
"hash": -7288887728823608000,
"line_mean": 21.6930693069,
"line_max": 70,
"alpha_frac": 0.6544502618,
"autogenerated": false,
"ratio": 2.628440366972477,... |
#Alex Holcombe alex.holcombe@sydney.edu.au
#See the github repository for more information: https://github.com/alexholcombe/twoWords
from __future__ import print_function
from psychopy import monitors, visual, event, data, logging, core, sound, gui
import psychopy.info
import numpy as np
from math import atan, log, cei... | {
"repo_name": "alexholcombe/twoWords",
"path": "specialFieldsStudentCode/twoWordsExperimentInvertedbackMayAlexContinue2.py",
"copies": "2",
"size": "52750",
"license": "mit",
"hash": 3622063637666545700,
"line_mean": 58.6056497175,
"line_max": 209,
"alpha_frac": 0.6849478673,
"autogenerated": false... |
#Alex Holcombe alex.holcombe@sydney.edu.au
#See the github repository for more information: https://github.com/alexholcombe/twoWords
from __future__ import print_function #use python3 style print
from psychopy import monitors, visual, event, data, logging, core, sound, gui
import psychopy.info
import numpy as np
from m... | {
"repo_name": "alexholcombe/twoWords",
"path": "specialFieldsStudentCode/twoWordsForJoel.py",
"copies": "2",
"size": "49800",
"license": "mit",
"hash": 399086273864182200,
"line_mean": 58.0747330961,
"line_max": 209,
"alpha_frac": 0.6828313253,
"autogenerated": false,
"ratio": 3.5591766723842198,... |
#Alex Holcombe alex.holcombe@sydney.edu.au
#Modified by Kim Ransley from the twoWords.py program at the github repository: https://github.com/alexholcombe/twoWords
#1. Inverted
#2. One word
#3. duration
#4. trail, trial
#5. numCharsInResponse to fit words of diff length?
#6. total number of trials?
#7. corre... | {
"repo_name": "alexholcombe/twoWords",
"path": "RansleySingleshotVersion/Justine/Hubert_18May2015_14-26.py",
"copies": "2",
"size": "49209",
"license": "mit",
"hash": 3745289546652181500,
"line_mean": 55.0870069606,
"line_max": 209,
"alpha_frac": 0.6636387653,
"autogenerated": false,
"ratio": 3.5... |
#Alex Holcombe alex.holcombe@sydney.edu.au
#See the github repository for more information: https://github.com/alexholcombe/twoWords
from __future__ import print_function
from psychopy import monitors, visual, event, data, logging, core, sound, gui
import psychopy.info
import numpy as np
from math import atan, lo... | {
"repo_name": "alexholcombe/twoWords",
"path": "Charlie/twoWords.py",
"copies": "3",
"size": "52843",
"license": "mit",
"hash": -8010308503823285000,
"line_mean": 55.9418859649,
"line_max": 209,
"alpha_frac": 0.6681490453,
"autogenerated": false,
"ratio": 3.5582115682445625,
"config_test": fals... |
#Alex Holcombe alex.holcombe@sydney.edu.au
#See the github repository for more information: https://github.com/alexholcombe/twoWords
from __future__ import print_function #use python3 style print
from psychopy import monitors, visual, event, data, logging, core, sound, gui
import psychopy.info
import numpy as np
... | {
"repo_name": "alexholcombe/twoWords",
"path": "twoWordsCherylwithIntro.py",
"copies": "1",
"size": "64243",
"license": "mit",
"hash": 5398931645272247000,
"line_mean": 57.4861111111,
"line_max": 419,
"alpha_frac": 0.6738633003,
"autogenerated": false,
"ratio": 3.602882620155908,
"config_test":... |
"""A lexical analyzer class for simple shell-like syntaxes."""
# Module and documentation by Eric S. Raymond, 21 Dec 1998
# Input stacking and error message cleanup added by ESR, March 2000
import os.path
import sys
class shlex:
"A lexical analyzer class for simple shell-like syntaxes."
def __init__(self,... | {
"repo_name": "MalloyPower/parsing-python",
"path": "front-end/testsuite-python-lib/Python-2.0/Lib/shlex.py",
"copies": "4",
"size": "7034",
"license": "mit",
"hash": -7187342638287929000,
"line_mean": 36.0210526316,
"line_max": 79,
"alpha_frac": 0.4746943418,
"autogenerated": false,
"ratio": 4.5... |
"""A lexical analyzer class for simple shell-like syntaxes."""
# Module and documentation by Eric S. Raymond, 21 Dec 1998
# Input stacking and error message cleanup added by ESR, March 2000
# push_source() and pop_source() made explicit by ESR, January 2001.
import os.path
import sys
__all__ = ["shlex"]
class shlex... | {
"repo_name": "jrabbit/ubotu-fr",
"path": "src/shlex.py",
"copies": "14",
"size": "8159",
"license": "bsd-3-clause",
"hash": 3730988352145723400,
"line_mean": 36.4266055046,
"line_max": 76,
"alpha_frac": 0.4799607795,
"autogenerated": false,
"ratio": 4.5888638920134985,
"config_test": false,
... |
# Alex Kim - Minesweeper
import atexit as _atexit
import random as _random
import sys as _sys
def render_board(game_board):
# 0 1 2 3 4 5 6
# +-------------+
# 0|O O O O O O O|
# 1|O O O O O O O|
# 2|O O O O O O O|
# 3|O O O O O O O|
# 4|O O O O O O O|
# 5|O O O O O O O|
# 6|O O O ... | {
"repo_name": "ajkim141/minesweeper-flask",
"path": "minesweeper.py",
"copies": "1",
"size": "11288",
"license": "mit",
"hash": -2069639437521127400,
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"line_max": 120,
"alpha_frac": 0.5948795181,
"autogenerated": false,
"ratio": 3.6828711256117455,
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"""AlexNet cnn model"""
import numpy as np
import tensorflow as tf
from model_utils import *
from model import Model
class AlexNet(Model):
def __init__(self, class_count, input_size_w=28,
input_size_h=None, params_path=None,
is_training=False):
"""Constructor.
I... | {
"repo_name": "Lazea/TensorFlow",
"path": "models/alexnet.py",
"copies": "1",
"size": "4969",
"license": "apache-2.0",
"hash": -4367961704092285400,
"line_mean": 40.0661157025,
"line_max": 83,
"alpha_frac": 0.5636949084,
"autogenerated": false,
"ratio": 3.1251572327044026,
"config_test": false,... |
'''AlexNet for CIFAR10. FC layers are removed. Paddings are adjusted.
Without BN, the start learning rate should be 0.01
(c) YANG, Wei
'''
import torch.nn as nn
__all__ = ['alexnet']
class AlexNet(nn.Module):
def __init__(self, num_classes=10, nchannels=3):
super(AlexNet, self).__init__()
self... | {
"repo_name": "google-research/understanding-curricula",
"path": "third_party/models/alexnet.py",
"copies": "1",
"size": "1380",
"license": "apache-2.0",
"hash": 3504049144795640300,
"line_mean": 30.3636363636,
"line_max": 74,
"alpha_frac": 0.5753623188,
"autogenerated": false,
"ratio": 3.1435079... |
"""AlexNet model."""
from athenet import Network
from athenet.layers import ConvolutionalLayer, ReLU, LRN, MaxPool, \
FullyConnectedLayer, Dropout, Softmax
from athenet.utils import load_data, get_bin_path
ALEXNET_FILENAME = 'alexnet_weights.pkl.gz'
def alexnet(trained=True, weights_filename=ALEXNET_FILENAME, w... | {
"repo_name": "heurezjusz/Athenet",
"path": "athenet/models/alexnet.py",
"copies": "2",
"size": "2032",
"license": "bsd-2-clause",
"hash": 6651648401164641000,
"line_mean": 28.8823529412,
"line_max": 79,
"alpha_frac": 0.4970472441,
"autogenerated": false,
"ratio": 3.89272030651341,
"config_test... |
""" AlexNet
Dataset classification task: 28x28x3 shape (adjusted AlexNet)
References:
ImageNet Classification with Deep Convolutional Neural Networks.
K. Simonyan, A. Zisserman. arXiv technical report, 2014.
Links:
http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
"... | {
"repo_name": "migueldsw/TL-DA-TF",
"path": "NETWORKS/alexnet.py",
"copies": "1",
"size": "4145",
"license": "apache-2.0",
"hash": -7972507101517669000,
"line_mean": 54.28,
"line_max": 113,
"alpha_frac": 0.6728588661,
"autogenerated": false,
"ratio": 3.49789029535865,
"config_test": false,
"h... |
""" Algebraic formalism for hard objects
Hard objects are defined as products of ``eta_i`` where ``i`` is
the index of a node of the graph on which the hard object is defined.
The ``eta`` elements are commuting and nilpotent.
They satisfy
``<eta_{i_1} ...eta_{i_k}> = 1``
if ``i_1,..,i_k`` are all different... | {
"repo_name": "pernici/hobj",
"path": "src/hobj.py",
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"size": "24509",
"license": "bsd-3-clause",
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"line_mean": 26.2322222222,
"line_max": 85,
"alpha_frac": 0.4682361581,
"autogenerated": false,
"ratio": 3.4393769295537466,
"config_test": false,
"has_n... |
'''algebric relationship framework using multimethods
'''
from __future__ import absolute_import
from functools import partial
try:
from numpy import number as numpy_number_type
except ImportError:
numpy_number_type = None
from jamenson.runtime.multimethod import MultiMethod, defmethod, defboth_wrapper
from... | {
"repo_name": "matthagy/physmath",
"path": "physmath/algebra.py",
"copies": "1",
"size": "3665",
"license": "apache-2.0",
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"line_mean": 25.1785714286,
"line_max": 95,
"alpha_frac": 0.6054570259,
"autogenerated": false,
"ratio": 3.31374321880651,
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'''Algebric Type System
Supports arbriarty types rules as well as algebric combinations through
set joins and complements. Designed to extend the Python type system to
incorporate a more robust definition of types. This is espically useful
for multimethods.
Examples:
In[0]: from jamenson.runtim... | {
"repo_name": "matthagy/Jamenson",
"path": "jamenson/runtime/atypes/_atypes.py",
"copies": "1",
"size": "15179",
"license": "apache-2.0",
"hash": -5570458838699171000,
"line_mean": 30.5571725572,
"line_max": 103,
"alpha_frac": 0.6001712893,
"autogenerated": false,
"ratio": 3.574894017899199,
"c... |
"""alge URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.9/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based ... | {
"repo_name": "teknologkoren/Strequelistan",
"path": "alge/urls.py",
"copies": "1",
"size": "1195",
"license": "mpl-2.0",
"hash": -7276990372810533000,
"line_mean": 33.1428571429,
"line_max": 83,
"alpha_frac": 0.6811715481,
"autogenerated": false,
"ratio": 3.4941520467836256,
"config_test": fal... |
alg = 'mf'
nu = 480189
nv = 17770
traindata='~/works/data/netflix_protobuf_train_4by500'
testdata='~/works/data/netflix_protobuf_valid'
#nu = 1000990
#nv = 624961
#traindata='~/works/data/yahoo_protobuf_train_4by500'
#testdata='~/works/data/yahoo_protobuf_valid'
it=10
fly=4
dim=2048
#mf
eta=2.4e-2
lam=4e-2
#dpmf
eps... | {
"repo_name": "dmlc/experimental-mf",
"path": "src/run.py",
"copies": "1",
"size": "1303",
"license": "apache-2.0",
"hash": -5188413155825594000,
"line_mean": 30.7804878049,
"line_max": 324,
"alpha_frac": 0.6185725249,
"autogenerated": false,
"ratio": 2.3908256880733947,
"config_test": true,
... |
"""AlgoContest URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Clas... | {
"repo_name": "xudianc/AlgoContest",
"path": "AlgoContest/urls.py",
"copies": "1",
"size": "2654",
"license": "mit",
"hash": -4128191568891630600,
"line_mean": 41.6129032258,
"line_max": 79,
"alpha_frac": 0.6949280848,
"autogenerated": false,
"ratio": 3.10093896713615,
"config_test": false,
"... |
"""Algolia is a funny word."""
from algoliasearch import algoliasearch
class AlgoliaHelper(object):
"""Handles the Algolia stuff."""
def __init__(self):
self.app_id = 'PBF4ZR3KBT'
self.api_key = '9188cd13a0dbf3d0af949802b0e31489' # search-only
self.index_name = 'ubuntu_irc_logs'
... | {
"repo_name": "phrawzty/ubunolia",
"path": "algoliahelper/algoliahelper.py",
"copies": "1",
"size": "3275",
"license": "mpl-2.0",
"hash": -2960955746323192300,
"line_mean": 29.3240740741,
"line_max": 77,
"alpha_frac": 0.546870229,
"autogenerated": false,
"ratio": 4.166666666666667,
"config_test... |
ALGOL_STYLE = {
'BACKGROUND_BLACK': '48;5;59',
'BACKGROUND_BLUE': '48;5;59',
'BACKGROUND_CYAN': '48;5;59',
'BACKGROUND_GREEN': '48;5;59',
'BACKGROUND_INTENSE_BLACK': '48;5;59',
'BACKGROUND_INTENSE_BLUE': '48;5;102',
'BACKGROUND_INTENSE_CYAN': '48;5;102',
'BACKGROUND_INTENSE_GREEN': '48;5... | {
"repo_name": "scopatz/xolors",
"path": "ansi_colors.py",
"copies": "1",
"size": "77701",
"license": "bsd-2-clause",
"hash": -1005166823304271000,
"line_mean": 35.5136278195,
"line_max": 52,
"alpha_frac": 0.5694778703,
"autogenerated": false,
"ratio": 2.1441264935566653,
"config_test": false,
... |
# Algorithm-2017-09-11.py
# coding: utf-8
# In[ ]:
import pandas as pd
import numpy as np
from pprint import pprint
import math
from utils import import_net, export_net
from time import time
# In[ ]:
def sort_nodes(t):
v = t[1]
return -v[0], -v[1]
def BFS(graph, start):
""" André, please clarify:
... | {
"repo_name": "lucasosouza/graph-competition",
"path": "Algorithm-2017-09-11.py",
"copies": "1",
"size": "3441",
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"hash": 261155934437804740,
"line_mean": 25.2595419847,
"line_max": 89,
"alpha_frac": 0.499127907,
"autogenerated": false,
"ratio": 3.675213675213675,
"config_test"... |
""" Algorithm evaluators for Rigor """
from __future__ import print_function
from collections import defaultdict
import sys
class ObjectAreaEvaluator(object):
"""
Compares ground truth to detections using Wolf and Jolion's algorithm.
:param scatter_punishment: :math:`f_{sc}(k)` "a parameter function of the evalua... | {
"repo_name": "blindsightcorp/rigor",
"path": "lib/evaluator.py",
"copies": "1",
"size": "11995",
"license": "bsd-2-clause",
"hash": 2494505943603799600,
"line_mean": 48.1598360656,
"line_max": 197,
"alpha_frac": 0.7323051271,
"autogenerated": false,
"ratio": 3.319955715471907,
"config_test": f... |
"""Algorithm for 2048 game."""
import random
OFFSETS = {'UP': (1, 0),
'DOWN': (-1, 0),
'LEFT': (0, 1),
'RIGHT': (0, -1)}
def openFile(score):
try:
scoreFile = file("highscore.txt", "r+")
scoreList = [0]
for line in scoreFile:
scoreList.appen... | {
"repo_name": "WillSkywalker/2048_monte_carlo",
"path": "_2048.py",
"copies": "1",
"size": "4116",
"license": "apache-2.0",
"hash": -5386982993453591000,
"line_mean": 27.3862068966,
"line_max": 122,
"alpha_frac": 0.5160349854,
"autogenerated": false,
"ratio": 3.6619217081850532,
"config_test": ... |
"""Algorithm for basic classtering."""
import numpy as np
from mutil import p_info
from bunch import Bunch
L_algorithm = ['alg1']
Default_param = Bunch(name='lr')
def example():
comat, l_freq = generate_comat()
group_comat(comat, l_freq, n_sample=100)
def group_comat(comat, hist, n_sample, th_pmi=0.0, th_j... | {
"repo_name": "belemizz/mimic2_tools",
"path": "clinical_db/alg/clustering.py",
"copies": "1",
"size": "2348",
"license": "mit",
"hash": 5551519073343978000,
"line_mean": 25.9885057471,
"line_max": 78,
"alpha_frac": 0.5195911414,
"autogenerated": false,
"ratio": 2.705069124423963,
"config_test"... |
# Algorithm for determining chord symbols based on frequency spectrum
from __future__ import division
import math
samplingFrequency = 2000
bufferSize = 1024
referenceFrequency = 130.81278265 # C
numHarmonics = 2
numOctaves = 4
numBinsToSearch = 2
noteFrequencies = []
chromagram = [0.0000000000000000000]*12
noteNames ... | {
"repo_name": "gmittal/joey-alexander",
"path": "util/chords.py",
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"""Algorithm for finding strongly connected components from a directed graph.
Component is strongly connected when there's a path from every vertex in to
every other vertex within the component.
Time complexity: O(V + E)
"""
from collections import defaultdict
from algolib.graph.dfs import DFS
def __process_vertex_ea... | {
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"path": "algolib/graph/strong_components.py",
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# Algorithm for maximum coverage problem
import numpy as np
def maximum_k_coverage(sets, k):
covered = set()
selected_sets = []
if k >= len(sets):
return sets
for i in xrange(k):
max_set = max(sets, key=lambda s: len(s - covered))
selected_sets.append(max_set)
... | {
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"path": "max_cover.py",
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"size": "1054",
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"alpha_frac": 0.6110056926,
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"config_test": false,
"has_no_key... |
"""Algorithm for simulating a 2048 game using Monte-Carlo method."""
import random, _2048
SIMULATE_TIMES = 100000
DIRECTIONS = ('UP', 'DOWN', 'LEFT', 'RIGHT')
def simulate_to_end(game):
while game.get_state():
dircts = list(DIRECTIONS)
for i in xrange(3):
c = random.choice(dircts)
... | {
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"path": "monte_carlo.py",
"copies": "1",
"size": "1244",
"license": "apache-2.0",
"hash": -7812411360253709000,
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"line_max": 68,
"alpha_frac": 0.5699356913,
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"config_te... |
algorithm = "fourier"
propagator = "chinchen"
T = 2 * 4.4
dt = 0.05
dimension = 1
ncomponents = 1
eps = 0.1
potential = "quartic"
sigma = 4.0
# The grid of our simulation domain
limits = [(-6.283185307179586, 6.283185307179586)]
number_nodes = [8192]
# The parameter set of the initial wavepacket
Q = [[1.0 ]]
P = ... | {
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"path": "examples/quartic/quartic_1D_f_cc.py",
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algorithm = 'fourier'
propagator = 'fourier'
T = 6
dt = 0.01
dimension = 2
ncomponents = 1
eps = 0.05
potential = 'henon_heiles'
a = 1
b = 3
limits = [[-1.5707963267948966, 1.5707963267948966],
[-1.5707963267948966, 1.5707963267948966]]
number_nodes = [2**10, 2**10]
# The parameter set of the initial wa... | {
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"path": "examples/henon_heiles/henon2_f.py",
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algorithm = "fourier"
propagator = "fourier"
T = 70
dt = 0.005
dimension = 1
ncomponents = 1
# Note: the eps in the paper is our eps**2
eps = 0.1530417681822
potential = "eckart"
sigma = 100 * 3.8008 * 10**(-4.0)
a = 1.0 / (2.0 * 0.52918)
# The grid of our simulation domain
limits = [[-9 * 3.141592653589793, 9 * 3... | {
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"path": "examples/tunneling_eckart/eckart_phi0_f.py",
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algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T = 10
dt = 0.01
dimension = 1
ncomponents = 2
eps = 0.2
delta = eps
potential = "delta_gap"
leading_component = 0
# The parameter set of the initial wavepacket
Q = [[1.0 - 5.0j]]
P = [[ 1.0j]]
q = [[-5.0]]
p = [[ 1.0]]
S = [[0.0]]
... | {
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"path": "examples/single_avoided_crossing/single_crossing_1D_p.py",
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"rat... |
algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T = 12
dt = 0.001
dimension = 1
ncomponents = 1
eps = 0.01
potential = "cosh_osc"
# The parameter set of the initial wavepacket
Q = [[1.0]]
P = [[1.0j]]
q = [[1.0]]
p = [[0.0]]
S = [[0.0]]
wp0 = {
"type": "HagedornWavepacket",
"di... | {
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"path": "examples/cosh_oscillators/cosh_1D_p_nsd.py",
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"ratio": 3.17412935... |
algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T = 12
dt = 0.01
dimension = 1
ncomponents = 1
eps = 0.1
potential = "quadratic"
# The parameter set of the initial wavepacket
Q = [[1.0]]
P = [[1.0j]]
q = [[1.0]]
p = [[0.0]]
S = [[0.0]]
# What it takes to specify a wavepacket!
wp0 = {
... | {
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"path": "examples/harmonic_oscillators/harmonic_1D_p.py",
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"autogenerated": false,
"ratio": 3.0700... |
algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T = 12
dt = 0.01
dimension = 1
ncomponents = 1
eps = 0.24
potential = "quadratic"
sigma = 0.5
# The parameter set of the initial wavepacket
# Parameter values computed by 'ComputeGroundstate.py'
Q = [[1.18920712 + 0.0j]]
P = [[0.0 + 0.8408... | {
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"path": "examples/harmonic_oscillators/harmonic_1D_p_stationary_groundstate.py",
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"autogenerated": f... |
algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T = 12
dt = 0.01
dimension = 2
ncomponents = 1
eps = 1.0
potential = "quadratic_2d"
# The parameter set of the initial wavepacket
# Parameter values computed by 'ComputeGroundstate.py'
Q = [[1.18920712, 0.00000000],
[0.00000000, 1.189... | {
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"path": "examples/harmonic_oscillators/harmonic_2D_p_stationary_groundstate.py",
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"autogenerated": f... |
algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T = 2 * 4.4
dt = 0.0005
dimension = 1
ncomponents = 1
eps = 0.1
potential = {}
potential["variables"] = ["x"]
potential["potential"] = "x**4 - x**2"
# The parameter set of the initial wavepacket
Q = [[1.0]]
P = [[1.0j]]
q = [[1.0]]
p = [[0... | {
"repo_name": "WaveBlocks/WaveBlocksND",
"path": "examples/double_well/double_well_1D_p.py",
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algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T = 70
dt = 0.005
dimension = 1
ncomponents = 1
# Note: the eps in the paper is our eps**2
eps = 0.1530417681822
potential = "eckart"
sigma = 100 * 3.8008 * 10**(-4.0)
a = 1.0 / (2.0 * 0.52918)
# The parameter set of the initial wavepacket... | {
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"path": "examples/tunneling_eckart/eckart_phi2_p.py",
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algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T =
dt =
dimension = 1
ncomponents = 1
eps =
potential =
# The parameter set of the initial wavepacket
Q = [[1.0]]
P = [[1.0j]]
q = [[1.0]]
p = [[0.0]]
S = [[0.0]]
leading_component =
# How often do we write data to disk
write_nth = 1
m... | {
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"path": "examples/templates/template_1D_p.py",
"copies": "1",
"size": "1070",
"license": "bsd-3-clause",
"hash": -467067753189527230,
"line_mean": 18.1071428571,
"line_max": 85,
"alpha_frac": 0.5289719626,
"autogenerated": false,
"ratio": 3.101449275362319... |
algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y4"
T =
dt =
dimension = 2
ncomponents = 1
eps =
potential =
# The parameter set of the initial wavepacket
Q = [[1.0, 0.0],
[0.0, 1.0]]
P = [[1.0j, 0.0 ],
[0.0, 1.0j]]
q = [[-1.0],
[ 0.0]]
p = [[0.0],
[0.0]]
S = [[0.0]... | {
"repo_name": "WaveBlocks/WaveBlocksND",
"path": "examples/templates/template_2D_p.py",
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"ratio": 2.97355769230769... |
algorithm = "hagedorn"
propagator = "semiclassical"
splitting_method = "Y61"
T = 15
dt = 0.01
dimension = 1
ncomponents = 1
eps = 0.1104536
potential = 'morse_zero'
D = 0.0572
a = 0.983
x0 = 5.03855
# The parameter set of the initial wavepacket
# Sigma = PQ^{-1} = 1.3836
Q = [[1.0]]
P = [[1.0j]]
q = [[4.53]]
p = [... | {
"repo_name": "WaveBlocks/WaveBlocksND",
"path": "examples/morse_oscillators/I2_morse.py",
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'Algorithmia Algorithm API Client (python)'
import base64
import json
import re
from Algorithmia.async_response import AsyncResponse
from Algorithmia.algo_response import AlgoResponse
from Algorithmia.errors import ApiError, ApiInternalError, raiseAlgoApiError
from enum import Enum
from algorithmia_api_client.rest imp... | {
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"path": "Algorithmia/algorithm.py",
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"line_max": 144,
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"conf... |
'Algorithmia API Client (python)'
from Algorithmia.client import Client
from Algorithmia.handler import Handler
import sys
import sys
if sys.version_info[0] >= 3:
from adk import ADK
import os
apiKey = None
apiAddress = None
# Get reference to an algorithm using a default client
def algo(algoRef):
# Return ... | {
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'Algorithmia Data API Client (python)'
import json
import re
import os
import six
import tempfile
import Algorithmia
from Algorithmia.datafile import DataFile
from Algorithmia.data import DataObject, DataObjectType
from Algorithmia.errors import DataApiError
from Algorithmia.util import getParentAndBase, pathJoin
fro... | {
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"path": "Algorithmia/datadirectory.py",
"copies": "1",
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"hash": -2204779883851168800,
"line_mean": 31.625,
"line_max": 102,
"alpha_frac": 0.6006996502,
"autogenerated": false,
"ratio": 4.165857043719639,
"config_... |
'Algorithmia Data API Client (python)'
import re
import json
import six
import tempfile
from datetime import datetime
import os.path
import pkgutil
from Algorithmia.util import getParentAndBase
from Algorithmia.data import DataObject, DataObjectType
from Algorithmia.errors import DataApiError, raiseDataApiError
cla... | {
"repo_name": "algorithmiaio/algorithmia-python",
"path": "Algorithmia/datafile.py",
"copies": "1",
"size": "7937",
"license": "mit",
"hash": -8500454176323640000,
"line_mean": 32.4894514768,
"line_max": 112,
"alpha_frac": 0.5911553484,
"autogenerated": false,
"ratio": 4.068170169144029,
"confi... |
'''Algorithmically determine soft selector strings.
.. This software is released under an MIT/X11 open source license.
Copyright 2012-2014 Diffeo, Inc.
Soft selectors
==============
.. autofunction:: find_soft_selectors
.. autofunction:: make_ngram_corpus
'''
from __future__ import absolute_import, division, prin... | {
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"path": "dossier/models/soft_selectors.py",
"copies": "1",
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"line_max": 88,
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"autogenerated": false,
"ratio": 4.035395408163265,
"config_... |
# Algorithm information
import re, sys, numpy, copy, math
from numpy.random import *
from xml.dom import minidom
#### Regular expression for implemented priors or posterior placeholder#############################
re_prior_const=re.compile('constant')
re_prior_uni=re.compile('uniform')
re_prior_normal=re.compile('n... | {
"repo_name": "MichaelPHStumpf/Peitho",
"path": "peitho/errors_and_parsers/ode_parsers/parse_object_info.py",
"copies": "1",
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# algorithm:
# 0. remove from consideration any QC test that fails to produce TPR / FPR >= some tunable threshold
# 1. remove from consideration any bad profile not flagged by any test; put these aside for new qc test design
# 2. accept all individual qc tests with 0% fpr; remove these from consideration, along with al... | {
"repo_name": "BillMills/AutoQC",
"path": "catchall.py",
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"autogenerated": false,
"ratio": 3.3580433686333837,
"config_test": true,
"has_no_ke... |
# Algorithm
# =========
algorithm = "fourier"
# Time stepping
# =============
# Perform a simulation in the time interval [0, T].
T = 3.0
# Duration of a single time step.
dt = 0.02
# Semi-classical parameter
# ========================
# The epsilon parameter in the semiclassical scaling
eps = 0.2
# Potentia... | {
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"path": "doc/manual/examples/parameters_01.py",
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import csv
import path
__author__ = "Dan Rugeles"
__copyright__ = "Copyright 2013, Accelerometrics"
__credits__ = ["Dan Rugeles"]
__license__ = "GPL"
__version__ = "1.0.1"
__maintainer__ = "Dan Rugeles"
__email__ = "danrugeles@gmail.com"
__status__ = "Production"
#1.0.1
"main(): Added Main to easily preprocess sever... | {
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"path": "preprocess.py",
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... |
""" Algorithm predicting a random rating.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from .algo_base import AlgoBase
class NormalPredictor(AlgoBase):
"""Algorithm predicting a random rating based on the distribution of the... | {
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"path": "surprise/prediction_algorithms/random_pred.py",
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"""Algorithms and strategies to play 2048 and collect experience."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import math
import itertools
import numpy as np
from qlearning4k.games.twenty48 import Twenty48
def random_strategy(_, actions... | {
"repo_name": "bhillmann/2048-rl",
"path": "qlearning4k/agents/agents_twenty48.py",
"copies": "1",
"size": "5024",
"license": "mit",
"hash": 1192259145059650000,
"line_mean": 31,
"line_max": 141,
"alpha_frac": 0.5905652866,
"autogenerated": false,
"ratio": 3.7746055597295265,
"config_test": fal... |
""" Algorithms for clustering : Meanshift, Affinity propagation and spectral
clustering.
"""
# Author: Alexandre Gramfort alexandre.gramfort@inria.fr
# Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
import numpy as np
from ..base import BaseEstimator, ClusterMixin
from ..utils import a... | {
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""" Algorithms for computing spanning trees of entity graphs. """
__author__ = 'smartschat'
def precision_system_output(entity, partitioned_entity):
""" Compute a spanning tree from antecedent information.
All edges in the spanning tree correspond to anaphor-antecedent pairs. In
order to access this an... | {
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"path": "cort/analysis/spanning_tree_algorithms.py",
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"co... |
"""Algorithms for computing symbolic roots of polynomials. """
from __future__ import print_function, division
import math
from sympy.core import S, I, pi
from sympy.core.compatibility import ordered, range, reduce
from sympy.core.exprtools import factor_terms
from sympy.core.function import _mexpand
from sympy.core... | {
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"""Algorithms for computing symbolic roots of polynomials. """
from __future__ import print_function, division
import math
from sympy.core.symbol import Dummy, Symbol, symbols
from sympy.core import S, I, pi
from sympy.core.compatibility import ordered
from sympy.core.mul import expand_2arg, Mul
from sympy.core.powe... | {
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"line_mean": 27.969588551,
"line_max": 92,
"alpha_frac": 0.5136779054,
"autogenerated": false,
"ratio": 3.233626198083067,
"config_test": f... |
"""Algorithms for computing symbolic roots of polynomials. """
from sympy.core.symbol import Dummy
from sympy.core.add import Add
from sympy.core.mul import Mul
from sympy.core import S, I, Basic
from sympy.core.sympify import sympify
from sympy.core.numbers import Rational, igcd
from sympy.ntheory import divisors, i... | {
"repo_name": "Cuuuurzel/KiPyCalc",
"path": "sympy_old/polys/polyroots.py",
"copies": "2",
"size": "18242",
"license": "mit",
"hash": -8679868022038474000,
"line_mean": 25.06,
"line_max": 91,
"alpha_frac": 0.5099769762,
"autogenerated": false,
"ratio": 3.3781481481481483,
"config_test": false,
... |
"""Algorithms for computing symbolic roots of polynomials. """
from sympy.core.symbol import Dummy
from sympy.core import S, I
from sympy.core.sympify import sympify
from sympy.core.numbers import Rational, igcd
from sympy.ntheory import divisors, isprime, nextprime
from sympy.functions import exp, sqrt
from sympy.p... | {
"repo_name": "srjoglekar246/sympy",
"path": "sympy/polys/polyroots.py",
"copies": "3",
"size": "18233",
"license": "bsd-3-clause",
"hash": -5527631685905393000,
"line_mean": 24.9729344729,
"line_max": 91,
"alpha_frac": 0.5080897274,
"autogenerated": false,
"ratio": 3.385886722376973,
"config_t... |
"""Algorithms for computing symbolic roots of polynomials. """
from sympy.core.symbol import Dummy, Symbol, symbols
from sympy.core import S, I, pi
from sympy.core.sympify import sympify
from sympy.core.numbers import Rational, igcd
from sympy.ntheory import divisors, isprime, nextprime
from sympy.functions import ex... | {
"repo_name": "amitjamadagni/sympy",
"path": "sympy/polys/polyroots.py",
"copies": "1",
"size": "23045",
"license": "bsd-3-clause",
"hash": -3459258910794162000,
"line_mean": 25.921728972,
"line_max": 91,
"alpha_frac": 0.5182035149,
"autogenerated": false,
"ratio": 3.186091524955067,
"config_te... |
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