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smapy/utils.py | pvk-developer/smapy | 3 | 154718 | # -*- coding: utf-8 -*-
import configparser
import copy
import importlib
import os
import pkgutil
from collections import defaultdict
def find_subclasses(parent_class, recursive=False):
"""Find the subclasses of a given parent class."""
subclasses = parent_class.__subclasses__()
if recursive:
for i in range(0, len(subclasses)):
temp = find_subclasses(subclasses[i])
if temp:
subclasses.extend(temp)
return subclasses
def recursivedict():
return defaultdict(recursivedict)
def read_conf(conf_file):
conf = configparser.ConfigParser(interpolation=None)
conf.optionxform = str # Prevent lowercase keys
if conf_file:
if not os.path.isfile(conf_file):
raise FileNotFoundError("File {} not found".format(conf_file))
conf.read(conf_file)
conf_dict = recursivedict()
for section, params in conf.items():
for key, value in params.items():
conf_dict[section][key] = eval(value)
return conf_dict
def setenv(conf):
for key, value in conf.items():
if key not in os.environ:
os.environ[key] = value
def get_bool(message, key, default=False):
if key not in message:
return default
value = message.get(key)
if not value:
return True
string = str(value).lower()
if string in ['true', 'yes', 'y', '1']:
return True
elif string in ['false', 'no', 'n', '0']:
return False
raise ValueError('Invalid boolean: {}'.format(string))
def sum_dicts(a, b):
"""Sum the values of the two dicts, no matter which type they are.
>>> sum_dicts({}, {})
{}
>>> sum_dicts({'a': 1}, {'a': 2})
{'a': 3}
>>> sum_dicts({'a': [1]}, {'a': [2], 'b': 3})
{'b': 3, 'a': [1, 2]}
>>> sum_dicts({'a': 1, 'b': 2, 'c': [1]}, {'b': 3, 'c': [4], 'd': [5]})
{'c': [1, 4], 'b': 5, 'd': [5], 'a': 1}
"""
merged = dict()
if a is None or b is None:
return a if a else b or {}
for key in set(a) | set(b):
value_a = a.get(key, type(b.get(key))())
value_b = b.get(key, type(a.get(key))())
if isinstance(value_a, dict) and isinstance(value_b, dict):
merged[key] = sum_dicts(value_a, value_b)
else:
merged[key] = value_a + value_b
return merged
def safecopy(obj):
# if isinstance(obj, dict):
# clone = dict()
# for key, value in obj.items():
# clone[key] = safecopy(value)
# return clone
# elif isinstance(obj, bs4.BeautifulSoup):
# return str(obj)
return copy.deepcopy(obj)
def get_ms(delta):
"""Convert a datetime.timedelta into the corresponding milliseconds.
>>> from datetime import timedelta
>>> get_ms(timedelta(1, 1, 1, 1))
86401001.001
>>> get_ms(timedelta(days=1))
86400000.0
>>> get_ms(timedelta(seconds=15))
15000.0
>>> get_ms(timedelta(milliseconds=15, microseconds=222))
15.222
"""
return delta.days * 24 * 60 * 60 * 1000 + delta.seconds * 1000 + delta.microseconds / 1000
def find_submodules(package):
if isinstance(package, str):
package = importlib.import_module(package)
submodules = list()
# Otherwise it is not a package but a module
if hasattr(package, '__path__'):
for _, name, __ in pkgutil.iter_modules(package.__path__):
full_name = package.__name__ + '.' + name
module = importlib.import_module(full_name)
submodules.append(module)
return submodules
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handlers/play.py | Gautampro1834/Telegram_Vc_Bot | 0 | 192982 | <reponame>Gautampro1834/Telegram_Vc_Bot
from os import path
from typing import Dict
from pyrogram import Client
from pyrogram.types import Message, Voice
from typing import Callable, Coroutine, Dict, List, Tuple, Union
from callsmusic import callsmusic, queues
from helpers.admins import get_administrators
from os import path
import requests
import aiohttp
import youtube_dl
from youtube_search import YoutubeSearch
from pyrogram import filters, emoji
from pyrogram.types import InputMediaPhoto
from pyrogram.errors.exceptions.bad_request_400 import ChatAdminRequired
from pyrogram.errors.exceptions.flood_420 import FloodWait
import traceback
import os
import sys
from callsmusic.callsmusic import client as USER
from pyrogram.errors import UserAlreadyParticipant
import converter
from downloaders import youtube
from config import BOT_NAME as bn, DURATION_LIMIT
from helpers.filters import command, other_filters
from helpers.decorators import errors, authorized_users_only
from helpers.errors import DurationLimitError
from helpers.gets import get_url, get_file_name
from pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup
from cache.admins import admins as a
import os
import aiohttp
import aiofiles
import ffmpeg
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
from config import que
from Python_ARQ import ARQ
from pyrogram.errors import UserNotParticipant
import json
import wget
chat_id = None
JOIN_ASAP = "You need to Join @GautamS_Mirror for using me :/"
def cb_admin_check(func: Callable) -> Callable:
async def decorator(client, cb):
admemes = a.get(cb.message.chat.id)
if cb.from_user.id in admemes:
return await func(client, cb)
else:
await cb.answer('You ain\'t allowed!', show_alert=True)
return
return decorator
def transcode(filename):
ffmpeg.input(filename).output("input.raw", format='s16le', acodec='pcm_s16le', ac=2, ar='48k').overwrite_output().run()
os.remove(filename)
# Convert seconds to mm:ss
def convert_seconds(seconds):
seconds = seconds % (24 * 3600)
seconds %= 3600
minutes = seconds // 60
seconds %= 60
return "%02d:%02d" % (minutes, seconds)
# Convert hh:mm:ss to seconds
def time_to_seconds(time):
stringt = str(time)
return sum(int(x) * 60 ** i for i, x in enumerate(reversed(stringt.split(':'))))
# Change image size
def changeImageSize(maxWidth, maxHeight, image):
widthRatio = maxWidth / image.size[0]
heightRatio = maxHeight / image.size[1]
newWidth = int(widthRatio * image.size[0])
newHeight = int(heightRatio * image.size[1])
newImage = image.resize((newWidth, newHeight))
return newImage
async def generate_cover(requested_by, title, views, duration, thumbnail):
async with aiohttp.ClientSession() as session:
async with session.get(thumbnail) as resp:
if resp.status == 200:
f = await aiofiles.open("background.png", mode="wb")
await f.write(await resp.read())
await f.close()
image1 = Image.open("./background.png")
image2 = Image.open("etc/foreground.png")
image3 = changeImageSize(1280, 720, image1)
image4 = changeImageSize(1280, 720, image2)
image5 = image3.convert("RGBA")
image6 = image4.convert("RGBA")
Image.alpha_composite(image5, image6).save("temp.png")
img = Image.open("temp.png")
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("etc/font.otf", 32)
draw.text((205, 550), f"Title: {title}", (51, 215, 255), font=font)
draw.text(
(205, 590), f"Duration: {duration}", (255, 255, 255), font=font
)
draw.text((205, 630), f"Views: {views}", (255, 255, 255), font=font)
draw.text((205, 670),
f"Added By: {requested_by}",
(255, 255, 255),
font=font,
)
img.save("final.png")
os.remove("temp.png")
os.remove("background.png")
@Client.on_message(
filters.command("playlist")
& filters.group
& ~ filters.edited
)
async def playlist(client, message):
try:
await message._client.get_chat_member(int("-1001292906791"), message.from_user.id)
except UserNotParticipant:
await message.reply_text(JOIN_ASAP)
return
global que
queue = que.get(message.chat.id)
if not queue:
await message.reply_text('Player is idle')
temp = []
for t in queue:
temp.append(t)
now_playing = temp[0][0]
by = temp[0][1].mention(style='md')
msg = "**Now Playing** in {}".format(message.chat.title)
msg += "\n"+ now_playing
msg += "\n- Req by "+by
temp.pop(0)
if temp:
msg += '\n\n'
msg += '**Queue**'
for song in temp:
name = song[0]
usr = song[1].mention(style='md')
msg += f'\n- {name}'
msg += f'\n- Req by {usr}\n'
await message.reply_text(msg)
# ============================= Settings =========================================
def updated_stats(chat, queue, vol=100):
if chat.id in callsmusic.pytgcalls.active_calls:
#if chat.id in active_chats:
stats = 'Settings of **{}**'.format(chat.title)
if len(que) > 0:
stats += '\n\n'
stats += 'Volume : {}%\n'.format(vol)
stats += 'Songs in queue : `{}`\n'.format(len(que))
stats += 'Now Playing : **{}**\n'.format(queue[0][0])
stats += 'Requested by : {}'.format(queue[0][1].mention)
else:
stats = None
return stats
def r_ply(type_):
if type_ == 'play':
ico = '▶'
else:
ico = '⏸'
mar = InlineKeyboardMarkup(
[
[
InlineKeyboardButton('⏹', 'leave'),
InlineKeyboardButton('⏸', 'puse'),
InlineKeyboardButton('▶️', 'resume'),
InlineKeyboardButton('⏭', 'skip')
],
[
InlineKeyboardButton('Playlist 📖', 'playlist'),
],
[
InlineKeyboardButton("❌ Close",'cls')
]
]
)
return mar
@Client.on_message(
filters.command("current")
& filters.group
& ~ filters.edited
)
async def ee(client, message):
try:
await message._client.get_chat_member(int("-1001292906791"), message.from_user.id)
except UserNotParticipant:
await message.reply_text(JOIN_ASAP)
return
queue = que.get(message.chat.id)
stats = updated_stats(message.chat, queue)
if stats:
await message.reply(stats)
else:
await message.reply('No VoiceChat instances running in this chat')
@Client.on_message(
filters.command("player")
& filters.group
& ~ filters.edited
)
@authorized_users_only
async def settings(client, message):
try:
await message._client.get_chat_member(int("-1001292906791"), message.from_user.id)
except UserNotParticipant:
await message.reply_text(JOIN_ASAP)
return
playing = None
if message.chat.id in callsmusic.pytgcalls.active_calls:
playing = True
queue = que.get(message.chat.id)
stats = updated_stats(message.chat, queue)
if stats:
if playing:
await message.reply(stats, reply_markup=r_ply('pause'))
else:
await message.reply(stats, reply_markup=r_ply('play'))
else:
await message.reply('No VC instances running in this chat')
@Client.on_callback_query(filters.regex(pattern=r'^(playlist)$'))
@Client.on_callback_query(filters.regex(pattern=r'^(playlist)$'))
async def p_cb(b, cb):
global que
qeue = que.get(cb.message.chat.id)
type_ = cb.matches[0].group(1)
chat_id = cb.message.chat.id
m_chat = cb.message.chat
the_data = cb.message.reply_markup.inline_keyboard[1][0].callback_data
if type_ == 'playlist':
queue = que.get(cb.message.chat.id)
if not queue:
await cb.message.edit('Player is idle')
temp = []
for t in queue:
temp.append(t)
now_playing = temp[0][0]
by = temp[0][1].mention(style='md')
msg = "**Now Playing** in {}".format(cb.message.chat.title)
msg += "\n- "+ now_playing
msg += "\n- Req by "+by
temp.pop(0)
if temp:
msg += '\n\n'
msg += '**Queue**'
for song in temp:
name = song[0]
usr = song[1].mention(style='md')
msg += f'\n- {name}'
msg += f'\n- Req by {usr}\n'
await cb.message.edit(msg)
@Client.on_callback_query(filters.regex(pattern=r'^(play|pause|skip|leave|puse|resume|menu|cls)$'))
@cb_admin_check
async def m_cb(b, cb):
global que
qeue = que.get(cb.message.chat.id)
type_ = cb.matches[0].group(1)
chat_id = cb.message.chat.id
m_chat = cb.message.chat
the_data = cb.message.reply_markup.inline_keyboard[1][0].callback_data
if type_ == 'pause':
if (
chat_id not in callsmusic.pytgcalls.active_calls
) or (
callsmusic.pytgcalls.active_calls[chat_id] == 'paused'
):
await cb.answer('Chat is not connected!', show_alert=True)
else:
callsmusic.pytgcalls.pause_stream(chat_id)
await cb.answer('Music Paused!')
await cb.message.edit(updated_stats(m_chat, qeue), reply_markup=r_ply('play'))
elif type_ == 'play':
if (
chat_id not in callsmusic.pytgcalls.active_calls
) or (
callsmusic.pytgcalls.active_calls[chat_id] == 'playing'
):
await cb.answer('Chat is not connected!', show_alert=True)
else:
callsmusic.pytgcalls.resume_stream(chat_id)
await cb.answer('Music Resumed!')
await cb.message.edit(updated_stats(m_chat, qeue), reply_markup=r_ply('pause'))
elif type_ == 'playlist':
queue = que.get(cb.message.chat.id)
if not queue:
await cb.message.edit('Player is idle')
temp = []
for t in queue:
temp.append(t)
now_playing = temp[0][0]
by = temp[0][1].mention(style='md')
msg = "**Now Playing** in {}".format(cb.message.chat.title)
msg += "\n- "+ now_playing
msg += "\n- Req by "+by
temp.pop(0)
if temp:
msg += '\n\n'
msg += '**Queue**'
for song in temp:
name = song[0]
usr = song[1].mention(style='md')
msg += f'\n- {name}'
msg += f'\n- Req by {usr}\n'
await cb.message.edit(msg)
elif type_ == 'resume':
if (
chat_id not in callsmusic.pytgcalls.active_calls
) or (
callsmusic.pytgcalls.active_calls[chat_id] == 'playing'
):
await cb.answer('Chat is not connected or already playng', show_alert=True)
else:
callsmusic.pytgcalls.resume_stream(chat_id)
await cb.answer('Music Resumed!')
elif type_ == 'puse':
if (
chat_id not in callsmusic.pytgcalls.active_calls
) or (
callsmusic.pytgcalls.active_calls[chat_id] == 'paused'
):
await cb.answer('Chat is not connected or already paused', show_alert=True)
else:
callsmusic.pytgcalls.pause_stream(chat_id)
await cb.answer('Music Paused!')
elif type_ == 'cls':
await cb.answer('Closed menu')
await cb.message.delete()
elif type_ == 'menu':
stats = updated_stats(cb.message.chat, qeue)
await cb.answer('Menu opened')
marr = InlineKeyboardMarkup(
[
[
InlineKeyboardButton('⏹', 'leave'),
InlineKeyboardButton('⏸', 'puse'),
InlineKeyboardButton('▶️', 'resume'),
InlineKeyboardButton('⏭', 'skip')
],
[
InlineKeyboardButton('Playlist 📖', 'playlist'),
],
[
InlineKeyboardButton("❌ Close",'cls')
]
]
)
await cb.message.edit(stats, reply_markup=marr)
elif type_ == 'skip':
if qeue:
skip = qeue.pop(0)
if chat_id not in callsmusic.pytgcalls.active_calls:
await cb.answer('Chat is not connected!', show_alert=True)
else:
callsmusic.queues.task_done(chat_id)
if callsmusic.queues.is_empty(chat_id):
callsmusic.pytgcalls.leave_group_call(chat_id)
await cb.message.edit('- No More Playlist..\n- Leaving VC!')
else:
callsmusic.pytgcalls.change_stream(
chat_id,
callsmusic.queues.get(chat_id)["file"]
)
await cb.answer('Skipped')
await cb.message.edit((m_chat, qeue), reply_markup=r_ply(the_data))
await cb.message.reply_text(f'- Skipped track\n- Now Playing **{qeue[0][0]}**')
else:
if chat_id in callsmusic.pytgcalls.active_calls:
try:
callsmusic.queues.clear(chat_id)
except QueueEmpty:
pass
callsmusic.pytgcalls.leave_group_call(chat_id)
await cb.message.edit('Successfully Left the Chat!')
else:
await cb.answer('Chat is not connected!', show_alert=True)
@Client.on_message(command("play") & other_filters)
async def play(_, message: Message):
try:
await message._client.get_chat_member(int("-1001292906791"), message.from_user.id)
except UserNotParticipant:
await message.reply_text(JOIN_ASAP)
return
global que
lel = await message.reply("🔄 **Processing**")
administrators = await get_administrators(message.chat)
chid = message.chat.id
try:
user = await USER.get_me()
except:
user.first_name = "helper"
usar = user
wew = usar.id
try:
#chatdetails = await USER.get_chat(chid)
lmoa = await _.get_chat_member(chid,wew)
except:
for administrator in administrators:
if administrator == message.from_user.id:
try:
invitelink = await _.export_chat_invite_link(chid)
except:
await lel.edit(
"<b>Add me as admin of yor group first</b>",
)
return
try:
await USER.join_chat(invitelink)
await USER.send_message(message.chat.id,"I joined this group for playing music in VC")
await lel.edit(
"<b>helper userbot joined your chat</b>",
)
except UserAlreadyParticipant:
pass
except Exception as e:
#print(e)
await lel.edit(
f"<b>🔴 Flood Wait Error 🔴 \nUser {user.first_name} couldn't join your group due to heavy requests for userbot! Make sure user is not banned in group."
"\n\nOr manually ask @Gautam180304 to your Group and try again</b>",
)
pass
try:
chatdetails = await USER.get_chat(chid)
#lmoa = await client.get_chat_member(chid,wew)
except:
await lel.edit(
f"<i> {user.first_name} Userbot not in this chat, Ask admin to send /play command for first time or add {user.first_name} manually</i>"
)
return
sender_id = message.from_user.id
sender_name = message.from_user.first_name
await lel.edit("**__Searching Your Song__**")
sender_id = message.from_user.id
user_id = message.from_user.id
sender_name = message.from_user.first_name
user_name = message.from_user.first_name
rpk = "["+user_name+"](tg://user?id="+str(user_id)+")"
query = ''
for i in message.command[1:]:
query += ' ' + str(i)
print(query)
await lel.edit("**__Processing Your Song__**")
ydl_opts = {"format": "bestaudio[ext=m4a]"}
try:
results = YoutubeSearch(query, max_results=1).to_dict()
url = f"https://youtube.com{results[0]['url_suffix']}"
#print(results)
title = results[0]["title"][:40]
thumbnail = results[0]["thumbnails"][0]
thumb_name = f'thumb{title}.jpg'
thumb = requests.get(thumbnail, allow_redirects=True)
open(thumb_name, 'wb').write(thumb.content)
duration = results[0]["duration"]
url_suffix = results[0]["url_suffix"]
views = results[0]["views"]
except Exception as e:
await lel.edit("Song not found.Try another song or maybe spell it properly.")
print(str(e))
return
keyboard = InlineKeyboardMarkup(
[
[
InlineKeyboardButton('📖 Playlist', callback_data='playlist'),
InlineKeyboardButton('Menu ⏯ ', callback_data='menu')
],
[
InlineKeyboardButton(
text="Watch On YouTube 🎬",
url=f"{url}")
],
[
InlineKeyboardButton(
text="❌ Close",
callback_data='cls')
]
]
)
requested_by = message.from_user.first_name
await generate_cover(requested_by, title, views, duration, thumbnail)
file_path = await converter.convert(youtube.download(url))
if message.chat.id in callsmusic.pytgcalls.active_calls:
position = await queues.put(message.chat.id, file=file_path)
qeue = que.get(message.chat.id)
s_name = title
r_by = message.from_user
loc = file_path
appendable = [s_name, r_by, loc]
qeue.append(appendable)
await message.reply_photo(
photo="final.png",
caption=f"#⃣ Your requested song **queued** at position {position}!",
reply_markup=keyboard)
os.remove("final.png")
return await lel.delete()
else:
chat_id = message.chat.id
que[chat_id] = []
qeue = que.get(message.chat.id)
s_name = title
r_by = message.from_user
loc = file_path
appendable = [s_name, r_by, loc]
qeue.append(appendable)
callsmusic.pytgcalls.join_group_call(message.chat.id, file_path)
await message.reply_photo(
photo="final.png",
reply_markup=keyboard,
caption="▶️ **Playing** here the song requested by {} via Gautam'S Music Bot 😜".format(
message.from_user.mention()
),
)
os.remove("final.png")
return await lel.delete()
@Client.on_message(
filters.command("dplay")
& filters.group
& ~ filters.edited
)
async def deezer(client: Client, message_: Message):
try:
await message._client.get_chat_member(int("-1001292906791"), message.from_user.id)
except UserNotParticipant:
await message.reply_text(JOIN_ASAP)
return
global que
lel = await message_.reply("🔄 **Processing**")
administrators = await get_administrators(message_.chat)
chid = message_.chat.id
try:
user = await USER.get_me()
except:
user.first_name = "<NAME>"
usar = user
wew = usar.id
try:
#chatdetails = await USER.get_chat(chid)
lmoa = await client.get_chat_member(chid,wew)
except:
for administrator in administrators:
if administrator == message_.from_user.id:
try:
invitelink = await client.export_chat_invite_link(chid)
except:
await lel.edit(
"<b>Add me as admin of yor group first</b>",
)
return
try:
await USER.join_chat(invitelink)
await USER.send_message(message_.chat.id,"I joined this group for playing music in VC")
await lel.edit(
"<b>helper userbot joined your chat</b>",
)
except UserAlreadyParticipant:
pass
except Exception as e:
#print(e)
await lel.edit(
f"<b>🔴 Flood Wait Error 🔴 \nUser {user.first_name} couldn't join your group due to heavy requests for userbot! Make sure user is not banned in group."
"\n\nOr manually add @Gautam180304 to your Group and try again</b>",
)
pass
try:
chatdetails = await USER.get_chat(chid)
#lmoa = await client.get_chat_member(chid,wew)
except:
await lel.edit(
f"<i> {user.first_name} Userbot not in this chat, Ask admin to send /play command for first time or add {user.first_name} manually</i>"
)
return
requested_by = message_.from_user.first_name
text = message_.text.split(" ", 1)
queryy = text[1]
res = lel
await res.edit(f"Searching 👀👀👀 for `{queryy}` on deezer")
try:
arq = ARQ("https://thearq.tech")
r = await arq.deezer(query=queryy, limit=1)
title = r[0]["title"]
duration = int(r[0]["duration"])
thumbnail = r[0]["thumbnail"]
artist = r[0]["artist"]
url = r[0]["url"]
except:
await res.edit(
"Found Literally Nothing, You Should Work On Your English!"
)
is_playing = False
return
keyboard = InlineKeyboardMarkup(
[
[
InlineKeyboardButton('📖 Playlist', callback_data='playlist'),
InlineKeyboardButton('Menu ⏯ ', callback_data='menu')
],
[
InlineKeyboardButton(
text="Listen On Deezer 🎬",
url=f"{url}")
],
[
InlineKeyboardButton(
text="❌ Close",
callback_data='cls')
]
]
)
file_path= await converter.convert(wget.download(url))
await res.edit("Generating Thumbnail")
await generate_cover(requested_by, title, artist, duration, thumbnail)
if message_.chat.id in callsmusic.pytgcalls.active_calls:
await res.edit("adding in queue")
position = await queues.put(message_.chat.id, file=file_path)
qeue = que.get(message_.chat.id)
s_name = title
r_by = message_.from_user
loc = file_path
appendable = [s_name, r_by, loc]
qeue.append(appendable)
await res.edit_text(f"Music= #️⃣ Queued at position {position}")
else:
await res.edit_text("Music=▶️ Playing.....")
chat_id = message_.chat.id
que[chat_id] = []
qeue = que.get(message_.chat.id)
s_name = title
r_by = message_.from_user
loc = file_path
appendable = [s_name, r_by, loc]
qeue.append(appendable)
callsmusic.pytgcalls.join_group_call(message_.chat.id, file_path)
await res.delete()
m = await client.send_photo(
chat_id=message_.chat.id,
reply_markup=keyboard,
photo="final.png",
caption=f"Playing [{title}]({url}) Via Deezer"
)
os.remove("final.png")
@Client.on_message(
filters.command("splay")
& filters.group
& ~ filters.edited
)
async def jiosaavn(client: Client, message_: Message):
try:
await message._client.get_chat_member(int("-1001292906791"), message.from_user.id)
except UserNotParticipant:
await message.reply_text(JOIN_ASAP)
return
global que
lel = await message_.reply("🔄 **Processing**")
administrators = await get_administrators(message_.chat)
chid = message_.chat.id
try:
user = await USER.get_me()
except:
user.first_name = "Gautam'S Music Bot"
usar = user
wew = usar.id
try:
#chatdetails = await USER.get_chat(chid)
lmoa = await client.get_chat_member(chid,wew)
except:
for administrator in administrators:
if administrator == message_.from_user.id:
try:
invitelink = await client.export_chat_invite_link(chid)
except:
await lel.edit(
"<b>Add me as admin of yor group first</b>",
)
return
try:
await USER.join_chat(invitelink)
await USER.send_message(message_.chat.id,"I joined this group for playing music in VC")
await lel.edit(
"<b>helper userbot joined your chat</b>",
)
except UserAlreadyParticipant:
pass
except Exception as e:
#print(e)
await lel.edit(
f"<b>🔴 Flood Wait Error 🔴 \nUser {user.first_name} couldn't join your group due to heavy requests for userbot! Make sure user is not banned in group."
"\n\nOr manually add @Gautam180304 to your Group and try again</b>",
)
pass
try:
chatdetails = await USER.get_chat(chid)
#lmoa = await client.get_chat_member(chid,wew)
except:
await lel.edit(
"<i> helper Userbot not in this chat, Ask admin to send /play command for first time or add assistant manually</i>"
)
return
requested_by = message_.from_user.first_name
chat_id=message_.chat.id
text = message_.text.split(" ", 1)
query = text[1]
res = lel
await res.edit(f"Searching for `{query}` on jio saavn")
try:
async with aiohttp.ClientSession() as session:
async with session.get(
f"https://jiosaavnapi.bhadoo.uk/result/?query={query}"
) as resp:
r = json.loads(await resp.text())
sname = r[0]["song"]
slink = r[0]["media_url"]
ssingers = r[0]["singers"]
sthumb = r[0]["image"]
sduration = int(r[0]["duration"])
except Exception as e:
await res.edit(
"Found Literally Nothing!, You Should Work On Your English."
)
print(str(e))
is_playing = False
return
keyboard = InlineKeyboardMarkup(
[
[
InlineKeyboardButton('📖 Playlist', callback_data='playlist'),
InlineKeyboardButton('Menu ⏯ ', callback_data='menu')
],
[
InlineKeyboardButton(
text="Join Updates Channel",
url='https://t.me/GautamS_Mirror')
],
[
InlineKeyboardButton(
text="❌ Close",
callback_data='cls')
]
]
)
file_path= await converter.convert(wget.download(slink))
if message_.chat.id in callsmusic.pytgcalls.active_calls:
position = await queues.put(message_.chat.id, file=file_path)
qeue = que.get(message_.chat.id)
s_name = sname
r_by = message_.from_user
loc = file_path
appendable = [s_name, r_by, loc]
qeue.append(appendable)
await res.delete()
m = await client.send_photo(
chat_id=message_.chat.id,
reply_markup=keyboard,
photo="final.png",
caption=f"#️⃣ Queued at position {position}",
)
else:
await res.edit_text("▶️ Playing.....")
chat_id = message_.chat.id
que[chat_id] = []
qeue = que.get(message_.chat.id)
s_name = sname
r_by = message_.from_user
loc = file_path
appendable = [s_name, r_by, loc]
qeue.append(appendable)
callsmusic.pytgcalls.join_group_call(message_.chat.id, file_path)
await res.edit("Generating Thumbnail.")
await generate_cover(requested_by, sname, ssingers, sduration, sthumb)
await res.delete()
m = await client.send_photo(
chat_id=message_.chat.id,
reply_markup=keyboard,
photo="final.png",
caption=f"Playing {sname} Via Jiosaavn",
)
os.remove("final.png")
# Have u read all. If read RESPECT :-)
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] |
codesignal/arcade/python/intro_51_delete_digit.py | tinesife94/random | 0 | 104224 | <reponame>tinesife94/random
def solution(n):
s = str(n)
return max(int('{}{}'.format(s[:i], s[i+1:])) for i in range(len(s)))
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elasticapm/contrib/starlette/__init__.py | lavr/apm-agent-python | 0 | 195435 | # BSD 3-Clause License
#
# Copyright (c) 2012, the Sentry Team, see AUTHORS for more details
# Copyright (c) 2019, Elasticsearch BV
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
from __future__ import absolute_import
import starlette
from starlette.middleware.base import BaseHTTPMiddleware, RequestResponseEndpoint
from starlette.requests import Request
from starlette.responses import Response
from starlette.types import ASGIApp
import elasticapm
import elasticapm.instrumentation.control
from elasticapm.base import Client
from elasticapm.conf import constants
from elasticapm.contrib.asyncio.traces import set_context
from elasticapm.contrib.starlette.utils import get_data_from_request, get_data_from_response
from elasticapm.utils.disttracing import TraceParent
from elasticapm.utils.logging import get_logger
logger = get_logger("elasticapm.errors.client")
def make_apm_client(config: dict, client_cls=Client, **defaults) -> Client:
"""Builds ElasticAPM client.
Args:
config (dict): Dictionary of Client configuration. All keys must be uppercase. See `elasticapm.conf.Config`.
client_cls (Client): Must be Client or its child.
**defaults: Additional parameters for Client. See `elasticapm.base.Client`
Returns:
Client
"""
if "framework_name" not in defaults:
defaults["framework_name"] = "starlette"
defaults["framework_version"] = starlette.__version__
return client_cls(config, **defaults)
class ElasticAPM(BaseHTTPMiddleware):
"""
Starlette / FastAPI middleware for Elastic APM capturing.
>>> elasticapm = make_apm_client({
>>> 'SERVICE_NAME': 'myapp',
>>> 'DEBUG': True,
>>> 'SERVER_URL': 'http://localhost:8200',
>>> 'CAPTURE_HEADERS': True,
>>> 'CAPTURE_BODY': 'all'
>>> })
>>> app.add_middleware(ElasticAPM, client=elasticapm)
Pass an arbitrary APP_NAME and SECRET_TOKEN::
>>> elasticapm = ElasticAPM(app, service_name='myapp', secret_token='<PASSWORD>')
Pass an explicit client::
>>> elasticapm = ElasticAPM(app, client=client)
Automatically configure logging::
>>> elasticapm = ElasticAPM(app, logging=True)
Capture an exception::
>>> try:
>>> 1 / 0
>>> except ZeroDivisionError:
>>> elasticapm.capture_exception()
Capture a message::
>>> elasticapm.capture_message('hello, world!')
"""
def __init__(self, app: ASGIApp, client: Client):
"""
Args:
app (ASGIApp): Starlette app
client (Client): ElasticAPM Client
"""
self.client = client
if self.client.config.instrument:
elasticapm.instrumentation.control.instrument()
super().__init__(app)
async def dispatch(self, request: Request, call_next: RequestResponseEndpoint) -> Response:
"""Processes the whole request APM capturing.
Args:
request (Request)
call_next (RequestResponseEndpoint): Next request process in Starlette.
Returns:
Response
"""
await self._request_started(request)
try:
response = await call_next(request)
except Exception:
await self.capture_exception(
context={"request": await get_data_from_request(request, self.client.config, constants.ERROR)}
)
elasticapm.set_transaction_result("HTTP 5xx", override=False)
elasticapm.set_context({"status_code": 500}, "response")
raise
else:
await self._request_finished(response)
finally:
self.client.end_transaction()
return response
async def capture_exception(self, *args, **kwargs):
"""Captures your exception.
Args:
*args:
**kwargs:
"""
self.client.capture_exception(*args, **kwargs)
async def capture_message(self, *args, **kwargs):
"""Captures your message.
Args:
*args: Whatever
**kwargs: Whatever
"""
self.client.capture_message(*args, **kwargs)
async def _request_started(self, request: Request):
"""Captures the begin of the request processing to APM.
Args:
request (Request)
"""
trace_parent = TraceParent.from_headers(dict(request.headers))
self.client.begin_transaction("request", trace_parent=trace_parent)
await set_context(lambda: get_data_from_request(request, self.client.config, constants.TRANSACTION), "request")
elasticapm.set_transaction_name("{} {}".format(request.method, request.url.path), override=False)
async def _request_finished(self, response: Response):
"""Captures the end of the request processing to APM.
Args:
response (Response)
"""
await set_context(
lambda: get_data_from_response(response, self.client.config, constants.TRANSACTION), "response"
)
result = "HTTP {}xx".format(response.status_code // 100)
elasticapm.set_transaction_result(result, override=False)
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] |
tests/integration/commands/test_transfer.py | real-digital/esque | 29 | 113808 | <gh_stars>10-100
import time
from typing import Tuple
import pytest
from click.testing import CliRunner
from confluent_kafka.avro import AvroConsumer, AvroProducer
from confluent_kafka.cimpl import Consumer
from confluent_kafka.cimpl import Producer as ConfluentProducer
from confluent_kafka.cimpl import TopicPartition
from pytest_cases import fixture
from esque.cli.commands import esque
from esque.config import Config
from tests.utils import (
produce_avro_test_messages,
produce_binary_test_messages,
produce_text_test_messages,
produce_text_test_messages_with_headers,
)
@fixture
def target_topic_consumer(unittest_config: Config, target_topic: Tuple[str, int]) -> Consumer:
consumer = Consumer(
{
"group.id": "asdf",
"enable.auto.commit": False,
"enable.partition.eof": False,
**unittest_config.create_confluent_config(),
}
)
consumer.assign([TopicPartition(topic=target_topic[0], partition=i, offset=0) for i in range(target_topic[1])])
yield consumer
consumer.close()
@fixture
def target_topic_avro_consumer(unittest_config: Config, target_topic: Tuple[str, int]) -> AvroConsumer:
consumer = AvroConsumer(
{
"group.id": "asdf",
"enable.auto.commit": False,
"enable.partition.eof": False,
**unittest_config.create_confluent_config(include_schema_registry=True),
}
)
consumer.assign([TopicPartition(topic=target_topic[0], partition=i, offset=0) for i in range(target_topic[1])])
yield consumer
consumer.close()
@pytest.mark.integration
def test_transfer_plain_text_message_using_cli_pipe(
producer: ConfluentProducer,
target_topic_consumer: Consumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
):
expected_messages = produce_text_test_messages(topic_name=source_topic[0], producer=producer)
result1 = non_interactive_cli_runner.invoke(
esque, args=["consume", "--stdout", "--number", "10", source_topic[0]], catch_exceptions=False
)
non_interactive_cli_runner.invoke(
esque, args=["produce", "--stdin", target_topic[0]], input=result1.output, catch_exceptions=False
)
actual_messages = {
(msg.key().decode(), msg.value().decode(), msg.partition())
for msg in target_topic_consumer.consume(10, timeout=20)
}
expected_messages = {(msg.key, msg.value, msg.partition) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_plain_text_message_with_headers_using_cli_pipe(
producer: ConfluentProducer,
target_topic_consumer: Consumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
):
expected_messages = produce_text_test_messages_with_headers(topic_name=source_topic[0], producer=producer)
result1 = non_interactive_cli_runner.invoke(
esque, args=["consume", "--stdout", "--number", "10", source_topic[0]], catch_exceptions=False
)
non_interactive_cli_runner.invoke(
esque, args=["produce", "--stdin", target_topic[0]], input=result1.output, catch_exceptions=False
)
actual_messages = {
(msg.key().decode(), msg.value().decode(), msg.partition(), tuple(msg.headers() or []))
for msg in target_topic_consumer.consume(10, timeout=20)
}
expected_messages = {(msg.key, msg.value, msg.partition, tuple(msg.headers)) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_binary_message_using_cli_pipe(
producer: ConfluentProducer,
target_topic_consumer: Consumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner,
):
expected_messages = produce_binary_test_messages(topic_name=source_topic[0], producer=producer)
result1 = non_interactive_cli_runner.invoke(
esque, args=["consume", "--stdout", "--binary", "--number", "10", source_topic[0]], catch_exceptions=False
)
non_interactive_cli_runner.invoke(
esque, args=["produce", "--stdin", "--binary", target_topic[0]], input=result1.output, catch_exceptions=False
)
actual_messages = {
(msg.key(), msg.value(), msg.partition()) for msg in target_topic_consumer.consume(10, timeout=20)
}
expected_messages = {(msg.key, msg.value, msg.partition) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_plain_text_message_using_file(
producer: ConfluentProducer,
target_topic_consumer: Consumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
tmpdir_factory,
):
output_directory = tmpdir_factory.mktemp("output_directory")
expected_messages = produce_text_test_messages(topic_name=source_topic[0], producer=producer)
non_interactive_cli_runner.invoke(
esque, args=["consume", "-d", str(output_directory), "--number", "10", source_topic[0]], catch_exceptions=False
)
non_interactive_cli_runner.invoke(
esque, args=["produce", "-d", str(output_directory), target_topic[0]], catch_exceptions=False
)
actual_messages = {
(msg.key().decode(), msg.value().decode(), msg.partition())
for msg in target_topic_consumer.consume(10, timeout=20)
}
expected_messages = {(msg.key, msg.value, msg.partition) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_plain_text_message_with_headers_using_file(
producer: ConfluentProducer,
target_topic_consumer: Consumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
tmpdir_factory,
):
output_directory = tmpdir_factory.mktemp("output_directory")
expected_messages = produce_text_test_messages_with_headers(topic_name=source_topic[0], producer=producer)
non_interactive_cli_runner.invoke(
esque, args=["consume", "-d", str(output_directory), "--number", "10", source_topic[0]], catch_exceptions=False
)
non_interactive_cli_runner.invoke(
esque, args=["produce", "-d", str(output_directory), target_topic[0]], catch_exceptions=False
)
actual_messages = {
(msg.key().decode(), msg.value().decode(), msg.partition(), tuple((msg.headers() or [])))
for msg in target_topic_consumer.consume(10, timeout=20)
}
expected_messages = {(msg.key, msg.value, msg.partition, tuple(msg.headers)) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_binary_message_using_file(
producer: ConfluentProducer,
target_topic_consumer: Consumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
tmpdir_factory,
):
output_directory = tmpdir_factory.mktemp("output_directory")
expected_messages = produce_binary_test_messages(topic_name=source_topic[0], producer=producer)
non_interactive_cli_runner.invoke(
esque,
args=["consume", "-d", str(output_directory), "--binary", "--number", "10", source_topic[0]],
catch_exceptions=False,
)
non_interactive_cli_runner.invoke(
esque, args=["produce", "-d", str(output_directory), "--binary", target_topic[0]], catch_exceptions=False
)
actual_messages = {
(msg.key(), msg.value(), msg.partition()) for msg in target_topic_consumer.consume(10, timeout=20)
}
expected_messages = {(msg.key, msg.value, msg.partition) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_avro_message_using_file(
avro_producer: AvroProducer,
target_topic_avro_consumer: AvroConsumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
tmpdir_factory,
):
output_directory = tmpdir_factory.mktemp("output_directory")
expected_messages = produce_avro_test_messages(topic_name=source_topic[0], avro_producer=avro_producer)
non_interactive_cli_runner.invoke(
esque,
args=["consume", "-d", str(output_directory), "--avro", "--number", "10", source_topic[0]],
catch_exceptions=False,
)
non_interactive_cli_runner.invoke(
esque, args=["produce", "-d", str(output_directory), "--avro", target_topic[0]], catch_exceptions=False
)
actual_messages = set()
start = time.monotonic()
while len(actual_messages) < 10:
msg = target_topic_avro_consumer.poll(timeout=2)
if msg is not None:
actual_messages.add((msg.key()["key"], msg.value()["value"], msg.partition()))
elif time.monotonic() - start >= 20:
raise TimeoutError("Timeout reading data from topic")
expected_messages = {(msg.key["key"], msg.value["value"], msg.partition) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_avro_with_single_command(
avro_producer: AvroProducer,
target_topic_avro_consumer: AvroConsumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
):
expected_messages = produce_avro_test_messages(topic_name=source_topic[0], avro_producer=avro_producer)
non_interactive_cli_runner.invoke(
esque,
args=[
"transfer",
"--from-topic",
source_topic[0],
"--to-topic",
target_topic[0],
"--avro",
"--number",
"10",
"--first",
],
catch_exceptions=False,
)
actual_messages = set()
start = time.monotonic()
while len(actual_messages) < 10:
msg = target_topic_avro_consumer.poll(timeout=2)
if msg is not None:
actual_messages.add((msg.key()["key"], msg.value()["value"], msg.partition()))
elif time.monotonic() - start >= 20:
raise TimeoutError("Timeout reading data from topic")
expected_messages = {(msg.key["key"], msg.value["value"], msg.partition) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_binary_with_single_command(
producer: ConfluentProducer,
target_topic_consumer: Consumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
):
expected_messages = produce_binary_test_messages(topic_name=source_topic[0], producer=producer)
non_interactive_cli_runner.invoke(
esque,
args=[
"transfer",
"--from-topic",
source_topic[0],
"--to-topic",
target_topic[0],
"--binary",
"--number",
"10",
"--first",
],
catch_exceptions=False,
)
actual_messages = {
(msg.key(), msg.value(), msg.partition()) for msg in target_topic_consumer.consume(10, timeout=20)
}
expected_messages = {(msg.key, msg.value, msg.partition) for msg in expected_messages}
assert expected_messages == actual_messages
@pytest.mark.integration
def test_transfer_plain_with_single_command(
producer: ConfluentProducer,
target_topic_consumer: Consumer,
source_topic: Tuple[str, int],
target_topic: Tuple[str, int],
non_interactive_cli_runner: CliRunner,
):
expected_messages = produce_text_test_messages_with_headers(topic_name=source_topic[0], producer=producer)
non_interactive_cli_runner.invoke(
esque,
args=["transfer", "--from-topic", source_topic[0], "--to-topic", target_topic[0], "--number", "10", "--first"],
catch_exceptions=False,
)
actual_messages = {
(msg.key().decode(), msg.value().decode(), msg.partition())
for msg in target_topic_consumer.consume(10, timeout=20)
}
expected_messages = {(msg.key, msg.value, msg.partition) for msg in expected_messages}
assert expected_messages == actual_messages
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13,
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3502,
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5498,
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4925,
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802,
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938,
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] |
World 03/Class 17/ex079.py | DanielRios549/PythonExcercises | 6 | 127765 | <reponame>DanielRios549/PythonExcercises
'''
Get an undefined numbers of values and put them in a list.
In the end, show all the unique values in ascendent order.
'''
values = []
while True:
number = int(input('Choose a number: '))
if number not in values:
print(f'\033[32mAdd the number {number} to the list.\033[m')
values.append(number)
else:
print(f'\033[31mThe number {number} already exists in the list. Not added.\033[m')
again = input('Do you want to continue? [Y/N]').upper()
if again == 'N':
break
values.sort()
print(f'You choose the values {values}')
| [
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] |
sorting/bubbleSort.py | rp581998/data-structures | 0 | 185692 | <reponame>rp581998/data-structures<gh_stars>0
'''def bubbleSort(arr):
n = len(arr)
for i in range(n):
for j in range(0,n-i-1):
if arr[j] > arr[j+1]:
temp = arr[j]
arr[j] = arr[j+1]
arr[j+1] = temp
arr = [10,9,15,1,5,2,6,3]
bubbleSort(arr)
print('Sorted array: ')
for i in range(len(arr)):
print(arr[i])
'''
# OR use this
'''
def bubbleSort(arr):
n = len(arr)
for i in range(n):
for j in range(0,n-i-1):
if arr[j] > arr[j+1]:
swap(arr, j, j+1)
def swap(arr, x, y):
temp = arr[x]
arr[x] = arr[y]
arr[y] = temp
'''
# OR use this,swap from last
def bubbleSort(arr):
n = len(arr)
for i in range(n):
for j in range(n-1,i,-1):
if arr[j] < arr[j-1]:
swap(arr, j, j-1)
def swap(arr, x, y):
temp = arr[x]
arr[x] = arr[y]
arr[y] = temp
arr = [10,9,15,1,5,2,6,3]
bubbleSort(arr)
print('Sorted array: ')
print(arr) | [
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2
] |
hard-gists/1191457/snippet.py | jjhenkel/dockerizeme | 21 | 21081 | #!/usr/bin/env python
import urllib
import sys
import json
from mwlib import parser
from mwlib.refine import compat
if __name__ == "__main__":
params = urllib.urlencode({
"format": "json",
"action": "query",
"prop": "revisions",
"rvprop": "content",
"titles": "ISO_3166-1",
"rvsection": "4",
})
wc = urllib.urlopen("http://en.wikipedia.org/w/api.php?%s" % params)
if wc.getcode() != 200:
print "Fail!"
sys.exit(2)
raw = wc.read()
rdata = json.loads(raw)
wc.close()
page = rdata['query']['pages'].itervalues().next()
if not page:
print "NO page found"
sys.exit(3)
revision = page['revisions'][0]
if not revision:
print "NO revision found"
sys.exit(4)
content = revision[str(revision.keys()[0])]
parsed = compat.parse_txt(content)
table = parsed.find(parser.Table)[0]
if not table:
print "Table not found"
sys.exit(5)
for row in table.children:
cells = row.find(parser.Cell)
print cells[0].asText().replace("}}", "").replace("{{", "").strip() + \
" || " + cells[1].asText().strip() + " || " + cells[2].asText().strip() \
+ " || " + cells[3].asText().strip()
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] |
exercises/fit_gaussian_estimators.py | talazulay27/IML.HUJI | 0 | 170652 | <gh_stars>0
import numpy
from plotly.subplots import make_subplots
from IMLearn.learners import UnivariateGaussian, MultivariateGaussian
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
pio.templates.default = "simple_white"
def test_univariate_gaussian():
# Question 1 - Draw samples and print fitted model
samples = np.random.normal(10, 1, 1000)
univariate_gaussian = UnivariateGaussian()
univariate_gaussian.fit(samples)
print((univariate_gaussian.mu_, univariate_gaussian.var_))
# Question 2 - Empirically showing sample mean is consistent
axis_x = np.linspace(10, 1000, 100)
results = numpy.zeros(100)
counter = 0
for i in range(10, 1001, 10):
results[counter] = np.abs(np.mean(samples[:i]) - 10)
counter = counter + 1
fig = make_subplots(rows=1, cols=1) \
.add_traces([go.Scatter(x=axis_x, y=results, mode='lines', marker=dict(color="black"),
showlegend=False)]) \
.update_layout(title_text=r"$\text{Univariate Gaussian Estimation - Question 2}$",
height=800)
fig.update_xaxes(title_text="Sample size", row=1, col=1)
fig.update_yaxes(title_text="Absolute distance", row=1, col=1)
fig.show()
# Question 3 - Plotting Empirical PDF of fitted model
np.sort(samples)
pdf_results = univariate_gaussian.pdf(samples)
fig = make_subplots(rows=1, cols=1) \
.add_traces([go.Scatter(x=samples, y=pdf_results, mode='markers',
marker=dict(color="red"), showlegend=False)])
fig.update_xaxes(title_text="Sample value", row=1, col=1)
fig.update_yaxes(title_text="PDF Value", row=1, col=1)
fig.update_layout(title_text=r"$\text{Empirical PDF Function- Question 3}$", height=800)
fig.show()
def test_multivariate_gaussian():
# Question 4 - Draw samples and print fitted model
multivariate_gaussian = MultivariateGaussian()
mu = np.array([0, 0, 4, 0])
sigma = np.array([[1, 0.2, 0, 0.5], [0.2, 2, 0, 0], [0, 0, 1, 0], [0.5, 0, 0, 1]])
samples = np.random.multivariate_normal(mu, sigma, 1000)
multivariate_gaussian.fit(samples)
print(multivariate_gaussian.mu_)
print(multivariate_gaussian.cov_)
# Question 5 - Likelihood evaluation
f1 = np.linspace(-10, 10, 200)
f3 = np.linspace(-10, 10, 200)
results = np.zeros([200, 200])
for i in range(0, 200):
for j in range(0, 200):
mu = np.array([f1[i], 0, f3[j], 0])
results[i][j] = multivariate_gaussian.log_likelihood(mu, sigma, samples)
fig = make_subplots(rows=1, cols=1).add_traces(go.Heatmap(x=f1, y=f3, z=results))
fig.update_xaxes(title_text="f1 values", row=1, col=1)
fig.update_yaxes(title_text="f3 values", row=1, col=1)
fig.update_layout(title_text=r"$\text{HeatMap - Question 5}$", height=800)
fig.show()
# Question 6 - Maximum likelihood
indices = np.unravel_index(np.argmax(results, axis=None), results.shape)
print("The model achieved the maximum log-likelihood value is:")
print("f1 = " + str(f1[indices[0]]))
print("f3 = " + str(f1[indices[1]]))
if __name__ == '__main__':
np.random.seed(0)
test_univariate_gaussian()
test_multivariate_gaussian()
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tests/artificial/test_memory.py | anrddh/memsight | 1 | 1614889 | import angr
import claripy
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
from memory import factory
def check(state, obj, exp_values, conditions=()):
r = state.se.eval_upto(obj, len(exp_values) + 1, extra_constraints=conditions)
if len(r) != len(exp_values) or set(r) != set(exp_values):
print("Mismatch:")
print("\tobtained: " + str(r))
print("\texpected: " + str(exp_values))
assert len(r) == len(exp_values) and set(r) == set(exp_values)
def test_store_with_symbolic_size(state):
val = 0x01020304
state.memory.store(0x0, claripy.BVV(val, 32))
res = state.memory.load(0x0, 4)
assert not state.se.symbolic(res) and state.se.eval(res) == val
val_2 = 0x0506
s_size = claripy.BVS('size', 32)
state.se.add(s_size <= 2)
state.memory.store(0x1, claripy.BVV(val_2, 16), s_size)
res = state.se.eval_upto(state.memory.load(0x0, 4), 10)
# print ' '.join([hex(x) for x in res])
assert len(state.se.eval_upto(state.memory.load(0x0, 4), 10)) == 3
s0 = state.copy()
assert len(s0.se.eval_upto(s0.memory.load(0x0, 4), 10)) == 3
s1 = state.copy()
s1.se.add(s_size == 0)
res = s1.se.eval_upto(s1.memory.load(0x0, 4), 2)
assert len(res) == 1 and res[0] == val
s2 = state.copy()
s2.se.add(s_size == 1)
res = s2.se.eval_upto(s2.memory.load(0x0, 4), 2)
assert len(res) == 1 and res[0] == 0x01050304
s3 = state.copy()
s3.se.add(s_size == 2)
res = s3.se.eval_upto(s3.memory.load(0x0, 4), 2)
assert len(res) == 1 and res[0] == 0x01050604
def test_store_with_symbolic_addr_and_symbolic_size(state):
#state.memory.set_verbose(True)
val = 0x01020304
addr = claripy.BVS('addr', 64)
state.se.add(addr < 8)
state.memory.store(addr, claripy.BVV(val, 32), 4)
res = state.memory.load(addr, 4)
res = state.se.eval_upto(res, 20)
assert len(res) == 1 and res[0] == val
val_2 = 0x0506
s_size = claripy.BVS('size', 32)
state.se.add(s_size <= 2)
state.memory.store(addr + 1, claripy.BVV(val_2, 16), s_size)
s0 = state.copy()
assert len(s0.se.eval_upto(s0.memory.load(addr, 4), 10)) == 3
s1 = state.copy()
s1.se.add(s_size == 0)
res = s1.se.eval_upto(s1.memory.load(addr, 4), 2)
assert len(res) == 1 and res[0] == val
s2 = state.copy()
s2.se.add(s_size == 1)
res = s2.se.eval_upto(s2.memory.load(addr, 4), 2)
assert len(res) == 1 and res[0] == 0x01050304
s3 = state.copy()
s3.se.add(s_size == 2)
res = s3.se.eval_upto(s3.memory.load(addr, 4), 2)
assert len(res) == 1 and res[0] == 0x01050604
def test_concrete_merge(state):
val = 0x01020304
state.memory.store(0x0, claripy.BVV(val, 32))
s1 = state.copy()
s2 = state.copy()
s1.memory.store(0x1, claripy.BVV(0x05, 8))
s2.memory.store(0x1, claripy.BVV(0x06, 8))
s3 = s1.copy()
guard = claripy.BVS('branch', 32)
s3.memory.merge([s2.memory], [guard > 0, guard <= 0], state.memory)
res = s3.memory.load(0x0, 4)
r1 = s3.se.eval_upto(res, 2, extra_constraints=(guard > 0,))
assert len(r1) == 1 and r1[0] == 0x01050304
r2 = s3.se.eval_upto(res, 2, extra_constraints=(guard <= 0,))
assert len(r2) == 1 and r2[0] == 0x01060304
def test_concrete_merge_with_condition(state):
val = 0x01020304
state.memory.store(0x0, claripy.BVV(val, 32))
s1 = state.copy()
s2 = state.copy()
s1.memory.store(0x1, claripy.BVV(0x05, 8))
cond = claripy.BVS('cond', 32)
s2.memory.store(0x1, claripy.BVV(0x06, 8), condition=cond != 0)
s3 = s1.copy()
guard = claripy.BVS('guard', 32)
s3.memory.merge([s2.memory], [guard > 1, guard <= 1], s1.memory)
res = s3.memory.load(0x0, 4)
r1 = s3.se.eval_upto(res, 2, extra_constraints=(guard > 1,))
assert len(r1) == 1 and r1[0] == 0x01050304
r2 = s3.se.eval_upto(res, 3, extra_constraints=(guard <= 1,))
assert len(r2) == 2 and set(r2) == set([0x01020304, 0x01060304])
s4 = s3.copy()
s4.se.add(guard == 1)
s4.se.add(cond != 0)
res = s4.memory.load(0x0, 4)
r3 = s4.se.eval_upto(res, 2)
assert len(r3) == 1 and r3[0] == 0x01060304
def test_symbolic_merge(state):
val = 0x01020304
state.memory.store(0x0, claripy.BVV(val, 32))
a = claripy.BVS('a0', 64)
state.se.add(a <= 1)
state.memory.store(a, claripy.BVV(0x5, 8))
res = state.memory.load(0x0, 1)
check(state, res, [5, 1])
s1 = state.copy()
s1.memory.store(0x1, claripy.BVV(0x6, 8))
a1 = claripy.BVS('a1', 64)
s1.se.add(a1 >= 1)
s1.se.add(a1 <= 2)
s1.memory.store(a1, claripy.BVV(0x7, 8))
s2 = state.copy()
s2.memory.store(0x1, claripy.BVV(0x8, 8))
a2 = claripy.BVS('a2', 64)
s2.se.add(a2 >= 1)
s2.se.add(a2 <= 2)
s2.memory.store(a2, claripy.BVV(0x9, 8))
s3 = s1.copy()
guard = claripy.BVS('guard', 32)
s3.memory.merge([s2.memory], [guard > 1, guard <= 1], s1.memory)
res = s3.memory.load(0x0, 1)
check(s3, res, [5], (a == 0,))
check(s3, res, [1], (a == 1,))
res = s3.memory.load(0x1, 1)
check(s3, res, [7], (guard > 1, a1 == 1, ))
check(s3, res, [6], (guard > 1, a1 == 2,))
check(s3, res, [9], (guard <= 1, a2 == 1,))
check(s3, res, [8], (guard <= 1, a2 == 2,))
res = s3.memory.load(0x2, 1)
check(s3, res, [7], (guard > 1, a1 == 2,))
check(s3, res, [3], (guard > 1, a1 == 1,))
check(s3, res, [9], (guard <= 1, a2 == 2,))
check(s3, res, [3], (guard <= 1, a2 == 1,))
res = s3.memory.load(0x3, 1)
check(s3, res, [4], set())
def test_symbolic_access(state):
# an address which is in a valid region
start_addr = state.heap.allocate(32)
#assert state.se.any_int(state.memory.permissions(start_addr)) == 0x3
#assert state.se.any_int(state.memory.permissions(start_addr + 1)) == 0x3
#assert state.se.any_int(state.memory.permissions(start_addr + 2)) == 0x3
# init memory 3 bytes starting at start_addr
state.memory.store(start_addr, claripy.BVV(0x0, 24), 3)
# a symbolic pointer that can be equal to [start_addr, start_addr + 1]
addr = claripy.BVS('addr', 64)
state.se.add(addr >= start_addr)
state.se.add(addr <= start_addr + 1)
addrs = state.se.eval_upto(addr, 10)
assert len(addrs) == 2 and set(addrs) == set([start_addr, start_addr + 1])
val = 0xABCD
# symbolic store at addr
state.memory.store(addr, claripy.BVV(val, 16), 2)
# symbolic load at addr
res = state.memory.load(addr, 2)
res = state.se.eval_upto(res, 20)
assert len(res) == 1 and res[0] == val
def test_same_operator(state):
a = claripy.BVS('a', 8)
b = claripy.BVS('b', 8)
assert not state.memory.same(a, b)
state.se.add(a == b)
assert state.memory.same(a, b)
state.se.add(a < 5)
assert state.memory.same(a, b)
zero = claripy.BVV(0x0, 8)
assert not state.memory.same(a, zero)
state.se.add(a < 1)
assert state.memory.same(a, zero)
if __name__ == '__main__':
t = 0
angr_project = angr.Project("/bin/ls", load_options={'auto_load_libs': False})
if t == 0:
mem_memory, reg_memory = None, None
elif t == 1:
mem_memory, reg_memory = factory.get_range_fully_symbolic_memory(angr_project)
#mem_memory.set_verbose(True)
plugins = {}
if mem_memory is not None:
plugins['memory'] = mem_memory
if reg_memory is not None:
plugins['registers'] = reg_memory
add_options = {None}
#add_options = {simuvex.o.STRICT_PAGE_ACCESS}
# add_options = {simuvex.o.CGC_ZERO_FILL_UNCONSTRAINED_MEMORY}
state = angr_project.factory.entry_state(remove_options={angr.options.LAZY_SOLVES},
add_options=add_options, plugins=plugins)
if t == 0:
# store: add new concretization strategy
state.memory.write_strategies.insert(0, angr.concretization_strategies.SimConcretizationStrategyRange(2048))
state.memory.read_strategies.insert(0, angr.concretization_strategies.SimConcretizationStrategyRange(2048))
pass
test_symbolic_access(state.copy())
test_store_with_symbolic_size(state.copy())
test_store_with_symbolic_addr_and_symbolic_size(state.copy())
test_concrete_merge(state.copy())
test_concrete_merge_with_condition(state.copy())
test_symbolic_merge(state.copy())
if t == 1:
test_same_operator(state.copy())
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config/looper.py | panoramichq/data-collection-fb | 0 | 129464 | <gh_stars>0
# flake8: noqa: E722
RUN_SWEEP_TIMEOUT = 60 * 60 # seconds
RUN_TASKS_TIMEOUT = 30 * 60 # 30 minutes.
FB_THROTTLING_WINDOW = 20 * 60 # 20 minutes.
OOZER_ENABLE_LEARNING = True
OOZER_LEARNING_RATE = 0.05
OOZER_REVIEW_INTERVAL = 10
OOZER_START_RATE = 100.0
OOZER_MIN_RATE = 10.0
# Hour, DMA, AgeGender * about 2 years back + 3 levels of lifetime + 3 levels of Entities
_number_of_long_tasks_per_aa = 3 * 600 + 3 + 3
# there is also a way to get to this real number by scanning FacebookAdAccount table,
# but, we'd like to dream.
# This does not really limit anything, just defines some optimistic upper range for
# "normal" when it comes estimating "total possible" number of tasks to run per sweep.
MAX_EXPECTED_AD_ACCOUNTS = 500 # yeah, optimistic
# Real number of tasks can be much greater, as we are not counting
# per-entity report type permutations. There there can be millions per single AdAccount
# for that reason:
_looney_balooney_multiplier = 10 # yeah, this is NOT millions, but something, right
# When we relax prioritizer logic to schedule per-entity tasks along with per-parent
# tasks, you need to crank this ^ up quite a bit, to 10,000 at least.
# Used to define "max normal" estimatable tasks when it comes to Early Exit logic in looper
SANE_MAX_TASKS = _number_of_long_tasks_per_aa * MAX_EXPECTED_AD_ACCOUNTS * _looney_balooney_multiplier
from common.updatefromenv import update_from_env
update_from_env(__name__)
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] |
spira/gdsii/cell.py | cloudcalvin/spira | 0 | 153881 | <filename>spira/gdsii/cell.py
import gdspy
import numpy as np
from copy import copy, deepcopy
from spira import param
from spira.core.lists import ElementList
from spira.lne import *
from spira.gdsii import *
from spira.core.initializer import CellInitializer
from spira.core.mixin.property import CellMixin
from spira.core.mixin.gdsii_output import OutputMixin
from spira.gdsii.elemental.port import __Port__
from spira.core.mixin.transform import TranformationMixin
class __Cell__(gdspy.Cell, CellInitializer):
__mixins__ = [OutputMixin, CellMixin, TranformationMixin]
def __init__(self, name=None, elementals=None, ports=None, library=None, **kwargs):
CellInitializer.__init__(self, **kwargs)
gdspy.Cell.__init__(self, name, exclude_from_current=True)
if name is not None:
self.__dict__['__name__'] = name
Cell.name.__set__(self, name)
if library is not None:
self.library = library
if elementals is not None:
self.elementals = elementals
if ports is not None:
self.ports = ports
# self.move(midpoint=self.center, destination=(0,0))
# self.center = (0,0)
def __add__(self, other):
if other is None:
return self
if issubclass(type(other), __Port__):
self.ports += other
else:
self.elementals += other
return self
# if issubclass(type(other), Cell):
# print(other)
# self.elementals += SRef(other)
# # for e in other.elementals:
# # self.elementals += e
# # for p in other.ports:
# # self.ports += p
def __sub__(self, other):
pass
class CellAbstract(__Cell__):
name = param.StringField()
ports = param.ElementListField(fdef_name='create_ports')
elementals = param.ElementListField(fdef_name='create_elementals')
def create_elementals(self, elems):
result = ElementList()
return result
def create_ports(self, ports):
return ports
def flatten(self):
self.elementals = self.elementals.flatten()
return self.elementals
def flat_copy(self, level=-1, commit_to_gdspy=False):
self.elementals = self.elementals.flat_copy(level, commit_to_gdspy)
return self.elementals
def dependencies(self):
deps = self.elementals.dependencies()
deps += self
return deps
def commit_to_gdspy(self):
cell = gdspy.Cell(self.name, exclude_from_current=True)
for e in self.elementals:
if issubclass(type(e), Cell):
for elem in e.elementals:
elem.commit_to_gdspy(cell=cell)
for port in e.ports:
port.commit_to_gdspy(cell=cell)
elif not isinstance(e, (SRef, ElementList, Graph, Mesh)):
e.commit_to_gdspy(cell=cell)
return cell
def move(self, midpoint=(0,0), destination=None, axis=None):
"""
Moves elements of the Device from the midpoint point to
the destination. Both midpoint and destination can be 1x2
array-like, Port, or a key corresponding to
one of the Ports in this device
"""
if destination is None:
destination = midpoint
midpoint = [0,0]
if issubclass(type(midpoint), __Port__):
o = midpoint.midpoint
elif np.array(midpoint).size == 2:
o = midpoint
elif midpoint in self.ports:
o = self.ports[midpoint].midpoint
else:
raise ValueError('[DeviceReference.move()] ``midpoint`` ' + \
'not array-like, a port, or port name')
if issubclass(type(destination), __Port__):
d = destination.midpoint
elif np.array(destination).size == 2:
d = destination
elif destination in self.ports:
d = self.ports[destination].midpoint
else:
raise ValueError('[DeviceReference.move()] ``destination`` ' + \
'not array-like, a port, or port name')
if axis == 'x':
d = (d[0], o[1])
if axis == 'y':
d = (o[0], d[1])
dx, dy = np.array(d) - o
for e in self.elementals:
if issubclass(type(e), (LabelAbstract, PolygonAbstract)):
e.translate(dx, dy)
if isinstance(e, (Cell, SRef)):
e.move(destination=d, midpoint=o)
for p in self.ports:
mc = np.array(p.midpoint) + np.array(d) - np.array(o)
p.move(midpoint=p.midpoint, destination=mc)
return self
def reflect(self, p1=(0,1), p2=(0,0)):
""" Reflects the cell around the line [p1, p2]. """
for e in self.elementals:
if not issubclass(type(e), (LabelAbstract, __Port__)):
e.reflect(p1, p2)
for p in self.ports:
p.midpoint = self.__reflect__(p.midpoint, p1, p2)
phi = np.arctan2(p2[1]-p1[1], p2[0]-p1[0])*180 / np.pi
p.orientation = 2*phi - p.orientation
return self
def rotate(self, angle=45, center=(0,0)):
""" Rotates the cell with angle around a center. """
if angle == 0:
return self
for e in self.elementals:
if issubclass(type(e), PolygonAbstract):
e.rotate(angle=angle, center=center)
elif isinstance(e, SRef):
e.rotate(angle, center)
ports = self.ports
self.ports = ElementList()
for p in ports:
if issubclass(type(p), __Port__):
p.midpoint = self.__rotate__(p.midpoint, angle, center)
p.orientation = np.mod(p.orientation + angle, 360)
self.ports += p
return self
def get_ports(self, level=None):
""" Returns copies of all the ports of the Device """
port_list = [p._copy() for p in self.ports]
if level is None or level > 0:
for r in self.elementals.sref:
if level is None:
new_level = None
else:
new_level = level - 1
ref_ports = r.ref.get_ports(level=new_level)
tf = {
'midpoint': r.midpoint,
'rotation': r.rotation,
'magnification': r.magnification,
'reflection': r.reflection
}
ref_ports_transformed = []
for rp in ref_ports:
new_port = rp._copy()
new_port = new_port.transform(tf)
ref_ports_transformed.append(new_port)
port_list += ref_ports_transformed
return port_list
class Cell(CellAbstract):
""" A Cell encapsulates a set of elementals that
describes the layout being generated. """
def __repr__(self):
if hasattr(self, 'elementals'):
elems = self.elementals
return ("[SPiRA: Cell(\'{}\')] " +
"({} elementals: {} sref, {} polygons, " +
"{} labels, {} ports)").format(
self.name,
elems.__len__(),
elems.sref.__len__(),
elems.polygons.__len__(),
elems.labels.__len__(),
self.ports.__len__()
)
else:
return "[SPiRA: Cell(\'{}\')]".format(self.__class__.__name__)
def __str__(self):
return self.__repr__()
@property
def id(self):
return self.__str__()
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] |
spockpy/config/app.py | gavindsouza/spockpy | 58 | 1601267 | # imports - compatibility imports
from __future__ import absolute_import
# imports - module imports
from spockpy.config import Config
from spockpy._util import _round_int
def _get_video_panel_size(length):
size = _round_int(length * 0.25)
return (size, size)
class AppConfig(Config):
WINDOW_WIDTH = 1024
WINDOW_HEIGHT = 768
WINDOW_SIZE = (WINDOW_WIDTH, WINDOW_HEIGHT)
VIDEO_PANEL_SIZE = _get_video_panel_size(WINDOW_WIDTH) | [
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] |
tests/test_cell.py | nat-chan/nokogiri | 0 | 1606812 | <gh_stars>0
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import unittest
from nokogiri.cell import cell
class test_cell(unittest.TestCase):
def test_スクリプトとしてとして実行したときはラムダ式を返す(self):
self.assertEqual("<lambda>", cell(globals()).__name__)
if __name__ == '__main__':
unittest.main()
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mamonsu/lib/zbx_template.py | dan-aksenov/mamonsu | 0 | 30304 | <gh_stars>0
# -*- coding: utf-8 -*-
from mamonsu.lib.const import Template
class ZbxTemplate(object):
mainTemplate = u"""<?xml version="1.0" encoding="UTF-8"?>
<zabbix_export>
<version>2.0</version>
<groups>
<group>
<name>Templates</name>
</group>
</groups>
<templates>
<template>
<template>{template}</template>
<name>{template}</name>
<groups>
<group>
<name>Templates</name>
</group>
</groups>
<applications>
<application>
<name>{application}</name>
</application>
</applications>
<items>{items}</items>
<discovery_rules>{discovery_rules}</discovery_rules>
<macros>{macros}</macros>
</template>
</templates>
<triggers>{triggers}</triggers>
<graphs>{graphs}</graphs>
</zabbix_export>"""
# https://www.zabbix.com/documentation/2.0/manual/appendix/api/item/definitions
item_defaults = [
('name', None), ('type', 2), ('snmp_community', None),
('multiplier', 0), ('inventory_link', 0),
('key', None), ('snmp_oid', None), ('history', 7),
('trends', 365), ('status', 0), ('delay', 60),
('value_type', Template.VALUE_TYPE.numeric_float),
('allowed_hosts', None), ('valuemap', None),
('units', Template.UNITS.none), ('delta', Template.DELTA.as_is),
('snmpv3_contextname', None), ('snmpv3_securityname', None),
('snmpv3_securitylevel', 0), ('snmpv3_authprotocol', 0),
('snmpv3_authpassphrase', None), ('snmpv3_privprotocol', 0),
('snmpv3_privpassphrase', None), ('formula', 1),
('delay_flex', None), ('params', None),
('ipmi_sensor', None), ('data_type', 0), ('authtype', 0),
('username', None), ('password', None), ('publickey', None),
('privatekey', None), ('port', None), ('description', None)
]
trigger_defaults = [
('expression', None), ('name', None), ('url', None),
('status', 0), ('priority', 3), ('description', None),
('type', 0), ('dependencies', None)
]
trigger_discovery_defaults = [
('expression', None), ('name', None), ('url', None),
('status', 0), ('priority', 3), ('description', None),
('type', 0)
]
graph_values_defaults = [
('name', None), ('width', 900), ('height', 200),
('yaxismin', 0.0000), ('yaxismax', 100.0), ('show_work_period', 1),
('show_triggers', 1), ('type', 0), ('show_legend', 1),
('show_3d', 0), ('percent_left', 0.0), ('percent_right', 0.0),
('ymin_type_1', 0), ('ymax_type_1', 0), ('ymin_item_1', 0),
('ymax_item_1', 0)
]
graph_items_defaults = [
('sortorder', None), ('drawtype', 0),
('color', '00CC00'), ('yaxisside', 0),
('calc_fnc', 2), ('type', Template.GRAPH_TYPE.normal)
]
discovery_defaults = [
('name', None), ('type', 2), ('snmp_community', None),
('snmp_oid', None), ('delay', 60), ('status', 0),
('allowed_hosts', None), ('snmpv3_contextname', None),
('snmpv3_securityname', None), ('snmpv3_securitylevel', 0),
('snmpv3_authprotocol', 0), ('snmpv3_authpassphrase', None),
('snmpv3_privprotocol', 0), ('snmpv3_privpassphrase', None),
('delay_flex', None), ('params', None), ('filter', None),
('ipmi_sensor', None), ('authtype', 0),
('username', None), ('password', None), ('publickey', None),
('privatekey', None), ('port', None), ('lifetime', 7),
('description', None), ('key', None)
]
def __init__(self, name, app):
self.Application = app
self.Template = name
def xml(self, plugins=[]):
# sort plugins!
plugins.sort(key=lambda x: x.__class__.__name__)
# create template
template_data = {}
template_data['template'] = self.Template
template_data['application'] = self.Application
template_data['items'] = self._get_all('items', plugins)
template_data['macros'] = ''
template_data['triggers'] = self._get_all('triggers', plugins)
template_data['graphs'] = self._get_all('graphs', plugins)
template_data['discovery_rules'] = self._get_all(
'discovery_rules', plugins)
return self.mainTemplate.format(**template_data)
def _get_all(self, items='items', plugins=[]):
result = ''
for plugin in plugins:
row = getattr(plugin, items)(self)
if row is None:
continue
result += row
return result
def item(self, args={}, xml_key='item'):
return '<{2}>{0}{1}</{2}>'.format(
self._format_args(self.item_defaults, args),
self._application(),
xml_key)
def trigger(self, args={}, xml_key='trigger', defaults=None):
if defaults is None:
defaults = self.trigger_defaults
try:
expression = args['expression']
except KeyError:
raise LookupError(
'Miss expression in trigger: {0}.'.format(args))
args['expression'] = expression.replace('#TEMPLATE', self.Template)
return '<{1}>{0}</{1}>'.format(
self._format_args(defaults, args),
xml_key)
def graph(self, args={}, xml_key='graph'):
try:
items = args['items']
except KeyError:
raise LookupError(
'Miss item in graph: {0}.'.format(args))
graph_items = ''
for idx, item in enumerate(items):
try:
key = item['key']
except KeyError:
raise LookupError(
'Missed key in graph item: {0}.'.format(item))
if 'sortorder' not in item:
item['sortorder'] = idx
row = '<graph_item>{0}<item><host>{1}'
row += '</host><key>{2}</key></item></graph_item>'
graph_items += row.format(
self._format_args(self.graph_items_defaults, item),
self.Template, key)
result = '<{2}>{0}<graph_items>{1}</graph_items></{2}>'
return result.format(
self._format_args(self.graph_values_defaults, args),
graph_items, xml_key)
def discovery_rule(self, rule={}, items=[], triggers=[], graphs=[]):
result_items = '<item_prototypes>'
for item in items:
result_items += self.item(item, xml_key='item_prototype')
result_items += '</item_prototypes>'
result_triggers = '<trigger_prototypes>'
for trigger in triggers:
result_triggers += self.trigger(
trigger, xml_key='trigger_prototype',
defaults=self.trigger_discovery_defaults)
result_triggers += '</trigger_prototypes>'
result_graphs = '<graph_prototypes>'
for graph in graphs:
result_graphs += self.graph(
graph, xml_key='graph_prototype')
result_graphs += '</graph_prototypes>'
result = '<discovery_rule>{0}{1}{2}{3}</discovery_rule>'
return result.format(
self._format_args(self.discovery_defaults, rule),
result_items, result_triggers, result_graphs)
def _application(self):
result = '<applications><application><name>{0}'
result += '</name></application></applications>'
return result.format(self.Application)
def _format_args(self, defaults, override):
result = ''
for pair in defaults:
key = pair[0]
try:
val = override[key]
except KeyError:
val = pair[1]
if val is None:
row = '<{0}/>'.format(key)
else:
row = '<{0}>{1}</{0}>'.format(key, val)
result += row
return result
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] |
tests/hadoop-etl/test/compression_types.py | mace84/script-languages | 6 | 103490 | <filename>tests/hadoop-etl/test/compression_types.py
#!/usr/bin/env python2.7
# encoding: utf8
import os
import sys
sys.path.append(os.path.realpath(__file__ + '/../../../lib'))
sys.path.append(os.path.realpath(__file__ + '/../../lib'))
import udf
import utils
import datagen
import hadoopenv
class TestDeflate(utils.HiveTestCase):
hive_file_format = 'textfile'
hive_table = '{file_format}_%s'.format(file_format = hive_file_format)
hive_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'string', 'char(20)', 'varchar(50)', 'boolean', 'binary', \
'array<timestamp>', 'map<int, string>', 'struct<s1:int, s2:smallint, s3:date>', \
'uniontype<bigint, timestamp, boolean, varchar(100)>']
exa_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'varchar(5000)', 'char(20)', 'varchar(50)', 'boolean', 'varchar(5000) ASCII', \
'varchar(5000)', 'varchar(5000)', 'varchar(5000)', \
'varchar(5000)']
hive_config_props = ['hive.exec.compress.output=true', \
'mapred.output.compression.codec=org.apache.hadoop.io.compress.DefaultCodec']
num_rows = 1000
has_id_col = True
def test(self):
utils.test_import(self)
utils.validate_import_odbc(self)
class TestGzip(utils.HiveTestCase):
hive_file_format = 'textfile'
hive_table = '{file_format}_%s'.format(file_format = hive_file_format)
hive_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'string', 'char(20)', 'varchar(50)', 'boolean', 'binary', \
'array<timestamp>', 'map<int, string>', 'struct<s1:int, s2:smallint, s3:date>', \
'uniontype<bigint, timestamp, boolean, varchar(100)>']
exa_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'varchar(5000)', 'char(20)', 'varchar(50)', 'boolean', 'varchar(5000) ASCII', \
'varchar(5000)', 'varchar(5000)', 'varchar(5000)', \
'varchar(5000)']
hive_config_props = ['hive.exec.compress.output=true', \
'mapred.output.compression.codec=org.apache.hadoop.io.compress.GzipCodec']
num_rows = 1000
has_id_col = True
def test(self):
utils.test_import(self)
utils.validate_import_odbc(self)
class TestBzip2(utils.HiveTestCase):
hive_file_format = 'textfile'
hive_table = '{file_format}_%s'.format(file_format = hive_file_format)
hive_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'string', 'char(20)', 'varchar(50)', 'boolean', 'binary', \
'array<timestamp>', 'map<int, string>', 'struct<s1:int, s2:smallint, s3:date>', \
'uniontype<bigint, timestamp, boolean, varchar(100)>']
exa_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'varchar(5000)', 'char(20)', 'varchar(50)', 'boolean', 'varchar(5000) ASCII', \
'varchar(5000)', 'varchar(5000)', 'varchar(5000)', \
'varchar(5000)']
hive_config_props = ['hive.exec.compress.output=true', \
'mapred.output.compression.codec=org.apache.hadoop.io.compress.BZip2Codec']
num_rows = 1000
has_id_col = True
def test(self):
utils.test_import(self)
utils.validate_import_odbc(self)
class TestLzo(utils.HiveTestCase):
hive_file_format = 'textfile'
hive_table = '{file_format}_%s'.format(file_format = hive_file_format)
hive_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'string', 'char(20)', 'varchar(50)', 'boolean', 'binary', \
'array<timestamp>', 'map<int, string>', 'struct<s1:int, s2:smallint, s3:date>', \
'uniontype<bigint, timestamp, boolean, varchar(100)>']
exa_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'varchar(5000)', 'char(20)', 'varchar(50)', 'boolean', 'varchar(5000) ASCII', \
'varchar(5000)', 'varchar(5000)', 'varchar(5000)', \
'varchar(5000)']
hive_config_props = ['hive.exec.compress.output=true', \
'mapred.output.compression.codec=com.hadoop.compression.lzo.LzoCodec']
num_rows = 1000
has_id_col = True
def test(self):
utils.test_import(self)
utils.validate_import_odbc(self)
class TestLzop(utils.HiveTestCase):
hive_file_format = 'textfile'
hive_table = '{file_format}_%s'.format(file_format = hive_file_format)
hive_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'string', 'char(20)', 'varchar(50)', 'boolean', 'binary', \
'array<timestamp>', 'map<int, string>', 'struct<s1:int, s2:smallint, s3:date>', \
'uniontype<bigint, timestamp, boolean, varchar(100)>']
exa_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'varchar(5000)', 'char(20)', 'varchar(50)', 'boolean', 'varchar(5000) ASCII', \
'varchar(5000)', 'varchar(5000)', 'varchar(5000)', \
'varchar(5000)']
hive_config_props = ['hive.exec.compress.output=true', \
'mapred.output.compression.codec=com.hadoop.compression.lzo.LzopCodec']
num_rows = 1000
has_id_col = True
def test(self):
utils.test_import(self)
utils.validate_import_odbc(self)
'''
# Requires native libraries
class TestLz4(utils.HiveTestCase):
hive_file_format = 'textfile'
hive_table = '{file_format}_%s'.format(file_format = hive_file_format)
hive_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'string', 'char(20)', 'varchar(50)', 'boolean', 'binary', \
'array<timestamp>', 'map<int, string>', 'struct<s1:int, s2:smallint, s3:date>', \
'uniontype<bigint, timestamp, boolean, varchar(100)>']
exa_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'varchar(5000)', 'char(20)', 'varchar(50)', 'boolean', 'varchar(5000) ASCII', \
'varchar(5000)', 'varchar(5000)', 'varchar(5000)', \
'varchar(5000)']
hive_config_props = ['hive.exec.compress.output=true', \
'mapred.output.compression.codec=org.apache.hadoop.io.compress.Lz4Codec']
num_rows = 1000
has_id_col = True
def test(self):
utils.test_import(self)
utils.validate_import_odbc(self)
'''
'''
# Requires native libraries
class TestSnappy(utils.HiveTestCase):
hive_file_format = 'textfile'
hive_table = '{file_format}_%s'.format(file_format = hive_file_format)
hive_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'string', 'char(20)', 'varchar(50)', 'boolean', 'binary', \
'array<timestamp>', 'map<int, string>', 'struct<s1:int, s2:smallint, s3:date>', \
'uniontype<bigint, timestamp, boolean, varchar(100)>']
exa_col_types = ['decimal(36,0)', 'tinyint', 'smallint', 'int', 'bigint', 'decimal(18,5)', 'float', 'double', \
'date', 'timestamp', 'varchar(5000)', 'char(20)', 'varchar(50)', 'boolean', 'varchar(5000) ASCII', \
'varchar(5000)', 'varchar(5000)', 'varchar(5000)', \
'varchar(5000)']
hive_config_props = ['hive.exec.compress.output=true', \
'mapred.output.compression.codec=org.apache.hadoop.io.compress.SnappyCodec']
num_rows = 1000
has_id_col = True
def test(self):
utils.test_import(self)
utils.validate_import_odbc(self)
'''
if __name__ == '__main__':
udf.main()
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open_sea_v1/endpoints/abc.py | MyNameIsBondJamesBond/opensea-python-wrapper | 14 | 191919 | <gh_stars>10-100
from abc import ABC, abstractmethod
from typing import Generator, Type
from open_sea_v1.endpoints.client import ClientParams
from open_sea_v1.responses.abc import BaseResponse
class BaseEndpoint(ABC):
@property
@abstractmethod
def __post_init__(self):
"""Using post_init to run param validation"""
@property
@abstractmethod
def client_params(self) -> ClientParams:
"""Instance of common OpenSea Endpoint parameters."""
@property
@abstractmethod
def _response_type(self) -> Type[BaseResponse]:
""""""
@property
@abstractmethod
def url(self) -> str:
"""Endpoint URL"""
@abstractmethod
def _parse_json(self) -> Generator[list[list[BaseResponse]], None, None]:
"""Returns all pages for the query."""
@property
@abstractmethod
def get_params(self) -> str:
"""Endpoint URL"""
@property
@abstractmethod
def _validate_request_params(self) -> None:
""""""
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parsifal/apps/reviews/migrations/0020_searchresult.py | ShivamPytho/parsifal | 342 | 29244 | <reponame>ShivamPytho/parsifal<filename>parsifal/apps/reviews/migrations/0020_searchresult.py
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
import parsifal.apps.reviews.models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('library', '0013_auto_20150710_1614'),
('reviews', '0019_study_comments'),
]
operations = [
migrations.CreateModel(
name='SearchResult',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('imported_file', models.FileField(null=True, upload_to=parsifal.apps.reviews.models.search_result_file_upload_to)),
('documents', models.ManyToManyField(to='library.Document')),
('review', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='reviews.Review')),
('search_session', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='reviews.SearchSession', null=True)),
('source', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='reviews.Source')),
],
),
]
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emotion_classifier/data/data_utilities/pkl_emotion_fixer.py | IronEdward/chatbot | 0 | 97028 | <gh_stars>0
import pickle as pkl
from Functions.functions import *
data = pkl.load(open("Data(new)/data.pkl", "rb"))
emotion_type = pkl.load(open("Data(new)/emotion_types.pkl", "rb"))
new_data = []
for i in data:
new_data.append([i[0], emotion_type.index(i[1])])
pkl.dump(new_data, open("Data(new)/final_data.pkl", "wb")) | [
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models/LASSO.py | MattAHarrington/crypto-comovement | 1 | 29226 | #!/usr/bin/env python
# Standard imports
import pandas as pd
import numpy as np
# Pytorch
import torch
from torch import nn
# Using sklearn's LASSO implementation
from sklearn.linear_model import Lasso
# Local Files
from models.model_interface import CryptoModel
class LASSO(CryptoModel):
"""Wrapper around the sklearn LASSO class"""
def __init__(self, alpha=0.1, warm_start=True, verbose_training=False):
"""Create the LASSO model.
:input_size: Input size to the AutoEncoder, should be n_coins
"""
# Arguments
self.alpha = alpha
self.verbose_training = verbose_training
self.model = Lasso(alpha=alpha, fit_intercept=True, warm_start=warm_start)
# set the default plotting color
self.set_plotting_color()
def predict(self, sample):
"""Predict the next out of sample timestep
:sample: Vector or DataFrame of timesteps to use as input for the predictor(s).
:returns: [batch_size, 1, n_coins] Tensor of predictions
"""
n_samp, _, n_features = sample.shape
yhat = self.model.predict(sample.reshape((n_samp, n_features)))
if self.verbose_training:
print(f'prediction: {yhat}')
return yhat
def train(self, training_set):
"""Train, or re-train, the LSTM and AE
:training_set: DataFrame of training samples
"""
X, Y = [], []
for data, target in training_set:
X.append(data.numpy())
Y.append(target.numpy())
X = np.vstack(X)
Y = np.vstack(Y)
n_samples, _, n_features = X.shape
X = X.reshape((n_samples, n_features))
Y = Y.reshape((n_samples, n_features))
self.model.fit(X, Y)
#TODO: print out that coefficients (or at least num of) that the L1 normalization leaves
coef = self.model.coef_
all_zeros = np.isin([0,-0], coef)
if self.verbose_training:
print(f'All zeros? {all_zeros}')
print(f'Coefs? {coef.shape}')
if np.isin(False, all_zeros):
print(self.model.coef_)
print(type(self.model.coef_))
print(self.model.coef_.shape)
def get_fullname(self):
"""Get the full-grammar name for this model
:returns: English phrase as string
"""
return f"LASSO_alpha-{self.alpha}"
def get_filename(self):
"""Get the abbreviated (file)name for this model
:returns: Abbreviated string with underscores
"""
return f"LASSO_alpha-{self.alpha}"
def needs_retraining(self):
"""Does this model need regular retraining while forecasting?
:returns: bool
"""
return True
def set_plotting_color(self, color="#FCB97D"):
"""Set color used for plotting
:color: Hex value string
"""
self.color = color
def get_plotting_color(self):
"""return color for graphing distinction
:returns: str of color
"""
return self.color
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2
] |
src/util.py | BobToninho/cybersecurity-project | 0 | 142713 | <filename>src/util.py
# Taken from https://github.com/ojroques/garbled-circuit
import json
# HELPER FUNCTIONS
def parse_json(json_path):
with open(json_path) as json_file:
return json.load(json_file)
| [
1,
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app.py | iio1989/oshite | 0 | 12954 | <gh_stars>0
from flask import Flask, render_template, request, redirect, url_for, Markup
import app_helper as apHelp
app = Flask(__name__)
@app.route('/')
def root():
return render_template('home.html')
# click convetBtn. get HttpParam.
@app.route('/post', methods=['GET', 'POST'])
def post():
if request.method == 'POST':
input_kana = request.form['input_kana']
converted_input_list = apHelp.getConvetedStr_kanaToOshite(input_kana)
# rendering for home.html.
return render_template('home.html',
input_kana=input_kana,
converted_input_list=converted_input_list,
fileType= apHelp.FILE_TYPE)
else: # error redirect.
return redirect(url_for('home'))
# click homeBtn from header.
@app.route('/home', methods=['GET', 'POST'])
def home():
return render_template('home.html')
# click aboutBtn from header.
@app.route('/about', methods=['GET', 'POST'])
def about():
return render_template('about.html')
# click historyBtn from header.
@app.route('/history', methods=['GET', 'POST'])
def history():
return render_template('history.html')
if __name__ == '__main__':
app.run(debug=True) | [
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tests/test_cache.py | phaustin/jupyter-cache | 0 | 128945 | <gh_stars>0
import os
from textwrap import dedent
import nbformat as nbf
import pytest
from jupyter_cache.cache.main import JupyterCacheBase
from jupyter_cache.base import NbValidityError
NB_PATH = os.path.join(os.path.realpath(os.path.dirname(__file__)), "notebooks")
def test_basic_workflow(tmp_path):
cache = JupyterCacheBase(str(tmp_path))
with pytest.raises(NbValidityError):
cache.cache_notebook_file(path=os.path.join(NB_PATH, "basic.ipynb"))
cache.cache_notebook_file(
path=os.path.join(NB_PATH, "basic.ipynb"),
uri="basic.ipynb",
check_validity=False,
)
assert cache.list_cache_records()[0].uri == "basic.ipynb"
pk = cache.match_cache_file(path=os.path.join(NB_PATH, "basic.ipynb")).pk
nb_bundle = cache.get_cache_bundle(pk)
assert nb_bundle.nb.metadata["kernelspec"] == {
"display_name": "Python 3",
"language": "python",
"name": "python3",
}
assert set(nb_bundle.record.to_dict().keys()) == {
"pk",
"hashkey",
"uri",
"data",
"created",
"accessed",
"description",
}
# assert cache.get_cache_codecell(pk, 0).source == "a=1\nprint(a)"
path = os.path.join(NB_PATH, "basic_failing.ipynb")
diff = cache.diff_nbfile_with_cache(pk, path, as_str=True, use_color=False)
assert diff == dedent(
f"""\
nbdiff
--- cached pk=1
+++ other: {path}
## inserted before nb/cells/0:
+ code cell:
+ source:
+ raise Exception('oopsie!')
## deleted nb/cells/0:
- code cell:
- execution_count: 2
- source:
- a=1
- print(a)
- outputs:
- output 0:
- output_type: stream
- name: stdout
- text:
- 1
"""
)
cache.remove_cache(pk)
assert cache.list_cache_records() == []
cache.cache_notebook_file(
path=os.path.join(NB_PATH, "basic.ipynb"),
uri="basic.ipynb",
check_validity=False,
)
with pytest.raises(ValueError):
cache.stage_notebook_file(os.path.join(NB_PATH, "basic.ipynb"), assets=[""])
cache.stage_notebook_file(
os.path.join(NB_PATH, "basic.ipynb"),
assets=[os.path.join(NB_PATH, "basic.ipynb")],
)
assert [r.pk for r in cache.list_staged_records()] == [1]
assert [r.pk for r in cache.list_staged_unexecuted()] == []
cache.stage_notebook_file(os.path.join(NB_PATH, "basic_failing.ipynb"))
assert [r.pk for r in cache.list_staged_records()] == [1, 2]
assert [r.pk for r in cache.list_staged_unexecuted()] == [2]
bundle = cache.get_staged_notebook(os.path.join(NB_PATH, "basic_failing.ipynb"))
assert bundle.nb.metadata
cache.clear_cache()
assert cache.list_cache_records() == []
def test_merge_match_into_notebook(tmp_path):
cache = JupyterCacheBase(str(tmp_path))
cache.cache_notebook_file(
path=os.path.join(NB_PATH, "basic.ipynb"), check_validity=False
)
nb = nbf.read(os.path.join(NB_PATH, "basic_unrun.ipynb"), 4)
pk, merged = cache.merge_match_into_notebook(nb)
assert merged.cells[1] == {
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [{"name": "stdout", "output_type": "stream", "text": "1\n"}],
"source": "a=1\nprint(a)",
}
def test_artifacts(tmp_path):
cache = JupyterCacheBase(str(tmp_path))
with pytest.raises(IOError):
cache.cache_notebook_file(
path=os.path.join(NB_PATH, "basic.ipynb"),
uri="basic.ipynb",
artifacts=(os.path.join(NB_PATH),),
check_validity=False,
)
cache.cache_notebook_file(
path=os.path.join(NB_PATH, "basic.ipynb"),
uri="basic.ipynb",
artifacts=(os.path.join(NB_PATH, "artifact_folder", "artifact.txt"),),
check_validity=False,
)
hashkey = cache.get_cache_record(1).hashkey
assert {
str(p.relative_to(tmp_path)) for p in tmp_path.glob("**/*") if p.is_file()
} == {
"global.db",
f"executed/{hashkey}/base.ipynb",
f"executed/{hashkey}/artifacts/artifact_folder/artifact.txt",
}
bundle = cache.get_cache_bundle(1)
assert {str(p) for p in bundle.artifacts.relative_paths} == {
"artifact_folder/artifact.txt"
}
text = list(h.read().decode() for r, h in bundle.artifacts)[0]
assert text.rstrip() == "An artifact"
with cache.cache_artefacts_temppath(1) as path:
assert path.joinpath("artifact_folder").exists()
# jupyter_client/session.py:371: DeprecationWarning:
# Session._key_changed is deprecated in traitlets: use @observe and @unobserve instead
@pytest.mark.filterwarnings("ignore")
def test_execution(tmp_path):
from jupyter_cache.executors import load_executor
db = JupyterCacheBase(str(tmp_path))
db.stage_notebook_file(path=os.path.join(NB_PATH, "basic_unrun.ipynb"))
db.stage_notebook_file(path=os.path.join(NB_PATH, "basic_failing.ipynb"))
db.stage_notebook_file(
path=os.path.join(NB_PATH, "external_output.ipynb"),
assets=(os.path.join(NB_PATH, "basic.ipynb"),),
)
executor = load_executor("basic", db)
result = executor.run_and_cache()
print(result)
assert result == {
"succeeded": [
os.path.join(NB_PATH, "basic_unrun.ipynb"),
os.path.join(NB_PATH, "external_output.ipynb"),
],
"excepted": [os.path.join(NB_PATH, "basic_failing.ipynb")],
"errored": [],
}
assert len(db.list_cache_records()) == 2
bundle = db.get_cache_bundle(1)
assert bundle.nb.cells[0] == {
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [{"name": "stdout", "output_type": "stream", "text": "1\n"}],
"source": "a=1\nprint(a)",
}
assert "execution_seconds" in bundle.record.data
with db.cache_artefacts_temppath(2) as path:
paths = [str(p.relative_to(path)) for p in path.glob("**/*") if p.is_file()]
assert paths == ["artifact.txt"]
assert path.joinpath("artifact.txt").read_text() == "hi"
stage_record = db.get_staged_record(2)
assert stage_record.traceback is not None
assert "Exception: oopsie!" in stage_record.traceback
@pytest.mark.filterwarnings("ignore")
def test_execution_timeout_config(tmp_path):
""" tests the timeout value passed to the executor"""
from jupyter_cache.executors import load_executor
db = JupyterCacheBase(str(tmp_path))
db.stage_notebook_file(path=os.path.join(NB_PATH, "complex_outputs_unrun.ipynb"))
executor = load_executor("basic", db)
result = executor.run_and_cache(timeout=60)
assert result == {
"succeeded": [os.path.join(NB_PATH, "complex_outputs_unrun.ipynb")],
"excepted": [],
"errored": [],
}
db.clear_cache()
db.stage_notebook_file(path=os.path.join(NB_PATH, "complex_outputs_unrun.ipynb"))
executor = load_executor("basic", db)
result = executor.run_and_cache(timeout=1)
assert result == {
"succeeded": [],
"excepted": [os.path.join(NB_PATH, "complex_outputs_unrun.ipynb")],
"errored": [],
}
@pytest.mark.filterwarnings("ignore")
def test_execution_timeout_metadata(tmp_path):
""" tests the timeout metadata key in notebooks"""
from jupyter_cache.executors import load_executor
db = JupyterCacheBase(str(tmp_path))
db.stage_notebook_file(
path=os.path.join(NB_PATH, "complex_outputs_unrun_timeout.ipynb")
)
executor = load_executor("basic", db)
result = executor.run_and_cache()
assert result == {
"succeeded": [],
"excepted": [os.path.join(NB_PATH, "complex_outputs_unrun_timeout.ipynb")],
"errored": [],
}
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] |
tests/main_test.py | emilianomoscato/stl | 0 | 189336 | import pytest as pytest
from main import select_tasks
from task_selector.taskexceptions import EmptyFileException, \
JSONMissingFieldsExepction
def test_empty_json_list():
assert select_tasks("[]") == "[]"
@pytest.mark.parametrize('json_str,exception', [
(""
, EmptyFileException),
("[{'name': 'A', 'resources': ['1', '2', '3']}]"
, JSONMissingFieldsExepction),
("[{'name': 'A', 'profit': 9.2}]"
, JSONMissingFieldsExepction),
("[{'name': 'A', 'resources': [1], 'profit': 1},"
" {'resources': ['2','3'], 'profit': 1},"
" {'name': 'C', 'resources': ['3','4'], 'profit': 2}]"
, JSONMissingFieldsExepction)
])
def test_missing_json_fields(json_str, exception):
with pytest.raises(exception):
select_tasks(json_str)
@pytest.mark.parametrize('json_str,result', [
("[{'name': 'A', 'resources': ['1', '2', '3'], 'profit': 9.2}]",
"[{'name': 'A', 'resources': ['1', '2', '3'], 'profit': 9.2}]"
),
("[{'name': 'A', 'resources': ['1', '2', '3'], 'profit': 9.2},"
" {'name': 'B', 'resources': ['2'] , 'profit': 0.4},"
" {'name': 'C', 'resources': ['3'] , 'profit': 2.9}]",
"[{'name': 'A', 'resources': ['1', '2', '3'], 'profit': 9.2}]"
),
("[{'name': 'A', 'resources': ['1', '2'], 'profit': 1},"
" {'name': 'B', 'resources': ['2', '3'], 'profit': 1},"
" {'name': 'C', 'resources': ['3', '4'], 'profit': 1}]",
"[{'name': 'A', 'resources': ['1', '2'], 'profit': 1.0},"
" {'name': 'C', 'resources': ['3', '4'], 'profit': 1.0}]"),
("[{'name': 'A', 'resources': [], 'profit': 1},"
" {'name': 'B', 'resources': ['2', '3'], 'profit': 1},"
" {'name': 'C', 'resources': ['3', '4'], 'profit': 2}]",
"[{'name': 'A', 'resources': [], 'profit': 1.0},"
" {'name': 'C', 'resources': ['3', '4'], 'profit': 2.0}]")
])
def test_select_tasks(json_str, result):
assert select_tasks(json_str) == result
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] |
15-3SUM/solution.py | alfmunny/leetcode | 0 | 36238 | <gh_stars>0
class Solution:
def threeSum(self, nums: List[int]) -> List[List[int]]:
if len(nums) < 3:
return []
ans = []
nums.sort()
for i in range(0, len(nums)-2):
if nums[i] > 0:
break
if i > 0 and nums[i-1] == nums[i]:
continue
left, right = i+1, len(nums)-1
while right > left:
s = nums[left] + nums[right] + nums[i]
if s == 0:
ans.append([nums[i], nums[left], nums[right]])
left += 1
right -= 1
while right > left and nums[left] == nums[left-1]:
left += 1
while right > left and nums[right] == nums[right+1]:
right -= 1
elif s < 0:
left += 1
else:
right -= 1
return ans
| [
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] |
web/python/flask_api_mysql_pagination/movies_api/app.py | CALlanoR/virtual_environments | 0 | 188535 | <filename>web/python/flask_api_mysql_pagination/movies_api/app.py
from flask import Flask
from flask_cors import CORS, cross_origin
from flask_jwt_extended import (
JWTManager, jwt_required, create_access_token,
get_jwt_identity
)
app = Flask(__name__)
CORS(app) | [
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models/three_layers_nn.py | erwanlecarpentier/optimistic-regressor | 0 | 24140 | <gh_stars>0
"""
N layers optimistic neural network
"""
import torch
class ThreeLayersNN(torch.nn.Module):
def __init__(self, in_dim, out_dim, h_dim, activation='relu'):
"""
:param in_dim: (int) input dimension
:param out_dim: (int) output dimension
"""
super(ThreeLayersNN, self).__init__()
self.activation = activation
# Initialize layers
self.h_dim = h_dim
self.linear1 = torch.nn.Linear(in_dim, h_dim[0])
self.linear2 = torch.nn.Linear(h_dim[0], h_dim[1])
self.linear3 = torch.nn.Linear(h_dim[1], out_dim)
def forward(self, x):
if self.activation == 'sigmoid':
h = torch.sigmoid(self.linear1(x))
h = torch.sigmoid(self.linear2(h))
return self.linear3(h)
elif self.activation == 'relu':
h = self.linear1(x).clamp(min=0)
h = self.linear2(h).clamp(min=0)
return self.linear3(h)
| [
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] |
day13.py | jm-projects/AdventOfCode2021 | 0 | 166793 | from numpy.core.numeric import count_nonzero
import pandas as pd
import numpy as np
import re
data = pd.read_csv("data/day13.csv", header = None, dtype=str, delimiter= '\n')[0]
codes = [re.split("\s\S\S\s", word) for word in data.values][1:]
# Challenge 1
word = np.array(data.values)[0]
c_dic = {c[0]:c[1] for c in codes}
c_dic['0'+word[0]] = ''
c_dic[word[-1]+'0'] = ''
word = '0'+word+'0'
steps = 10
dic = {key:0 for key in c_dic}
for i in range(len(word)-1): dic[word[i]+word[i+1]] += 1
for step in range(steps):
dic2 = {k:val for k, val in dic.items()}
for key in dic:
if key[0] != '0' and key[-1] != '0':
res = c_dic[key]
dic2[key[0]+res] += dic[key]
dic2[res+key[1]] += dic[key]
dic2[key] -= dic[key]
dic = {k:val for k, val in dic2.items()}
occ = {val:0 for k,val in c_dic.items()}
for char in word: occ[char] = 0
for key in dic:
occ[key[0]] += dic[key]
occ[key[1]] += dic[key]
out = np.sort([val for k,val in occ.items()])
print((out[-1]-out[2])//2)
# Challenge 2
word = np.array(data.values)[0]
c_dic = {c[0]:c[1] for c in codes}
c_dic['0'+word[0]] = ''
c_dic[word[-1]+'0'] = ''
word = '0'+word+'0'
steps = 40
dic = {key:0 for key in c_dic}
for i in range(len(word)-1): dic[word[i]+word[i+1]] += 1
for step in range(steps):
dic2 = {k:val for k, val in dic.items()}
for key in dic:
if key[0] != '0' and key[-1] != '0':
res = c_dic[key]
dic2[key[0]+res] += dic[key]
dic2[res+key[1]] += dic[key]
dic2[key] -= dic[key]
dic = {k:val for k, val in dic2.items()}
occ = {val:0 for k,val in c_dic.items()}
for char in word: occ[char] = 0
for key in dic:
occ[key[0]] += dic[key]
occ[key[1]] += dic[key]
out = np.sort([val for k,val in occ.items()])
print((out[-1]-out[2])//2) | [
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] |
timbr/machine/base_machine.py | timbr-io/timbr-python | 2 | 187294 | <reponame>timbr-io/timbr-python<gh_stars>1-10
from __future__ import print_function
from multiprocessing.pool import ThreadPool
import dask as da
# NOTE: sync mode wil likely be faster
# from dask.async import get_sync as get
from dask.threaded import get
_pool = ThreadPool()
da.set_options(pool=_pool)
try:
from Queue import Empty, Full, Queue # Python 2
except ImportError:
from queue import Empty, Full, Queue # Python 3
from bson.objectid import ObjectId
from functools import wraps, partial # should be used but isn't currently
from collections import defaultdict, deque
import inspect
import zmq
import json
from .util import identity, wrap_transform, json_serializable_exception
from .profiler import MachineProfiler
from .exception import UpstreamError
from .display import Display
from .dashboard import Dashboard
def json_serialize(obj):
try:
return json.dumps(obj)
except TypeError as te:
return json.dumps(json_serializable_exception(te))
def time_from_objectidstr(oid):
return ObjectId(oid).generation_time.isoformat()
def is_serialization_task(task):
if task[-2:] == "_s":
return True
return False
class BaseMachine(object):
def __init__(self, stages=8, bufsize=1024):
self.q = Queue(bufsize)
self.tbl = {}
self._status = {"last_oid": None, "processed": 0, "errored": 0, "queue_size": self.q.qsize()}
self.stages = stages
self._dsk = None
self._dirty = True
self._getter = partial(get, num_workers=1)
self.serialize_fn = json_serialize
self.REFERENCE_DASK = {
"oid_s": (str, "oid"),
"in_s": (self.serialize_fn, "in")
}
self.REFERENCE_DASK.update({"f{}_s".format(i): (self.serialize_fn, "f{}".format(i)) for i in xrange(self.stages)})
def put(self, msg):
# NOTE: Non-blocking
self.q.put(msg, False)
def get(self, block=False, timeout=0.5):
dsk = dict(self.dsk)
dsk["in"] = (self.q.get, block, timeout)
output = self._getter(dsk, ["oid_s", "in_s"] + ["f{}_s".format(i) for i in xrange(self.stages)])
return output
def display_status(self, interval=1):
self._display = Display(self, interval=interval)
def dashboard(self):
self._dashboard = Dashboard(self)
@property
def status(self):
self._status["last_processed_time"] = time_from_objectidstr(self._status["last_oid"])
self._status["queue_size"] = self.q.qsize()
return self._status
def __len__(self):
return self.stages
def __setitem__(self, pos, fn):
assert isinstance(pos, (int, long))
assert pos >=0 and pos < self.stages
wrapped_fn = wrap_transform(fn)
self.tbl["f{}".format(pos)] = wrapped_fn
self.dirty = True
def __getitem__(self, pos):
assert isinstance(pos, (int, long))
assert pos >=0 and pos < self.stages
return self.tbl["f{}".format(pos)]
def __missing__(self, pos):
return wrap_transform(identity)
def __delitem__(self, pos):
assert isinstance(pos, (int, long))
assert pos >=0 and pos < self.stages
del self.tbl["f{}".format(pos)]
self.dirty = True
def __call__(self, data, include_serialized=False):
dsk = dict(self.dsk)
dsk["in"] = data
args = ["oid", "in"] + ["f{}".format(i) for i in xrange(self.stages)]
if include_serialized:
args.extend(["oid_s", "in_s"] + ["f{}_s".format(i) for i in xrange(self.stages)])
output = self._getter(dsk, args)
return output
@property
def dsk(self):
if self._dsk is None or self.dirty:
self._dsk = {}
self._dsk["oid"] = (ObjectId,)
for i in xrange(self.stages):
fkey = "f{}".format(i)
cmd = [self.tbl.get(fkey, wrap_transform(identity))]
cmd.extend(["f{}".format(j) for j in reversed(xrange(i)) if i > 0])
cmd.append("in")
cmd.extend(["f{}_s".format(j) for j in reversed(xrange(i)) if i > 0])
cmd.append("in_s")
self._dsk[fkey] = tuple(cmd)
self._dsk.update(self.REFERENCE_DASK)
self.dirty = False
return self._dsk
def format_status(self):
stats = self.status
hmap = {k: " ".join(k.split("_")).upper() for k in stats.keys()}
s = ""
for h in sorted(stats.keys()):
s += "{:<5} --- {:<5}\n\n".format(hmap[h], stats[h])
return s
def print_status(self):
print(self.format_status())
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apps/organization/urls.py | stormsha/StormOnline | 18 | 1922 | # _*_ coding: utf-8 _*_
# ---------------------------
__author__ = 'StormSha'
__date__ = '2018/3/28 18:01'
# ---------------------------
# -------------------------django----------------------
from django.conf.urls import url
from .views import OrgView, AddUserAskView, OrgHomeView, OrgCourseView, OrgDescView, OrgTeacherView, AddFavView
from .views import TeacherListView, TeacherDetailView
urlpatterns = [
url(r'^list/$', OrgView.as_view(), name="org_list"),
url(r'^add_ask/$', AddUserAskView.as_view(), name="add_ask"),
url(r'^home/(?P<org_id>\d+)/$', OrgHomeView.as_view(), name="org_home"),
url(r'^course/(?P<org_id>\d+)/$', OrgCourseView.as_view(), name="org_course"),
url(r'^desc/(?P<org_id>\d+)/$', OrgDescView.as_view(), name="org_desc"),
url(r'^org_teacher/(?P<org_id>\d+)/$', OrgTeacherView.as_view(), name="org_teacher"),
# --------------机构收藏-------------------------
url(r'^add_fav/$', AddFavView.as_view(), name="add_fav"),
# -----------------------teacher------------------------------
url(r'^teacher/list/$', TeacherListView.as_view(), name="teacher_list"),
url(r'^teacher/detail/(?P<teacher_id>\d+)/$', TeacherDetailView.as_view(), name="teacher_detail")
] | [
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gen_phone.py | jfsantos/ift6266h14 | 8 | 198605 | import cPickle as pickle
import numpy as np
import theano
_mean = 0.0035805809921434142
_std = 542.48824133746177
def gen_phone(mdl, phones, noise_level):
terr_monitor = mdl.monitor.channels['test_objective']
terr = min(terr_monitor.val_record)
X = theano.tensor.dmatrix('X')
P = theano.tensor.dmatrix('P')
y = mdl.fprop([X,P])
predict = theano.function([X, P], y)
# Let's start with a all zero vector, then use the prediction to populate the next sample
duration = 3
fs = 16000
frame_length = mdl.input_space.components[0].dim
x0 = np.asmatrix(np.zeros((1,duration*fs)))
# phones = np.load('test_phones.npy')
phone_code = np.asmatrix(np.zeros((duration*fs,3*62)))
for pi, p in enumerate(phones):
phone_code[pi,[p, p+62, p+2*62]] = 1 # code for 'aw'
for k in np.arange(frame_length,duration*fs):
frame = x0[:,k-frame_length:k]
x0[0,k] = np.random.normal(predict(frame + np.random.normal(0, noise_level[0]*np.sqrt(terr), frame.shape), phone_code[k]), noise_level[1]*np.sqrt(terr))
x0 = x0.T
x0_scaled = x0*_std + _mean
x0a = np.asarray(x0_scaled, dtype=np.int16)
return x0a
if __name__ == "__main__":
from sys import argv
from scipy.io import wavfile
mdl = pickle.load(open(argv[1]))
x0a = gen_phone(mdl)
wavfile.write(argv[2], 16000, x0a)
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aldryn_redirects/middleware.py | chaosk/aldryn-redirects | 1 | 1617895 | from __future__ import unicode_literals
from django import http
from django.conf import settings
from django.contrib.sites.models import Site
from django.db.models import Q
from .models import Redirect, StaticRedirect
class RedirectFallbackMiddleware(object):
def process_request(self, request):
static_redirect = StaticRedirect.objects.get_for_request(request)
if static_redirect:
full_domain = '{}://{}'.format(request.scheme, Site.objects.get(id=settings.SITE_ID).domain)
return http.HttpResponsePermanentRedirect(static_redirect.get_outbound_url(full_domain))
path = request.path_info
path_with_queries = request.get_full_path()
queries = (
Q(old_path__iexact=path)
| Q(old_path__iexact=path_with_queries)
)
if settings.APPEND_SLASH and path.endswith('/'):
path_with_queries_no_slash = path[:-1] + path_with_queries[len(path):]
queries |= (
Q(old_path__iexact=path[:-1])
| Q(old_path__iexact=path_with_queries_no_slash)
)
try:
r = Redirect.objects.filter(
queries,
site__id__exact=settings.SITE_ID
).distinct().get()
except Redirect.DoesNotExist:
return
new_path = r.safe_translation_getter('new_path', any_language=True)
if new_path in (None, ''):
return http.HttpResponseGone()
return http.HttpResponsePermanentRedirect(new_path)
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magmap/cv/colocalizer.py | kaparna126/magellanmapper | 10 | 165376 | <reponame>kaparna126/magellanmapper<gh_stars>1-10
# Object colocalization
# Copyright The MagellanMapper Contributors
"""Colocalize objects in an image, typically in separate channels."""
from enum import Enum
import multiprocessing as mp
import pandas as pd
import numpy as np
from skimage import morphology
from magmap.cv import chunking, detector, stack_detect, verifier
from magmap.io import cli, df_io, libmag, sqlite
from magmap.settings import config
class BlobMatch:
"""Blob match storage class as a wrapper for a data frame of matches.
Attributes:
df (:class:`pandas.DataFrame`): Data frame of matches with column
names given by :class:`BlobMatch.Cols`.
"""
class Cols(Enum):
"""Blob match column names."""
MATCH_ID = "MatchID"
ROI_ID = "RoiID"
BLOB1_ID = "Blob1ID"
BLOB1 = "Blob1"
BLOB2_ID = "Blob2ID"
BLOB2 = "Blob2"
DIST = "Distance"
def __init__(self, matches=None, match_id=None, roi_id=None, blob1_id=None,
blob2_id=None, df=None):
"""Initialize blob match object.
Args:
matches (list[list[
:class:`numpy.ndarray`, :class:`numpy.ndarray`, float]]:
List of blob match lists, which each contain,
``blob1, blob2, distance``. Defaults to None, which
sets the data frame to None.
match_id (Sequence[int]): Sequence of match IDs, which should be
of the same length as ``matches``; defaults to None.
roi_id (Sequence[int]): Sequence of ROI IDs, which should be
of the same length as ``matches``; defaults to None.
blob1_id (Sequence[int]): Sequence of blob 1 IDs, which should be
of the same length as ``matches``; defaults to None.
blob2_id (Sequence[int]): Sequence of blob2 IDs, which should be
of the same length as ``matches``; defaults to None.
df (:class:`pandas.DataFrame`): Pandas data frame to set in
place of any other arguments; defaults to None.
"""
if df is not None:
# set data frame directly and ignore any other arguments
self.df = df
return
if matches is None:
# set data frame to None and return since any other arguments
# must correspond to matches
self.df = None
return
matches_dict = {}
for i, match in enumerate(matches):
# assumes that all first sequences are of the same length
vals = {
BlobMatch.Cols.BLOB1: match[0],
BlobMatch.Cols.BLOB2: match[1],
BlobMatch.Cols.DIST: match[2],
}
if match_id is not None:
vals[BlobMatch.Cols.MATCH_ID] = match_id[i]
if roi_id is not None:
vals[BlobMatch.Cols.ROI_ID] = roi_id[i]
if blob1_id is not None:
vals[BlobMatch.Cols.BLOB1_ID] = blob1_id[i]
if blob2_id is not None:
vals[BlobMatch.Cols.BLOB2_ID] = blob2_id[i]
for key in BlobMatch.Cols:
matches_dict.setdefault(key, []).append(
vals[key] if key in vals else None)
self.df = df_io.dict_to_data_frame(matches_dict)
def __repr__(self):
"""Format the underlying data frame."""
if self.df is None:
return "Empty blob matches"
return df_io.print_data_frame(self.df, show=False)
def get_blobs(self, n):
"""Get blobs as a numpy array.
Args:
n (int): 1 for blob1, otherwise blob 2.
Returns:
:class:`numpy.ndarray`: Numpy array of the given blob type, or
None if the :attr:`df` is None or the blob column does not exist.
"""
col = BlobMatch.Cols.BLOB1 if n == 1 else BlobMatch.Cols.BLOB2
if self.df is None or col.value not in self.df:
return None
return np.vstack(self.df[col.value])
def get_blobs_all(self):
"""Get all blobs in the blob matches.
Returns:
tuple[:class:`numpy.ndarray`, :class:`numpy.ndarray`]:
Tuple of ``(blobs1, blobs2)``, or None if either are None.
"""
blobs_all = []
for n in (1, 2):
blobs = self.get_blobs(n)
if blobs is None:
return None
blobs_all.append(blobs)
return blobs_all
def update_blobs(self, fn, *args):
"""Update all blobs with the given function.
Args:
fn (func): Function that accepts the output of :meth:`get_blobs`
separately for each set of blobs.
*args (Any): Additional arguments to ``fn``.
"""
if self.df is None: return
for i, col in enumerate((BlobMatch.Cols.BLOB1, BlobMatch.Cols.BLOB2)):
blobs = self.get_blobs(i + 1)
if blobs is not None:
self.df[col.value] = fn(blobs, *args).tolist()
class StackColocalizer(object):
"""Colocalize blobs in blocks based on matching blobs across channels.
Support shared memory for spawned multiprocessing, with fallback to
pickling in forked multiprocessing.
"""
blobs = None
match_tol = None
@classmethod
def colocalize_block(cls, coord, offset, shape, blobs=None,
tol=None, setup_cli=False):
"""Colocalize blobs from different channels within a block.
Args:
coord (Tuple[int]): Block coordinate.
offset (List[int]): Block offset within the full image in z,y,x.
shape (List[int]): Block shape in z,y,x.
blobs (:obj:`np.ndarray`): 2D blobs array; defaults to None to
use :attr:`blobs`.
tol (List[float]): Tolerance for colocalizing blobs; defaults
to None to use :attr:`match_tol`.
setup_cli (bool): True to set up CLI arguments, typically for
a spawned (rather than forked) environment; defaults to False.
Returns:
Tuple[int], dict[Tuple[int], Tuple]: ``coord`` for tracking
multiprocessing and the dictionary of matches.
"""
if blobs is None:
blobs = cls.blobs
if tol is None:
tol = cls.match_tol
if setup_cli:
# reload command-line parameters
cli.process_cli_args()
matches = colocalize_blobs_match(blobs, offset[::-1], shape[::-1], tol)
return coord, matches
@classmethod
def colocalize_stack(cls, shape, blobs):
"""Entry point to colocalizing blobs within a stack.
Args:
shape (List[int]): Image shape in z,y,x.
blobs (:obj:`np.ndarray`): 2D Numpy array of blobs.
Returns:
dict[tuple[int, int], :class:`BlobMatch`]: The
dictionary of matches, where keys are tuples of the channel pairs,
and values are blob match objects.
"""
print("Colocalizing blobs based on matching blobs in each pair of "
"channels")
# set up ROI blocks from which to select blobs in each block
sub_roi_slices, sub_rois_offsets, _, _, _, overlap_base, _, _ \
= stack_detect.setup_blocks(config.roi_profile, shape)
match_tol = np.multiply(
overlap_base, config.roi_profile["verify_tol_factor"])
is_fork = chunking.is_fork()
if is_fork:
# set shared data in forked multiprocessing
cls.blobs = blobs
cls.match_tol = match_tol
pool = mp.Pool(processes=config.cpus)
pool_results = []
for z in range(sub_roi_slices.shape[0]):
for y in range(sub_roi_slices.shape[1]):
for x in range(sub_roi_slices.shape[2]):
coord = (z, y, x)
offset = sub_rois_offsets[coord]
slices = sub_roi_slices[coord]
shape = [s.stop - s.start for s in slices]
if is_fork:
# use variables stored as class attributes
pool_results.append(pool.apply_async(
StackColocalizer.colocalize_block,
args=(coord, offset, shape)))
else:
# pickle full set of variables
pool_results.append(pool.apply_async(
StackColocalizer.colocalize_block,
args=(coord, offset, shape,
detector.get_blobs_in_roi(
blobs, offset, shape)[0], match_tol,
True)))
# dict of channel combos to blob matches data frame
matches_all = {}
for result in pool_results:
coord, matches = result.get()
count = 0
for key, val in matches.items():
matches_all.setdefault(key, []).append(val.df)
count += len(val.df)
print("adding {} matches from block at {} of {}"
.format(count, coord, np.add(sub_roi_slices.shape, -1)))
pool.close()
pool.join()
# prune duplicates by taking matches with shortest distance
for key in matches_all.keys():
matches_all[key] = pd.concat(matches_all[key])
for blobi in (BlobMatch.Cols.BLOB1, BlobMatch.Cols.BLOB2):
# convert blob column to ndarray to extract coords by column
matches = matches_all[key]
matches_uniq, matches_i, matches_inv, matches_cts = np.unique(
np.vstack(matches[blobi.value])[:, :3], axis=0,
return_index=True, return_inverse=True, return_counts=True)
if np.sum(matches_cts > 1) > 0:
# prune if at least one blob has been matched to multiple
# other blobs
singles = matches.iloc[matches_i[matches_cts == 1]]
dups = []
for i, ct in enumerate(matches_cts):
# include non-duplicates to retain index
if ct <= 1: continue
# get indices in orig matches at given unique array
# index and take match with lowest dist
matches_mult = matches.loc[matches_inv == i]
dists = matches_mult[BlobMatch.Cols.DIST.value]
min_dist = np.amin(dists)
num_matches = len(matches_mult)
if config.verbose and num_matches > 1:
print("pruning from", num_matches,
"matches of dist:", dists)
matches_mult = matches_mult.loc[dists == min_dist]
# take first in case of any ties
dups.append(matches_mult.iloc[[0]])
matches_all[key] = pd.concat((singles, pd.concat(dups)))
print("Colocalization matches for channels {}: {}"
.format(key, len(matches_all[key])))
libmag.printv(print(matches_all[key]))
# store data frame in BlobMatch object
matches_all[key] = BlobMatch(df=matches_all[key])
return matches_all
def colocalize_blobs(roi, blobs, thresh=None):
"""Co-localize blobs from different channels based on surrounding
intensities.
Thresholds for detection are first identified in each channel by taking
the blobs in the given channel, finding the surrounding intensities,
and taking a low (5th) percentile. Then for each channel, the
surrounding intensities of blobs in that channel are compared with
the thresholds in the other channels. Blobs exceeding any given
threshold are considered to co-localize in that channel.
Args:
roi (:obj:`np.ndarray`): Region of interest as a 3D+channel array.
blobs (:obj:`np.ndarray`): Blobs as a 2D array in the format
``[[z, y, x, radius, confirmation, truth, channel...], ...]``.
thresh (int, float, str): Threshold percentile of intensities from
pixels surrounding each blob in the given channel. Use "min"
to instead take the mininimum average intensity of all blobs
in the channel. Defaults to None to use "min".
Returns:
:obj:`np.ndarray`: 2D Numpy array of same length as ``blobs`` with
a column for each channel where 1 indicates that the corresponding
blob has signal is present in the given channels at the blob's
location, and 0 indicates insufficient signal.
"""
if blobs is None or roi is None or len(roi.shape) < 4:
return None
if thresh is None:
thresh = "min"
print("Colocalizing blobs based on image intensity across channels")
threshs = []
selem = morphology.ball(2)
# find only blobs in ROI since blobs list may include blobs from immediate
# surrounds, but ROI is not available for them
blobs_roi, blobs_roi_mask = detector.get_blobs_in_roi(
blobs, (0, 0, 0), roi.shape[:3], reverse=False)
blobs_chl = detector.get_blobs_channel(blobs_roi)
blobs_range_chls = []
# get labeled masks of blobs for each channel and threshold intensities
mask_roi = np.ones(roi.shape[:3], dtype=int)
mask_roi_chls = []
for chl in range(roi.shape[3]):
# label a mask with blob indices surrounding each blob
blobs_chl_mask = np.isin(blobs_chl, chl)
blobs_range = np.where(blobs_chl_mask)[0]
blobs_range_chls.append(blobs_range)
mask = np.copy(mask_roi) * -1
mask[tuple(libmag.coords_for_indexing(
blobs_roi[blobs_chl_mask, :3].astype(int)))] = blobs_range
mask = morphology.dilation(mask, selem=selem)
mask_roi_chls.append(mask)
if thresh == "min":
# set minimum average surrounding intensity of all blobs as thresh
threshs.append(
None if len(blobs_range) == 0 else np.amin([
np.mean(roi[mask == b, chl]) for b in blobs_range]))
else:
# set a percentile of intensities surrounding all blobs in channel
# as threshold for that channel, or the whole ROI if no blobs
mask_blobs = mask >= 0
roi_mask = roi if np.sum(mask_blobs) < 1 else roi[mask_blobs, chl]
threshs.append(np.percentile(roi_mask, thresh))
channels = np.unique(detector.get_blobs_channel(blobs_roi)).astype(int)
colocs_roi = np.zeros((blobs_roi.shape[0], roi.shape[3]), dtype=np.uint8)
for chl in channels:
# get labeled mask of blobs in the given channel
mask = mask_roi_chls[chl]
blobs_range = blobs_range_chls[chl]
for chl_other in channels:
if threshs[chl_other] is None: continue
for blobi in blobs_range:
# find surrounding intensity of blob in another channel
mask_blob = mask == blobi
blob_avg = np.mean(roi[mask_blob, chl_other])
if config.verbose:
print(blobi, detector.get_blob_channel(blobs_roi[blobi]),
blobs_roi[blobi, :3], blob_avg, threshs[chl_other])
if blob_avg >= threshs[chl_other]:
# intensities in another channel around blob's position
# is above that channel's threshold
colocs_roi[blobi, chl_other] = 1
# create array for all blobs including those outside ROI
colocs = np.zeros((blobs.shape[0], roi.shape[3]), dtype=np.uint8)
colocs[blobs_roi_mask] = colocs_roi
if config.verbose:
for i, (blob, coloc) in enumerate(zip(blobs_roi, colocs)):
print(i, detector.get_blob_channel(blob), blob[:3], coloc)
return colocs
def colocalize_blobs_match(blobs, offset, size, tol, inner_padding=None):
"""Co-localize blobs in separate channels but the same ROI by finding
optimal blob matches.
Args:
blobs (:obj:`np.ndarray`): Blobs from separate channels.
offset (List[int]): ROI offset given as x,y,z.
size (List[int]): ROI shape given as x,y,z.
tol (List[float]): Tolerances for matching given as x,y,z
inner_padding (List[int]): ROI padding given as x,y,z; defaults
to None to use the padding based on ``tol``.
Returns:
dict[tuple[int, int], :class:`BlobMatch`]:
Dictionary where keys are tuples of the two channels compared and
values are blob matches objects.
"""
if blobs is None:
return None
thresh, scaling, inner_pad, resize, blobs = verifier.setup_match_blobs_roi(
blobs, tol)
if inner_padding is None:
inner_padding = inner_pad
matches_chls = {}
channels = np.unique(detector.get_blobs_channel(blobs)).astype(int)
for chl in channels:
# pair channels
blobs_chl = detector.blobs_in_channel(blobs, chl)
for chl_other in channels:
# prevent duplicates by skipping other channels below given channel
if chl >= chl_other: continue
# find colocalizations between blobs from one channel to blobs
# in another channel
blobs_chl_other = detector.blobs_in_channel(blobs, chl_other)
blobs_inner_plus, blobs_truth_inner_plus, offset_inner, \
size_inner, matches = verifier.match_blobs_roi(
blobs_chl_other, blobs_chl, offset, size, thresh, scaling,
inner_padding, resize)
# reset truth and confirmation blob flags in matches
chl_combo = (chl, chl_other)
matches.update_blobs(detector.set_blob_truth, -1)
matches.update_blobs(detector.set_blob_confirmed, -1)
matches_chls[chl_combo] = matches
return matches_chls
def _get_roi_id(db, offset, shape, exp_name=None):
"""Get database ROI ID for the given ROI position within the main image5d.
Args:
db (:obj:`sqlite.ClrDB`): Database object.
offset (List[int]): ROI offset in z,y,x.
shape (List[int]): ROI shape in z,y,x.
exp_name (str): Name of experiment; defaults to None to attempt
discovery through any image loaded to :attr:`config.img5d`.
Returns:
int: ROI ID or found or inserted ROI.
"""
if exp_name is None:
exp_name = sqlite.get_exp_name(
config.img5d.path_img if config.img5d else None)
exp_id = sqlite.select_or_insert_experiment(
db.conn, db.cur, exp_name, None)
roi_id = sqlite.select_or_insert_roi(
db.conn, db.cur, exp_id, config.series, offset, shape)[0]
return roi_id
def insert_matches(db, matches):
"""Insert matches into database for a whole image.
Args:
db (:obj:`sqlite.ClrDB`): Database object.
matches (dict[tuple[int, int], :class:`BlobMatch`):
Dictionary of channel combo tuples to blob match objects.
"""
# use size of 0 for each dimension for whole-image ROI, which avoids
# the need to discover the image size
roi_id = _get_roi_id(db, (0, 0, 0), (0, 0, 0))
for chl_matches in matches.values():
# insert blobs and matches for the given channel combo
blobs = chl_matches.get_blobs_all()
if blobs is not None:
sqlite.insert_blobs(db.conn, db.cur, roi_id, np.vstack(blobs))
config.db.insert_blob_matches(roi_id, chl_matches)
def select_matches(db, channels, offset=None, shape=None, exp_name=None):
"""Select blob matches for the given region from a database.
Blob matches are assumed to have been processed from the whole image.
To retrieve matches from a selected ROI, use
:meth:`magmap.io.sqlite.ClrDB.select_blob_matches` instead.
Args:
db (:obj:`sqlite.ClrDB`): Database object.
channels (list[int]): List of channels.
offset (list[int]): ROI offset in z,y,x; defaults to None to use
``(0, 0, 0)``.
shape (list[int]): ROI shape in z,y,x; defaults to None to use
``(0, 0, 0)``.
exp_name (str): Name of experiment in ``db``.
Returns:
dict[tuple[int, int], list[:obj:`BlobMatch`]: Dictionary where
keys are tuples of the two channels compared and values are a list
of blob matches. None if no blob matches are found.
"""
# get ROI for whole image
roi_id = _get_roi_id(db, (0, 0, 0), (0, 0, 0), exp_name)
if offset is not None and shape is not None:
# get blob from matches within ROI
blobs, blob_ids = db.select_blobs_by_position(
roi_id, offset[::-1], shape[::-1])
else:
# get blobs from matches within the whole image
blobs, blob_ids = db.select_blobs_by_roi(roi_id)
if blobs is None or len(blobs) == 0:
print("No blob matches found")
return None
blob_ids = np.array(blob_ids)
matches = {}
for chl in channels:
# pair channels
for chl_other in channels:
if chl >= chl_other: continue
# select matches for blobs in the given first channel of the pair
# of channels, assuming chls were paired this way during insertion
chl_matches = db.select_blob_matches_by_blob_id(
roi_id, 1,
blob_ids[detector.get_blobs_channel(blobs) == chl])
blobs2 = chl_matches.get_blobs(2)
if blobs2 is not None:
chl_matches = chl_matches.df.loc[detector.get_blobs_channel(
blobs2) == chl_other]
matches[(chl, chl_other)] = BlobMatch(df=chl_matches)
return matches
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app.py | fdoliveira/infosystem-seed | 1 | 161225 | <filename>app.py
import os
import app
if __name__ == '__main__':
app_host = os.environ.get('HOST', '0.0.0.0')
app_port = int(os.environ.get('PORT', 5000))
system = app.System()
system.run(host=app_host, port=app_port)
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irspack/split/userwise.py | tnakae/irspack | 0 | 131831 | <reponame>tnakae/irspack
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
from numpy.lib.arraysetops import unique
from scipy import sparse as sps
from irspack.definitions import InteractionMatrix, OptionalRandomState
from irspack.split.time import split_last_n_interaction_df
from irspack.utils import df_to_sparse, rowwise_train_test_split
from irspack.utils.random import convert_randomstate
def _split_list(
ids: List[Any], test_size: int, rns: np.random.RandomState
) -> Tuple[List[Any], List[Any]]:
rns.shuffle(ids)
return ids[test_size:], ids[:test_size]
class UserTrainTestInteractionPair:
"""A class to hold users' train & test (if any) interactions and their ids.
Args:
user_ids:
List of user ids. Its ``i``-th element should correspond to ``i``-th row
of ``X_train``.
X_train:
The train part of interactions.
X_test:
The test part of interactions (if any).
If ``None``, an empty matrix with the shape of ``X_train``
will be created. Defaults to None.
Raises:
ValueError:
when ``X_train`` and ``user_ids`` have inconsistent size.
ValueError:
when ``X_train`` and ``X_test`` have inconsistent size.
"""
X_train: sps.csr_matrix
"""The train part of users' interactions."""
X_test: sps.csr_matrix
"""The test part of users' interactions."""
n_users: int
"""The number of users"""
n_items: int
"""The number of items"""
X_all: sps.csr_matrix
"""If ``X_test`` is not ``None``, equal to ``X_train + X_test``.Otherwise equals X_train."""
def __init__(
self,
user_ids: List[Any],
X_train: InteractionMatrix,
X_test: Optional[InteractionMatrix],
item_ids: Optional[List[Any]] = None,
):
if len(user_ids) != X_train.shape[0]:
raise ValueError("user_ids and X_train have different shapes.")
if X_test is not None:
if X_train.shape != X_test.shape:
raise ValueError("X_train and X_test have different shapes.")
X_test = sps.csr_matrix(X_test)
else:
X_test = sps.csr_matrix(X_train.shape, dtype=X_train.dtype)
self.user_ids = [x for x in user_ids]
self.X_train = sps.csr_matrix(X_train)
self.X_test = X_test
self.n_users = self.X_train.shape[0]
self.n_items = self.X_train.shape[1]
self.X_all = sps.csr_matrix(self.X_train + self.X_test)
if item_ids is not None:
if len(item_ids) != self.X_test.shape[1]:
raise ValueError("X_train.shape[1] != len(item_ids)")
self.item_ids = item_ids
def _X_to_df(self, X: sps.csr_matrix, user_ids: List[Any]) -> pd.DataFrame:
if self.item_ids is None:
raise RuntimeError("Setting item_ids is required to use this method.")
X.sort_indices()
row, col = X.nonzero()
data = X.data
return pd.DataFrame(
dict(
user_id=[user_ids[r] for r in row],
item_id=[self.item_ids[c] for c in col],
rating=data,
)
)
def df_train(self) -> pd.DataFrame:
return self._X_to_df(self.X_train, self.user_ids)
def df_test(self) -> pd.DataFrame:
return self._X_to_df(self.X_test, self.user_ids)
def concat(
self, other: "UserTrainTestInteractionPair"
) -> "UserTrainTestInteractionPair":
"""Concatenate the users data.
user_id will be ``self.user_ids + self.item_ids``.
Returns:
[type]: [description]
ValueError:
when ``self`` and ``other`` have unequal ``n_items``.
"""
if self.n_items != other.n_items:
raise ValueError("inconsistent n_items.")
return UserTrainTestInteractionPair(
self.user_ids + other.user_ids,
sps.vstack([self.X_train, other.X_train], format="csr"),
sps.vstack([self.X_test, other.X_test], format="csr"),
)
def split_train_test_userwise_random(
df_: pd.DataFrame,
user_colname: str,
item_colname: str,
item_ids: List[Any],
heldout_ratio: float,
n_heldout: Optional[int],
rns: np.random.RandomState,
rating_column: Optional[str] = None,
) -> UserTrainTestInteractionPair:
"""Split the user x item data frame into a pair of sparse matrix (represented as a UserDataSet).
Parameters
----------
df_:
user x item interaction matrix.
user_colname:
The column name for the users.
item_colname:
The column name for the items.
item_id_to_iid:
The mapper from item id to item index. If not supplied, create own mapping from df_.
heldout_ratio:
The percentage of items (per-user) to be held out as a test(validation) ones.
n_heldout:
The maximal number of items (per-user) to be held out as a test(validation) ones.
rns:
The random state
rating_column:
The column for the rating values. If None, the rating values will be all equal (1), by default None
Returns
-------
UserDataSet
Resulting train-test split dataset.
"""
X_all, user_ids, _ = df_to_sparse(
df_,
user_colname=user_colname,
item_colname=item_colname,
item_ids=item_ids,
rating_colname=rating_column,
)
X_learn, X_predict = rowwise_train_test_split(
X_all,
heldout_ratio,
n_heldout,
random_state=rns,
)
return UserTrainTestInteractionPair(
user_ids, X_learn.tocsr(), X_predict.tocsr(), item_ids
)
def split_train_test_userwise_time(
df_: pd.DataFrame,
user_colname: str,
item_colname: str,
time_colname: str,
item_ids: List[Any],
heldout_ratio: float,
n_heldout: Optional[int],
rating_column: Optional[str] = None,
) -> UserTrainTestInteractionPair:
unique_user_ids = np.asarray(list(set(df_[user_colname])))
df_train, df_test = split_last_n_interaction_df(
df_[[user_colname, item_colname, time_colname]],
user_colname,
time_colname,
n_heldout=n_heldout,
heldout_ratio=heldout_ratio,
)
X_train, _, __ = df_to_sparse(
df_train,
user_colname,
item_colname,
user_ids=unique_user_ids,
item_ids=item_ids,
rating_colname=rating_column,
)
X_test, _, __ = df_to_sparse(
df_test,
user_colname,
item_colname,
user_ids=unique_user_ids,
item_ids=item_ids,
rating_colname=rating_column,
)
return UserTrainTestInteractionPair(unique_user_ids, X_train, X_test, item_ids)
def split_dataframe_partial_user_holdout(
df_all: pd.DataFrame,
user_column: str,
item_column: str,
time_column: Optional[str] = None,
rating_column: Optional[str] = None,
n_val_user: Optional[int] = None,
n_test_user: Optional[int] = None,
val_user_ratio: float = 0.1,
test_user_ratio: float = 0.1,
heldout_ratio_val: float = 0.5,
n_heldout_val: Optional[int] = None,
heldout_ratio_test: float = 0.5,
n_heldout_test: Optional[int] = None,
random_state: OptionalRandomState = None,
) -> Tuple[Dict[str, UserTrainTestInteractionPair], List[Any]]:
"""Splits the DataFrame and build an interaction matrix,
holding out random interactions for a subset of randomly selected users
(whom we call "validation users" and "test users").
Args:
df_all:
The user-item interaction event log.
user_column:
The column name for user_id.
item_column:
The column name for movie_id.
time_column:
The column name (if any) specifying the time of the interaction.
If this is set, the split will be based on time, and some of the most recent interactions will be held out for each user.
Defaults to None.
rating_column:
The column name for ratings. If ``None``, the rating will be treated as
``1`` for all interactions. Defaults to None.
n_val_user:
The number of "validation users". Defaults to None.
n_test_user:
The number of "test users". Defaults to None.
val_user_ratio:
The percentage of "validation users" with respect to all users.
Ignored when ``n_val_user`` is set. Defaults to 0.1.
test_user_ratio:
The percentage of "test users" with respect to all users.
Ignored when ``n_text_user`` is set. Defaults to 0.1.
heldout_ratio_val:
The percentage of held-out interactions for "validation users".
Ignored if ``n_heldout_val`` is specified. Defaults to 0.5.
n_heldout_val:
The maximal number of held-out interactions for "validation users".
heldout_ratio_test:
The percentage of held-out interactions for "test users".
Ignored if ``n_heldout_test`` is specified. Defaults to 0.5.
n_heldout_val:
The maximal number of held-out interactions for "test users".
random_state:
The random state for this procedure. Defaults to `None`.
Raises:
ValueError: When ``n_val_user + n_test_user`` is greater than the number of total users.
Returns:
A tuple consisting of:
1. A dictionary with ``"train"``, ``"val"``, ``"test"`` as its keys and the
coressponding dataset as its values.
2. List of unique item ids (which corresponds to the columns of the datasets).
"""
assert (test_user_ratio <= 1) and (test_user_ratio >= 0)
assert (val_user_ratio <= 1) and (val_user_ratio >= 0)
uids: List[Any] = df_all[user_column].unique()
n_users_all = len(uids)
if n_val_user is None:
n_val_user = int(n_users_all * val_user_ratio)
val_user_ratio = n_val_user / n_users_all
else:
if n_val_user > n_users_all:
raise ValueError("n_val_user exceeds the number of total users.")
if n_test_user is None:
n_test_user = int(n_users_all * test_user_ratio)
test_user_ratio = n_test_user / n_users_all
else:
if (n_test_user + n_val_user) > n_users_all:
raise ValueError(
"n_val_user + n_test_users exceeds the number of total users."
)
df_all = df_all.drop_duplicates([user_column, item_column])
rns = convert_randomstate(random_state)
train_uids, val_test_uids = _split_list(uids, (n_val_user + n_test_user), rns)
if (test_user_ratio * len(uids)) >= 1:
val_uids, test_uids = _split_list(
val_test_uids,
n_test_user,
rns,
)
else:
val_uids = val_test_uids
test_uids = np.asarray([])
df_train = df_all[df_all[user_column].isin(train_uids)].copy()
df_val = df_all[df_all[user_column].isin(val_uids)].copy()
df_test = df_all[df_all[user_column].isin(test_uids)].copy()
item_all: List[Any] = list(set(df_all[item_column]))
train_user_interactions, _, __ = df_to_sparse(
df_train, user_column, item_column, user_ids=train_uids, item_ids=item_all
)
valid_data: Dict[str, UserTrainTestInteractionPair] = dict(
train=UserTrainTestInteractionPair(train_uids, train_user_interactions, None)
)
val_test_info_: List[Tuple[pd.DataFrame, str, float, Optional[int]]] = [
(df_val, "val", heldout_ratio_val, n_heldout_val),
(df_test, "test", heldout_ratio_test, n_heldout_test),
]
for df_, dataset_name, heldout_ratio, n_heldout in val_test_info_:
if time_column is None:
valid_data[dataset_name] = split_train_test_userwise_random(
df_,
user_column,
item_column,
item_all,
heldout_ratio,
n_heldout,
rns,
rating_column=rating_column,
)
else:
valid_data[dataset_name] = split_train_test_userwise_time(
df_,
user_column,
item_column,
time_column,
item_all,
heldout_ratio,
n_heldout,
rating_column=rating_column,
)
return valid_data, item_all
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src/pyon/util/poller.py | scionrep/scioncc_new | 2 | 94429 | #!/usr/bin/env python
'''
@author <NAME> <<EMAIL>>
@date Thu Oct 25 15:41:44 EDT 2012
@file pyon/util/poller.py
@brief Utility for polling
'''
import gevent
import functools
def poll(poller, *args, **kwargs):
'''
Polls a callback (poller) until success is met
poller must be a valid callback and returns True on success
'''
timeout = 10
if 'timeout' in kwargs:
timeout = kwargs['timeout']
kwargs.pop('timeout')
success = False
with gevent.timeout.Timeout(timeout):
while not success:
success = poller(*args, **kwargs)
gevent.sleep(0.2)
return success
def poll_wrapper(timeout):
def decorator(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
success = False
with gevent.timeout.Timeout(timeout):
while not success:
success = func(*args, **kwargs)
gevent.sleep(0.2)
return success
return wrapper
return decorator
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marconi/tests/queues/storage/base.py | andrew-vant/marconi | 0 | 1613600 | <reponame>andrew-vant/marconi
# Copyright (c) 2013 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import time
import uuid
import ddt
import six
from testtools import matchers
from marconi.openstack.common.cache import cache as oslo_cache
from marconi.openstack.common import timeutils
from marconi.queues import storage
from marconi.queues.storage import errors
from marconi import tests as testing
from marconi.tests import helpers
class ControllerBaseTest(testing.TestBase):
project = 'project'
driver_class = None
controller_class = None
controller_base_class = None
def setUp(self):
super(ControllerBaseTest, self).setUp()
if not self.driver_class:
self.skipTest('No driver class specified')
if not issubclass(self.controller_class, self.controller_base_class):
self.skipTest('{0} is not an instance of {1}. '
'Tests not supported'.format(
self.controller_class,
self.controller_base_class))
oslo_cache.register_oslo_configs(self.conf)
cache = oslo_cache.get_cache(self.conf.cache_url)
# pylint: disable=not-callable
self.driver = self.driver_class(self.conf, cache)
self._prepare_conf()
self.addCleanup(self._purge_databases)
# pylint: disable=not-callable
self.controller = self.controller_class(self.driver)
def _prepare_conf(self):
"""Prepare the conf before running tests
Classes overriding this method, must use
the `self.conf` instance and alter its state.
"""
def _purge_databases(self):
"""Override to clean databases."""
@ddt.ddt
class QueueControllerTest(ControllerBaseTest):
"""Queue Controller base tests."""
controller_base_class = storage.Queue
def setUp(self):
super(QueueControllerTest, self).setUp()
self.queue_controller = self.driver.queue_controller
self.message_controller = self.driver.message_controller
self.claim_controller = self.driver.claim_controller
@ddt.data(None, ControllerBaseTest.project)
def test_list(self, project):
# NOTE(kgriffs): Ensure we mix global and scoped queues
# in order to verify that queue records are exluded that
# are not at the same level.
project_alt = self.project if project is None else None
num = 15
for queue in six.moves.xrange(num):
self.controller.create(str(queue), project=project)
self.controller.create(str(queue), project=project_alt)
interaction = self.controller.list(project=project,
detailed=True)
queues = list(next(interaction))
self.assertEqual(all(map(lambda queue:
'name' in queue and
'metadata' in queue, queues)), True)
self.assertEqual(len(queues), 10)
interaction = self.controller.list(project=project,
marker=next(interaction))
queues = list(next(interaction))
self.assertEqual(all(map(lambda queue:
'name' in queue and
'metadata' not in queue, queues)), True)
self.assertEqual(len(queues), 5)
def test_queue_lifecycle(self):
# Test queue creation
created = self.controller.create('test', project=self.project)
self.assertTrue(created)
# Test queue existence
self.assertTrue(self.controller.exists('test', project=self.project))
# Test queue retrieval
interaction = self.controller.list(project=self.project)
queue = list(next(interaction))[0]
self.assertEqual(queue['name'], 'test')
# Test queue metadata retrieval
metadata = self.controller.get_metadata('test', project=self.project)
self.assertEqual(metadata, {})
# Test queue update
created = self.controller.set_metadata('test', project=self.project,
metadata=dict(meta='test_meta'))
metadata = self.controller.get_metadata('test', project=self.project)
self.assertEqual(metadata['meta'], 'test_meta')
# Touching an existing queue does not affect metadata
created = self.controller.create('test', project=self.project)
self.assertFalse(created)
metadata = self.controller.get_metadata('test', project=self.project)
self.assertEqual(metadata['meta'], 'test_meta')
client_uuid = uuid.uuid4()
# Test queue statistic
_insert_fixtures(self.message_controller, 'test',
project=self.project, client_uuid=client_uuid,
num=6)
# NOTE(kgriffs): We can't get around doing this, because
# we don't know how the storage drive may be calculating
# message timestamps (and may not be monkey-patchable).
time.sleep(1.2)
_insert_fixtures(self.message_controller, 'test',
project=self.project, client_uuid=client_uuid,
num=6)
stats = self.controller.stats('test', project=self.project)
message_stats = stats['messages']
self.assertEqual(message_stats['free'], 12)
self.assertEqual(message_stats['claimed'], 0)
self.assertEqual(message_stats['total'], 12)
oldest = message_stats['oldest']
newest = message_stats['newest']
self.assertNotEqual(oldest, newest)
age = oldest['age']
self.assertThat(age, matchers.GreaterThan(0))
# NOTE(kgriffs): Ensure is different enough
# for the next comparison to work.
soon = timeutils.utcnow() + datetime.timedelta(seconds=60)
for message_stat in (oldest, newest):
created_iso = message_stat['created']
created = timeutils.parse_isotime(created_iso)
self.assertThat(timeutils.normalize_time(created),
matchers.LessThan(soon))
self.assertIn('id', message_stat)
self.assertThat(oldest['created'],
matchers.LessThan(newest['created']))
# Test queue deletion
self.controller.delete('test', project=self.project)
# Test queue existence
self.assertFalse(self.controller.exists('test', project=self.project))
# Test DoesNotExist exception
with testing.expect(storage.errors.DoesNotExist):
self.controller.get_metadata('test', project=self.project)
with testing.expect(storage.errors.DoesNotExist):
self.controller.set_metadata('test', '{}', project=self.project)
def test_stats_for_empty_queue(self):
created = self.controller.create('test', project=self.project)
self.assertTrue(created)
stats = self.controller.stats('test', project=self.project)
message_stats = stats['messages']
self.assertEqual(message_stats['free'], 0)
self.assertEqual(message_stats['claimed'], 0)
self.assertEqual(message_stats['total'], 0)
self.assertNotIn('newest', message_stats)
self.assertNotIn('oldest', message_stats)
class MessageControllerTest(ControllerBaseTest):
"""Message Controller base tests.
NOTE(flaper87): Implementations of this class should
override the tearDown method in order
to clean up storage's state.
"""
queue_name = 'test_queue'
controller_base_class = storage.Message
# Specifies how often expired messages are purged, in sec.
gc_interval = 0
def setUp(self):
super(MessageControllerTest, self).setUp()
# Lets create a queue
self.queue_controller = self.driver.queue_controller
self.claim_controller = self.driver.claim_controller
self.queue_controller.create(self.queue_name, project=self.project)
def tearDown(self):
self.queue_controller.delete(self.queue_name, project=self.project)
super(MessageControllerTest, self).tearDown()
def test_message_lifecycle(self):
queue_name = self.queue_name
message = {
'ttl': 60,
'body': {
'event': 'BackupStarted',
'backupId': 'c378813c-3f0b-11e2-ad92-7823d2b0f3ce'
}
}
# Test Message Creation
created = list(self.controller.post(queue_name, [message],
project=self.project,
client_uuid=uuid.uuid4()))
self.assertEqual(len(created), 1)
message_id = created[0]
# Test Message Get
message_out = self.controller.get(queue_name, message_id,
project=self.project)
self.assertEqual(set(message_out), set(('id', 'body', 'ttl', 'age')))
self.assertEqual(message_out['id'], message_id)
self.assertEqual(message_out['body'], message['body'])
self.assertEqual(message_out['ttl'], message['ttl'])
# Test Message Deletion
self.controller.delete(queue_name, message_id, project=self.project)
# Test does not exist
with testing.expect(storage.errors.DoesNotExist):
self.controller.get(queue_name, message_id, project=self.project)
def test_get_multi(self):
client_uuid = uuid.uuid4()
_insert_fixtures(self.controller, self.queue_name,
project=self.project, client_uuid=client_uuid, num=15)
def load_messages(expected, *args, **kwargs):
interaction = self.controller.list(*args, **kwargs)
msgs = list(next(interaction))
self.assertEqual(len(msgs), expected)
return interaction
# Test all messages, echo False and uuid
load_messages(0, self.queue_name, project=self.project,
client_uuid=client_uuid)
# Test all messages and limit
load_messages(15, self.queue_name, project=self.project, limit=20,
echo=True)
# Test all messages, echo True, and uuid
interaction = load_messages(10, self.queue_name, echo=True,
project=self.project,
client_uuid=client_uuid)
# Test all messages, echo True, uuid and marker
load_messages(5, self.queue_name, echo=True, project=self.project,
marker=next(interaction), client_uuid=client_uuid)
def test_multi_ids(self):
messages_in = [{'ttl': 120, 'body': 0}, {'ttl': 240, 'body': 1}]
ids = self.controller.post(self.queue_name, messages_in,
project=self.project,
client_uuid=uuid.uuid4())
messages_out = self.controller.bulk_get(self.queue_name, ids,
project=self.project)
for idx, message in enumerate(messages_out):
self.assertEqual(set(message), set(('id', 'body', 'ttl', 'age')))
self.assertEqual(message['body'], idx)
self.controller.bulk_delete(self.queue_name, ids,
project=self.project)
with testing.expect(StopIteration):
result = self.controller.bulk_get(self.queue_name, ids,
project=self.project)
next(result)
def test_claim_effects(self):
client_uuid = uuid.uuid4()
_insert_fixtures(self.controller, self.queue_name,
project=self.project, client_uuid=client_uuid, num=12)
def list_messages(include_claimed=None):
kwargs = {
'project': self.project,
'client_uuid': client_uuid,
'echo': True,
}
# Properly test default value
if include_claimed is not None:
kwargs['include_claimed'] = include_claimed
interaction = self.controller.list(self.queue_name, **kwargs)
messages = next(interaction)
return [msg['id'] for msg in messages]
messages_before = list_messages(True)
meta = {'ttl': 70, 'grace': 60}
another_cid, _ = self.claim_controller.create(self.queue_name, meta,
project=self.project)
messages_after = list_messages(True)
self.assertEqual(messages_before, messages_after)
messages_excluding_claimed = list_messages()
self.assertNotEqual(messages_before, messages_excluding_claimed)
self.assertEqual(2, len(messages_excluding_claimed))
cid, msgs = self.claim_controller.create(self.queue_name, meta,
project=self.project)
[msg1, msg2] = msgs
# A wrong claim does not ensure the message deletion
with testing.expect(storage.errors.NotPermitted):
self.controller.delete(self.queue_name, msg1['id'],
project=self.project,
claim=another_cid)
# Make sure a message can be deleted with a claim
self.controller.delete(self.queue_name, msg1['id'],
project=self.project,
claim=cid)
with testing.expect(storage.errors.DoesNotExist):
self.controller.get(self.queue_name, msg1['id'],
project=self.project)
# Make sure such a deletion is idempotent
self.controller.delete(self.queue_name, msg1['id'],
project=self.project,
claim=cid)
# A non-existing claim does not ensure the message deletion
self.claim_controller.delete(self.queue_name, cid,
project=self.project)
with testing.expect(storage.errors.NotPermitted):
self.controller.delete(self.queue_name, msg2['id'],
project=self.project,
claim=cid)
@testing.is_slow(condition=lambda self: self.gc_interval != 0)
def test_expired_messages(self):
messages = [{'body': 3.14, 'ttl': 0}]
client_uuid = uuid.uuid4()
[msgid] = self.controller.post(self.queue_name, messages,
project=self.project,
client_uuid=client_uuid)
time.sleep(self.gc_interval)
with testing.expect(storage.errors.DoesNotExist):
self.controller.get(self.queue_name, msgid,
project=self.project)
stats = self.queue_controller.stats(self.queue_name,
project=self.project)
self.assertEqual(stats['messages']['free'], 0)
def test_bad_id(self):
# NOTE(cpp-cabrera): A malformed ID should result in an empty
# query. Raising an exception for validating IDs makes the
# implementation more verbose instead of taking advantage of
# the Maybe/Optional protocol, particularly when dealing with
# bulk operations.
bad_message_id = 'xyz'
self.controller.delete(self.queue_name,
bad_message_id,
project=self.project)
with testing.expect(errors.MessageDoesNotExist):
self.controller.get(self.queue_name,
bad_message_id,
project=self.project)
def test_bad_claim_id(self):
[msgid] = self.controller.post(self.queue_name,
[{'body': {}, 'ttl': 10}],
project=self.project,
client_uuid=uuid.uuid4())
bad_claim_id = '; DROP TABLE queues'
self.controller.delete(self.queue_name,
msgid,
project=self.project,
claim=bad_claim_id)
def test_bad_marker(self):
bad_marker = 'xyz'
interaction = self.controller.list(self.queue_name,
project=self.project,
client_uuid=uuid.uuid4(),
marker=bad_marker)
messages = list(next(interaction))
self.assertEqual(messages, [])
def test_sort_for_first(self):
client_uuid = uuid.uuid4()
[msgid_first] = self.controller.post(self.queue_name,
[{'body': {}, 'ttl': 120}],
project=self.project,
client_uuid=client_uuid)
_insert_fixtures(self.controller, self.queue_name,
project=self.project, client_uuid=client_uuid, num=10)
[msgid_last] = self.controller.post(self.queue_name,
[{'body': {}, 'ttl': 120}],
project=self.project,
client_uuid=client_uuid)
msg_asc = self.controller.first(self.queue_name,
self.project,
1)
self.assertEqual(msg_asc['id'], msgid_first)
msg_desc = self.controller.first(self.queue_name,
self.project,
-1)
self.assertEqual(msg_desc['id'], msgid_last)
class ClaimControllerTest(ControllerBaseTest):
"""Claim Controller base tests.
NOTE(flaper87): Implementations of this class should
override the tearDown method in order
to clean up storage's state.
"""
queue_name = 'test_queue'
controller_base_class = storage.Claim
def setUp(self):
super(ClaimControllerTest, self).setUp()
# Lets create a queue
self.queue_controller = self.driver.queue_controller
self.message_controller = self.driver.message_controller
self.queue_controller.create(self.queue_name, project=self.project)
def tearDown(self):
self.queue_controller.delete(self.queue_name, project=self.project)
super(ClaimControllerTest, self).tearDown()
def test_claim_lifecycle(self):
_insert_fixtures(self.message_controller, self.queue_name,
project=self.project, client_uuid=uuid.uuid4(),
num=20)
meta = {'ttl': 70, 'grace': 30}
# Make sure create works
claim_id, messages = self.controller.create(self.queue_name, meta,
project=self.project,
limit=15)
messages = list(messages)
self.assertEqual(len(messages), 15)
# Ensure Queue stats
countof = self.queue_controller.stats(self.queue_name,
project=self.project)
self.assertEqual(countof['messages']['claimed'], 15)
self.assertEqual(countof['messages']['free'], 5)
self.assertEqual(countof['messages']['total'], 20)
# Make sure get works
claim, messages2 = self.controller.get(self.queue_name, claim_id,
project=self.project)
messages2 = list(messages2)
self.assertEqual(len(messages2), 15)
self.assertEqual(messages, messages2)
self.assertEqual(claim['ttl'], 70)
self.assertEqual(claim['id'], claim_id)
new_meta = {'ttl': 100, 'grace': 60}
self.controller.update(self.queue_name, claim_id,
new_meta, project=self.project)
# Make sure update works
claim, messages2 = self.controller.get(self.queue_name, claim_id,
project=self.project)
messages2 = list(messages2)
self.assertEqual(len(messages2), 15)
# TODO(zyuan): Add some tests to ensure the ttl is
# extended/not-extended.
for msg1, msg2 in zip(messages, messages2):
self.assertEqual(msg1['body'], msg2['body'])
self.assertEqual(claim['ttl'], 100)
self.assertEqual(claim['id'], claim_id)
# Make sure delete works
self.controller.delete(self.queue_name, claim_id,
project=self.project)
self.assertRaises(storage.errors.ClaimDoesNotExist,
self.controller.get, self.queue_name,
claim_id, project=self.project)
def test_extend_lifetime(self):
_insert_fixtures(self.message_controller, self.queue_name,
project=self.project, client_uuid=uuid.uuid4(),
num=20, ttl=120)
meta = {'ttl': 777, 'grace': 0}
claim_id, messages = self.controller.create(self.queue_name, meta,
project=self.project)
for message in messages:
self.assertEqual(message['ttl'], 777)
def test_extend_lifetime_with_grace_1(self):
_insert_fixtures(self.message_controller, self.queue_name,
project=self.project, client_uuid=uuid.uuid4(),
num=20, ttl=120)
meta = {'ttl': 777, 'grace': 23}
claim_id, messages = self.controller.create(self.queue_name, meta,
project=self.project)
for message in messages:
self.assertEqual(message['ttl'], 800)
def test_extend_lifetime_with_grace_2(self):
_insert_fixtures(self.message_controller, self.queue_name,
project=self.project, client_uuid=uuid.uuid4(),
num=20, ttl=120)
# Although ttl is less than the message's TTL, the grace
# period puts it just over the edge.
meta = {'ttl': 100, 'grace': 22}
claim_id, messages = self.controller.create(self.queue_name, meta,
project=self.project)
for message in messages:
self.assertEqual(message['ttl'], 122)
def test_do_not_extend_lifetime(self):
_insert_fixtures(self.message_controller, self.queue_name,
project=self.project, client_uuid=uuid.uuid4(),
num=20, ttl=120)
# Choose a ttl that is less than the message's current TTL
meta = {'ttl': 60, 'grace': 30}
claim_id, messages = self.controller.create(self.queue_name, meta,
project=self.project)
for message in messages:
self.assertEqual(message['ttl'], 120)
def test_expired_claim(self):
meta = {'ttl': 0, 'grace': 60}
claim_id, messages = self.controller.create(self.queue_name, meta,
project=self.project)
with testing.expect(storage.errors.DoesNotExist):
self.controller.get(self.queue_name, claim_id,
project=self.project)
with testing.expect(storage.errors.DoesNotExist):
self.controller.update(self.queue_name, claim_id,
meta, project=self.project)
def test_illformed_id(self):
# any ill-formed IDs should be regarded as non-existing ones.
self.controller.delete(self.queue_name,
'illformed',
project=self.project)
with testing.expect(errors.DoesNotExist):
self.controller.get(self.queue_name,
'illformed',
project=self.project)
with testing.expect(errors.DoesNotExist):
self.controller.update(self.queue_name,
'illformed',
{'ttl': 40},
project=self.project)
class ShardsControllerTest(ControllerBaseTest):
"""Shards Controller base tests.
NOTE(flaper87): Implementations of this class should
override the tearDown method in order
to clean up storage's state.
"""
controller_base_class = storage.ShardsBase
def setUp(self):
super(ShardsControllerTest, self).setUp()
self.shards_controller = self.driver.shards_controller
# Let's create one shard
self.shard = str(uuid.uuid1())
self.shards_controller.create(self.shard, 100, 'localhost', {})
def tearDown(self):
self.shards_controller.drop_all()
super(ShardsControllerTest, self).tearDown()
def test_create_succeeds(self):
self.shards_controller.create(str(uuid.uuid1()),
100, 'localhost', {})
def test_create_replaces_on_duplicate_insert(self):
name = str(uuid.uuid1())
self.shards_controller.create(name,
100, 'localhost', {})
self.shards_controller.create(name,
111, 'localhost2', {})
entry = self.shards_controller.get(name)
self._shard_expects(entry, xname=name, xweight=111,
xlocation='localhost2')
def _shard_expects(self, shard, xname, xweight, xlocation):
self.assertIn('name', shard)
self.assertEqual(shard['name'], xname)
self.assertIn('weight', shard)
self.assertEqual(shard['weight'], xweight)
self.assertIn('uri', shard)
self.assertEqual(shard['uri'], xlocation)
def test_get_returns_expected_content(self):
res = self.shards_controller.get(self.shard)
self._shard_expects(res, self.shard, 100, 'localhost')
self.assertNotIn('options', res)
def test_detailed_get_returns_expected_content(self):
res = self.shards_controller.get(self.shard, detailed=True)
self.assertIn('options', res)
self.assertEqual(res['options'], {})
def test_get_raises_if_not_found(self):
self.assertRaises(storage.errors.ShardDoesNotExist,
self.shards_controller.get, 'notexists')
def test_exists(self):
self.assertTrue(self.shards_controller.exists(self.shard))
self.assertFalse(self.shards_controller.exists('notexists'))
def test_update_raises_assertion_error_on_bad_fields(self):
self.assertRaises(AssertionError, self.shards_controller.update,
self.shard)
def test_update_works(self):
self.shards_controller.update(self.shard, weight=101,
uri='redis://localhost',
options={'a': 1})
res = self.shards_controller.get(self.shard, detailed=True)
self._shard_expects(res, self.shard, 101, 'redis://localhost')
self.assertEqual(res['options'], {'a': 1})
def test_delete_works(self):
self.shards_controller.delete(self.shard)
self.assertFalse(self.shards_controller.exists(self.shard))
def test_delete_nonexistent_is_silent(self):
self.shards_controller.delete('nonexisting')
def test_drop_all_leads_to_empty_listing(self):
self.shards_controller.drop_all()
cursor = self.shards_controller.list()
self.assertRaises(StopIteration, next, cursor)
def test_listing_simple(self):
# NOTE(cpp-cabrera): base entry interferes with listing results
self.shards_controller.delete(self.shard)
name_gen = lambda i: chr(ord('A') + i)
for i in range(15):
self.shards_controller.create(name_gen(i), i, str(i), {})
res = list(self.shards_controller.list())
self.assertEqual(len(res), 10)
for i, entry in enumerate(res):
self._shard_expects(entry, name_gen(i), i, str(i))
self.assertNotIn('options', entry)
res = list(self.shards_controller.list(limit=5))
self.assertEqual(len(res), 5)
res = next(self.shards_controller.list(marker=name_gen(3)))
self._shard_expects(res, name_gen(4), 4, '4')
res = list(self.shards_controller.list(detailed=True))
self.assertEqual(len(res), 10)
for i, entry in enumerate(res):
self._shard_expects(entry, name_gen(i), i, str(i))
self.assertIn('options', entry)
self.assertEqual(entry['options'], {})
class CatalogueControllerTest(ControllerBaseTest):
controller_base_class = storage.CatalogueBase
def setUp(self):
super(CatalogueControllerTest, self).setUp()
self.controller = self.driver.catalogue_controller
self.queue = six.text_type(uuid.uuid1())
self.project = six.text_type(uuid.uuid1())
def tearDown(self):
self.controller.drop_all()
super(CatalogueControllerTest, self).tearDown()
def _check_structure(self, entry):
self.assertIn('queue', entry)
self.assertIn('project', entry)
self.assertIn('shard', entry)
self.assertIsInstance(entry['queue'], six.text_type)
self.assertIsInstance(entry['project'], six.text_type)
self.assertIsInstance(entry['shard'], six.text_type)
def _check_value(self, entry, xqueue, xproject, xshard):
self.assertEqual(entry['queue'], xqueue)
self.assertEqual(entry['project'], xproject)
self.assertEqual(entry['shard'], xshard)
def test_catalogue_entry_life_cycle(self):
queue = self.queue
project = self.project
# check listing is initially empty
for p in self.controller.list(project):
self.fail('There should be no entries at this time')
# create a listing, check its length
with helpers.shard_entries(self.controller, 10) as expect:
project = expect[0][0]
xs = list(self.controller.list(project))
self.assertEqual(len(xs), 10)
# create, check existence, delete
with helpers.shard_entry(self.controller, project, queue, u'a'):
self.assertTrue(self.controller.exists(project, queue))
# verify it no longer exists
self.assertFalse(self.controller.exists(project, queue))
# verify it isn't listable
self.assertEqual(len(list(self.controller.list(project))), 0)
def test_list(self):
with helpers.shard_entries(self.controller, 10) as expect:
values = zip(self.controller.list(u'_'), expect)
for e, x in values:
p, q, s = x
self._check_structure(e)
self._check_value(e, xqueue=q, xproject=p, xshard=s)
def test_update(self):
with helpers.shard_entry(self.controller, self.project,
self.queue, u'a') as expect:
p, q, s = expect
self.controller.update(p, q, shard=u'b')
entry = self.controller.get(p, q)
self._check_value(entry, xqueue=q, xproject=p, xshard=u'b')
def test_update_raises_when_entry_does_not_exist(self):
self.assertRaises(errors.QueueNotMapped,
self.controller.update,
'not', 'not', 'a')
def test_get(self):
with helpers.shard_entry(self.controller,
self.project,
self.queue, u'a') as expect:
p, q, s = expect
e = self.controller.get(p, q)
self._check_value(e, xqueue=q, xproject=p, xshard=s)
def test_get_raises_if_does_not_exist(self):
with helpers.shard_entry(self.controller,
self.project,
self.queue, u'a') as expect:
p, q, _ = expect
self.assertRaises(errors.QueueNotMapped,
self.controller.get,
p, 'non_existing')
self.assertRaises(errors.QueueNotMapped,
self.controller.get,
'non_existing', q)
self.assertRaises(errors.QueueNotMapped,
self.controller.get,
'non_existing', 'non_existing')
def test_exists(self):
with helpers.shard_entry(self.controller,
self.project,
self.queue, u'a') as expect:
p, q, _ = expect
self.assertTrue(self.controller.exists(p, q))
self.assertFalse(self.controller.exists('nada', 'not_here'))
def _insert_fixtures(controller, queue_name, project=None,
client_uuid=None, num=4, ttl=120):
def messages():
for n in six.moves.xrange(num):
yield {
'ttl': ttl,
'body': {
'event': 'Event number {0}'.format(n)
}}
controller.post(queue_name, messages(),
project=project, client_uuid=client_uuid)
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] |
examples/pervasive/modules/wip/ll_controls.py | EricLina/attn2d | 490 | 87177 | import sys
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
def progressive_max(x):
T = x.size(1)
x = F.pad(x, (T-1, 0), 'constant', -1)
x = F.max_pool1d(x.unsqueeze(1).float(), # shape into B, C, T
T, # kernel size
1, # stride
0, # padding
1, # dilation
False, # ceil_mode
False, # return indices
)
return x.squeeze(1) # B, Tt
def logsumexp(a, b):
m = torch.max(a, b)
return torch.log(torch.exp(a - m) + torch.exp(b - m))
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.)
return m
class LLControls(nn.Module):
"""
LL based controller
"""
def __init__(self, args, controller_dim):
nn.Module.__init__(self)
self.gate = nn.Linear(controller_dim, 1, bias=True)
nn.init.normal_(self.gate.weight, 0, 1/controller_dim)
nn.init.constant_(self.gate.bias, 0)
self.penalty = args.oracle_penalty
self.write_right = args.write_right
def get_positions_proba(self, rw_logits):
"""
Inputs:
rw_logits [log(rho), log(1-rho)] : (Tt, B, Ts, 2)
Returns the probabilities of being at position (t,j) (Tt, B, Ts)
"""
Tt, B, Ts, _ = rw_logits.size()
Mr1 = rw_logits[0:1,:,:-1,0].exp()
Mc1 = rw_logits[:,:,0:1,1].exp()
M = rw_logits[1:,:,:-1,0].exp() + rw_logits[:-1,:,1:,1].exp()
M = torch.cat((Mr1, M), dim=0)
M = torch.cat((Mc1, M), dim=-1)
return M
def predict_read_write(self, x):
""" Returns log(rho), log(1-rho) in B, Tt, Ts, 2 """
x = self.gate(x)
s = F.logsigmoid(x)
return torch.cat((s, s-x), dim=-1).float()
def forward(self, observations, scores):
"""
Inputs:
observations : Input for the controller: B, Tt, Ts, C
Scores : log p(y_t | x<j) : B, Tt, Ts
"""
controls = self.predict_read_write(observations) # B,Tt,Ts,2
B, Tt, Ts = scores.size()
with torch.no_grad():
if self.penalty:
# Penalize large contexts:
indices = torch.arange(
Ts,
dtype=scores.dtype,
device=scores.device
) / Ts
scores = scores - self.penalty * indices.unsqueeze(0).unsqueeze(0)
best_context = scores.max(-1)[1] # B, Tt
best_context = progressive_max(best_context).type_as(best_context)
AP = best_context.float().mean(dim=1) / Ts
print('AP:', ' '.join(map(lambda x: '{:.2f}'.format(x), AP.tolist())))
gamma = torch.zeros_like(scores).scatter_(-1, best_context.unsqueeze(-1), 1.0) # B, Tt, Ts
if self.write_right:
gamma = gamma.cumsum(dim=-1)
# Write beyond the ideal context
if self.write_right:
write = gamma[:, 1:] # B, Tt-1, Ts
else:
write = gamma[:, 1:].cumsum(dim=-1) # B, Tt-1, Ts
read = 1 - write
return controls[:, :-1], gamma, read, write
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gpvdm_data/shape/Gaus/example.py | roderickmackenzie/gpvdm | 12 | 45840 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
from gpvdm_api import gpvdm_api
def run():
a=gpvdm_api(verbose=True)
a.set_save_dir(device_data)
a.edit("light.inp","#light_model","qe")
a.edit("jv0.inp","#Vstop","0.8")
a.run() | [
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src/orion/algo/robo/__init__.py | lebrice/orion.algo.robo | 0 | 15272 | # -*- coding: utf-8 -*-
"""
Wrapper for RoBO
"""
__descr__ = "TODO"
__license__ = "BSD 3-Clause"
__author__ = u"Epistímio"
__author_short__ = u"Epistímio"
__author_email__ = "<EMAIL>"
__copyright__ = u"2021, Epistímio"
__url__ = "https://github.com/Epistimio/orion.algo.robo"
from ._version import get_versions
__version__ = get_versions()["version"]
del get_versions
| [
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rgd/geodata/migrations/0004_auto_20210302_2017.py | Erotemic/ResonantGeoData | 0 | 98492 | # Generated by Django 3.2a1 on 2021-03-02 20:17
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('geodata', '0003_auto_20210301_1639'),
]
operations = [
migrations.RemoveField(
model_name='imageentry',
name='failure_reason',
),
migrations.RemoveField(
model_name='imageentry',
name='metadata',
),
migrations.RemoveField(
model_name='imageentry',
name='status',
),
migrations.DeleteModel(
name='Thumbnail',
),
]
| [
1,
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13,
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13,
4706,
10353,
13,
1678,
4514,
13,
2
] |
examples/gto/13-symmetry.py | robert-anderson/pyscf | 2 | 120520 | <gh_stars>1-10
#!/usr/bin/env python
#
# Author: <NAME> <<EMAIL>>
#
from pyscf import gto
'''
Specify symmetry.
Mole.symmetry can be True/False to turn on/off the symmetry (default is off),
or a string to specify the symmetry of molecule.
If symmetry is bool type, the atom coordinates might be changed. The molecule
will be rotated to a propper orientation in which the highest rotation axis
is parallel to Z-axis. Also, symmetry_subgroup keyword can be used to
generate a subgroup of the dectected symmetry. symmetry_subgroup keyword has
no effect when symmetry symbol is explicitly given.
NOTE: to assign Mole.symmetry with a given symbol, the molecule must be put
in a proper orientation. The program will check the given symmetry to ensure
that the molecule has the required symmetry. If the detected symmetry does
not match to the assigend symmetry, you will see a warning message on the
screen.
Symmetry adapted basis are stored in Mole object.
'''
mol = gto.M(
atom = 'C 0 .2 0; O 0 0 1.1',
symmetry = True,
)
print('Symmetry %-4s, using subgroup %s. The molecule geometry is changed' %
(mol.topgroup, mol.groupname))
for x in mol._atom:
print(x)
print('--\n')
mol = gto.M(
atom = 'C 0 .2 0; O 0 0 1.1',
symmetry = True,
symmetry_subgroup = 'C2v',
)
print('Symmetry %-4s, using subgroup %s. The molecule geometry is changed' %
(mol.topgroup, mol.groupname))
for x in mol._atom:
print(x)
print('--\n')
try:
mol = gto.M(
atom = 'C 0 .2 0; O 0 0 1.1',
symmetry = 'C2v',
)
except RuntimeWarning as e:
print('Unable to identify the symmetry with the input geometry. Error msg:')
print(e)
print('--\n')
mol = gto.M(
atom = 'C 0 0 0; O 0 0 1.5',
symmetry = 'C2v',
)
print('Symmetry %-4s, using subgroup %s.' % (mol.topgroup, mol.groupname))
print('If "symmetry=string" was specified, the string is taken as the '
'group name and the geometry is kept')
for x in mol._atom:
print(x)
for k, ir in enumerate(mol.irrep_name):
print('Irrep name %s (ID %d), symm-adapted-basis shape %s' %
(ir, mol.irrep_id[k], mol.symm_orb[k].shape))
| [
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13,
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413,
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297,
26985,
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324,
29889,
381,
3445,
29918,
978,
1125,
13,
1678,
1596,
877,
29902,
29878,
3445,
1024,
1273,
29879,
29871,
313,
1367,
1273,
29881,
511,
5016,
29885,
29899,
1114,
415,
287,
29899,
6500,
275,
8267,
1273,
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29889,
11967,
29885,
29918,
11831,
29961,
29895,
1822,
12181,
876,
13,
2
] |
example_project/myshop/urls.py | bluedisk/django-iamport | 6 | 122996 | <reponame>bluedisk/django-iamport
# -*- coding: utf-8 -*-
from django.conf.urls import url
from django.urls import path
from . import views
app_name = "myshop"
urlpatterns = [
path('', views.home, name='home'),
path('retry_order/<int:order_id>', views.retry_order, name='retry_order'),
]
| [
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5477,
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13,
29962,
13,
2
] |
defs/__init__.py | scionrep/scioncc_new | 2 | 120067 | # Declare as Python package, such that setuptools can include the data files
# (YML, SQL, etc) with the binary egg distribution. | [
1,
396,
3826,
8663,
408,
5132,
3577,
29892,
1316,
393,
731,
21245,
8789,
508,
3160,
278,
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2066,
13,
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29892,
2992,
29897,
411,
278,
7581,
19710,
4978,
29889,
2
] |
maxsmi/utils.py | t-kimber/maxsmi | 1 | 149721 | """
utils.py
Python utils functionalities.
Handles the primary functions
"""
import argparse
from maxsmi.augmentation_strategies import (
no_augmentation,
augmentation_with_duplication,
augmentation_without_duplication,
augmentation_with_reduced_duplication,
augmentation_maximum_estimation,
)
def string_to_bool(string):
"""
Converts a string to a bool.
Parameters
----------
string : str
String.
Returns
-------
bool :
True or False depending on the string.
"""
if isinstance(string, bool):
return string
if string in ("yes", "True", "true", "1"):
return True
elif string in ("no", "False", "false", "0"):
return False
else:
raise argparse.ArgumentTypeError("Choose between 'yes' or 'no'.")
def augmentation_strategy(string):
"""
Converts a string to an augmentation strategy.
Parameters
----------
string : str
String describing the augmentation strategy to be used.
Returns
-------
function :
The augmentation strategy.
"""
if string == "no_augmentation":
return no_augmentation
elif string == "augmentation_with_duplication":
return augmentation_with_duplication
if string == "augmentation_without_duplication":
return augmentation_without_duplication
elif string == "augmentation_with_reduced_duplication":
return augmentation_with_reduced_duplication
elif string == "augmentation_maximum_estimation":
return augmentation_maximum_estimation
else:
raise argparse.ArgumentTypeError(
"Choose between 'no_augmentation',"
"'augmentation_with_duplication', 'augmentation_without_duplication"
"'augmentation_with_reduced_duplication' and 'augmentation_maximum_estimation'."
)
| [
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358,
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13,
9651,
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358,
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2541,
29918,
9313,
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1414,
29915,
322,
525,
2987,
358,
362,
29918,
27525,
398,
29918,
342,
7715,
29915,
1213,
13,
4706,
1723,
13,
2
] |
tests/mac-logs/examples/tag/tag.py | andrewp-as-is/mac-logs.py | 1 | 39579 | <filename>tests/mac-logs/examples/tag/tag.py
#!/usr/bin/env python
import mac_logs
mac_logs.tag()
| [
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] |
Task1E.py | Yichen-Cai-yc539/Lab87-Flood-Warning-System | 0 | 169581 | <filename>Task1E.py
from floodsystem.stationdata import build_station_list
from floodsystem.geo import rivers_by_station_number
def run():
stations = build_station_list()
print(rivers_by_station_number(stations, 10))
run() | [
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] |
CtCI_custom_classes/linked_list.py | enyquist/Cracking_the_Coding_Interview | 0 | 1613701 | from random import randint
from CtCI_custom_classes.node import Node
class LinkedList:
def __init__(self):
self.head = None
def insert(self, data):
node = Node(data)
node.set_next(self.head)
self.head = node
def size(self):
current = self.head
count = 0
while current:
count += 1
current = current.get_next()
return count
def kth_to_last(self, k):
current = runner = self.head
for _ in range(k):
if runner is None:
return None
runner = runner.get_next()
while runner:
current = current.get_next()
runner = runner.get_next()
return current.get_data()
def search(self, data):
current = self.head
found = False
while current and found is False:
if current.get_data() == data:
found = True
else:
current = current.get_next()
if current is None:
raise ValueError('Data is not in list')
return current
def delete(self, data):
current = self.head
previous = None
found = False
while current and found is False:
if current.get_data() == data:
found = True
else:
previous = current
current = current.get_next()
if current is None:
raise ValueError('Data is not in list')
if previous is None:
self.head = current.get_next()
else:
previous.set_next(current.get_next())
def print_list(self):
temp = self.head
while temp:
print(temp.get_data())
temp = temp.get_next()
def generate(self, length, start, stop):
for _ in range(length):
val = randint(a=start, b=stop)
self.insert(val)
| [
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13,
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1583,
29889,
7851,
29898,
791,
29897,
13,
2
] |
list_tuple/10.py | mdazharuddin1011999/IoT_Assignment_2 | 0 | 108477 | <filename>list_tuple/10.py
from math import pi, exp
consts = (pi, exp)
print("Area of circle is:", consts[0]*int(input("Enter radius of circle: "))**2) | [
1,
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] |
python_reddit_img_download/main.py | brandonyuh/python_reddit_img_download | 1 | 120269 | <filename>python_reddit_img_download/main.py<gh_stars>1-10
import sys
import praw
import re
import pyperclip
import requests
#delete this line
import reddit_variables
# Replace with your own at https://www.reddit.com/prefs/apps
client_id = reddit_variables.client_id
client_secret = reddit_variables.client_secret
user_agent = reddit_variables.user_agent
reddit = praw.Reddit(client_id=client_id, client_secret=client_secret, user_agent=user_agent)
regex_url = re.compile(
r'^(?:http|ftp)s?://' # http:// or https://
r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|' # domain...
r'localhost|' # localhost...
r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip
r'(?::\d+)?' # optional port
r'(?:/?|[/?]\S+)$', re.IGNORECASE)
def main():
url = pyperclip.paste()
if (not re.match(regex_url, url)):
url = sys.argv[1]
if (not re.match(regex_url, url)):
url = input("URL?")
if (not re.match(regex_url, url)):
print("URL invalid quitting")
sys.exit(1)
submission = reddit.submission(url=url)
author = str(submission.author)
image_url = submission.url
id = str(submission.id)
local_image = author + "_" + id + ".png"
with open(author + "_" + id + ".txt", "w") as file:
file.write(url)
img_data = requests.get(image_url).content
with open(local_image, 'wb') as handler:
handler.write(img_data)
if __name__ == "__main__":
main() | [
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api/core/threads_core.py | michael-pryor/GeoTweetSearch | 1 | 86725 | <reponame>michael-pryor/GeoTweetSearch<filename>api/core/threads_core.py
import logging
import os
from threading import Thread
import traceback
import time
from api.config import Configuration
from api.core.utility import EventTimer
logger = logging.getLogger(__name__)
class BaseThread(Thread):
def __init__(self, threadName, onTerminateFunc=None, criticalThread=None, maxFailures=None, failureBackoffMaximumMs=None):
if criticalThread is None:
criticalThread = True
if criticalThread:
critString = '(critical)'
else:
critString = '(non critical)'
name = '%s %s' % (threadName, critString)
super(BaseThread,self).__init__(name=name)
if failureBackoffMaximumMs is None:
failureBackoffMaximumMs = Configuration.THREAD_FAILURE_DEFAULT_MAXIMUM_BACKOFF_MS
if maxFailures is None:
maxFailures = Configuration.THREAD_FAILURE_DEFAULT_MAXIMUM_COUNT
self.stopped = False
self.on_terminate_func = onTerminateFunc
self.is_critical_thread = criticalThread
self.failure_event_timer = EventTimer(maxFailures, failureBackoffMaximumMs * 2)
self.on_failure_wait_before_restart_seconds = float(1)
self.on_failure_wait_before_restart_seconds_maximum = float(failureBackoffMaximumMs) / 1000
def _run(self):
raise NotImplementedError
def _onRestart(self, e):
""" Called when thread fails and attempts to restart,
run method will be called afterwards.
@param e exception which triggered restart.
@return True if """
traceback.print_exc()
def _onFailure(self, e):
""" Called when restarts failed and thread is terminating. """
traceback.print_exc()
def run(self):
needRestart = None
while True:
logger.info('Starting thread: %s' % self.getName())
try:
if needRestart:
self._onRestart(needRestart)
needRestart = None
self._run()
if not self.stopped:
raise Exception('Thread illegally exited')
logger.warn('Thread legally exited and will be allowed to terminate: %s' % self.getName()) # looking at this, keep as warn for now.
break # if we reach this point thread is terminating normally.
except Exception as e:
logger.error('Exception in thread: %s - %s (%s)' % (self.getName(), e.message, type(e)))
if not self.failure_event_timer.onEvent():
if self.on_failure_wait_before_restart_seconds > 0:
if self.failure_event_timer.triggered_reset:
logger.error('Reset failure back off time to 1 second of thread: %s' % self.getName())
self.on_failure_wait_before_restart_seconds = float(1)
logger.error('Waiting %.2f seconds before restarting thread: %s' % (self.on_failure_wait_before_restart_seconds, self.getName()))
time.sleep(self.on_failure_wait_before_restart_seconds)
# Exponential increase in wait time (up to maximum).
if self.on_failure_wait_before_restart_seconds < self.on_failure_wait_before_restart_seconds_maximum:
self.on_failure_wait_before_restart_seconds *= 2
if self.on_failure_wait_before_restart_seconds > self.on_failure_wait_before_restart_seconds_maximum:
self.on_failure_wait_before_restart_seconds = self.on_failure_wait_before_restart_seconds_maximum
logger.error('Attempting to restart thread: %s' % self.getName())
needRestart = e
continue
else:
try:
self._onFailure(e)
except Exception as e:
logger.error('Exception while terminating thread: %s, details: %s' % (self.getName(), e.message))
if self.is_critical_thread:
logger.error('Critical thread failed, terminating application - thread responsible: %s' % self.getName())
os._exit(0)
else:
logger.error('Thread failed without terminating application: %s' % self.getName())
break
logger.warn('Thread terminating: %s' % self.getName()) # looking at this, keep as warn for now.
if self.on_terminate_func:
self.on_terminate_func()
def stop(self):
self.stopped = True | [
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] |
main/PublicEmotionDatasets/Adobe/process/train_glove.py | cvlab-stonybrook/Emotion-Prediction | 10 | 31650 | <filename>main/PublicEmotionDatasets/Adobe/process/train_glove.py
"""
Copyright (c) 2019 <NAME>
Licensed under the MIT License
Author: <NAME>
Email: <EMAIL>
Last modified: 18/12/2019
Usage:
Run this script to train GloVe model on Adobe tags.
"""
import os
import gluonnlp as nlp
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
from glove import Corpus, Glove
import glob
import pickle
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
model_folder = "/nfs/bigfovea/add_disk0/eugenia/Emotion/wordembedding_models/"
#Creating a corpus object
corpus = Corpus()
#Training the corpus to generate the co occurence matrix which is used in GloVe
corpus.fit(lines, window=10)
# train the model
glove = Glove(no_components=5, learning_rate=0.05)
glove.fit(corpus.matrix, epochs=30, no_threads=4, verbose=True)
glove.add_dictionary(corpus.dictionary)
# save the model
glove.save(os.path.join(model_folder, 'glove_adobe.model')) | [
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] |
tests/contrib/test_discord.py | eblume/flask-dance | 0 | 130882 | <filename>tests/contrib/test_discord.py<gh_stars>0
from __future__ import unicode_literals
import pytest
import responses
from urlobject import URLObject
from flask import Flask
from flask_dance.contrib.discord import make_discord_blueprint, discord
from flask_dance.consumer import OAuth2ConsumerBlueprint
from flask_dance.consumer.storage import MemoryStorage
@pytest.fixture
def make_app():
def _make_app(*args, **kwargs):
app = Flask(__name__)
app.secret_key = "whatever"
blueprint = make_discord_blueprint(*args, **kwargs)
app.register_blueprint(blueprint)
return app
return _make_app
def test_blueprint_factory():
discord_bp = make_discord_blueprint(
client_id="foo",
client_secret="bar",
scope=["identify", "email"],
redirect_to="index",
)
assert isinstance(discord_bp, OAuth2ConsumerBlueprint)
assert discord_bp.session.scope == ["identify", "email"]
assert discord_bp.session.base_url == "https://discordapp.com/"
assert discord_bp.session.client_id == "foo"
assert discord_bp.client_secret == "bar"
assert discord_bp.authorization_url == "https://discordapp.com/api/oauth2/authorize"
assert discord_bp.token_url == "https://discordapp.com/api/oauth2/token"
def test_load_from_config(make_app):
app = make_app(redirect_to="index")
app.config["DISCORD_OAUTH_CLIENT_ID"] = "foo"
app.config["DISCORD_OAUTH_CLIENT_SECRET"] = "bar"
resp = app.test_client().get("/discord")
url = resp.headers["Location"]
client_id = URLObject(url).query.dict.get("client_id")
assert client_id == "foo"
@responses.activate
def test_context_local(make_app):
responses.add(responses.GET, "https://google.com")
# set up two apps with two different set of auth tokens
app1 = make_app(
"foo1",
"bar1",
redirect_to="url1",
storage=MemoryStorage({"access_token": "<PASSWORD>"}),
)
app2 = make_app(
"foo2",
"bar2",
redirect_to="url2",
storage=MemoryStorage({"access_token": "<PASSWORD>"}),
)
# outside of a request context, referencing functions on the `discord` object
# will raise an exception
with pytest.raises(RuntimeError):
discord.get("https://google.com")
# inside of a request context, `discord` should be a proxy to the correct
# blueprint session
with app1.test_request_context("/"):
app1.preprocess_request()
discord.get("https://google.com")
request = responses.calls[0].request
assert request.headers["Authorization"] == "Bearer app1"
with app2.test_request_context("/"):
app2.preprocess_request()
discord.get("https://google.com")
request = responses.calls[1].request
assert request.headers["Authorization"] == "Bearer app2"
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] |
examples/python/macOS/hack_or_die.py | kitazaki/NORA_Badge | 0 | 20383 | from __future__ import print_function
import time
import uuid
import Adafruit_BluefruitLE
CHARACTERISTIC_SERVICE_UUID = uuid.UUID('0000fee0-0000-1000-8000-00805f9b34fb')
CHARACTERISTIC_DATA_UUID = uuid.UUID('0000fee1-0000-1000-8000-00805f9b34fb')
provider = Adafruit_BluefruitLE.get_provider()
def main():
provider.clear_cached_data()
adapter = provider.get_default_adapter()
if not adapter.is_powered:
adapter.power_on()
print('Searching for device...')
try:
adapter.start_scan()
device = provider.find_device(service_uuids=[CHARACTERISTIC_SERVICE_UUID])
if device is None:
raise RuntimeError('Failed to find device!')
else:
print(device)
print('device: {0}'.format(device.name))
print('id: {0}'.format(device.id))
finally:
adapter.stop_scan()
print('Connecting to device...')
device.connect()
try:
print('Discovering services...')
device.discover([CHARACTERISTIC_SERVICE_UUID], [CHARACTERISTIC_DATA_UUID])
service = device.find_service(CHARACTERISTIC_SERVICE_UUID)
print('service uuid: {0}'.format(service.uuid))
data = service.find_characteristic(CHARACTERISTIC_DATA_UUID)
print('characteristic uuid: {0}'.format(data.uuid))
print('Writing Data..')
bs = bytes(range(16))
bs = b'\x77\x61\x6E\x67\x00\x00\x00\x00\x30\x00\x00\x00\x00\x00\x00\x00'
data.write_value(bs)
time.sleep(0.1)
bs = b'\x00\x0b\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
data.write_value(bs)
time.sleep(0.1)
bs = b'\x00\x00\x00\x00\x00\x00\xE1\x0C\x06\x17\x2D\x23\x00\x00\x00\x00'
data.write_value(bs)
time.sleep(0.1)
bs = b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
data.write_value(bs)
time.sleep(0.1)
bs = b'\x00\x00\xc6\xc6\xc6\xfe\xc6\xc6\xc6\xc6\x00\x00\x00\xfe\xc6\xc6'
data.write_value(bs)
time.sleep(0.1)
bs = b'\xfe\xc6\xc6\xc6\xc6\x00\x00\x00\xfe\xc6\xc0\xc0\xc6\xc6\xc6\xfe'
data.write_value(bs)
time.sleep(0.1)
bs = b'\x00\x00\x00\xc6\xcc\xd8\xf0\xd8\xcc\xc6\xc6\x00\x00\x00\x00\x00'
data.write_value(bs)
time.sleep(0.1)
bs = b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x7c\x6c\x6c'
data.write_value(bs)
time.sleep(0.1)
bs = b'\x7c\x00\x00\x00\x00\x00\x00\x00\x6c\x78\x70\x60\x00\x00\x00\x00'
data.write_value(bs)
time.sleep(0.1)
bs = b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xf8\xce\xc6\xc6\xc6\xc6'
data.write_value(bs)
time.sleep(0.1)
bs = b'\xce\xf8\x00\x00\x00\x30\x30\x30\x30\x30\x30\x30\x30\x00\x00\x00'
data.write_value(bs)
time.sleep(0.1)
bs = b'\xfe\xc0\xc0\xfe\xc0\xc0\xc0\xfe\x00\x00\x00\x00\x00\x00\x00\x00'
data.write_value(bs)
time.sleep(3)
print('Writing done.')
finally:
device.disconnect()
provider.initialize()
provider.run_mainloop_with(main)
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] |
isimip_qc/exceptions.py | rouxter/isimip-qc | 4 | 124618 | class FileWarning(Exception):
def __init__(self, file, message, *args, **kwargs):
file.warn(message, *args, **kwargs)
super().__init__(message % args)
class FileError(Exception):
def __init__(self, file, message, *args, **kwargs):
file.error(message, *args, **kwargs)
super().__init__(message % args)
class FileCritical(Exception):
def __init__(self, file, message, *args, **kwargs):
file.critical(message, *args, **kwargs)
super().__init__(message % args)
| [
1,
770,
3497,
22709,
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src/Step5-extract-votes-for-categories-having-them-all.py | YossiCohen/Cross-Domain-Transfer-Learning-RecSys | 3 | 127866 | import csv
import time
import os
import pandas as pd
DATA_ROOT = "C:\\RS\\Amazon\\All\\"
MINIMUM_X_CATEGORIES_FILENAME = 'minimum_2_Categories.csv'
# MINIMUM_X_CATEGORIES_FILENAME = 'minimum_2_196k.csv'
SOURCE_RATING_FILES_TO_USE = ['ratings_Movies_and_TV.csv','ratings_CDs_and_Vinyl.csv']
TARGET_RATING_FILE = 'ratings_Toys_and_Games.csv'
##Items that untouched by users with both catogories will be ignored in both categories
timestamp = time.strftime('%y%m%d%H%M%S')
for category_filename in SOURCE_RATING_FILES_TO_USE:
big_table = pd.read_csv(os.path.join(DATA_ROOT, MINIMUM_X_CATEGORIES_FILENAME), index_col=['user_id'], usecols=['user_id', category_filename, TARGET_RATING_FILE])
out_filename = os.path.join(DATA_ROOT, timestamp + category_filename + '_FILTERED_BY_' + TARGET_RATING_FILE)
with open(out_filename, 'w', newline='', encoding='utf8') as filtered_ratings:
writer = csv.writer(filtered_ratings, delimiter=',', lineterminator='\n')
cat_file = open(os.path.join(DATA_ROOT, category_filename), 'rt')
try:
cat_file_reader = csv.reader(cat_file)
for row in cat_file_reader:
if row[0] in big_table.index:
if big_table.get_value(row[0],TARGET_RATING_FILE) != 0 and big_table.get_value(row[0],category_filename) !=0:
# print(row, big_table.get_value(row[0],TARGET_RATING_FILE), big_table.get_value(row[0],category_filename)) #TBD - Many more
writer.writerow(row)
filtered_ratings.flush()
finally:
cat_file.close()
out_filename2 = os.path.join(DATA_ROOT, timestamp + TARGET_RATING_FILE+ '_FILTERED_BY_' + category_filename )
with open(out_filename2, 'w', newline='', encoding='utf8') as filtered_ratings:
writer = csv.writer(filtered_ratings, delimiter=',', lineterminator='\n')
cat_file = open(os.path.join(DATA_ROOT, TARGET_RATING_FILE), 'rt')
try:
cat_file_reader = csv.reader(cat_file)
for row in cat_file_reader:
if row[0] in big_table.index:
if big_table.get_value(row[0],TARGET_RATING_FILE) != 0 and big_table.get_value(row[0],category_filename) !=0:
# print(row, big_table.get_value(row[0],TARGET_RATING_FILE), big_table.get_value(row[0],category_filename)) #TBD - Many more
writer.writerow(row)
filtered_ratings.flush()
finally:
cat_file.close()
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] |
src/pengbot/adapters/facebook/adapter.py | mariocesar/pengbot | 1 | 27520 | <reponame>mariocesar/pengbot<filename>src/pengbot/adapters/facebook/adapter.py
import json
from pengbot.utils import isbound
from .api import Facebook
from ..web import WebAdapter
class Adapter(WebAdapter):
_fbapi = None
@property
def fbapi(self):
if not self._fbapi:
self._fbapi = Facebook(self)
return self._fbapi
@WebAdapter.route('GET', r'/')
def handle_verify_token(self, start_response, request):
verify_token = request.GET.get('hub.verify_token', None)
challenge = request.GET.get('hub.challenge', None)
if verify_token == self.context.verify_token:
start_response('200 OK', [('Content-Type', 'text/plain')])
return challenge
else:
start_response('401 OK', [('Content-Type', 'text/plain')])
return "Invalid Token"
@WebAdapter.route('POST', r'/')
def handle_payload(self, start_response, request):
payload = json.loads(request.body.decode())
assert 'object' in payload, 'Missing object type in payload: %r' % payload
assert payload['object'] == 'page', 'Unknown object type: %r' % payload['object']
assert 'entry' in payload, 'Missing entry'
for entry in payload['entry']:
try:
assert 'id' in entry, 'Missing id in entry: %r' % entry
assert 'time' in entry, 'Missing timestamp in entry: %r' % entry
self.handle_message(entry)
except Exception as err:
start_response('500 OK', [('Content-Type', 'text/plain')])
return '%r' % err
else:
start_response('200 OK', [('Content-Type', 'text/plain')])
return ''
def say(self, recipient, text):
response = self.fbapi.send_message({
'recipient': {'id': recipient},
'message': {'text': text}}
)
self.logger.debug(response.content)
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] |
index.py | PrinceRaiN/Rain-player | 0 | 171572 |
import sys
import os
from os.path import expanduser
from PyQt5.QtWidgets import *
from PyQt5.QtMultimedia import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.uic import loadUiType
import time
scriptDir = os.path.dirname(os.path.realpath(__file__))
SCREEN,_ = loadUiType(os.path.join(os.path.dirname(__file__),"first.ui"))
PLAYER,_ = loadUiType(os.path.join(os.path.dirname(__file__),"player.ui"))
class Threading(QThread):
mysignal = pyqtSignal(int)
def __init__(self, parent=None):
QThread.__init__(self, parent)
def run(self):
time.sleep(4)
self.mysignal.emit(1)
class ThreadAnimation(QThread):
myAnimation = pyqtSignal(int)
def __init__(self, parent=None):
QThread.__init__(self, parent)
self.runs = True
def run(self):
while self.runs:
time.sleep(2)
self.myAnimation.emit(1)
def stop(self):
self.runs = False
class Screen(QMainWindow, SCREEN):
def __init__(self, parent=None):
super(Screen, self).__init__(parent)
QMainWindow.__init__(self)
self.setWindowIcon(QIcon(scriptDir + os.path.sep + 'icons/rain.png'))
self.setupUi(self)
pitmap = QPixmap("icons/rain.png")
self.image.setPixmap(pitmap)
thread = Threading(self)
thread.mysignal.connect(self.onTimeEnd)
thread.start()
@pyqtSlot(int)
def onTimeEnd(self, value):
self.hide()
window = Player(self)
window.show()
class Player(QMainWindow, PLAYER):
def __init__(self, parent=None):
super(Player, self).__init__(parent)
self.setupUi(self)
self.setWindowIcon(QIcon(scriptDir + os.path.sep + 'icons/rain.png'))
self.play_icon = QIcon("icons/play.png")
self.next_icon = QIcon("icons/next.png")
self.back_icon = QIcon("icons/back.png")
self.pause_icon = QIcon("icons/pause.png")
self.stop_icon = QIcon("icons/stop.png")
self.volume_icon = QIcon("icons/volume.png")
self.mute_icon = QIcon("icons/mute.png")
self.default_image = QPixmap("icons/rain.png")
self.currentPlaylist = QMediaPlaylist()
self.icon_button()
self.setAcceptDrops(True)
self.sound = True
self.music = {}
self.player = QMediaPlayer(self)
self.player.mediaStatusChanged.connect(self.mediaStatusChange)
self.player.positionChanged.connect(self.positionChanged)
self.player.durationChanged.connect(self.durationChanged)
self.musicList.itemClicked.connect(self.on_item_clicked)
self._buffer = QBuffer()
self.musicProgress.setValue(0)
self.musicProgress.setRange(0, 0)
self.musicProgress.sliderMoved.connect(self.onSliderMoved)
self.musicVolume.valueChanged.connect(self.onVolumeChange)
self.player.setPlaylist(self.currentPlaylist)
self.volumeProgress.clicked.connect(self.onSoundClicked)
self.stopButton.clicked.connect(self.onStopClicked)
self.nextButton.clicked.connect(self.onNextClicked)
self.backButton.clicked.connect(self.onBackClicked)
self.playButton.clicked.connect(self.onPlayClicked)
self.pauseButton.clicked.connect(self.onPauseClicked)
self.musicVolume.setValue(50)
self.index = 0
self.item = ""
self.animation = ThreadAnimation(self)
self.animation.myAnimation.connect(self.startAni)
self.animation.start()
self.aniType = "left"
self.actionExit_3.triggered.connect(self.exit_gui)
def onVolumeChange(self, value):
self.player.setVolume(value)
def onNextClicked(self):
self.player.playlist().next()
index = self.player.playlist().currentIndex()
try:
self.musicList.item(index).setSelected(True)
except:
self.player.playlist().setCurrentIndex(0)
self.musicList.item(0).setSelected(True)
self.player.play()
def onBackClicked(self):
self.player.playlist().previous()
index = self.player.playlist().currentIndex()
try:
self.musicList.item(index).setSelected(True)
except:
self.player.playlist().setCurrentIndex(0)
self.musicList.item(0).setSelected(True)
self.player.play()
def onPlayClicked(self):
if self.currentPlaylist.mediaCount() == 0:
self.openFile()
return
if self.player.state() == QMediaPlayer.PausedState:
self.player.play()
elif self.player.state() == QMediaPlayer.StoppedState:
self.player.play()
elif self.player.state() == QMediaPlayer.PlayingState:
return
else:
self.player.play()
def openFile(self):
fileChoosen = QFileDialog.getOpenFileUrl(self, 'Open Music File', expanduser('~'),'Audio (*.mp3 *.ogg *.wav)','*.mp3 *.ogg *.wav')
if fileChoosen != None:
self.musicList.addItem(fileChoosen[0].fileName())
self.currentPlaylist.addMedia(QMediaContent(fileChoosen[0]))
def onPauseClicked(self):
if self.player.state() == QMediaPlayer.PausedState:
self.player.play()
elif self.player.state() == QMediaPlayer.StoppedState:
self.player.play()
else:
self.player.pause()
def onStopClicked(self):
if self.player.state() == QMediaPlayer.PlayingState:
self.player.stop()
elif self.player.state() == QMediaPlayer.PausedState:
self.player.stop()
def onSoundClicked(self):
if self.sound:
self.volumeProgress.setIcon(self.mute_icon)
self.player.setVolume(0)
self.sound = False
else:
self.sound = True
self.player.setVolume(self.musicVolume.value())
self.volumeProgress.setIcon(self.volume_icon)
def durationChanged(self, duration):
self.musicProgress.setRange(0, duration)
def onSliderMoved(self, position):
sender = self.sender()
if isinstance(sender, QSlider):
if self.player.isSeekable():
self.player.setPosition(position)
def on_item_clicked(self, item):
data = self.musicList.selectedIndexes()[0]
index = data.row()
self.playMusic(index=index)
def playMusic(self, index=0):
self.player.setPlaylist(self.currentPlaylist)
self.player.playlist().setCurrentIndex(index)
self.player.play()
self.player.setVolume(self.musicVolume.value())
def onVolumeChange(self, value):
if value > 50:
self.player.setVolume(value+1)
else:
self.player.setVolume(value)
def positionChanged(self, position):
tm = '%d:%02d'%(int(position/60000),int((position/1000)%60))
self.startTime.setText(tm)
self.musicProgress.setValue(position)
def mediaStatusChange(self):
durationT = self.player.duration()
tm = "%d:%02d"%(int(durationT/60000),int((durationT/1000)%60))
self.musicTime.setText(tm)
index = self.player.playlist().currentIndex()
try:
self.musicList.item(index).setSelected(True)
except:
pass
def icon_button(self):
self.playButton.setIcon(self.play_icon)
self.pauseButton.setIcon(self.pause_icon)
self.nextButton.setIcon(self.next_icon)
self.backButton.setIcon(self.back_icon)
self.stopButton.setIcon(self.stop_icon)
self.image.setPixmap(self.default_image)
self.image.setAlignment(Qt.AlignCenter)
self.volumeProgress.setIcon(self.volume_icon)
def dropEvent(self, e):
for x in e.mimeData().urls():
mp3 = x.toString()
name = x.fileName()
path = x.toLocalFile()
self.musicList.addItem(name)
self.currentPlaylist.addMedia(QMediaContent(QUrl.fromLocalFile(path)))
def dragEnterEvent(self, e):
for x in e.mimeData().urls():
mp3 = x.toString()
if mp3.endswith(".mp3") or mp3.endswith(".MP3"):
e.accept()
else:
e.ignore()
def closeEvent(self, event):
reply = QMessageBox.question(self,'Exit?','Are you sure you want to close?',QMessageBox.Yes|QMessageBox.No,QMessageBox.Yes)
if reply == QMessageBox.Yes :
qApp.quit()
else :
try:
event.ignore()
except AttributeError:
pass
@pyqtSlot(int)
def startAni(self, i):
anim = QPropertyAnimation(self.image, b"geometry")
anim.setDuration(30)
if self.aniType == "left":
anim.setStartValue(QRect(180, 100, 411, 271))
anim.setEndValue(QRect(190, 110, 420, 280))
self.aniType = "right"
elif self.aniType == "right":
anim.setStartValue(QRect(190, 110, 420, 280))
anim.setEndValue(QRect(180, 100, 411, 271))
self.aniType = "dort"
elif self.aniType == "dort":
anim.setStartValue(QRect(180, 100, 411, 271))
anim.setEndValue(QRect(122, 302, 395, 261))
self.aniType = "left"
anim.start()
def exit_gui(self):
app.exit()
if __name__ == "__main__":
app=QApplication(sys.argv)
window = Screen()
window.show()
app.exec_()
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] |
editor/scheduler/__init__.py | iamsayem/smart-editor | 0 | 30851 | """
Created by Sayem on 14 March, 2021
All rights reserved. Copyright © 2020.
"""
from .celery import app as celery_app
__author__ = "Sayem"
__all__ = ["celery_app"]
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main/migrations/0028_auto_20170103_1634.py | jsmnbom/htxaarhuslan | 1 | 14708 | <gh_stars>1-10
# -*- coding: utf-8 -*-
# Generated by Django 1.10.2 on 2017-01-03 15:34
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('main', '0027_auto_20170103_1130'),
]
operations = [
migrations.CreateModel(
name='Event',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=255, verbose_name='navn')),
('url', models.URLField(blank=True, help_text='Valgfri. Link som kan klikkes på kalenderen.', max_length=255, null=True, verbose_name='link')),
('start', models.DateTimeField(null=True, verbose_name='Start')),
('end', models.DateTimeField(blank=True, null=True, verbose_name='Slut')),
('lan', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.Lan', verbose_name='lan')),
],
options={
'verbose_name_plural': 'begivenheder',
'verbose_name': 'begivenhed',
},
),
migrations.AlterField(
model_name='tournament',
name='end',
field=models.DateTimeField(blank=True, null=True, verbose_name='Slut'),
),
]
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main/migration_types.py | sotheara-leang/xFlask | 2 | 159130 | from xflask.sqlalchemy import StringEnum, IntegerEnum
from xflask.type import *
from main.type.edu_level import *
| [
1,
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] |
src/tests/models/test_model.py | ChristianHinge/geometric | 3 | 150443 | import pytest
import torch
from torch_geometric.data import Batch
from src.models.model import GCN
@pytest.fixture
def model():
return GCN(2, 2)
class TestGCN:
def test_model_structure(self, model):
assert model.conv_layers[0].in_channels == 2
assert model.conv_layers[0].out_channels == 64
assert model.conv_layers[1].in_channels == 64
assert model.conv_layers[1].out_channels == 64
assert model.conv_layers[2].in_channels == 64
assert model.conv_layers[2].out_channels == 64
assert model.linear.in_features == 64
assert model.linear.out_features == 2
def test_forward_correct(self, model):
x = torch.zeros([1, 2])
edge_index = torch.zeros([2, 2], dtype=torch.long)
batch = torch.zeros([1, 1], dtype=torch.int64)
data = Batch(x=x, edge_index=edge_index, batch=batch)
output = model.forward(data.x, data.edge_index, data.batch)
assert output.shape == torch.Size([1, 2])
def test_forward_empty(self, model):
data = Batch()
with pytest.raises(TypeError):
model.forward(data.x, data.edge_index, data.batch)
def test_forward_wrong_shape(self, model):
x = torch.zeros([1, 2, 3])
edge_index = torch.zeros([2], dtype=torch.long)
batch = torch.zeros([1, 1, 1, 1], dtype=torch.int64)
data = Batch(x=x, edge_index=edge_index, batch=batch)
with pytest.raises(IndexError):
model.forward(data.x, data.edge_index, data.batch)
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rotkehlchen/tests/exchanges/test_independentreserve.py | rotkehlchenio/rotkehlchen | 137 | 41963 | <reponame>rotkehlchenio/rotkehlchen<gh_stars>100-1000
import warnings as test_warnings
from unittest.mock import patch
import pytest
from rotkehlchen.accounting.structures.balance import Balance
from rotkehlchen.constants.assets import A_AUD, A_ETC, A_ETH
from rotkehlchen.errors.asset import UnknownAsset
from rotkehlchen.exchanges.data_structures import Location, Trade, TradeType
from rotkehlchen.exchanges.independentreserve import (
IR_TO_WORLD,
Independentreserve,
independentreserve_asset,
)
from rotkehlchen.fval import FVal
from rotkehlchen.tests.utils.mock import MockResponse
def test_location():
exchange = Independentreserve('independentreserve1', 'a', b'a', object(), object())
assert exchange.location == Location.INDEPENDENTRESERVE
assert exchange.name == 'independentreserve1'
def test_assets_are_known():
exchange = Independentreserve('independentreserve1', 'a', b'a', object(), object())
response = exchange._api_query('get', 'Public', 'GetValidPrimaryCurrencyCodes')
for currency in response:
try:
independentreserve_asset(currency)
except UnknownAsset:
test_warnings.warn(UserWarning(
f'Found unknown primary asset {currency} in IndependentReserve. '
f'Support for it has to be added',
))
response = exchange._api_query('get', 'Public', 'GetValidSecondaryCurrencyCodes')
for currency in response:
try:
independentreserve_asset(currency)
except UnknownAsset:
test_warnings.warn(UserWarning(
f'Found unknown secondary asset {currency} in IndependentReserve. '
f'Support for it has to be added',
))
@pytest.mark.parametrize('should_mock_current_price_queries', [True])
def test_query_balances(
function_scope_independentreserve,
inquirer, # pylint: disable=unused-argument
):
"""Test all balances returned by IndependentReserve are proccessed properly"""
exchange = function_scope_independentreserve
def mock_api_return(method, url, **kwargs): # pylint: disable=unused-argument
assert method == 'post'
response = """[{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Aud", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Usd", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Nzd", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Sgd", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Xbt", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Eth", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Xrp", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Ada", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Dot", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Uni", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Link", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Usdt", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Usdc", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Bch", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Ltc", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Mkr", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Dai", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Comp", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Snx", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Grt", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Eos", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Xlm", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Etc", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Bat", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Pmgt", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Yfi", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Aave", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Zrx", "TotalBalance": 150.55},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 150.55, "CurrencyCode": "Omg", "TotalBalance": 150.55}]""" # noqa: E501
return MockResponse(200, response)
with patch.object(exchange.session, 'request', side_effect=mock_api_return):
balances, msg = exchange.query_balances()
assert msg == ''
assets_seen = {0}
for asset, balance in balances.items():
assert asset in IR_TO_WORLD.values()
assert asset not in assets_seen
assets_seen.add(asset)
assert balance.amount == FVal('150.55')
@pytest.mark.parametrize('should_mock_current_price_queries', [True])
def test_query_some_balances(
function_scope_independentreserve,
inquirer, # pylint: disable=unused-argument
):
"""Just like test_query_balances but make sure 0 balances are skipped"""
exchange = function_scope_independentreserve
def mock_api_return(method, url, **kwargs): # pylint: disable=unused-argument
assert method == 'post'
response = """[{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 1.2, "CurrencyCode": "Aud", "TotalBalance": 2.5},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Usd", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Nzd", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Sgd", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Xbt", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Eth", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Xrp", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Ada", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Dot", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Uni", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Link", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Usdt", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Usdc", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Bch", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Ltc", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Mkr", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Dai", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Comp", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Snx", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Grt", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Eos", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Xlm", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Etc", "TotalBalance": 100.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Bat", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Pmgt", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Yfi", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Aave", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Zrx", "TotalBalance": 0.0},
{"AccountGuid": "foo", "AccountStatus": "Active", "AvailableBalance": 0.0, "CurrencyCode": "Omg", "TotalBalance": 0.0}]""" # noqa: E501
return MockResponse(200, response)
with patch.object(exchange.session, 'request', side_effect=mock_api_return):
balances, msg = exchange.query_balances()
assert msg == ''
assert balances == {
A_AUD: Balance(amount=FVal(2.5), usd_value=FVal(3.75)),
A_ETC: Balance(amount=FVal(100), usd_value=FVal(150)),
}
def test_query_trade_history(function_scope_independentreserve):
"""Happy path test for independentreserve trade history querying"""
exchange = function_scope_independentreserve
def mock_api_return(method, url, **kwargs): # pylint: disable=unused-argument
assert method == 'post'
response = """{"Data": [
{"AvgPrice": 603.7,
"CreatedTimestampUtc": "2017-11-22T22:54:40.3249401Z",
"FeePercent": 0.005,
"OrderGuid": "foo1",
"OrderType": "MarketOffer",
"Original": {"Outstanding": 0.0, "Volume": 0.5, "VolumeCurrencyType": "Primary"},
"Outstanding": 0.0,
"Price": null,
"PrimaryCurrencyCode": "Eth",
"SecondaryCurrencyCode": "Aud",
"Status": "Filled",
"Value": 301.85,
"Volume": 0.5
}, {
"AvgPrice": 257.25,
"CreatedTimestampUtc": "2017-07-28T09:39:19.8799244Z",
"FeePercent": 0.005,
"OrderGuid": "foo2",
"OrderType": "MarketBid",
"Original": {"Outstanding": 0.0, "Volume": 2.64117379, "VolumeCurrencyType": "Primary"},
"Outstanding": 0.0,
"Price": null,
"PrimaryCurrencyCode": "Eth",
"SecondaryCurrencyCode": "Aud",
"Status": "Filled",
"Value": 679.44,
"Volume": 2.64117379
}],
"PageSize": 50,
"TotalItems": 2,
"TotalPages": 1}
""" # noqa: E501
return MockResponse(200, response)
with patch.object(exchange.session, 'request', side_effect=mock_api_return):
trades = exchange.query_trade_history(
start_ts=0,
end_ts=1565732120,
only_cache=False,
)
expected_trades = [
Trade(
timestamp=1501234760,
location=Location.INDEPENDENTRESERVE,
base_asset=A_ETH,
quote_asset=A_AUD,
trade_type=TradeType.BUY,
amount=FVal('2.64117379'),
rate=FVal('257.25'),
fee=FVal('0.01320586895'),
fee_currency=A_ETH,
link='foo2',
), Trade(
timestamp=1511391280,
location=Location.INDEPENDENTRESERVE,
base_asset=A_ETH,
quote_asset=A_AUD,
trade_type=TradeType.SELL,
amount=FVal('0.5'),
rate=FVal('603.7'),
fee=FVal('0.0025'),
fee_currency=A_ETH,
link='foo1',
)]
assert trades == expected_trades[::-1]
# TODO: Make a test for asset movements.
# Would need more mocking as it would require mocking of multiple calls
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explore.py | lribiere/explore-mit-bih-arrhythmia-db | 3 | 2193 | import plotly.graph_objects as go
import streamlit as st
import pandas as pd
from utils import *
import glob
import wfdb
import os
ANNOTATIONS_COL_NAME = 'annotations'
'''
# MIT-BIH Arrhythmia DB Exploration
'''
record_ids = [os.path.basename(file)[:-4] for file in glob.glob('data/*.dat')]
if len(record_ids) == 0:
st.write('Warning ! No data could be found under the ./data/ directory.',
'*\*.dat*, *\*.hea*, *\*.atr* files and such should be placed ',
'immediately under the ./data/ directory')
else:
record_ids.sort()
record_id = st.selectbox('Select a record id', record_ids)
record = wfdb.rdrecord(f'data/{record_id}')
annotation = wfdb.rdann(f'data/{record_id}', 'atr')
st.write('Signals found in this record :')
for idx, signal in enumerate(record.sig_name):
st.write(f'- `{signal}` : in {record.units[idx]}, with a frequency of '
f'{record.fs * record.samps_per_frame[idx]}hz')
st.write(f'Comments for this record : {record.comments}')
signals_df = pd.DataFrame(record.p_signal, columns=record.sig_name)
annot_serie = pd.Series(annotation.symbol, index=annotation.sample,
name=ANNOTATIONS_COL_NAME)
full_df = pd.concat([signals_df, annot_serie], axis=1)
''' ## Annotations '''
beat_annot_count = annot_serie.isin(dict(beat_annotations)).sum()
non_beat_annot_count = annot_serie.isin(dict(non_beat_annotations)).sum()
unique_annot = annot_serie.value_counts().index.values
st.write(f'This record contains `{annot_serie.size}` annotations '
f'among which `{beat_annot_count}` beat annotations and '
f'`{non_beat_annot_count}` non beat annotation(s).')
st.write('The annotations are the followings :')
for annot in unique_annot:
st.write(f'- `{annot}` : {annotation_definitions[annot]}')
st.write('More explanations on the annotations are available here : '
'https://archive.physionet.org/physiobank/annotations.shtml')
# Plot counts for each annotation
annot_counts_df = annot_serie \
.value_counts() \
.rename_axis(ANNOTATIONS_COL_NAME) \
.reset_index(name='counts')
bar_fig = go.Figure(data=[go.Bar(x=annot_counts_df[ANNOTATIONS_COL_NAME],
y=annot_counts_df['counts'],
text=annot_counts_df['counts'],
textposition='auto'
)])
bar_fig.update_layout(title='Annotations by count', yaxis_title='counts',
xaxis_title='annotations')
st.write(bar_fig)
''' ## Explore full dataset '''
signal = st.selectbox('Select a signal', record.sig_name)
# Plot signals and annotations
matching_rows_by_annot = {}
for annot in unique_annot:
matching_rows_by_annot[annot] = full_df[ANNOTATIONS_COL_NAME] == annot
fig = go.Figure(layout=go.Layout(title=go.layout.Title(
text='{} signal with annotations'.format(signal))))
fig.add_trace(go.Scatter(x=full_df.index.values,
y=full_df[signal],
mode='lines',
name=signal))
for annot, annot_matching_rows in matching_rows_by_annot.items():
fig.add_trace(go.Scatter(x=full_df.index[annot_matching_rows].values,
y=full_df[annot_matching_rows][signal].values,
mode='markers',
name='{} (annot)'.format(annot)))
st.plotly_chart(fig)
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hcam_finder/ucam_finder.py | HiPERCAM/hcam_finder | 0 | 172107 | <gh_stars>0
# -*- coding: utf-8 -*-
from __future__ import print_function, absolute_import, unicode_literals, division
import itertools
from ginga.canvas.types.all import Line, CompoundObject
from ginga.util import wcs
from hcam_widgets.tkutils import get_root
from .finders import FovSetter
class UCAMFovSetter(FovSetter):
def window_string(self):
g = get_root(self).globals
if g.ipars.isFF:
retval = ''
else:
wframe = g.ipars.wframe
winlist = [
'xsl: {}, xsr: {}, ys: {}, nx: {}, ny: {}'.format(xsl, xsr, ys, nx, ny)
for (xsl, xsr, ys, nx, ny) in wframe
]
retval = '\n'.join(winlist)
return retval
def _make_ccd(self, image):
"""
Converts the current instrument settings to a ginga canvas object
"""
# get window pair object from top widget
g = get_root(self).globals
wframe = g.ipars.wframe
# all values in pixel coords of the FITS frame
# get centre
ctr_x, ctr_y = image.radectopix(self.ctr_ra_deg, self.ctr_dec_deg)
self.ctr_x, self.ctr_y = ctr_x, ctr_y
nx, ny = self.nxtot.value, self.nytot.value
mainCCD = self._make_win(0, 0, nx, ny, image,
fill=True, color='red',
fillalpha=0.1, name='mainCCD')
# dashed lines to mark quadrants of CCD
chip_ctr_ra, chip_ctr_dec = self._chip_cen()
x1, y1 = wcs.add_offset_radec(chip_ctr_ra, chip_ctr_dec,
0, self.fov_y/2)
x2, y2 = wcs.add_offset_radec(chip_ctr_ra, chip_ctr_dec,
0, -self.fov_y/2)
points = [image.radectopix(ra, dec) for (ra, dec) in (
(x1, y1), (x2, y2)
)]
points = list(itertools.chain.from_iterable(points))
vline = Line(*points, color='red', linestyle='dash', linewidth=2)
# emphasis on bottom of CCD
x1, y1 = wcs.add_offset_radec(chip_ctr_ra, chip_ctr_dec,
-self.fov_x/2, -self.fov_y/2)
x2, y2 = wcs.add_offset_radec(chip_ctr_ra, chip_ctr_dec,
self.fov_x/2, -self.fov_y/2)
points = [image.radectopix(ra, dec) for (ra, dec) in (
(x1, y1), (x2, y2)
)]
points = list(itertools.chain.from_iterable(points))
eline = Line(*points, color='red', linewidth=4)
# list of objects for compound object
obl = [mainCCD, vline, eline]
# iterate over window pairs
# these coords in ccd pixel vaues
if not g.ipars.isFF:
params = dict(fill=True, fillcolor='blue', fillalpha=0.3)
for xsl, xsr, ys, nx, ny in wframe:
obl.append(self._make_win(xsl, ys, nx, ny, image, **params))
obl.append(self._make_win(xsr, ys, nx, ny, image, **params))
obj = CompoundObject(*obl)
obj.editable = True
return obj
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] |
webapi/webapi/resources/testurl.py | shovradas/proxy-fellow | 1 | 78404 | <reponame>shovradas/proxy-fellow
import datetime
import time
from flask import url_for
from flask_restful import Resource, abort
from webargs.flaskparser import use_args
from bson.objectid import ObjectId
from core.proxy_checker import HttpProxyChecker
from webapi import api, mongo
from webapi.common import util
from webapi.schemas import TesturlSchema, TesturlSchemaEmbedded, params_schema
testurl_schema = TesturlSchema()
testurls_schema = TesturlSchema(many=True)
testurl_schema_embedded = TesturlSchemaEmbedded()
testurls_schema_embedded = TesturlSchemaEmbedded(many=True)
@api.resource('/api/v1/testurls/<id>')
class TestUrl(Resource):
@use_args(params_schema, location="query")
def get(self, args, id):
util.abort_if_invalid_id_format(id)
if args['embed']:
docs = mongo.db.testurls.aggregate([
{'$match': {'_id': ObjectId(id)}},
{
"$lookup": {
"from": "proxytesturls",
"let": {"testurl_id": "$_id"},
"pipeline": [{"$match": {"$expr": {"$eq": ["$testurlId", "$$testurl_id"]}}},
{
"$lookup": {
"from": "proxies",
"let": {"proxy_id": "$proxyId"},
"pipeline": [{"$match": {"$expr": {"$eq": ["$_id", "$$proxy_id"]}}}],
"as": "proxy"
}
},
{"$unwind": "$proxy"},
{
"$project": {
"_id": "$proxy._id",
"providerId": "$proxy.providerId",
"ip": "$proxy.ip",
"port": "$proxy.port",
"funcTestDate": "$proxy.funcTestDate",
"lastFoundDate": "$proxy.lastFoundDate",
"discoveredDate": "$proxy.discoveredDate",
"anonymity": "$proxy.anonymity",
"validationTestDate": "$validationTestDate",
"validationTestStatus": "$validationTestStatus"
}
}],
"as": "proxies"
}
}
])
docs = list(docs)
util.abort_if_doesnt_exist(docs)
return testurl_schema_embedded.dump(docs[0])
doc = mongo.db.testurls.find_one({'_id': ObjectId(id)})
util.abort_if_doesnt_exist(doc)
return testurl_schema.dump(doc)
def delete(self, id):
util.abort_if_invalid_id_format(id)
mongo.db.proxytesturls.delete_many({'testurlId': ObjectId(id)})
mongo.db.testurls.delete_one({'_id': ObjectId(id)})
return '', 204
@use_args(testurl_schema, location='json_or_form')
@use_args(params_schema, location="query")
def put(self, testurl_new, params, id):
util.abort_if_invalid_id_format(id)
testurl = mongo.db.testurls.find_one({'_id': ObjectId(id)})
util.abort_if_doesnt_exist(testurl)
for k, v in testurl_new.items():
testurl[k] = v
mongo.db.testurls.update_one({'_id': ObjectId(id)}, {'$set': testurl})
if params['validate']:
validate_testurl(testurl['_id'])
return '', 204
def patch(self, id):
util.abort_if_invalid_id_format(id)
if not validate_testurl(id):
abort(500, message='Error occurred while syncing', status="error")
return '', 204
@api.resource('/api/v1/testurls')
class TestUrlList(Resource):
@use_args(params_schema, location="query")
def get(self, args):
if args['embed']:
docs = mongo.db.testurls.aggregate([
{
"$lookup": {
"from": "proxytesturls",
"let": {"testurl_id": "$_id"},
"pipeline": [{"$match": {"$expr": {"$eq": ["$testurlId", "$$testurl_id"]}}},
{
"$lookup": {
"from": "proxies",
"let": {"proxy_id": "$proxyId"},
"pipeline": [{"$match": {"$expr": {"$eq": ["$_id", "$$proxy_id"]}}}],
"as": "proxy"
}
},
{"$unwind": "$proxy"},
{
"$project": {
"_id": "$proxy._id",
"providerId": "$proxy.providerId",
"ip": "$proxy.ip",
"port": "$proxy.port",
"funcTestDate": "$proxy.funcTestDate",
"lastFoundDate": "$proxy.lastFoundDate",
"discoveredDate": "$proxy.discoveredDate",
"anonymity": "$proxy.anonymity",
"validationTestDate": "$validationTestDate",
"validationTestStatus": "$validationTestStatus"
}
}],
"as": "proxies"
}
},
{'$skip': args['offset']},
{'$limit': args['limit']}
])
return testurls_schema_embedded.dump(docs)
docs = mongo.db.testurls.find().skip(args['offset']).limit(args['limit'])
return testurls_schema.dump(docs)
@use_args(testurl_schema, location='json_or_form')
@use_args(params_schema, location="query")
def post(self, testurl, params):
result = mongo.db.testurls.insert_one(testurl)
testurl = mongo.db.testurls.find_one({'_id': result.inserted_id})
if params['validate']:
validate_testurl(testurl['_id'])
return testurl_schema.dump(testurl), 201, {'location': url_for('testurl', id=testurl['_id'])}
def patch(self):
testurls = mongo.db.testurls.find()
for testurl in testurls:
validate_testurl(testurl['_id'])
return '', 204
def validate_testurl(testurl_id):
testurl = mongo.db.testurls.find_one({'_id': ObjectId(testurl_id)})
util.abort_if_doesnt_exist(testurl)
cfg = mongo.db.configurations.find_one({'status': True})
if not cfg:
cfg = {
'syncInterval': 10,
'maxAge': 1,
'downloadDelay': 1,
'proxyTestTimeout': 1
}
proxies = list(mongo.db.proxies.find())
proxy_checker = HttpProxyChecker()
valid_proxy_count = 0
for proxy in proxies:
try:
proxy_address = f"{proxy['ip']}:{proxy['port']}"
# Checking if online
result = proxy_checker.check_proxy(proxy_address, cfg['proxyTestTimeout'])
if result['status'] == 'online':
proxy['anonymity'] = result['anonymity']
proxy['funcTestDate'] = datetime.datetime.now()
try:
mongo.db.proxies.update_one({'_id': proxy['_id']}, {'$set': proxy})
except:
print('error: while syncing proxy')
continue
else:
try:
mongo.db.proxytesturls.delete_many({'proxyId': proxy['_id']})
mongo.db.proxies.delete_one({'_id': proxy['_id']})
continue
except:
print('error: while deleting proxy')
continue
###############
# Validating
result = proxy_checker.validate_proxy(proxy_address, testurl['url'], cfg['proxyTestTimeout'])
proxytesturl = {
'proxyId': proxy['_id'],
'testurlId': testurl['_id'],
'validationTestDate': datetime.datetime.now(),
'validationTestStatus': result['status']
}
mongo.db.proxytesturls.update_one(
{'proxyId': proxy['_id'], 'testurlId': testurl['_id']},
{'$set': proxytesturl},
upsert=True
)
if result['status'] == 'success':
valid_proxy_count += 1
except:
print('error: validate_testurl')
continue
try:
testurl['validationAttempt'] = {
'attemptDate': datetime.datetime.now(),
'validProxyCount': valid_proxy_count,
'status': 'success' if (valid_proxy_count !=0 and valid_proxy_count == len(proxies)) else ('partial' if valid_proxy_count>0 else 'failed')
}
mongo.db.testurls.update_one({'_id': testurl['_id']}, {'$set': testurl})
return True
except:
return False
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] |
libs/clustering/ensembles/utils.py | greenelab/phenoplier | 3 | 6214 | """
Contains functions to generate and combine a clustering ensemble.
"""
import numpy as np
import pandas as pd
from sklearn.metrics import pairwise_distances
from sklearn.metrics import adjusted_rand_score as ari
from sklearn.metrics import adjusted_mutual_info_score as ami
from sklearn.metrics import normalized_mutual_info_score as nmi
from tqdm import tqdm
from clustering.utils import reset_estimator, compare_arrays
def generate_ensemble(data, clusterers: dict, attributes: list, affinity_matrix=None):
"""
It generates an ensemble from the data given a set of clusterers (a
clusterer is an instance of a clustering algorithm with a fixed set of
parameters).
Args:
data:
A numpy array, pandas dataframe, or any other structure supported
by the clusterers as data input.
clusterers:
A dictionary with clusterers specified in this format: { 'k-means
#1': KMeans(n_clusters=2), ... }
attributes:
A list of attributes to save in the final dataframe; for example,
including "n_clusters" will extract this attribute from the
estimator and include it in the final dataframe returned.
affinity_matrix:
If the clustering algorithm is AgglomerativeClustering (from
sklearn) and the linkage method is different than ward (which only
support euclidean distance), the affinity_matrix is given as data
input to the estimator instead of data.
Returns:
A pandas DataFrame with all the partitions generated by the clusterers.
Columns include the clusterer name/id, the partition, the estimator
parameters (obtained with the get_params() method) and any other
attribute specified.
"""
ensemble = []
for clus_name, clus_obj in tqdm(clusterers.items(), total=len(clusterers)):
# get partition
#
# for agglomerative clustering both data and affinity_matrix should be
# given; for ward linkage, data is used, and for the other linkage
# methods the affinity_matrix is used
if (type(clus_obj).__name__ == "AgglomerativeClustering") and (
clus_obj.linkage != "ward"
):
partition = clus_obj.fit_predict(affinity_matrix).astype(float)
else:
partition = clus_obj.fit_predict(data).astype(float)
# remove from partition noisy points (for example, if using DBSCAN)
partition[partition < 0] = np.nan
# get number of clusters
partition_no_nan = partition[~np.isnan(partition)]
n_clusters = np.unique(partition_no_nan).shape[0]
# stop if n_clusters <= 1
if n_clusters <= 1:
reset_estimator(clus_obj)
continue
res = pd.Series(
{
"clusterer_id": clus_name,
"clusterer_params": str(clus_obj.get_params()),
"partition": partition,
}
)
for attr in attributes:
if attr == "n_clusters" and not hasattr(clus_obj, attr):
res[attr] = n_clusters
else:
res[attr] = getattr(clus_obj, attr)
ensemble.append(res)
# for some estimators such as DBSCAN this is needed, because otherwise
# the estimator saves references of huge data structures not needed in
# this context
reset_estimator(clus_obj)
return pd.DataFrame(ensemble).set_index("clusterer_id")
def get_ensemble_distance_matrix(ensemble, n_jobs=1):
"""
Given an ensemble, it computes the coassociation matrix (a distance matrix
for all objects using the ensemble information). For each object pair, the
coassociation matrix contains the percentage of times the pair of objects
was clustered together in the ensemble.
Args:
ensemble:
A numpy array representing a set of clustering solutions on the same
data. Each row is a clustering solution (partition) and columns are
objects.
n_jobs:
The number of jobs used by the pairwise_distance matrix from
sklearn.
Returns:
A numpy array representing a square distance matrix for all objects
(coassociation matrix).
"""
def _compare(x, y):
xy = np.array([x, y]).T
xy = xy[~np.isnan(xy).any(axis=1)]
return (xy[:, 0] != xy[:, 1]).sum() / xy.shape[0]
return pairwise_distances(
ensemble.T, metric=_compare, n_jobs=n_jobs, force_all_finite="allow-nan"
)
def supraconsensus(ensemble, k, methods, selection_criterion, n_jobs=1, use_tqdm=False):
"""
It combines a clustering ensemble using a set of methods that the user can
specify. Each of these methods combines the ensemble and returns a single
partition. This function returns the combined partition that maximizes the
selection criterion.
Args:
ensemble:
a clustering ensemble (rows are partitions, columns are objects).
k:
the final number of clusters for the combined partition.
methods:
a list of methods to apply on the ensemble; each returns a combined
partition.
selection_criterion:
a function that represents the selection criterion; this function
has to accept an ensemble as the first argument, and a partition as
the second one.
n_jobs:
number of jobs.
use_tqdm:
ensembles/disables the use of tqdm to show a progress bar.
Returns:
Returns a tuple: (partition, best method name, best criterion value)
"""
from concurrent.futures import ProcessPoolExecutor, as_completed
methods_results = {}
with ProcessPoolExecutor(max_workers=n_jobs) as executor:
tasks = {executor.submit(m, ensemble, k): m.__name__ for m in methods}
for future in tqdm(
as_completed(tasks),
total=len(tasks),
disable=(not use_tqdm),
ncols=100,
):
method_name = tasks[future]
part = future.result()
criterion_value = selection_criterion(ensemble, part)
methods_results[method_name] = {
"partition": part,
"criterion_value": criterion_value,
}
# select the best performing method according to the selection criterion
best_method = max(
methods_results, key=lambda x: methods_results[x]["criterion_value"]
)
best_method_results = methods_results[best_method]
return (
best_method_results["partition"],
best_method,
best_method_results["criterion_value"],
)
def run_method_and_compute_agreement(method_func, ensemble_data, ensemble, k, **kwargs):
"""
Runs a consensus clustering method on the ensemble data, obtains the
consolidated partition with the desired number of clusters, and computes
a series of performance measures.
Args:
method_func:
A consensus function (first argument is either the ensemble or
the coassociation matrix derived from the ensemble).
ensemble_data:
A numpy array with the ensemble data that will be given to the
specified method. For evidence accumulation methods, this is the
coassociation matrix (a square matrix with the distance between
object pairs derived from the ensemble).
ensemble:
A numpy array representing the ensemble (partitions in rows, objects
in columns).
k:
The number of clusters to obtain from the ensemble data using the
specified method.
kwargs:
Other parameters passed to `method_func`.
Returns:
It returns a tuple with the data partition derived from the ensemble
data using the specified method, and some performance measures of this
partition.
"""
part = method_func(ensemble_data, k, **kwargs)
nmi_values = np.array(
[
compare_arrays(ensemble_member, part, nmi, use_weighting=True)
for ensemble_member in ensemble
]
)
ami_values = np.array(
[
compare_arrays(ensemble_member, part, ami, use_weighting=True)
for ensemble_member in ensemble
]
)
ari_values = np.array(
[
compare_arrays(ensemble_member, part, ari, use_weighting=True)
for ensemble_member in ensemble
]
)
performance_values = {
"ari_mean": np.mean(ari_values),
"ari_median": np.median(ari_values),
"ari_std": np.std(ari_values),
"ami_mean": np.mean(ami_values),
"ami_median": np.median(ami_values),
"ami_std": np.std(ami_values),
"nmi_mean": np.mean(nmi_values),
"nmi_median": np.median(nmi_values),
"nmi_std": np.std(nmi_values),
}
return part, performance_values
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736,
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4180,
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5975,
13,
2
] |
rest_slack/app_settings.py | jordaneremieff/django-rest-slack | 1 | 41413 | <reponame>jordaneremieff/django-rest-slack<gh_stars>1-10
import os
from django.conf import settings
SLACK_CLIENT_ID = getattr(settings, 'SLACK_CLIENT_ID', os.environ.get('SLACK_CLIENT_ID'))
SLACK_CLIENT_SECRET = getattr(settings, 'SLACK_CLIENT_SECRET', os.environ.get('SLACK_CLIENT_SECRET'))
SLACK_VERIFICATION_TOKEN = getattr(settings, 'SLACK_VERIFICATION_TOKEN', os.environ.get('SLACK_VERIFICATION_TOKEN'))
SLACK_BOT_USER_TOKEN = getattr(settings, 'SLACK_BOT_USER_TOKEN', os.environ.get('SLACK_BOT_USER_TOKEN'))
REST_FRAMEWORK = getattr(settings, 'REST_FRAMEWORK', {'EXCEPTION_HANDLER': 'rest_framework.views.exception_handler'})
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] |
Src/Python/Recently Used List/test_rul.py | foutoucour/TDD-Katas | 4 | 100603 | import unittest
from rul import RecentlyUsedList
class RecentlyUsedListTests(unittest.TestCase):
def test_creating_a_new_list(self):
rul = RecentlyUsedList()
self.assertEqual(len(rul), 0)
def test_inserting_an_item(self):
rul = RecentlyUsedList()
item = 'item'
rul.insert(item)
self.assertEqual(len(rul), 1)
self.assertEqual(rul[0], item)
def test_inserting_two_unique_items(self):
rul = RecentlyUsedList()
rul.insert('first')
most_recent_item = 'second'
rul.insert(most_recent_item)
self.assertEqual(rul[0], most_recent_item)
def test_inserting_a_duplicate(self):
rul = RecentlyUsedList()
duplicate = 'b'
for item in 'a', duplicate, 'c', duplicate:
rul.insert(item)
self.assertEqual(len(rul), 3)
self.assertEqual(rul[0], duplicate)
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2
] |
tests/BasicLrTraining.py | felipessalvatore/logical-robustness | 0 | 120083 | <reponame>felipessalvatore/logical-robustness<filename>tests/BasicLrTraining.py
import numpy as np
import pandas as pd
import os
import sys
import inspect
import unittest
from joblib import load
currentdir = os.path.dirname(
os.path.abspath(
inspect.getfile(
inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
from src.lr.text_processing.util import pre_process_nli_df # noqa
from src.lr.training.util import get_binary_label, get_ternary_label # noqa
from src.lr.models.logistic_regression import LRWrapper # noqa
from src.lr.training.language_representation import BOW, Tfidf # noqa
data_path = parentdir + "/src/data/toy/train.csv"
assert os.path.exists(data_path)
class BasicLrTraining(unittest.TestCase):
@classmethod
def setUp(cls):
df = pd.read_csv(data_path)
pre_process_nli_df(df)
cls.df = df
cls.path = "test.joblib"
@classmethod
def tearDown(cls):
if os.path.exists(cls.path):
os.remove(cls.path)
def test_train_binary_BOW(self):
hyperparams = {"RepresentationFunction": BOW,
"num_words": 500,
"label_translation": get_binary_label,
"penalty": "l2",
"C": 1,
'solver': 'lbfgs'}
lr = LRWrapper(hyperparams)
lr.fit(self.df)
acc = lr.get_acc(self.df)
baseline = (
(self.df.label.value_counts() /
self.df.shape[0])[
1:]).sum()
msg = "acc = {:.2f} baseline = {:.2f}".format(acc, baseline)
self.assertTrue(acc > baseline, msg)
def test_train_ternary_Tfidf(self):
hyperparams = {"RepresentationFunction": Tfidf,
"max_features": 500,
"label_translation": get_ternary_label,
"penalty": "l2",
"C": 1,
'solver': 'lbfgs'}
lr = LRWrapper(hyperparams)
lr.fit(self.df)
acc = lr.get_acc(self.df)
baseline = 0.4
msg = "acc = {:.2f} baseline = {:.2f}".format(acc, baseline)
self.assertTrue(acc > baseline, msg)
def test_save(self):
hyperparams = {"RepresentationFunction": Tfidf,
"max_features": 500,
"label_translation": get_ternary_label,
"penalty": "l2",
"C": 1,
'solver': 'lbfgs'}
lr = LRWrapper(hyperparams)
lr.fit(self.df)
scores = lr.get_acc(self.df)
lr.save(self.path)
del lr
lr = load(self.path)
new_scores = lr.get_acc(self.df)
msg = "problem to save results"
self.assertTrue(scores == new_scores, msg)
if __name__ == '__main__':
unittest.main(verbosity=2)
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Game_AI_and_Reinforcement_Learning/ConnectX/v2/actor.py | BEPb/Python-100-days | 16 | 46340 | """
Python 3.9 программа самостоятельной игры агентов текущего и предыдущего покаления
программа на Python по изучению обучения с подкреплением - Reinforcement Learning
Название файла actor.py
Version: 0.1
Author: <NAME>
Date: 2021-12-23
"""
import numpy as np
import parl
import os
from alphazero_agent import create_agent
from MCTS import MCTS
from Arena import Arena
from utils import win_loss_draw
@parl.remote_class(wait=False)
class Actor(object):
def __init__(self, game, args, seed): # инициализация класса
np.random.seed(seed)
os.environ['OMP_NUM_THREADS'] = "1"
self.game = game # экземпляр (объект) класса доски и игры между двумя игроками
self.args = args # принимает все аргументы из главной программы
# 'master_address': 'localhost:8010', # главный адрес кластера xparl
# 'actors_num': 1, # количество удаленных участников
# 'numIters': 1, # общее количество итераций
# 'numEps': 1, # Количество полных игр с самостоятельной игрой для моделирования во время новой итерации.
# 'arenaCompare': 50, # Количество игр, которые нужно сыграть во время игры на арене (питтинг)
# 'numMCTSSims': 800, # Количество игровых ходов для моделирования MCTS.
# 'updateThreshold': 0.8, # пороговое или большее количество игр
# 'cpuct': 4, # CPUCT parameter
# 'dirichletAlpha': 1.0, # альфа-параметр шума дирихле
# 'numItersForTrainExamplesHistory': 20, # история примеров из последних итераций
# 'checkpoint': './saved_model/', # папка для сохранения моделей и обучающих примеров
# neural network of previous generation
# нейронная сеть предыдущего поколения
self.previous_agent = create_agent(self.game, cuda=False)
# neural network of current generation
# нейронная сеть текущего поколения
self.current_agent = create_agent(self.game, cuda=False)
# MCTS of previous generation
# MCTS предыдущего поколения
self.previous_mcts = MCTS(
self.game, self.previous_agent, self.args, dirichlet_noise=True)
# MCTS of current generation
# MCTS текущего поколения
self.current_mcts = MCTS(
self.game, self.current_agent, self.args, dirichlet_noise=True)
def self_play(self, current_weights, game_num):
"""
Сбор данных о тренировках путем самостоятельной игры.
Аргументы:
current_weights (numpy.array): последние веса нейронной сети
game_num (int): номер игры для самостоятельной игры
Возврат:
train_examples (список): примеры формы (canonicalBoard, currPlayer, pi, v)
"""
print('Самостоятельная игра одного из созданных агентов (использует одно ядро)')
# update weights of current neural network with latest weights
# обновить веса текущей нейронной сети с последними весами
self.current_agent.set_weights(current_weights)
train_examples = [] # создаем пустую таблицу (список) тренировки
for _ in range(game_num):
print('Начинается игра №', _)
# reset node state of MCTS
print('сбросить состояние узла MCTS')
self.current_mcts = MCTS(self.game, self.current_agent, self.args, dirichlet_noise=True)
print('тренировка узла MCTS')
train_examples.extend(self._executeEpisode())
# _executeEpisode() - функция одной игры
return train_examples
def pitting(self, previous_weights, current_weights, games_num):
"""Борьба между агентом предыдущего поколения и агентом текущего поколения
Аргументы:
previous_weights (numpy.array): веса нейронной сети предыдущего поколения
current_weights (numpy.array): веса нейронной сети текущего поколения
game_num (int): количество боев в игре
Возврат:
кортеж из (номер игры, в которой выиграл предыдущий агент, номер игры, в которой выиграл текущий агент,
номер игры, в которой был проведен розыгрыш)
"""
print('Борьба')
# update weights of previous and current neural network
# обновить веса предыдущей и текущей нейронной сети
self.previous_agent.set_weights(previous_weights)
self.current_agent.set_weights(current_weights)
# reset node state of MCTS
# сбросить состояние узла MCTS
print('сбросить состояние узла MCTS перед ареной')
self.previous_mcts = MCTS(self.game, self.previous_agent, self.args)
self.current_mcts = MCTS(self.game, self.current_agent, self.args)
arena = Arena(
lambda x: np.argmax(self.previous_mcts.getActionProb(x, temp=0)),
lambda x: np.argmax(self.current_mcts.getActionProb(x, temp=0)),
self.game)
previous_wins, current_wins, draws = arena.playGames(games_num)
return (previous_wins, current_wins, draws) # возвращает количество предудущих побед, текущих побед и ничьих
def evaluate_test_dataset(self, current_weights, test_dataset):
"""
Оценить эффективность новейших нейронных сетей
Аргументы:
current_weights (numpy.array): последние веса нейронной сети
test_dataset (список): номер игры для самостоятельной игры
Возврат:
кортеж из (количество совершенных ходов, количество хороших ходов)
"""
print('Эволюция')
# update weights of current neural network with latest weights
# обновить веса текущей нейронной сети с последними весами
self.current_agent.set_weights(current_weights)
# определяем качество проведенной игры
perfect_move_count, good_move_count = 0, 0
for data in test_dataset:
self.current_mcts = MCTS(self.game, self.current_agent, self.args) # обращаемся к дереву MCTS
x = self.game.getCanonicalForm(data['board'], data['player'])
agent_move = int(np.argmax(self.current_mcts.getActionProb(x, temp=0))) # количество ходов
moves = data["move_score"] # список очков
perfect_score = max(moves) # определяем максимальное значение в списке очков
perfect_moves = [i for i in range(7) if moves[i] == perfect_score] # выбираем 7 лучших
if agent_move in perfect_moves:
perfect_move_count += 1 # подсчет идеальных ходов
print('perfect_move_count', perfect_move_count)
print('Определяем победа\пройгрыш\ничья - ', win_loss_draw(moves[agent_move]))
if win_loss_draw(moves[agent_move]) == win_loss_draw(perfect_score):
good_move_count += 1 # подсчет хороших ходов
print('good_move_count', good_move_count)
return (perfect_move_count, good_move_count)
def _executeEpisode(self): # функция одной игры
"""
Эта функция выполняет один эпизод самостоятельной игры, начиная с игрока 1.
По ходу игры каждый ход добавляется в качестве обучающего примера к
trainExamples. Игра длится до конца. После игры
заканчивается, результат игры используется для присвоения значений каждому примеру
в поезде Примеры.
Он использует temp = 1, если episodeStep <tempThresholdStep, и после этого
использует temp = 0.
Возврат:
trainExamples: список примеров формы (canonicalBoard, currPlayer, pi, v)
pi - вектор политики, проинформированный MCTS, v - +1, если
игрок в конце концов выиграл игру, иначе -1.
"""
print('Эпизод одной игры')
trainExamples = []
board = self.game.getInitBoard()
self.curPlayer = 1
episodeStep = 0
while True:
episodeStep += 1
print('Самостоятельная игра агентов текущего поколения и предыдущего, ход = ', episodeStep)
canonicalBoard = self.game.getCanonicalForm(board, self.curPlayer)
temp = int(episodeStep < self.args.tempThresholdStep)
pi = self.current_mcts.getActionProb(canonicalBoard, temp=temp)
sym = self.game.getSymmetries(canonicalBoard, pi)
for b, p in sym: # board, pi
trainExamples.append([b, self.curPlayer, p, None])
action = np.random.choice(len(pi), p=pi)
board, self.curPlayer = self.game.getNextState(
board, self.curPlayer, action)
r = self.game.getGameEnded(board, self.curPlayer)
if r != 0:
return [(x[0], x[2], r * ((-1)**(x[1] != self.curPlayer)))
for x in trainExamples]
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2019,
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2019,
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] |
src/common/timestamp.py | vkhaydarov/PlantEye | 1 | 48421 | <reponame>vkhaydarov/PlantEye
from time import time
def get_timestamp():
return int(round(time() * 1000)) | [
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Models/LeNet-5/train.py | sujatasaini/Japanese-character-recognition-using-DropBlock | 2 | 82995 | <reponame>sujatasaini/Japanese-character-recognition-using-DropBlock<gh_stars>1-10
# Import the libraries
from keras import backend as K
from keras.datasets import mnist
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.core import Activation, Flatten, Dense
from keras.models import Sequential
from keras.utils import np_utils
from keras.initializers import Constant
from keras.optimizers import Adam
import matplotlib.pyplot as plt
# Load the dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
y_test = np_utils.to_categorical(y_test, 10)
y_train = np_utils.to_categorical(y_train, 10)
# Build the model
K.set_image_dim_ordering("th")
#LeNet
model = Sequential()
model.add(Conv2D(20, kernel_size=5, padding="same", input_shape=(1,28,28)))
model.add(Activation("relu"))
model.add(MaxPooling2D())
model.add(Conv2D(50, kernel_size=5, border_mode="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(500))
model.add(Activation("relu"))
model.add(Dense(10))
model.add(Activation("softmax"))
# Compile the model
model.compile(loss="categorical_crossentropy", optimizer=Adam(), metrics=["accuracy"])
# Train the model
history = model.fit(X_train, y_train, batch_size=128, epochs=20, verbose=1, validation_split=0.2)
score = model.evaluate(X_test, y_test, verbose=1)
print("Test score:", score[0])
print("Test accuracy:", score[1])
print(history.history.keys())
# Plot the graph of the model
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
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scripts/main.py | xdze2/trilobot | 0 | 68144 | <gh_stars>0
import time
import asyncio
import rx
from rx.scheduler.eventloop import AsyncIOScheduler
import rx.operators as op
import time
import matplotlib.pyplot as plt
import numpy as np
import pigpio
from pyrobot.drive import Mobility
from pyrobot.hardware.sonar import UltraSoundSensor
from pyrobot.gpio_mapping import SonarGpio
from pyrobot.rgb_leds import RgbUnderlighting, HsvColor, ColorMap
gpio = pigpio.pi()
distance_sensor = UltraSoundSensor(gpio_echo=SonarGpio.ECHO, gpio_trig=SonarGpio.TRIG, gpio=gpio)
mob = Mobility()
cm = ColorMap(0, 1500, 'gist_heat')
leds = RgbUnderlighting()
async_loop = asyncio.get_event_loop()
scheduler = AsyncIOScheduler(async_loop)
print('init done')
distance_measure = (
rx.interval(0.100, scheduler=scheduler)
.pipe(
op.map( lambda t: (t, distance_sensor.measure_cm()) ),
op.scan( lambda acc, x: (x[0], 0.5*acc[1] + 0.5*x[1]) )
)
)
(
distance_measure.pipe(
op.buffer_with_time(.5),
op.map(lambda grp: np.mean([u[1] for u in grp]))
).subscribe(
lambda dist_avg: leds.change_color(cm.get_rgb(dist_avg), 'front'),
on_error = lambda e: print("Error : {0}".format(e)),
on_completed = lambda: print("Job Done!"),
scheduler=scheduler
)
)
distance_measure.subscribe(
lambda x: print(x),
)
# test.pipe(op.delay(1.4), op.map(lambda x:x**2)).subscribe(
# lambda x: print("The square is {0}".format(x)),
# on_error = lambda e: print("Error : {0}".format(e)),
# on_completed = lambda: print("Job Done!"),
# scheduler=scheduler
# )
# test2 = rx.interval(3., scheduler=scheduler)
# print('done')
# test2.subscribe(
# lambda x: print("The 2 value is {0}".format(x)),
# on_error = lambda e: print("Error : {0}".format(e)),
# on_completed = lambda: print("Job Done!"),
# scheduler=scheduler
# )
async_loop.run_forever()
print("done")
async_loop.close()
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DeepLearning/Utils/prepare_corpus.py | neuralconcept/DrWhoAI | 3 | 110026 | # coding=utf-8
"""
Build vocab with a set max vocab size.
Build token ids given the vocab.
Do get_data.py first.
"""
# from __future__ import unicode_literals, print_function, division
import os
import subprocess
import re
from unidecode import unidecode
from nltk import word_tokenize
from DeepLearning.Utils import CACHE_DIR
from DeepLearning.Utils.data_utils import create_vocabulary, data_to_token_ids
from DeepLearning.Utils.genTrainingData import PERSON_FILENAME, DOCTOR_FILENAME, TRAIN_SUFFIX, DEV_SUFFIX
from DeepLearning.Utils.genTrainingData import PERSON_PATH, DOCTOR_PATH, PERSON_TRAIN_PATH, PERSON_DEV_PATH, DOCTOR_TRAIN_PATH, DOCTOR_DEV_PATH
# TODO: integrate all this in AIBot.py
PERSON_VOCAB_FILENAME = "person.vocab"
DOCTOR_VOCAB_FILENAME = "doctor.vocab"
PERSON_VOCAB_MAX = 20000
DOCTOR_VOCAB_MAX = 20000
IDS_SUFFIX = ".ids"
PERSON_VOCAB_PATH = os.path.join(CACHE_DIR, PERSON_VOCAB_FILENAME)
DOCTOR_VOCAB_PATH = os.path.join(CACHE_DIR, DOCTOR_VOCAB_FILENAME)
PERSON_TRAIN_IDS_PATH = os.path.join(CACHE_DIR, "person" + TRAIN_SUFFIX + IDS_SUFFIX)
PERSON_DEV_IDS_PATH = os.path.join(CACHE_DIR, "person" + DEV_SUFFIX + IDS_SUFFIX)
DOCTOR_TRAIN_IDS_PATH = os.path.join(CACHE_DIR, "doctor" + TRAIN_SUFFIX + IDS_SUFFIX)
DOCTOR_DEV_IDS_PATH = os.path.join(CACHE_DIR, "doctor" + DEV_SUFFIX + IDS_SUFFIX)
_WORD_SPLIT = re.compile("([.,!?\"':;)(])")
def _tokenizer(sentence):
"""Very basic tokenizer: split the sentence into a list of tokens + lower()."""
words = []
for space_separated_fragment in sentence.lower().strip().split():
words.extend(re.split(_WORD_SPLIT, space_separated_fragment))
return [w for w in words if w]
def tokenizer(sentence): # TODO: not working for apostrophes
sentence = sentence.strip().lower()
if type(sentence) != unicode:
sentence = unicode(sentence, encoding='utf-8', errors='replace')
sentence = unidecode(sentence)
sentence = sentence.replace("' ", "'")
return word_tokenize(sentence)
def build_vocab():
create_vocabulary(PERSON_VOCAB_PATH, PERSON_PATH, PERSON_VOCAB_MAX, tokenizer=tokenizer)
create_vocabulary(DOCTOR_VOCAB_PATH, DOCTOR_PATH, DOCTOR_VOCAB_MAX, tokenizer=tokenizer)
print( subprocess.check_output(['wc', '-l', PERSON_VOCAB_PATH]) )
print( subprocess.check_output(['wc', '-l', DOCTOR_VOCAB_PATH]) )
def build_ids():
data_to_token_ids(PERSON_TRAIN_PATH, PERSON_TRAIN_IDS_PATH, PERSON_VOCAB_PATH, tokenizer=tokenizer)
data_to_token_ids(PERSON_DEV_PATH, PERSON_DEV_IDS_PATH, PERSON_VOCAB_PATH, tokenizer=tokenizer)
data_to_token_ids(DOCTOR_TRAIN_PATH, DOCTOR_TRAIN_IDS_PATH, DOCTOR_VOCAB_PATH, tokenizer=tokenizer)
data_to_token_ids(DOCTOR_DEV_PATH, DOCTOR_DEV_IDS_PATH, DOCTOR_VOCAB_PATH, tokenizer=tokenizer)
print( subprocess.check_output(['wc', '-l', PERSON_TRAIN_IDS_PATH]) )
print( subprocess.check_output(['wc', '-l', PERSON_DEV_IDS_PATH]) )
print( subprocess.check_output(['wc', '-l', DOCTOR_TRAIN_IDS_PATH]) )
print( subprocess.check_output(['wc', '-l', DOCTOR_DEV_IDS_PATH]) )
def split_parallel_set(split_size=30000):
# split the dataset into train and dev sets (i'm using shell scripts)
subprocess.call(['split', '-l', str(split_size), PERSON_PATH])
subprocess.call(['mv', 'xaa', PERSON_TRAIN_PATH])
subprocess.call(['mv', 'xab', PERSON_DEV_PATH])
subprocess.call(['split', '-l', str(split_size), DOCTOR_PATH])
subprocess.call(['mv', 'xaa', DOCTOR_TRAIN_PATH])
subprocess.call(['mv', 'xab', DOCTOR_DEV_PATH])
print( subprocess.check_output(['wc', '-l', PERSON_TRAIN_PATH]) )
print( subprocess.check_output(['wc', '-l', PERSON_DEV_PATH]) )
print( subprocess.check_output(['wc', '-l', DOCTOR_TRAIN_PATH]) )
print( subprocess.check_output(['wc', '-l', DOCTOR_DEV_PATH]) )
if __name__ == '__main__':
split_parallel_set()
build_vocab()
build_ids() | [
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] |
python_code/I_K_ATP_RMP_mg.py | mmaleck/chondrocyte | 1 | 26259 | import numpy as np
from scipy.integrate.odepack import odeint
import matplotlib.pyplot as plt
import functions
from chondrocyte import Voltage_clamp
from params import params_dict
import matplotlib as mpl
"""
The code is used to create Figure 4B for submitted paper
"Probing the putative role of KATP channels and biological variability in a mathematical model of chondrocyte electrophysiology”
"""
mpl.rcParams['font.family'] = 'Avenir'
plt.rcParams['font.size'] = 18
plt.rcParams['axes.linewidth'] = 2
# define time span
params_dict.update(t_final=180)
t_final = params_dict["t_final"]
dt = params_dict["dt"]
t = np.linspace(0, t_final, int(t_final/dt))
params_dict.update(Mg_i=1)
# Define initial condition vector
y0 = (params_dict["V_0"], params_dict["Na_i_0"], params_dict["K_i_0"], params_dict["Ca_i_0"], params_dict["H_i_0"],
params_dict["Cl_i_0"], params_dict["a_ur_0"], params_dict["i_ur_0"], params_dict["vol_i_0"],
params_dict["cal_0"])
fig, ax = plt.subplots()
params_dict.update(K_o_0=7, Mg_i=0.1)
solution1 = odeint(functions.rhs, y0, t, args=(params_dict,))
ax.plot(t, solution1[:,0], label="$\mathrm{[Mg^{2+}]_i}$=0.1 mM", color="k")
params_dict.update(Mg_i=1.0)
solution2 = odeint(functions.rhs, y0, t, args=(params_dict,))
ax.plot(t, solution2[:,0], label="$\mathrm{[Mg^{2+}]_i}$=1.0 mM", color="b")
params_dict.update(Mg_i=10)
solution3 = odeint(functions.rhs, y0, t, args=(params_dict,))
ax.plot(t, solution3[:,0], label="$\mathrm{[Mg^{2+}]_i}$=10 mM", color="r")
ax.set_xlabel("Time [s]", fontsize=16)
ax.set_ylabel("Membrane Potential [mV]", fontsize=16)
ax.xaxis.set_tick_params(which='major', size=14, width=2, direction='out')
ax.yaxis.set_tick_params(which='major', size=14, width=2, direction='out')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.legend(loc='upper right')
# plt.savefig("Fig4_B.png", bbox_inches='tight')
plt.show()
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src/icupy/number.py | miute/icupy | 0 | 181091 | <filename>src/icupy/number.py<gh_stars>0
"""
Module for icu::number namespace
"""
from .icu.number import * # noqa
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code/src/scripts/pause.py | nuvla/job-engine | 3 | 25578 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import docker
import socket
docker_client = docker.from_env()
myself = docker_client.containers.get(socket.gethostname())
myself.pause() | [
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scvelo/tools/pymc/pymc3_model.py | AlexanderAivazidis/scvelo | 0 | 90635 | <reponame>AlexanderAivazidis/scvelo<filename>scvelo/tools/pymc/pymc3_model.py
# -*- coding: utf-8 -*-
r"""Base Pymc3 model class for all models in pymc3"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
import theano
from pymc3.variational.callbacks import CheckParametersConvergence
from tqdm.auto import tqdm
from scvelo.tools.pymc.base_model import BaseModel
# base model class - defining shared methods but not the model itself
class Pymc3Model(BaseModel):
r"""This class provides functions to train PyMC3 models and sample their parameters.
A model must have a main X_data input and can have arbitrary self.extra_data inputs.
Parameters
----------
X_data :
Numpy array of gene expression (cols) in spatial locations (rows)
all other:
the rest are arguments for parent class BaseModel
"""
def __init__(
self,
X_data: np.ndarray,
data_type: str = 'float32',
n_iter: int = 200000,
learning_rate=0.001,
total_grad_norm_constraint=200,
verbose=True,
var_names=None, var_names_read=None,
obs_names=None, sample_id=None
):
############# Initialise parameters ################
super().__init__(X_data,
data_type, n_iter,
learning_rate, total_grad_norm_constraint,
verbose, var_names, var_names_read,
obs_names, sample_id)
# Create dictionaries storing results
self.advi = {}
self.mean_field = {}
self.samples = {}
self.node_samples = {}
self.n_type = 'restart' # default
# Pass data to theano
self.x_data = theano.shared(X_data.astype(self.data_type))
def sample_prior(self, samples=10):
r"""Take samples from the prior, see `pymc3.sample_prior_predictive` for details
Parameters
----------
samples :
(Default value = 10)
Returns
-------
dict
self.prior_trace dictionary with an element for each parameter of the mode
"""
# Take samples from the prior
with self.model:
self.prior_trace = pm.sample_prior_predictive(samples=samples)
def fit_advi_iterative(self, n=3, method='advi', n_type='restart',
n_iter=None,
learning_rate=None, reducing_lr=False,
progressbar=True,
scale_cost_to_minibatch=True):
"""Find posterior using pm.ADVI() method directly (allows continuing training through `refine` method.
(maximising likelihood of the data and minimising KL-divergence of posterior to prior - ELBO loss)
Parameters
----------
n :
number of independent initialisations (Default value = 3)
method :
advi', to allow for potential use of SVGD, MCMC, custom (currently only ADVI implemented). (Default value = 'advi')
n_type :
type of repeated initialisation:
* **'restart'** to pick different initial value,
* **'cv'** for molecular cross-validation - splits counts into n datasets, for now, only n=2 is implemented
* **'bootstrap'** for fitting the model to multiple downsampled datasets.
Run `mod.bootstrap_data()` to generate variants of data (Default value = 'restart')
n_iter :
number of iterations, supersedes self.n_iter specified when creating model instance. (Default value = None)
learning_rate :
learning rate, supersedes self.learning_rate specified when creating model instance. (Default value = None)
reducing_lr :
boolean, use decaying learning rate? (Default value = False)
progressbar :
boolean, show progress bar? (Default value = True)
scale_cost_to_minibatch :
when using training in minibatches, scale cost function appropriately?
See discussion https://discourse.pymc.io/t/effects-of-scale-cost-to-minibatch/1429 to understand the effects. (Default value = True)
Returns
-------
None
self.mean_field dictionary with MeanField pymc3 objects,
and self.advi dictionary with ADVI objects for each initialisation.
"""
self.n_type = n_type
self.scale_cost_to_minibatch = scale_cost_to_minibatch
if n_iter is None:
n_iter = self.n_iter
if learning_rate is None:
learning_rate = self.learning_rate
### Initialise optimiser ###
if reducing_lr:
# initialise the function for adaptive learning rate
s = theano.shared(np.array(learning_rate).astype(self.data_type))
def reduce_rate(a, h, i):
s.set_value(np.array(learning_rate / ((i / self.n_cells) + 1) ** .7).astype(self.data_type))
optimiser = pm.adam(learning_rate=s)
callbacks = [reduce_rate, CheckParametersConvergence()]
else:
optimiser = pm.adam(learning_rate=learning_rate)
callbacks = [CheckParametersConvergence()]
if np.isin(n_type, ['bootstrap']):
if self.X_data_sample is None:
self.bootstrap_data(n=n)
elif np.isin(n_type, ['cv']):
self.generate_cv_data() # cv data added to self.X_data_sample
init_names = ['init_' + str(i + 1) for i in np.arange(n)]
for i, name in enumerate(init_names):
with self.model:
self.advi[name] = pm.ADVI(random_seed = 99)
# when type is molecular cross-validation or bootstrap,
# replace self.x_data tensor with new data
if np.isin(n_type, ['cv', 'bootstrap']):
# defining minibatch
if self.minibatch_size is not None:
# minibatch main data - expression matrix
self.x_data_minibatch = pm.Minibatch(self.X_data_sample[i].astype(self.data_type),
batch_size=[self.minibatch_size, None],
random_seed=self.minibatch_seed[i])
more_replacements = {self.x_data: self.x_data_minibatch}
# if any other data inputs should be minibatched add them too
if self.extra_data is not None:
# for each parameter in the dictionary add it to more_replacements
for k in self.extra_data.keys():
more_replacements[self.extra_data_tt[k]] = \
pm.Minibatch(self.extra_data[k].astype(self.data_type),
batch_size=[self.minibatch_size, None],
random_seed=self.minibatch_seed[i])
# or using all data
else:
more_replacements = {self.x_data: self.X_data_sample[i].astype(self.data_type)}
# if any other data inputs should be added
if self.extra_data is not None:
# for each parameter in the dictionary add it to more_replacements
for k in self.extra_data.keys():
more_replacements[self.extra_data_tt[k]] = \
self.extra_data[k].astype(self.data_type)
else:
# defining minibatch
if self.minibatch_size is not None:
# minibatch main data - expression matrix
self.x_data_minibatch = pm.Minibatch(self.X_data.astype(self.data_type),
batch_size=[self.minibatch_size, None],
random_seed=self.minibatch_seed[i])
more_replacements = {self.x_data: self.x_data_minibatch}
# if any other data inputs should be minibatched add them too
if self.extra_data is not None:
# for each parameter in the dictionary add it to more_replacements
for k in self.extra_data.keys():
more_replacements[self.extra_data_tt[k]] = \
pm.Minibatch(self.extra_data[k].astype(self.data_type),
batch_size=[self.minibatch_size, None],
random_seed=self.minibatch_seed[i])
else:
more_replacements = {}
self.advi[name].scale_cost_to_minibatch = scale_cost_to_minibatch
# train the model
self.mean_field[name] = self.advi[name].fit(n_iter, callbacks=callbacks,
obj_optimizer=optimiser,
total_grad_norm_constraint=self.total_grad_norm_constraint,
progressbar=progressbar, more_replacements=more_replacements,
)
# plot training history
if self.verbose:
print(plt.plot(np.log10(self.mean_field[name].hist[15000:])));
def plot_history(self, iter_start=0, iter_end=-1):
"""Plot loss function (ELBO) across training history
Parameters
----------
iter_start :
omit initial iterations from the plot (Default value = 0)
iter_end :
omit last iterations from the plot (Default value = -1)
"""
for i in self.mean_field.keys():
print(plt.plot(np.log10(self.mean_field[i].hist[iter_start:iter_end])))
def sample_posterior(self, node='all', n_samples=1000,
save_samples=False, return_samples=True,
mean_field_slot='init_1'):
"""Sample posterior distribution of all parameters or single parameter
Parameters
----------
node :
pymc3 node to sample (e.g. default "all", self.spot_factors)
n_samples :
number of posterior samples to generate (1000 is recommended, reduce if you get GPU memory error) (Default value = 1000)
save_samples :
save all samples, not just the mean, 5% and 95% quantile, SD. (Default value = False)
return_samples :
return summarised samples (mean, etc) in addition to saving them in `self.samples`? (Default value = True)
mean_field_slot :
string, which training initialisation (mean_field slot) to sample? 'init_1' by default
Returns
-------
dict
dictionary `self.samples` (mean, 5% quantile, SD, optionally all samples) with dictionaries
with numpy arrays for each parameter.
Plus an optional dictionary in `self.samples` with all samples of parameters
as numpy arrays of shape ``(n_samples, ...)``
"""
theano.config.compute_test_value = 'ignore'
if node == 'all':
# Sample all parameters - might use a lot of GPU memory
post_samples = self.mean_field[mean_field_slot].sample(n_samples)
self.samples['post_sample_means'] = {v: post_samples[v].mean(axis=0) for v in post_samples.varnames}
self.samples['post_sample_q05'] = {v: np.quantile(post_samples[v], 0.05, axis=0) for v in
post_samples.varnames}
self.samples['post_sample_q95'] = {v: np.quantile(post_samples[v], 0.95, axis=0) for v in
post_samples.varnames}
self.samples['post_sample_q01'] = {v: np.quantile(post_samples[v], 0.01, axis=0) for v in
post_samples.varnames}
self.samples['post_sample_q99'] = {v: np.quantile(post_samples[v], 0.99, axis=0) for v in
post_samples.varnames}
self.samples['post_sample_sds'] = {v: post_samples[v].std(axis=0) for v in post_samples.varnames}
if (save_samples):
# convert multitrace object to a dictionary
post_samples = {v: post_samples[v] for v in post_samples.varnames}
self.samples['post_samples'] = post_samples
else:
# Sample a singe node
post_node = self.mean_field[mean_field_slot].sample_node(node, size=n_samples).eval()
post_node_mean = post_node.mean(0)
post_node_q05 = np.quantile(post_node, 0.05, axis=0)
post_node_q95 = np.quantile(post_node, 0.95, axis=0)
post_node_q01 = np.quantile(post_node, 0.01, axis=0)
post_node_q99 = np.quantile(post_node, 0.99, axis=0)
post_node_sds = post_node.std(0)
# extract the name of the node and save to samples dictionary
node_name = node.name
self.node_samples[str(node_name) + '_mean'] = post_node_mean
self.node_samples[str(node_name) + '_q05'] = post_node_q05
self.node_samples[str(node_name) + '_q95'] = post_node_q95
self.node_samples[str(node_name) + '_q01'] = post_node_q01
self.node_samples[str(node_name) + '_q99'] = post_node_q99
self.node_samples[str(node_name) + '_sds'] = post_node_sds
if save_samples:
self.node_samples[str(node_name) + '_post_samples'] = post_node
if return_samples:
return self.samples
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ml/sandbox/00-data.py | robk-dev/algo-trading | 1 | 2172 | from alpha_vantage.timeseries import TimeSeries
from pprint import pprint
import json
import argparse
def save_dataset(symbol='MSFT', time_window='daily_adj'):
credentials = json.load(open('creds.json', 'r'))
api_key = credentials['av_api_key']
print(symbol, time_window)
ts = TimeSeries(key=api_key, output_format='pandas')
if time_window == 'intraday':
data, meta_data = ts.get_intraday(
symbol=symbol, interval='1min', outputsize='full')
elif time_window == 'daily':
data, meta_data = ts.get_daily(symbol, outputsize='full')
elif time_window == 'daily_adj':
data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full')
pprint(data.head(10))
data.to_csv(f'./{symbol}_{time_window}.csv')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('symbol', type=str, help="the stock symbol you want to download")
parser.add_argument('time_window', type=str, choices=[
'intraday', 'daily', 'daily_adj'], help="the time period you want to download the stock history for")
namespace = parser.parse_args()
save_dataset(**vars(namespace))
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demo_bgl/time_based_mining_invariants.py | elieser1101/loglizer | 0 | 156893 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
__author__ = '<NAME>'
from utils import data_loader as data_loader
from models import mining_invariants as mi
para = {
'path':'../../Data/BGL_data/', # directory for input data
'log_file_name':'BGL_MERGED.log', # filename for log data file
'log_event_mapping':'logTemplateMap.csv', # filename for log-event mapping. A list of event index, where each row represents a log
'save_path': '../time_windows/', # dir for saving sliding window data files to avoid splitting
'select_column':[0,4], # select the corresponding columns (label and time) in the raw log file
'window_size':3, # time window (unit: hour)
'step_size': 1, # step size (unit: hour)
'epsilon':2.0, # threshold for the step of estimating invariant space
'threshold':0.98, # percentage of vector Xj in matrix satisfies the condition that |Xj*Vi|<epsilon
'scale_list':[1,2,3], # list used to sacle the theta of float into integer
'stop_invar_num':3 # stop if the invariant length is larger than stop_invar_num. None if not given
}
if __name__ == '__main__':
raw_data, event_mapping_data = data_loader.deepia_data_loader(para)
event_count_matrix = data_loader.deepia_preprocess_data(para, raw_data, event_mapping_data)
r = mi.estimate_invar_spce(para, event_count_matrix)
invar_dict = mi.invariant_search(para, event_count_matrix, r)
predictions = mi.deepia_evaluate(event_count_matrix, invar_dict) | [
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] |
project1/budget/admin.py | sujeethiremath/Project-1 | 0 | 1609350 | from django.contrib import admin
from budget.models import Budget,BudgetCategory
# Register your models here.
admin.site.register(Budget)
admin.site.register(BudgetCategory)
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lang/python/pptv_strip.py | liuyang1/test | 8 | 24786 | import sys
fn = sys.argv[1]
fp = open(fn)
fo = open(fn+".flv","wb")
data = fp.read()
offset = int(sys.argv[2])
if len(sys.argv)==3:
fo.write(data[offset:])
else:
l = int(sys.argsv[3])
fo.write(data[offset:offset+l])
fo.close()
fp.close()
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29889,
5358,
580,
13,
18091,
29889,
5358,
580,
13,
2
] |
setup.py | sibyjackgrove/gym-SolarPVDER-environment | 7 | 169503 | from setuptools import setup
setup(name='gym_PVDER',
version='0.0.1',
install_requires=['gym','scipy','numpy','matplotlib']#And any other dependencies required
)
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projects/jist/jist/datasets/generate2Ddataset.py | Cryptum169/gpmp2 | 0 | 148086 | <reponame>Cryptum169/gpmp2<filename>projects/jist/jist/datasets/generate2Ddataset.py
import numpy as np
from gtsam import *
from gpmp2 import *
import math
from random import Random
dataset_random = Random()
class Obstacle:
"""docstring for Dataset"""
def __init__(self):
self.x = None
self.y = None
self.v_x = None
self.v_y = None
self.a_x = None
self.a_y = None
self.size = None
class Dataset:
"""docstring for Dataset"""
def __init__(self):
self.cols = None
self.rows = None
self.origin_x = None
self.origin_y = None
self.cell_size = None
self.map = None
def random_number(min_val, max_val):
return min_val + (max_val - min_val) * dataset_random.random()
def get_center(x, y, dataset):
center = (
np.asarray([y - dataset.origin_y, x - dataset.origin_x]) / dataset.cell_size
)
return center.astype(int)
def get_dim(w, h, dataset):
return np.asarray([h, w]) / dataset.cell_size
def add_obstacle(
position, size, map, landmarks=None, origin_x=None, origin_y=None, cell_size=None
):
half_size_row = int(math.floor((size[0] - 1) / 2))
half_size_col = int(math.floor((size[1] - 1) / 2))
# occupency grid. here map is assumed to be numpy array
temp = map[
max(0, position[0] - half_size_row - 1) : min(map.shape[0],position[0] + half_size_row),
max(0, position[1] - half_size_col - 1) : min(map.shape[1],position[1] + half_size_col),
]
map[
max(0, position[0] - half_size_row - 1) :min(map.shape[0], position[0] + half_size_row),
max(0, position[1] - half_size_col - 1) :min(map.shape[1], position[1] + half_size_col),
] = np.ones(temp.shape)
# landmarks
if landmarks is not None and origin_x is not None and origin_y is not None:
raise NotImplementedError
return map
def generate2Ddataset(dataset_str):
# GENERATE2DDATASET Generate 2D dataset evidence grid
#
# Usage: dataset = GENERATE2DDATASET(dataset_str)
# @dataset_str dataset string, existing datasets:
# 'OneObstacleDataset', 'TwoObstaclesDataset'
#
# Output Format:
# dataset.map ground truth evidence grid
# dataset.rows number of rows (y)
# dataset.cols number of cols (x)
# dataset.origin_x origin of map x
# dataset.origin_y origin of map y
# dataset.cell_size cell size
dataset = Dataset()
# dataset 5: 1 obs dataset for 2D Arm obs avoid
if dataset_str is "OneObstacleDataset":
# params
dataset.cols = 300
dataset.rows = 300
dataset.origin_x = -1
dataset.origin_y = -1
dataset.cell_size = 0.01
# map
dataset.map = np.zeros((dataset.rows, dataset.cols))
# obstacles
dataset.map = add_obstacle([190, 160], [60, 80], dataset.map)
# dataset 6: obs dataset for 3D Arm obs avoid
elif dataset_str is "Empty":
# params
dataset.cols = 300
dataset.rows = 300
dataset.origin_x = -1
dataset.origin_y = -1
dataset.cell_size = 0.01
# map
dataset.map = np.zeros((dataset.rows, dataset.cols))
# obstacles
# dataset.map = add_obstacle([200, 200], [80, 100], dataset.map)
# dataset.map = add_obstacle([160, 80], [30, 80], dataset.map)
elif dataset_str is "TwoObstaclesDataset":
# params
dataset.cols = 300
dataset.rows = 300
dataset.origin_x = -1
dataset.origin_y = -1
dataset.cell_size = 0.01
# map
dataset.map = np.zeros((dataset.rows, dataset.cols))
# obstacles
dataset.map = add_obstacle([200, 200], [80, 100], dataset.map)
dataset.map = add_obstacle([160, 80], [30, 80], dataset.map)
# dataset 7: multiple obs dataset for 2D Arm obs avoid
elif dataset_str is "MultiObstacleDataset":
# params
dataset.cols = 400 # x
dataset.rows = 300 # y
dataset.origin_x = -20
dataset.origin_y = -10
dataset.cell_size = 0.1
# map
dataset.map = np.zeros((dataset.rows, dataset.cols))
# obstacles
dataset.map = add_obstacle(
get_center(12, 10, dataset), get_dim(5, 7, dataset), dataset.map
)
dataset.map = add_obstacle(
get_center(-7, 10, dataset), get_dim(10, 7, dataset), dataset.map
)
dataset.map = add_obstacle(
get_center(0, -5, dataset), get_dim(10, 5, dataset), dataset.map
)
# mobile 2d map
elif dataset_str is "MobileMap1":
# params
dataset.cols = 500 # x
dataset.rows = 500 # y
dataset.origin_x = -10
dataset.origin_y = -10
dataset.cell_size = 0.01
# map
dataset.map = np.zeros((dataset.rows, dataset.cols))
# obstacles
dataset.map = add_obstacle(
get_center(0, 0, dataset), get_dim(1, 5, dataset), dataset.map
)
# dataset.map = add_obstacle(get_center(-2.5,-2,dataset), get_dim(5,1,dataset), dataset.map);
# wall
dataset.map = add_obstacle(
get_center(0, 4.5, dataset), get_dim(10, 1, dataset), dataset.map
)
dataset.map = add_obstacle(
get_center(0, -4.5, dataset), get_dim(10, 1, dataset), dataset.map
)
dataset.map = add_obstacle(
get_center(4.5, 0, dataset), get_dim(1, 10, dataset), dataset.map
)
dataset.map = add_obstacle(
get_center(-4.5, 0, dataset), get_dim(1, 10, dataset), dataset.map
)
# no such dataset
else:
raise NameError("No such dataset exist")
return dataset
class Patrol2Ddataset(object):
# keep track of objets, and their velocity.
# add big boundary stationary walls on all 4 sides.
# randomly intialize objets of same size at different x, y
# set random velocity magnitudes and directions for each objects
# simulate / move by dt time.
# if y is less than bottow or y is > top vy *= -1
# if x is less than left or x is > right vx *= -1
def __init__(
self,
rows=400,
cols=200, # rows corresponds to y
origin_x=0.0,
origin_y=0.0,
cell_size=0.1,
):
self.dataset = Dataset()
self.dataset.cols = cols
self.dataset.rows = rows
self.dataset.origin_x = origin_x
self.dataset.origin_y = origin_y
self.dataset.cell_size = cell_size
# map
self.static_obstacles = []
self.dynamic_obstacles = []
# left, right, bottom, up
self.bounds = [
origin_x,
origin_x + cell_size * cols,
origin_y,
origin_y + cell_size * rows,
]
def init_obstacles(self, seed_val=None, obstacle_num=100, vel_limit=0.8, acc_limit=None,):
if seed_val is None:
seed_val = 0
dataset_random.seed(seed_val)
# make static obstacles
self.static_obstacles.append(
self.make_obstacle(10.0, 15.0, np.asarray([1.0, 3.0]))
)
self.static_obstacles.append(self.make_obstacle(3.0, 20.0, np.asarray([4, 40])))
self.static_obstacles.append(
self.make_obstacle(17.0, 20.0, np.asarray([4, 40]))
)
# make dynamic obstacles
self.dynamic_bounds = np.asarray(self.bounds)
self.dynamic_bounds[0] = 8.0
self.dynamic_bounds[1] = 12.0
self.dynamic_obstacles.append(
self.make_obstacle(
random_number(self.dynamic_bounds[0], self.dynamic_bounds[1]),
10,
np.asarray([5.0, 3.0]),
)
)
self.dynamic_obstacles[0].v_y = 0.0
self.dynamic_obstacles[0].v_x = vel_limit# * (2.0/3.0)
if obstacle_num == 2:
self.dynamic_obstacles.append(
self.make_obstacle(
random_number(self.dynamic_bounds[0], self.dynamic_bounds[1]),
25,
np.asarray([5.0, 3.0]),
)
)
self.dynamic_obstacles[1].v_y = 0.0
self.dynamic_obstacles[1].v_x = vel_limit
self.static_obstacles.append(
self.make_obstacle(10.0, 30.0, np.asarray([1.0, 3.0]))
)
self.obstacles = self.static_obstacles + self.dynamic_obstacles
def make_obstacle(self, x, y, size):
obs = Obstacle()
obs.x = x
obs.y = y
obs.size = size
return obs
def simulate(self, dt):
for obs in self.dynamic_obstacles:
obs.x += dt * obs.v_x
obs.y += dt * obs.v_y
# change velocities
if obs.x <= self.dynamic_bounds[0] or obs.x >= self.dynamic_bounds[1]:
obs.v_x *= -1
if obs.y <= self.dynamic_bounds[2] or obs.y >= self.dynamic_bounds[3]:
obs.v_y *= -1
def get_dataset(self, pose=None, map_size=None): # size in meters
self.dataset.map = np.zeros((self.dataset.rows, self.dataset.cols))
for obs in self.static_obstacles:
self.dataset.map = add_obstacle(
get_center(obs.x, obs.y, self.dataset),
get_dim(obs.size[0], obs.size[1], self.dataset),
self.dataset.map,
)
for obs in self.dynamic_obstacles:
self.dataset.map = add_obstacle(
get_center(obs.x, obs.y, self.dataset),
get_dim(obs.size[0], obs.size[1], self.dataset),
self.dataset.map,
)
if pose is None or map_size is None:
return self.dataset
temp_dataset = Dataset()
temp_dataset.origin_x = pose[0] - float(map_size[0]) / 2.0
temp_dataset.origin_y = pose[1] - float(map_size[1]) / 2.0
temp_dataset.cell_size = self.dataset.cell_size
lower_left = get_center(
temp_dataset.origin_x, temp_dataset.origin_y, self.dataset
)
if lower_left[0] < 0:
lower_left[0] = 0
temp_dataset.origin_y = self.dataset.origin_y
if lower_left[1] < 0:
lower_left[1] = 0
temp_dataset.origin_x = self.dataset.origin_x
upper_right = get_center(
pose[0] + float(map_size[0]) / 2.0,
pose[1] + float(map_size[1]) / 2.0,
self.dataset,
)
if upper_right[0] >= self.dataset.map.shape[0]:
upper_right[0] = self.dataset.map.shape[0] - 1
if upper_right[1] >= self.dataset.map.shape[1]:
upper_right[1] = self.dataset.map.shape[1] - 1
if (
upper_right[0] < 0
or upper_right[1] < 0
or lower_left[0] >= self.dataset.map.shape[0]
or lower_left[1] >= self.dataset.map.shape[1]
):
temp_dataset.map = np.zeros((5, 5)) # some map of zeros
else:
temp_dataset.map = self.dataset.map[
lower_left[0] : upper_right[0] + 1, lower_left[1] : upper_right[1] + 1
]
temp_dataset.rows = temp_dataset.map.shape[0]
temp_dataset.cols = temp_dataset.map.shape[1]
return temp_dataset
class Dynamic2Ddataset(object):
# keep track of objets, and their velocity.
# add big boundary stationary walls on all 4 sides.
# randomly intialize objets of same size at different x, y
# set random velocity magnitudes and directions for each objects
# simulate / move by dt time.
# if y is less than bottow or y is > top vy *= -1
# if x is less than left or x is > right vx *= -1
def __init__(
self,
rows=900,
cols=1200,
origin_x=0.0,
origin_y=0.0,
cell_size=0.1,
):
self.dataset = Dataset()
self.dataset.cols = cols
self.dataset.rows = rows
self.dataset.origin_x = origin_x
self.dataset.origin_y = origin_y
self.dataset.cell_size = cell_size
# map
self.obstacles = []
# left, right, bottom, up
self.bounds = [
origin_x,
origin_x + cell_size * cols,
origin_y,
origin_y + cell_size * rows,
]
self.obs_size = (self.bounds[1] - self.bounds[0]) / 20.0
self.obs_dist = 10.0
self.goal = None
self.start = None
self.uniform_grid = False
def init_obstacles(self, seed_val=None, obstacle_num=100, vel_limit=None, acc_limit=None,
start=None, goal=None):
if obstacle_num == -1:
self.uniform_grid = True
x = self.bounds[0]
while x < self. bounds[1]:
y = self.bounds[2]
while y < self.bounds[3]:
obs = Obstacle()
obs.x = x
obs.y = y
obs.size = np.asarray([self.obs_size, self.obs_size])
y += 15.0
if goal is not None:
if np.linalg.norm(np.array([obs.x-goal[0], obs.y-goal[1]])) < self.obs_dist:
continue
if start is not None:
if np.linalg.norm(np.array([obs.x-start[0], obs.y-start[1]])) < self.obs_dist:
continue
self.obstacles.append(obs)
x += 15.0
return
if start is not None:
self.start = start
if goal is not None:
self.goal = goal
if seed_val is None:
seed_val = 0
dataset_random.seed(seed_val)
if vel_limit is None:
vel_limit = self.obs_size / 6
self.obs_velocity = vel_limit
if acc_limit is None:
acc_limit = self.obs_size / 10
self.obs_acc = acc_limit
# change in acceleration with time
self.acc_dt = self.obs_acc / 10
while len(self.obstacles) < obstacle_num:
# choose random x,y for obstacles.
obs = Obstacle()
obs.x = random_number(self.bounds[0], self.bounds[1])
obs.y = random_number(self.bounds[2], self.bounds[3])
if goal is not None:
if np.linalg.norm(np.array([obs.x-goal[0], obs.y-goal[1]])) < self.obs_dist:
continue
if start is not None:
if np.linalg.norm(np.array([obs.x-start[0], obs.y-start[1]])) < self.obs_dist:
continue
obs.v_x = random_number(-1 * self.obs_velocity, self.obs_velocity)
obs.v_y = random_number(-1 * self.obs_velocity, self.obs_velocity)
obs.a_x = random_number(-1 * self.obs_acc, self.obs_acc)
obs.a_y = random_number(-1 * self.obs_acc, self.obs_acc)
obs.size = np.asarray([self.obs_size, self.obs_size])
self.obstacles.append(obs)
# print('bounds', self.bounds, self.obs_size)
def simulate(self, dt):
if self.uniform_grid:
return
for obs in self.obstacles:
# Set accelerations
if random_number(0.0, 1.0) > 0.5:
obs.a_x += self.acc_dt
else:
obs.a_x -= self.acc_dt
obs.a_x = min(obs.a_x, self.obs_acc)
obs.a_x = max(obs.a_x, -1 * self.obs_acc)
if random_number(0.0, 1.0) > 0.5:
obs.a_y += self.acc_dt
else:
obs.a_y -= self.acc_dt
obs.a_y = min(obs.a_y, self.obs_acc)
obs.a_y = max(obs.a_y, -1 * self.obs_acc)
obs.v_x += dt * obs.a_x
obs.v_y += dt * obs.a_y
obs.v_x = min(obs.v_x, self.obs_velocity)
obs.v_x = max(obs.v_x, -1 * self.obs_velocity)
obs.v_y = min(obs.v_y, self.obs_velocity)
obs.v_y = max(obs.v_y, -1 * self.obs_velocity)
obs.x += dt * obs.v_x
obs.y += dt * obs.v_y
if self.goal is not None:
if np.linalg.norm(np.array([obs.x-self.goal[0], obs.y-self.goal[1]])) < self.obs_dist:
# bounce off the circle around goal
v1 = np.array([obs.x-self.goal[0], obs.y-self.goal[1]])
v1 = v1/np.linalg.norm(v1)
v2 = -1 * np.array([obs.v_x, obs.v_y])
v2 = v2/np.linalg.norm(v2)
theta = math.acos(np.linalg.norm( np.dot(v1, v2)))
alpha = math.atan2(obs.v_y, obs.v_x)
beta = math.atan2(obs.y - self.goal[1], obs.x - self.goal[0])
vel_vect = np.array([obs.v_x, obs.v_y])
angle = beta - alpha - theta
rot_mat = np.array([[math.cos(angle), -1* math.sin(angle)], [ math.sin(angle), math.cos(angle)]])
new_vels = np.matmul(np.asarray([obs.v_x, obs.v_y]) , rot_mat.T)
obs.v_x = new_vels[0]
obs.v_y = new_vels[1]
if self.start is not None:
if np.linalg.norm(np.array([obs.x-self.start[0], obs.y-self.start[1]])) < self.obs_dist:
# bounce off the circle around start
v1 = np.array([obs.x-self.start[0], obs.y-self.start[1]])
v1 = v1/np.linalg.norm(v1)
v2 = -1 * np.array([obs.v_x, obs.v_y])
v2 = v2/np.linalg.norm(v2)
theta = math.acos(np.linalg.norm( np.dot(v1, v2)))
alpha = math.atan2(obs.v_y, obs.v_x)
beta = math.atan2(obs.y - self.start[1], obs.x - self.start[0])
vel_vect = np.array([obs.v_x, obs.v_y])
angle = beta - alpha - theta
rot_mat = np.array([[math.cos(angle), -1* math.sin(angle)], [ math.sin(angle), math.cos(angle)]])
new_vels = np.matmul(np.asarray([obs.v_x, obs.v_y]) , rot_mat.T)
obs.v_x = new_vels[0]
obs.v_y = new_vels[1]
# change velocities
if obs.x <= self.bounds[0] or obs.x >= self.bounds[1]:
obs.v_x *= -1
if obs.y <= self.bounds[2] or obs.y >= self.bounds[3]:
obs.v_y *= -1
def get_dataset(self, pose=None, map_size=None): # size in meters
self.dataset.map = np.zeros((self.dataset.rows, self.dataset.cols))
for obs in self.obstacles:
self.dataset.map = add_obstacle(
get_center(obs.x, obs.y, self.dataset),
get_dim(obs.size[0], obs.size[1], self.dataset),
self.dataset.map,
)
if pose is None or map_size is None:
return self.dataset
temp_dataset = Dataset()
temp_dataset.origin_x = pose[0] - float(map_size[0]) / 2.0
temp_dataset.origin_y = pose[1] - float(map_size[1]) / 2.0
temp_dataset.cell_size = self.dataset.cell_size
lower_left = get_center(
temp_dataset.origin_x, temp_dataset.origin_y, self.dataset
)
if lower_left[0] < 0:
lower_left[0] = 0
temp_dataset.origin_y = self.dataset.origin_y
if lower_left[1] < 0:
lower_left[1] = 0
temp_dataset.origin_x = self.dataset.origin_x
upper_right = get_center(
pose[0] + float(map_size[0]) / 2.0,
pose[1] + float(map_size[1]) / 2.0,
self.dataset,
)
if upper_right[0] >= self.dataset.map.shape[0]:
upper_right[0] = self.dataset.map.shape[0] - 1
if upper_right[1] >= self.dataset.map.shape[1]:
upper_right[1] = self.dataset.map.shape[1] - 1
if (
upper_right[0] < 0
or upper_right[1] < 0
or lower_left[0] >= self.dataset.map.shape[0]
or lower_left[1] >= self.dataset.map.shape[1]
):
temp_dataset.map = np.zeros((5, 5)) # some map of zeros
else:
temp_dataset.map = self.dataset.map[
lower_left[0] : upper_right[0] + 1, lower_left[1] : upper_right[1] + 1
]
temp_dataset.rows = temp_dataset.map.shape[0]
temp_dataset.cols = temp_dataset.map.shape[1]
return temp_dataset
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] |
EvalEmbeddings.py | satyarth934/SpaceForceDataSearch | 1 | 178128 | import scann
from argparse import ArgumentParser
from pl_bolts.models.self_supervised import SimCLR
from pl_bolts.models.self_supervised.resnets import resnet18
from pl_bolts.models.self_supervised.simclr.transforms import SimCLREvalDataTransform, SimCLRTrainDataTransform
from pathlib import Path
import torch
import os
import time
import random
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import seaborn as sn
import h5py
import numpy as np
import pandas as pd
from tqdm import tqdm
from sklearn.metrics import f1_score, accuracy_score
#imports from internal
from CustomDataset import FolderDataset
from SSLTrainer import Projection
def eval_embeddings(model, dataset, save_path, rank_to, filter_hur):
if filter_hur:
rank_to = rank_to*4
model.eval()
embeddings_matrix = torch.empty((0, 512)).cuda()
for batch in tqdm(dataset):
#this mirrors shared_step
(image, im1, _), y = batch
with torch.no_grad():
image = torch.unsqueeze(image, 0)
image = image.cuda()
h1 = model(image)
embedding = h1
embeddings_matrix = torch.cat((embeddings_matrix, embedding))
embeddings_test = embeddings_matrix.cpu().numpy()
if os.path.exists('data.h5'):
os.remove('data.h5')
f = h5py.File('data.h5', 'w')
f.create_dataset("embeddings", data=embeddings_test)
dataset_scann = f['embeddings']
normalized_dataset = dataset_scann / np.linalg.norm(dataset_scann, axis=1)[:, np.newaxis]
searcher = scann.scann_ops_pybind.builder(normalized_dataset, rank_to, "dot_product").tree(num_leaves = int(np.sqrt(len(dataset_scann))), num_leaves_to_search = 10).score_brute_force().build()
neighbors, distances = searcher.search_batched(normalized_dataset)
#gets label for each image by index
def labelLookup(index):
return dataset.labels[index]
lookup = np.vectorize(labelLookup)
result_array = lookup(neighbors)
###
def same_hurricane(reference_idx, neighbor_idx):
hur = dataset.dirs[reference_idx].split('/')[-1].split('_')[0]
hur2 = dataset.dirs[neighbor_idx].split('/')[-1].split('_')[0]
return hur == hur2
if filter_hur:
mask = np.empty((0, neighbors.shape[1]))
same_hur = np.vectorize(same_hurricane)
for row in neighbors:
mask = np.vstack((mask, same_hur(row, row[0])))
mask = mask.astype(bool)
temp_res = np.empty((0, int(rank_to/4)))
#goes through each row
for i in range(result_array.shape[0]):
row = result_array[i]
msk = mask[i]
row_slice = row[~msk]
row_slice = np.insert(row_slice, 0, row[0])
if len(row_slice) < rank_to/4:
row_slice = np.append(row_slice, np.full((int(rank_to/4) - len(row_slice)), -1))
else:
row_slice = row_slice[:int(rank_to/4)]
temp_res = np.vstack((temp_res, row_slice))
result_array = temp_res
neighbor_rank = 1
array = confusion_matrix(result_array[:,0], result_array[:,neighbor_rank], normalize='true')
for i, r in enumerate(array):
acc_row = r[i]/ sum(r)
v = len(array)
df_cm = pd.DataFrame(array, range(v), range(v))
plt.figure(figsize=(10,7))
plt.title('Rank 1 on embeddings using validation transform')
sn.set(font_scale=0.5) # for label size
res = sn.heatmap(df_cm, annot=True, annot_kws={"size": 6}) # font size
figure = res.get_figure()
figure.savefig(f'{save_path}/rank1_NN_heatmap.png', dpi=400)
plt.clf()
plt.cla()
reference_image_classes = result_array[:, 0]
accs_by_rank = []
ncols = result_array.shape[1]
nrows = result_array.shape[0]
for i in range(1, ncols):
accs_by_rank.append(np.sum(reference_image_classes == result_array[:, i])/nrows)
plt.rc('ytick', labelsize=10)
plt.plot(range(1, ncols), accs_by_rank)
plt.xlabel('Nearest Neighbor Rank')
plt.ylabel('Percent in Reference Image Class')
plt.title('SimCLR (All Data) Similarity Searching')
plt.savefig(f'{save_path}/NN_acc_by_rank.png', dpi=400)
plt.clf()
plt.cla()
def accs_list(g):
f1s = []
for col in g.columns[1:]:
f1s.append(accuracy_score(g['neighbor_0'], g[col]))
return f1s
labels_df = pd.DataFrame(result_array, columns = ['neighbor_'+ str(x) for x in range(ncols)])
gp = labels_df.groupby('neighbor_0', group_keys = True)
k = list(gp.groups.keys())
inv_map = {v: k for k, v in dataset.mydict.items()}
for i, arr in enumerate(gp.apply(accs_list)):
plt.plot(range(1,ncols), arr, label = inv_map[k[i]+1])
plt.legend()
plt.xlabel('Nearest Neighbor Rank')
plt.ylabel('Percent in Reference Image Class')
plt.savefig(f'{save_path}/NN_acc_by_class_and_rank.png', dpi=400)
if os.path.exists('data.h5'):
os.remove('data.h5')
def cli_main():
parser = ArgumentParser()
parser.add_argument("--MODEL_PATH", type=str, help="path to .pt file containing SSL-trained SimCLR Resnet18 Model")
parser.add_argument("--DATA_PATH", type = str, help = "path to data. If folder already contains validation data only, set val_split to 0")
parser.add_argument("--val_split", default = 0.2, type = float, help = "amount of data to use for validation as a decimal")
parser.add_argument("--image_type", default="tif", type=str, help="extension of image for PIL to open and parse - i.e. jpeg, gif, tif, etc. Only put the extension name, not the dot (.)")
parser.add_argument("--image_embedding_size", default=128, type=int, help="size of image representation of SIMCLR")
parser.add_argument("--image_size", default = 128, type=int, help="height of square image to pass through model")
parser.add_argument("--gpus", default=1, type=int, help="number of gpus to use for training")
parser.add_argument("--rank", default=50, type=int, help="number of neighbors to search for")
parser.add_argument("--filter_same_group", default= False, type=bool, help="custom arg for hurricane data to filter same hurricanes out")
args = parser.parse_args()
MODEL_PATH = args.MODEL_PATH
DATA_PATH = args.DATA_PATH
image_size = args.image_size
image_type = args.image_type
embedding_size = args.image_embedding_size
val_split = args.val_split
gpus = args.gpus
rank_to = args.rank
filter_hur = args.filter_same_group
#testing
# MODEL_PATH = '/content/models/SSL/SIMCLR_SSL_0.pt'
# DATA_PATH = '/content/UCMerced_LandUse/Images'
# image_size = 128
# image_type = 'tif'
# embedding_size = 128
# val_split = 0.2
# gpus = 1
# #gets dataset. We can't combine since validation data has different transform needed
train_dataset = FolderDataset(DATA_PATH, validation = False,
val_split = val_split,
transform = SimCLRTrainDataTransform(image_size),
image_type = image_type
)
print('Training Data Loaded...')
val_dataset = FolderDataset(DATA_PATH, validation = True,
val_split = val_split,
transform = SimCLREvalDataTransform(image_size),
image_type = image_type
)
print('Validation Data Loaded...')
#load model
num_samples = len(train_dataset)
#init model with batch size, num_samples (len of data), epochs to train, and autofinds learning rate
model = SimCLR(arch = 'resnet18', batch_size = 1, num_samples = num_samples, gpus = gpus, dataset = 'None') #
model.encoder = resnet18(pretrained=False, first_conv=model.first_conv, maxpool1=model.maxpool1, return_all_feature_maps=False)
model.projection = Projection(input_dim = 512, hidden_dim = 256, output_dim = embedding_size) #overrides
model.load_state_dict(torch.load(MODEL_PATH))
model.cuda()
print('Successfully loaded your model for evaluation.')
#running eval on validation data
save_path = f"{MODEL_PATH[:-3]}/Evaluation/validationMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
eval_embeddings(model, val_dataset, save_path, rank_to, filter_hur)
print('Validation Data Evaluation Complete.')
#running eval on training data
save_path = f"{MODEL_PATH[:-3]}/Evaluation/trainingMetrics"
Path(save_path).mkdir(parents=True, exist_ok=True)
eval_embeddings(model, train_dataset, save_path, rank_to, filter_hur)
print('Training Data Evaluation Complete.')
print(f'Please check {MODEL_PATH[:-3]}/Evaluation/ for your results')
if __name__ == '__main__':
cli_main()
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] |
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/lms/djangoapps/grades/course_grade.py | osoco/better-ways-of-thinking-about-software | 3 | 28320 | <reponame>osoco/better-ways-of-thinking-about-software
"""
CourseGrade Class
"""
from abc import abstractmethod
from collections import OrderedDict, defaultdict
from ccx_keys.locator import CCXLocator
from django.conf import settings
from lazy import lazy
from openedx.core.lib.grade_utils import round_away_from_zero
from xmodule import block_metadata_utils
from .config import assume_zero_if_absent
from .scores import compute_percent
from .subsection_grade import ZeroSubsectionGrade
from .subsection_grade_factory import SubsectionGradeFactory
class CourseGradeBase:
"""
Base class for Course Grades.
"""
def __init__(self, user, course_data, percent=0.0, letter_grade=None, passed=False, force_update_subsections=False):
self.user = user
self.course_data = course_data
self.percent = percent
self.passed = passed
# Convert empty strings to None when reading from the table
self.letter_grade = letter_grade or None
self.force_update_subsections = force_update_subsections
def __str__(self):
return 'Course Grade: percent: {}, letter_grade: {}, passed: {}'.format(
str(self.percent),
self.letter_grade,
self.passed,
)
@property
def attempted(self):
"""
Returns whether at least one problem was attempted
by the user in the course.
"""
return False
def subsection_grade(self, subsection_key):
"""
Returns the subsection grade for the given subsection usage key.
Raises `KeyError` if the course structure does not contain the key.
If the course structure contains the key, this will always succeed
(and return a grade) regardless of whether the user can access that section;
it is up to the caller to ensure that the grade isn't
shown to users that shouldn't be able to access it
(e.g. a student shouldn't see a grade for an unreleased subsection);
"""
# look in the user structure first and fallback to the collected;
# however, we assume the state of course_data is intentional,
# so we use effective_structure to avoid additional fetching
subsection = (
self.course_data.effective_structure[subsection_key]
if subsection_key in self.course_data.effective_structure
else self.course_data.collected_structure[subsection_key]
)
return self._get_subsection_grade(subsection)
@lazy
def graded_subsections_by_format(self):
"""
Returns grades for the subsections in the course in
a dict keyed by subsection format types.
"""
subsections_by_format = defaultdict(OrderedDict)
for chapter in self.chapter_grades.values():
for subsection_grade in chapter['sections']:
if subsection_grade.graded:
graded_total = subsection_grade.graded_total
if graded_total.possible > 0:
subsections_by_format[subsection_grade.format][subsection_grade.location] = subsection_grade
return subsections_by_format
@lazy
def chapter_grades(self):
"""
Returns a dictionary of dictionaries.
The primary dictionary is keyed by the chapter's usage_key.
The secondary dictionary contains the chapter's
subsection grades, display name, and url name.
"""
course_structure = self.course_data.structure
grades = OrderedDict()
for chapter_key in course_structure.get_children(self.course_data.location):
grades[chapter_key] = self._get_chapter_grade_info(course_structure[chapter_key], course_structure)
return grades
@lazy
def subsection_grades(self):
"""
Returns an ordered dictionary of subsection grades,
keyed by subsection location.
"""
subsection_grades = defaultdict(OrderedDict)
for chapter in self.chapter_grades.values():
for subsection_grade in chapter['sections']:
subsection_grades[subsection_grade.location] = subsection_grade
return subsection_grades
@lazy
def problem_scores(self):
"""
Returns a dict of problem scores keyed by their locations.
"""
problem_scores = {}
for chapter in self.chapter_grades.values():
for subsection_grade in chapter['sections']:
problem_scores.update(subsection_grade.problem_scores)
return problem_scores
def chapter_percentage(self, chapter_key):
"""
Returns the rounded aggregate weighted percentage for the given chapter.
Raises:
KeyError if the chapter is not found.
"""
earned, possible = 0.0, 0.0
chapter_grade = self.chapter_grades[chapter_key]
for section in chapter_grade['sections']:
earned += section.graded_total.earned
possible += section.graded_total.possible
return compute_percent(earned, possible)
def score_for_module(self, location):
"""
Calculate the aggregate weighted score for any location in the course.
This method returns a tuple containing (earned_score, possible_score).
If the location is of 'problem' type, this method will return the
possible and earned scores for that problem. If the location refers to a
composite module (a vertical or section ) the scores will be the sums of
all scored problems that are children of the chosen location.
"""
if location in self.problem_scores:
score = self.problem_scores[location]
return score.earned, score.possible
children = self.course_data.structure.get_children(location)
earned, possible = 0.0, 0.0
for child in children:
child_earned, child_possible = self.score_for_module(child)
earned += child_earned
possible += child_possible
return earned, possible
@lazy
def grader_result(self):
"""
Returns the result from the course grader.
"""
course = self._prep_course_for_grading(self.course_data.course)
return course.grader.grade(
self.graded_subsections_by_format,
generate_random_scores=settings.GENERATE_PROFILE_SCORES,
)
@property
def summary(self):
"""
Returns the grade summary as calculated by the course's grader.
DEPRECATED: To be removed as part of TNL-5291.
"""
# TODO(TNL-5291) Remove usages of this deprecated property.
grade_summary = self.grader_result
grade_summary['percent'] = self.percent
grade_summary['grade'] = self.letter_grade
return grade_summary
@classmethod
def get_subsection_type_graders(cls, course):
"""
Returns a dictionary mapping subsection types to their
corresponding configured graders, per grading policy.
"""
course = cls._prep_course_for_grading(course)
return {
subsection_type: subsection_type_grader
for (subsection_type_grader, subsection_type, _)
in course.grader.subgraders
}
@classmethod
def _prep_course_for_grading(cls, course):
"""
Make sure any overrides to the grading policy are used.
This is most relevant for CCX courses.
Right now, we still access the grading policy from the course
object. Once we get the grading policy from the BlockStructure
this will no longer be needed - since BlockStructure correctly
retrieves/uses all field overrides.
"""
if isinstance(course.id, CCXLocator):
# clean out any field values that may have been set from the
# parent course of the CCX course.
course._field_data_cache = {} # pylint: disable=protected-access
# this is "magic" code that automatically retrieves any overrides
# to the grading policy and updates the course object.
course.set_grading_policy(course.grading_policy)
return course
def _get_chapter_grade_info(self, chapter, course_structure):
"""
Helper that returns a dictionary of chapter grade information.
"""
chapter_subsection_grades = self._get_subsection_grades(course_structure, chapter.location)
return {
'display_name': block_metadata_utils.display_name_with_default(chapter),
'url_name': block_metadata_utils.url_name_for_block(chapter),
'sections': chapter_subsection_grades,
}
def _get_subsection_grades(self, course_structure, chapter_key):
"""
Returns a list of subsection grades for the given chapter.
"""
return [
self._get_subsection_grade(course_structure[subsection_key], self.force_update_subsections)
for subsection_key in _uniqueify_and_keep_order(course_structure.get_children(chapter_key))
]
@abstractmethod
def _get_subsection_grade(self, subsection, force_update_subsections=False):
"""
Abstract method to be implemented by subclasses for returning
the grade of the given subsection.
"""
raise NotImplementedError
class ZeroCourseGrade(CourseGradeBase):
"""
Course Grade class for Zero-value grades when no problems were
attempted in the course.
"""
def _get_subsection_grade(self, subsection, force_update_subsections=False):
return ZeroSubsectionGrade(subsection, self.course_data)
class CourseGrade(CourseGradeBase):
"""
Course Grade class when grades are updated or read from storage.
"""
def __init__(self, user, course_data, *args, **kwargs):
super().__init__(user, course_data, *args, **kwargs)
self._subsection_grade_factory = SubsectionGradeFactory(user, course_data=course_data)
def update(self):
"""
Updates the grade for the course. Also updates subsection grades
if self.force_update_subsections is true, via the lazy call
to self.grader_result.
"""
# TODO update this code to be more functional and readable.
# Currently, it is hard to follow since there are plenty of
# side-effects. Once functional, force_update_subsections
# can be passed through and not confusingly stored and used
# at a later time.
grade_cutoffs = self.course_data.course.grade_cutoffs
self.percent = self._compute_percent(self.grader_result)
self.letter_grade = self._compute_letter_grade(grade_cutoffs, self.percent)
self.passed = self._compute_passed(grade_cutoffs, self.percent)
return self
@lazy
def attempted(self): # lint-amnesty, pylint: disable=invalid-overridden-method
"""
Returns whether any of the subsections in this course
have been attempted by the student.
"""
if assume_zero_if_absent(self.course_data.course_key):
return True
for chapter in self.chapter_grades.values():
for subsection_grade in chapter['sections']:
if subsection_grade.all_total.first_attempted:
return True
return False
def _get_subsection_grade(self, subsection, force_update_subsections=False):
if self.force_update_subsections:
return self._subsection_grade_factory.update(subsection, force_update_subsections=force_update_subsections)
else:
# Pass read_only here so the subsection grades can be persisted in bulk at the end.
return self._subsection_grade_factory.create(subsection, read_only=True)
@staticmethod
def _compute_percent(grader_result):
"""
Computes and returns the grade percentage from the given
result from the grader.
"""
# Confused about the addition of .05 here? See https://openedx.atlassian.net/browse/TNL-6972
return round_away_from_zero(grader_result['percent'] * 100 + 0.05) / 100
@staticmethod
def _compute_letter_grade(grade_cutoffs, percent):
"""
Computes and returns the course letter grade given the
inputs, as defined in the grading_policy (e.g. 'A' 'B' 'C')
or None if not passed.
"""
letter_grade = None
# Possible grades, sorted in descending order of score
descending_grades = sorted(grade_cutoffs, key=lambda x: grade_cutoffs[x], reverse=True)
for possible_grade in descending_grades:
if percent >= grade_cutoffs[possible_grade]:
letter_grade = possible_grade
break
return letter_grade
@staticmethod
def _compute_passed(grade_cutoffs, percent):
"""
Computes and returns whether the given percent value
is a passing grade according to the given grade cutoffs.
"""
nonzero_cutoffs = [cutoff for cutoff in grade_cutoffs.values() if cutoff > 0]
success_cutoff = min(nonzero_cutoffs) if nonzero_cutoffs else None
return success_cutoff and percent >= success_cutoff
def _uniqueify_and_keep_order(iterable):
return list(OrderedDict([(item, None) for item in iterable]).keys())
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450,
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16723,
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3743,
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29915,
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322,
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1024,
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3076,
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21533,
580,
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297,
3236,
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23905,
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657,
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11991,
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1311,
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15775,
29918,
1272,
29889,
5479,
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3076,
29961,
27349,
29918,
1989,
29962,
353,
1583,
3032,
657,
29918,
27349,
29918,
8228,
29918,
3888,
29898,
15775,
29918,
23905,
29961,
27349,
29918,
1989,
1402,
3236,
29918,
23905,
29897,
13,
4706,
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867,
3076,
13,
13,
1678,
732,
433,
1537,
13,
1678,
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1014,
2042,
29918,
629,
3076,
29898,
1311,
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13,
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9995,
13,
4706,
16969,
385,
10372,
8600,
310,
1014,
2042,
867,
3076,
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13,
4706,
1820,
287,
491,
1014,
2042,
4423,
29889,
13,
4706,
9995,
13,
4706,
1014,
2042,
29918,
629,
3076,
353,
2322,
8977,
29898,
7514,
287,
21533,
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13,
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363,
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297,
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629,
3076,
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5975,
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297,
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1839,
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29901,
13,
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2042,
29918,
629,
3076,
29961,
7235,
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29962,
353,
1014,
2042,
29918,
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13,
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1014,
2042,
29918,
629,
3076,
13,
13,
1678,
732,
433,
1537,
13,
1678,
822,
1108,
29918,
1557,
2361,
29898,
1311,
1125,
13,
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9995,
13,
4706,
16969,
263,
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310,
1108,
19435,
1820,
287,
491,
1009,
14354,
29889,
13,
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13,
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6571,
13,
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297,
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629,
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5975,
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13,
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363,
1014,
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297,
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1839,
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2033,
29901,
13,
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1108,
29918,
1557,
2361,
29889,
5504,
29898,
7235,
29918,
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29889,
17199,
29918,
1557,
2361,
29897,
13,
4706,
736,
1108,
29918,
1557,
2361,
13,
13,
1678,
822,
16385,
29918,
25376,
482,
29898,
1311,
29892,
16385,
29918,
1989,
1125,
13,
4706,
9995,
13,
4706,
16969,
278,
28240,
20431,
7688,
287,
19649,
363,
278,
2183,
16385,
29889,
13,
4706,
390,
1759,
267,
29901,
13,
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7670,
2392,
565,
278,
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338,
451,
1476,
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13,
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9995,
13,
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20591,
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1950,
353,
29871,
29900,
29889,
29900,
29892,
29871,
29900,
29889,
29900,
13,
4706,
16385,
29918,
8228,
353,
1583,
29889,
27349,
29918,
629,
3076,
29961,
27349,
29918,
1989,
29962,
13,
4706,
363,
4004,
297,
16385,
29918,
8228,
1839,
27117,
2033,
29901,
13,
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20591,
4619,
4004,
29889,
5105,
287,
29918,
7827,
29889,
799,
9571,
13,
9651,
1950,
4619,
4004,
29889,
5105,
287,
29918,
7827,
29889,
27338,
13,
4706,
736,
10272,
29918,
25376,
29898,
799,
9571,
29892,
1950,
29897,
13,
13,
1678,
822,
8158,
29918,
1454,
29918,
5453,
29898,
1311,
29892,
4423,
1125,
13,
4706,
9995,
13,
4706,
20535,
403,
278,
20431,
7688,
287,
8158,
363,
738,
4423,
297,
278,
3236,
29889,
13,
4706,
910,
1158,
3639,
263,
18761,
6943,
313,
799,
9571,
29918,
13628,
29892,
1950,
29918,
13628,
467,
13,
4706,
960,
278,
4423,
338,
310,
525,
17199,
29915,
1134,
29892,
445,
1158,
674,
736,
278,
13,
4706,
1950,
322,
20591,
19435,
363,
393,
1108,
29889,
960,
278,
4423,
14637,
304,
263,
13,
4706,
20842,
3883,
313,
29874,
11408,
470,
4004,
1723,
278,
19435,
674,
367,
278,
25470,
310,
13,
4706,
599,
15569,
4828,
393,
526,
4344,
310,
278,
10434,
4423,
29889,
13,
4706,
9995,
13,
4706,
565,
4423,
297,
1583,
29889,
17199,
29918,
1557,
2361,
29901,
13,
9651,
8158,
353,
1583,
29889,
17199,
29918,
1557,
2361,
29961,
5479,
29962,
13,
9651,
736,
8158,
29889,
799,
9571,
29892,
8158,
29889,
27338,
13,
4706,
4344,
353,
1583,
29889,
15775,
29918,
1272,
29889,
23905,
29889,
657,
29918,
11991,
29898,
5479,
29897,
13,
4706,
20591,
29892,
1950,
353,
29871,
29900,
29889,
29900,
29892,
29871,
29900,
29889,
29900,
13,
4706,
363,
2278,
297,
4344,
29901,
13,
9651,
2278,
29918,
799,
9571,
29892,
2278,
29918,
27338,
353,
1583,
29889,
13628,
29918,
1454,
29918,
5453,
29898,
5145,
29897,
13,
9651,
20591,
4619,
2278,
29918,
799,
9571,
13,
9651,
1950,
4619,
2278,
29918,
27338,
13,
4706,
736,
20591,
29892,
1950,
13,
13,
1678,
732,
433,
1537,
13,
1678,
822,
867,
1664,
29918,
2914,
29898,
1311,
1125,
13,
4706,
9995,
13,
4706,
16969,
278,
1121,
515,
278,
3236,
867,
1664,
29889,
13,
4706,
9995,
13,
4706,
3236,
353,
1583,
3032,
15287,
29918,
15775,
29918,
1454,
29918,
5105,
292,
29898,
1311,
29889,
15775,
29918,
1272,
29889,
15775,
29897,
13,
4706,
736,
3236,
29889,
629,
1664,
29889,
8228,
29898,
13,
9651,
1583,
29889,
5105,
287,
29918,
1491,
27117,
29918,
1609,
29918,
4830,
29892,
13,
9651,
5706,
29918,
8172,
29918,
1557,
2361,
29922,
11027,
29889,
24647,
1001,
3040,
29918,
8618,
7724,
29918,
29903,
3217,
15989,
29892,
13,
4706,
1723,
13,
13,
1678,
732,
6799,
13,
1678,
822,
15837,
29898,
1311,
1125,
13,
4706,
9995,
13,
4706,
16969,
278,
19468,
15837,
408,
12833,
491,
278,
3236,
29915,
29879,
867,
1664,
29889,
13,
4706,
5012,
15094,
29907,
3040,
29928,
29901,
1763,
367,
6206,
408,
760,
310,
323,
25103,
29899,
29945,
29906,
29929,
29896,
29889,
13,
4706,
9995,
13,
4706,
396,
14402,
29898,
29911,
25103,
29899,
29945,
29906,
29929,
29896,
29897,
15154,
502,
1179,
310,
445,
18164,
2875,
29889,
13,
4706,
19468,
29918,
7727,
353,
1583,
29889,
629,
1664,
29918,
2914,
13,
4706,
19468,
29918,
7727,
1839,
25376,
2033,
353,
1583,
29889,
25376,
13,
4706,
19468,
29918,
7727,
1839,
8228,
2033,
353,
1583,
29889,
15670,
29918,
8228,
13,
4706,
736,
19468,
29918,
7727,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
679,
29918,
7235,
29918,
1853,
29918,
5105,
414,
29898,
25932,
29892,
3236,
1125,
13,
4706,
9995,
13,
4706,
16969,
263,
8600,
10417,
1014,
2042,
4072,
304,
1009,
13,
4706,
6590,
13252,
4656,
414,
29892,
639,
4656,
292,
8898,
29889,
13,
4706,
9995,
13,
4706,
3236,
353,
1067,
29879,
3032,
15287,
29918,
15775,
29918,
1454,
29918,
5105,
292,
29898,
15775,
29897,
13,
4706,
736,
426,
13,
9651,
1014,
2042,
29918,
1853,
29901,
1014,
2042,
29918,
1853,
29918,
629,
1664,
13,
9651,
363,
313,
7235,
29918,
1853,
29918,
629,
1664,
29892,
1014,
2042,
29918,
1853,
29892,
24459,
13,
9651,
297,
3236,
29889,
629,
1664,
29889,
1491,
5105,
414,
13,
4706,
500,
13,
13,
1678,
732,
1990,
5696,
13,
1678,
822,
903,
15287,
29918,
15775,
29918,
1454,
29918,
5105,
292,
29898,
25932,
29892,
3236,
1125,
13,
4706,
9995,
13,
4706,
8561,
1854,
738,
975,
24040,
304,
278,
4656,
292,
8898,
526,
1304,
29889,
13,
4706,
910,
338,
1556,
8018,
363,
19178,
29990,
21888,
29889,
13,
13,
4706,
10428,
1286,
29892,
591,
1603,
2130,
278,
4656,
292,
8898,
515,
278,
3236,
13,
4706,
1203,
29889,
9038,
591,
679,
278,
4656,
292,
8898,
515,
278,
15658,
5015,
12425,
13,
4706,
445,
674,
694,
5520,
367,
4312,
448,
1951,
15658,
5015,
12425,
5149,
13,
4706,
5663,
17180,
29914,
6394,
599,
1746,
975,
24040,
29889,
13,
4706,
9995,
13,
4706,
565,
338,
8758,
29898,
15775,
29889,
333,
29892,
19178,
29990,
3524,
1061,
1125,
13,
9651,
396,
5941,
714,
738,
1746,
1819,
393,
1122,
505,
1063,
731,
515,
278,
13,
9651,
396,
3847,
3236,
310,
278,
19178,
29990,
3236,
29889,
13,
9651,
3236,
3032,
2671,
29918,
1272,
29918,
8173,
353,
6571,
29871,
396,
282,
2904,
524,
29901,
11262,
29922,
24681,
29899,
5943,
13,
13,
9651,
396,
445,
338,
376,
11082,
293,
29908,
775,
393,
6336,
5663,
17180,
738,
975,
24040,
13,
9651,
396,
304,
278,
4656,
292,
8898,
322,
11217,
278,
3236,
1203,
29889,
13,
9651,
3236,
29889,
842,
29918,
5105,
292,
29918,
22197,
29898,
15775,
29889,
5105,
292,
29918,
22197,
29897,
13,
4706,
736,
3236,
13,
13,
1678,
822,
903,
657,
29918,
27349,
29918,
8228,
29918,
3888,
29898,
1311,
29892,
16385,
29892,
3236,
29918,
23905,
1125,
13,
4706,
9995,
13,
4706,
6162,
546,
393,
3639,
263,
8600,
310,
16385,
19468,
2472,
29889,
13,
4706,
9995,
13,
4706,
16385,
29918,
7235,
29918,
629,
3076,
353,
1583,
3032,
657,
29918,
7235,
29918,
629,
3076,
29898,
15775,
29918,
23905,
29892,
16385,
29889,
5479,
29897,
13,
4706,
736,
426,
13,
9651,
525,
4990,
29918,
978,
2396,
2908,
29918,
19635,
29918,
13239,
29889,
4990,
29918,
978,
29918,
2541,
29918,
4381,
29898,
27349,
511,
13,
9651,
525,
2271,
29918,
978,
2396,
2908,
29918,
19635,
29918,
13239,
29889,
2271,
29918,
978,
29918,
1454,
29918,
1271,
29898,
27349,
511,
13,
9651,
525,
27117,
2396,
16385,
29918,
7235,
29918,
629,
3076,
29892,
13,
4706,
500,
13,
13,
1678,
822,
903,
657,
29918,
7235,
29918,
629,
3076,
29898,
1311,
29892,
3236,
29918,
23905,
29892,
16385,
29918,
1989,
1125,
13,
4706,
9995,
13,
4706,
16969,
263,
1051,
310,
1014,
2042,
867,
3076,
363,
278,
2183,
16385,
29889,
13,
4706,
9995,
13,
4706,
736,
518,
13,
9651,
1583,
3032,
657,
29918,
7235,
29918,
8228,
29898,
15775,
29918,
23905,
29961,
7235,
29918,
1989,
1402,
1583,
29889,
10118,
29918,
5504,
29918,
1491,
27117,
29897,
13,
9651,
363,
1014,
2042,
29918,
1989,
297,
903,
13092,
1598,
29918,
392,
29918,
17462,
29918,
2098,
29898,
15775,
29918,
23905,
29889,
657,
29918,
11991,
29898,
27349,
29918,
1989,
876,
13,
4706,
4514,
13,
13,
1678,
732,
16595,
5696,
13,
1678,
822,
903,
657,
29918,
7235,
29918,
8228,
29898,
1311,
29892,
1014,
2042,
29892,
4889,
29918,
5504,
29918,
1491,
27117,
29922,
8824,
1125,
13,
4706,
9995,
13,
4706,
25513,
1158,
304,
367,
8762,
491,
1014,
13203,
363,
7863,
13,
4706,
278,
19468,
310,
278,
2183,
1014,
2042,
29889,
13,
4706,
9995,
13,
4706,
12020,
2216,
1888,
2037,
287,
2392,
13,
13,
13,
1990,
28933,
29907,
10242,
29954,
15464,
29898,
29907,
10242,
29954,
15464,
5160,
1125,
13,
1678,
9995,
13,
1678,
6325,
344,
4989,
311,
770,
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] |
Scripts/hilberttrans.py | TChauvire/EPR_ESR_Suite | 2 | 88749 | <reponame>TChauvire/EPR_ESR_Suite
import numpy as np
from scipy.signal import hilbert
def hilberttrans(data, N=None):
"""
Hilbert transform.
Reconstruct imaginary data via hilbert transform.
This script is freely inspired by the easyspin suite from the Stefan Stoll lab
(https://github.com/StollLab/EasySpin/)
(https://easyspin.org/easyspin/)
Script written by <NAME> (https://github.com/TChauvire/EPR_ESR_Suite/), 09/09/2020
Parameters
----------
data : numpy data array column vector
N : int or None
Number of Fourier components.
Returns
-------
z : Hilbert transformed complex vector.
TYPE : numpy data array column vector
References
----------
.. [1] Wikipedia, "Analytic signal".
https://en.wikipedia.org/wiki/Analytic_signal
.. [2] <NAME>, "Time-Frequency Analysis", 1995. Chapter 2.
.. [3] <NAME>, <NAME>. Discrete-Time Signal
Processing, Third Edition, 2009. Chapter 12.
ISBN 13: 978-1292-02572-8
"""
shape = data.shape
if (data.shape[0] == np.ravel(data).shape[0]): # Check if the data is a column vector
data = np.ravel(data)
if N == None:
N = data.shape[-1]
# create an empty output array
fac = N / data.shape[-1]
z = np.empty(data.shape, dtype=(data.flat[0] + data.flat[1] * 1.j).dtype)
if data.ndim == 1:
z[:] = hilbert(data.real, N)[:data.shape[-1]] * fac
else:
for i, vec in enumerate(data):
z[i] = hilbert(vec.real, N)[:data.shape[-1]] * fac
# correct the real data as sometimes it changes
z.real = data.real
z = z.reshape(shape)
return z | [
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13,
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736,
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2
] |
__main__.py | Arab-developers/PSHMode | 27 | 1602691 | <reponame>Arab-developers/PSHMode<gh_stars>10-100
import os
import runpy
runpy.run_path(os.path.join(os.path.abspath(__file__).rsplit("/", 1)[0], "bin/PSHMode"))
| [
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scripts/update_hashes.py | presalytics/amplitude | 1 | 1606866 | import yaml
import sys
import git # type: ignore
import os
import pathlib
if __name__ == '__main__':
repo = git.Repo(search_parent_directories=True)
sha = repo.head.object.hexsha
manifests_dir = pathlib.Path(__file__).parent.absolute().parent.absolute().joinpath('manifests')
for manifest in os.listdir(manifests_dir):
file = manifests_dir.joinpath(manifest)
try:
data = {}
with open(file) as f:
data = yaml.load(f, Loader=yaml.FullLoader)
if data['apiVersion'] == 'serving.knative.dev/v1' and data['kind'] == 'Service':
data['spec']['template']['metadata']['labels']['gitHash'] = sha
with open(file, "w") as f:
f.write(yaml.dump(data, default_flow_style=False))
print("Version label of manifest {0} updated to {1}".format(manifest, sha))
except Exception as ex:
print("Hash update operation failed")
print("Error: " + ex.args[1])
exit(1) | [
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] |
bot/handlers/bans.py | Mehavoid/telegram-feedback-bot | 41 | 148008 | from contextlib import suppress
from aiogram import Dispatcher, types
from aiogram.dispatcher.filters import IsReplyFilter, IDFilter
from bot.blocklists import banned, shadowbanned
from bot.handlers.adminmode import extract_id
async def cmd_ban(message: types.Message):
try:
user_id = extract_id(message)
except ValueError as ex:
return await message.reply(str(ex))
banned.add(int(user_id))
await message.reply(
f"ID {user_id} добавлен в список заблокированных. "
f"При попытке отправить сообщение пользователь получит уведомление о том, что заблокирован."
)
async def cmd_shadowban(message: types.Message):
try:
user_id = extract_id(message)
except ValueError as ex:
return await message.reply(str(ex))
shadowbanned.add(int(user_id))
await message.reply(
f"ID {user_id} добавлен в список скрытно заблокированных. "
f"При попытке отправить сообщение пользователь не узнает, что заблокирован."
)
async def cmd_unban(message: types.Message):
try:
user_id = extract_id(message)
except ValueError as ex:
return await message.reply(str(ex))
user_id = int(user_id)
with suppress(KeyError):
banned.remove(user_id)
with suppress(KeyError):
shadowbanned.remove(user_id)
await message.reply(f"ID {user_id} разблокирован")
async def cmd_list_banned(message: types.Message):
has_bans = len(banned) > 0 or len(shadowbanned) > 0
if not has_bans:
await message.answer("Нет заблокированных пользователей")
return
result = []
if len(banned) > 0:
result.append("Список заблокированных:")
for item in banned:
result.append(f"• #id{item}")
if len(shadowbanned) > 0:
result.append("\nСписок скрытно заблокированных:")
for item in shadowbanned:
result.append(f"• #id{item}")
await message.answer("\n".join(result))
def register_bans_handlers(dp: Dispatcher, admin_chat_id: int):
dp.register_message_handler(cmd_ban, IsReplyFilter(is_reply=True), IDFilter(chat_id=admin_chat_id),
commands="ban")
dp.register_message_handler(cmd_shadowban, IsReplyFilter(is_reply=True), IDFilter(chat_id=admin_chat_id),
commands="shadowban")
dp.register_message_handler(cmd_unban, IsReplyFilter(is_reply=True), IDFilter(chat_id=admin_chat_id),
commands="unban")
dp.register_message_handler(cmd_list_banned, IDFilter(chat_id=admin_chat_id),
commands="list_banned")
| [
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2
] |
app/views.py | TheWolfA2/cppstyle | 0 | 173640 | import os
from flask import render_template, redirect, session, url_for, request, send_from_directory, jsonify
from app import app
from werkzeug import secure_filename
from style_grader_main import style_grader_driver
app.config['UPLOAD_FOLDER'] = 'uploads/'
app.config['ALLOWED_EXTENSIONS'] = set(['cpp', 'h'])
# @app.before_request
# def before_request():
# g.user = current_user
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1] in app.config['ALLOWED_EXTENSIONS']
@app.route('/', methods = ['GET'])
@app.route('/index', methods = ['GET', 'POST'])
def index():
# user = g.user
if request.method == 'POST':
# Get the FileStorage instance from request
file = request.files['file']
filename = secure_filename(file.filename)
# Render template with file info
return render_template('file.html',
filename = filename,
type = file.content_type)
return render_template('index.html',
title = 'Home')
@app.route('/uploadajax', methods=['POST'])
def add_numbers():
# Get the name of the uploaded files
uploaded_files = request.files.getlist("file[]")
filenames = []
for file in uploaded_files:
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join("app/" + app.config['UPLOAD_FOLDER'], filename))
filenames.append(filename)
list_of_online_files = []
for filename in filenames:
online_file = os.path.join("app/" + app.config['UPLOAD_FOLDER'], filename)
list_of_online_files.append(online_file)
response = style_grader_driver(list_of_online_files)
# if response != []:
# sub = Submission(user_id = g.user.id, passed_grader = False)
# else:
# sub = Submission(umich_id = g.user.umich_id, user_id = g.user.id, passed_grader = True)
# db.session.add(sub)
# db.session.commit()
return jsonify(errors=response)
@app.route('/uploads/<filename>')
def uploaded_file(filename):
return send_from_directory(app.config['UPLOAD_FOLDER'],
filename)
# @app.route('/login', methods = ['GET', 'POST'])
# @oid.loginhandler
# def login():
# if g.user is not None and g.user.is_authenticated():
# return redirect(url_for('index'))
# form = LoginForm()
# if form.validate_on_submit():
# session['remember_me'] = form.remember_me.data
# return oid.try_login(form.openid.data, ask_for = ['nickname', 'email'])
# return render_template('login.html',
# title = 'Sign In',
# form = form,
# providers = app.config['OPENID_PROVIDERS'])
# @lm.user_loader
# def load_user(id):
# return User.query.get(int(id))
# @oid.after_login
# def after_login(resp):
# if resp.email is None or resp.email == "":
# flash('Invalid login. Please try again.')
# return redirect(url_for('login'))
# user = User.query.filter_by(email = resp.email).first()
# if user is None:
# nickname = resp.nickname
# if nickname is None or nickname == "":
# nickname = resp.email.split('@')[0]
# user = User(email = resp.email, passed_grader = 0)
# db.session.add(user)
# db.session.commit()
# remember_me = False
# if 'remember_me' in session:
# remember_me = session['remember_me']
# session.pop('remember_me', None)
# login_user(user, remember = remember_me)
# return redirect(request.args.get('next') or url_for('index'))
# @app.route('/logout')
# def logout():
# logout_user()
##return redirect(url_for('index'))
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] |
main.py | IgorZyktin/Colortex | 17 | 76010 | <filename>main.py
"""
Colortex - simple textual painter
Based on:
https://gist.github.com/BigglesZX/4016539
https://gist.github.com/almost/d2832d0998ad9dfec2cacef934e7d247
"""
import ct_images
import ct_files
def main():
"""
Main flow
"""
print('--- Colortex image conversion script ---')
total_converted = 0
local_files = ct_files.get_filenames()
if not local_files:
print('Nothing to convert')
return
for i, file_dict in enumerate(local_files, start=1):
total_converted += ct_images.convert(file_dict, i, len(local_files))
if total_converted:
print(f'\nConversion complete, {total_converted} files converted.')
else:
print('\nComplete. No files converted.')
if __name__ == '__main__':
main()
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icrawler/utils/session.py | awesome-archive/icrawler | 0 | 86127 | <filename>icrawler/utils/session.py
import requests
from six.moves.urllib.parse import urlsplit
class Session(requests.Session):
def __init__(self, proxy_pool):
super(Session, self).__init__()
self.proxy_pool = proxy_pool
def _url_scheme(self, url):
return urlsplit(url).scheme
def get(self, url, **kwargs):
proxy = self.proxy_pool.get_next(protocol=self._url_scheme(url))
if proxy is None:
return super(Session, self).get(url, **kwargs)
try:
response = super(Session, self).get(url, proxies=proxy.format(),
**kwargs)
except requests.exceptions.ConnectionError:
self.proxy_pool.decrease_weight(proxy)
raise
except:
raise
else:
self.proxy_pool.increase_weight(proxy)
return response
def post(self, url, data=None, json=None, **kwargs):
proxy = self.proxy_pool.get_next(protocol=self._url_scheme(url))
if proxy is None:
return super(Session, self).get(url, data, json, **kwargs)
try:
response = super(Session, self).post(url, data, json,
proxies=proxy.format(),
**kwargs)
except requests.exceptions.ConnectionError:
self.proxy_pool.decrease_weight(proxy)
raise
except:
raise
else:
self.proxy_pool.increase_weight(proxy)
return response
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passwordValidator.py | valentinsoare/currentlyLearningPythonProjectsBased | 1 | 108516 | #!/usr/bin/python3
import re
def catch_input():
password = input(f'\n\033[1;31m *Enter Your Password for Validation:\033[0m ')
return password
def password_validator_rules(input_rule, given_password):
rules = re.compile(input_rule)
pattern = re.search(rules, given_password)
return True if pattern else False
def main():
password_to_check = catch_input()
checking = False
while not checking:
if password_validator_rules(r'([A-Za-z0-9].*[\-\s.,_]){5,}', password_to_check):
print(f'\n\033[1;34m **Basic rule applies here, looks ok.\033[0m\n')
checking = True
elif password_validator_rules(r'(?=.{8,})([A-Z].*)([a-z].*)([0-9].*)([@#$%^&+=].*)', password_to_check):
print(f'\n\033[1;34m **Eight characters rule applies here, we good to go.\033[0m\n')
checking = True
else:
print(f'\n\033[1;41m***ERROR, given password is not within desired guidelines. Please try again.\033[0m')
password_to_check = catch_input()
main()
| [
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mainapp/migrations/0001_initial.py | CheboiDerrick/hood-alert | 0 | 170252 | <gh_stars>0
# Generated by Django 3.2.9 on 2021-11-02 22:27
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Neighborhood',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=70)),
('location', models.CharField(max_length=70)),
('occupant_count', models.IntegerField()),
('user', models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Profile',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('photo', models.ImageField(blank=True, null=True, upload_to='posts/')),
('name', models.CharField(max_length=100)),
('email', models.EmailField(max_length=254, unique=True)),
('neighborhood', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='mainapp.neighborhood')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Post',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100)),
('detail', models.TextField()),
('image', models.ImageField(blank=True, null=True, upload_to='posts/')),
('posted_on', models.DateTimeField(auto_now_add=True)),
('neighborhood', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='mainapp.neighborhood')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
options={
'ordering': ['-posted_on'],
},
),
migrations.CreateModel(
name='Contacts',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100)),
('phone', models.CharField(max_length=50)),
('neighborhood', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
('user', models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, related_name='users', to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Business',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100)),
('description', models.TextField(blank=True, null=True)),
('email', models.EmailField(blank=True, max_length=254, null=True, unique=True)),
('phone', models.CharField(blank=True, max_length=50, null=True)),
('neighborhood', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='mainapp.neighborhood')),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
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] |
pendulum.py | x75/playground | 0 | 1604798 | <gh_stars>0
"""Quick test of data asymmetry in a motor driven pendulum"""
import numpy as np
import matplotlib
import matplotlib.gridspec as gridspec
# matplotlib.use("Cairo")
import matplotlib.pyplot as plt
l = 2e-0
g = 9.81
m = 1.0
cf = -4.8
# cf = -1.45
fr = 1e-2
one_over_fr = 1 - fr
scale = 1.0 # 0.1
# dt = 0.5e-2
dt = 1e-2
# dt = 1e-1
numsteps = 10000
x = np.zeros((numsteps, 3))
# x[0,0] = -np.pi/2.0
x[0,0] = np.pi/2.1
g *= scale
cf *= scale
for i in range(1, numsteps):
x[i,2] = (-g * m) / l * np.sin(x[i-1,0]) + cf + np.random.normal(0.0, 5e-0)
x[i,1] = one_over_fr * x[i-1,1] + x[i,2] * dt
x[i,0] = x[i-1,0] + x[i,1] * dt
if x[i,0] > (1*np.pi):
print("larger", x)
x[i,0] = x[i,0] - (2 * np.pi)
elif x[i,0] < (-1*np.pi):
print("smaller", x)
x[i,0] = x[i,0] + (2 * np.pi)
gs = gridspec.GridSpec(2, 3)
fig = plt.figure()
ax1 = fig.add_subplot(gs[0,:2])
# ax1.plot(np.abs(x))
# ax1.plot(x, "k,")
ax1.plot(x, "k-")
ax2 = fig.add_subplot(gs[0,2])
# si = x[:,0] ==
sls = 2000 # 1980+130
print("sls", sls)
# sls = x[sls:,0] == np.pi * 0.4
print("x[sls:,0]", x[sls:,0].shape)
print("x[sls:,0] < (np.pi * 0.4)", (x[sls:,0] < (np.pi * 0.4)))
print("x[sls:,0] > (np.pi * 0.39)", (x[sls:,0] > (np.pi * 0.39)))
sls += np.argwhere(np.logical_and(x[sls:,0] > (np.pi * 0.40), x[sls:,0] < (np.pi * 0.41)))[0,0]
sloffs = 10
sle = sloffs + np.argwhere(np.logical_and(x[sls+sloffs:,0] > (np.pi * 0.40), x[sls+sloffs:,0] < (np.pi * 0.41)))[0,0]
print("sle", sle)
print("x[sls+sloffs+sle:,0]", x[sls+sloffs+sle:,0])
# sle = np.argwhere(np.logical_and(x[sls:,0] > (np.pi * 0.6), x[sls:,0] < (np.pi * 0.61)))[0,0]
print("sls", sls)
sll = sle # int(115*6.8)
sl = slice(sls, sls + sll)
# ax2.plot([0, np.cos(x[sl,0]) * l], [0, np.sin(x[sl,0]) * l], "k-", alpha=0.1)
for x_ in x[sl,0]:
ax2.plot([0, np.cos(x_) * l * 2], [0, np.sin(x_) * l * 2], "k-", linewidth=10.0, alpha=0.3)
ax2.plot(np.cos(x[sl,0]) * l * 2.05, np.sin(x[sl,0]) * l * 2.05, "ko", alpha=0.5)
ax2.set_aspect(1)
ax3 = fig.add_subplot(gs[1,:])
# print "x[:,0].shape", x[:,0].shape
# x_ = np.vstack((x[1:-1,0], x[2:,0])).T
rng = 1400 # detect freq of oscillation
x_ = np.vstack([x[i:-rng+i,0] for i in range(rng)]).T
print("x_", x_.shape)
# ax3.plot(x_[:,0], x_[:,1], "ko", alpha=0.25)
transoffs = 1000
x_comp = []
x_offs = []
y_comp = []
y_offs = []
for i in range(1, rng, 10):
# mp = ax3.scatter(x_[:,0], x_[:,i], c = x[:-rng,1], alpha=0.25)
# mp = ax3.scatter(x_[:,0], np.abs(x_[:,i]), c = x[:-rng,1], cmap=plt.get_cmap("gray"), alpha=float(i)/rng)
sl = slice(transoffs, transoffs + 1000)
x_comp.append(np.sin(x_[sl,0]))
x_offs.append(np.ones_like(x_comp[-1]) * (i % (rng/10)))
y_comp.append(np.sin(x_[sl,i]))
y_offs.append(np.ones_like(y_comp[-1]) * 2.5 * (i // (rng/10)))
# mp = ax3.scatter(x_comp + i, y_comp, c = x_comp - y_comp, cmap=plt.get_cmap("spectral"), alpha=float(i)/rng)
# ax3.set_yscale("log")
x_comp = np.array(x_comp).flatten()
x_offs = np.array(x_offs).flatten()
y_comp = np.array(y_comp).flatten()
y_offs = np.array(y_offs).flatten()
mp = ax3.scatter(x_comp + x_offs, y_comp + y_offs, c = x_comp - y_comp, cmap=plt.get_cmap("Spectral"), alpha=0.25, s=2, linewidths=0)
# mp = ax3.scatter(x_[:,0], x_[:,1], c = x[:-1,1], alpha=0.25)
fig.colorbar(mappable = mp, ax = ax3, orientation="horizontal", fraction=0.05)
# ax3.set_aspect(1)
plt.show()
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] |
golden_search.py | nandaVT/Optimization-Techniques-for-Engineering-design | 0 | 189907 | import sys
try:
import math
import sympy as sym
from sympy import sin,cos,tan,N
except:
print("Packages not installed. Please install 'sympy' package")
sys.exit()
print("Fibonacci search method")
x = sym.Symbol('x')
def substitute(k):
return (f.subs(x,k))
while(True):
s = ""
func = "x**2+(54/x)"#input("Enter the function = ") #"x**2+(54/x)"
for i in func:
s= s+i
try:
exec("f = "+s)
print(f)
break
except:
print("Entered function is incompatible. Try again.")
a = 0#float(input("Enter 'a' value = "))
b = 5#float(input("Enter 'b' value = "))
k = 1
epsilon = 0.8
l_o = b - a
iteration = 1
while(1):
print("\nIteration = ",iteration)
iteration = iteration + 1
l_k_star = ((0.618)**k)*l_o
print("l_k_star = ",l_k_star)
x1 = a + l_k_star
x2 = b - l_k_star
print("x1 = {} x2 = {}".format(x1,x2))
f_x1 = substitute(x1)
f_x2 = substitute(x2)
print("f_x1 = {} f_x2 = {}".format(f_x1,f_x2))
if(x1 > x2):
if(f_x1 < f_x2):
a = x2
print("f_x1 > f_x2")
if(f_x1 > f_x2):
b = x1
print("f_x1 < f_x2")
else:
if(f_x1 > f_x2):
a = x1
print("f_x1 > f_x2")
elif(f_x1 < f_x2):
b = x2
print("f_x1 < f_x2")
#print(k)
l = b - a
print("l = ", l)
if(l < epsilon):
print("l < epsilon")
break
print("l > epsilon")
k = k + 1
print("(a = {} , b = {})".format(a,b)) | [
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] |
kivy/input/providers/linuxwacom.py | VICTORVICKIE/kivy | 13,889 | 66757 | <filename>kivy/input/providers/linuxwacom.py
'''
Native support of Wacom tablet from linuxwacom driver
=====================================================
To configure LinuxWacom, add this to your configuration::
[input]
pen = linuxwacom,/dev/input/event2,mode=pen
finger = linuxwacom,/dev/input/event3,mode=touch
.. note::
You must have read access to the input event.
You can use a custom range for the X, Y and pressure values.
On some drivers, the range reported is invalid.
To fix that, you can add these options to the argument line:
* invert_x : 1 to invert X axis
* invert_y : 1 to invert Y axis
* min_position_x : X minimum
* max_position_x : X maximum
* min_position_y : Y minimum
* max_position_y : Y maximum
* min_pressure : pressure minimum
* max_pressure : pressure maximum
'''
__all__ = ('LinuxWacomMotionEventProvider', 'LinuxWacomMotionEvent')
import os
from kivy.input.motionevent import MotionEvent
from kivy.input.shape import ShapeRect
class LinuxWacomMotionEvent(MotionEvent):
def depack(self, args):
self.is_touch = True
self.sx = args['x']
self.sy = args['y']
self.profile = ['pos']
if 'size_w' in args and 'size_h' in args:
self.shape = ShapeRect()
self.shape.width = args['size_w']
self.shape.height = args['size_h']
self.profile.append('shape')
if 'pressure' in args:
self.pressure = args['pressure']
self.profile.append('pressure')
super(LinuxWacomMotionEvent, self).depack(args)
def __str__(self):
return '<LinuxWacomMotionEvent id=%d pos=(%f, %f) device=%s>' \
% (self.id, self.sx, self.sy, self.device)
if 'KIVY_DOC' in os.environ:
# documentation hack
LinuxWacomMotionEventProvider = None
else:
import threading
import collections
import struct
import fcntl
from kivy.input.provider import MotionEventProvider
from kivy.input.factory import MotionEventFactory
from kivy.logger import Logger
#
# This part is taken from linux-source-2.6.32/include/linux/input.h
#
# Event types
EV_SYN = 0x00
EV_KEY = 0x01
EV_REL = 0x02
EV_ABS = 0x03
EV_MSC = 0x04
EV_SW = 0x05
EV_LED = 0x11
EV_SND = 0x12
EV_REP = 0x14
EV_FF = 0x15
EV_PWR = 0x16
EV_FF_STATUS = 0x17
EV_MAX = 0x1f
EV_CNT = (EV_MAX + 1)
KEY_MAX = 0x2ff
# Synchronization events
SYN_REPORT = 0
SYN_CONFIG = 1
SYN_MT_REPORT = 2
# Misc events
MSC_SERIAL = 0x00
MSC_PULSELED = 0x01
MSC_GESTURE = 0x02
MSC_RAW = 0x03
MSC_SCAN = 0x04
MSC_MAX = 0x07
MSC_CNT = (MSC_MAX + 1)
ABS_X = 0x00
ABS_Y = 0x01
ABS_PRESSURE = 0x18
ABS_MISC = 0x28 # if 0, it's touch up
ABS_MT_TOUCH_MAJOR = 0x30 # Major axis of touching ellipse
ABS_MT_TOUCH_MINOR = 0x31 # Minor axis (omit if circular)
ABS_MT_WIDTH_MAJOR = 0x32 # Major axis of approaching ellipse
ABS_MT_WIDTH_MINOR = 0x33 # Minor axis (omit if circular)
ABS_MT_ORIENTATION = 0x34 # Ellipse orientation
ABS_MT_POSITION_X = 0x35 # Center X ellipse position
ABS_MT_POSITION_Y = 0x36 # Center Y ellipse position
ABS_MT_TOOL_TYPE = 0x37 # Type of touching device
ABS_MT_BLOB_ID = 0x38 # Group a set of packets as a blob
ABS_MT_TRACKING_ID = 0x39 # Unique ID of initiated contact
ABS_MT_PRESSURE = 0x3a # Pressure on contact area
# some ioctl base (with 0 value)
EVIOCGNAME = 2147501318
EVIOCGBIT = 2147501344
EVIOCGABS = 2149074240
# sizeof(struct input_event)
struct_input_event_sz = struct.calcsize('LLHHi')
struct_input_absinfo_sz = struct.calcsize('iiiiii')
sz_l = struct.calcsize('Q')
class LinuxWacomMotionEventProvider(MotionEventProvider):
options = ('min_position_x', 'max_position_x',
'min_position_y', 'max_position_y',
'min_pressure', 'max_pressure',
'invert_x', 'invert_y')
def __init__(self, device, args):
super(LinuxWacomMotionEventProvider, self).__init__(device, args)
self.input_fn = None
self.default_ranges = dict()
self.mode = 'touch'
# split arguments
args = args.split(',')
if not args:
Logger.error('LinuxWacom: No filename given in config')
Logger.error('LinuxWacom: Use /dev/input/event0 for example')
return
# read filename
self.input_fn = args[0]
Logger.info('LinuxWacom: Read event from <%s>' % self.input_fn)
# read parameters
for arg in args[1:]:
if arg == '':
continue
arg = arg.split('=')
# ensure it's a key = value
if len(arg) != 2:
err = 'LinuxWacom: Bad parameter' \
'%s: Not in key=value format.' % arg
Logger.error(err)
continue
# ensure the key exist
key, value = arg
if key == 'mode':
self.mode = value
continue
if key not in LinuxWacomMotionEventProvider.options:
Logger.error('LinuxWacom: unknown %s option' % key)
continue
# ensure the value
try:
self.default_ranges[key] = int(value)
except ValueError:
err = 'LinuxWacom: value %s invalid for %s' % (key, value)
Logger.error(err)
continue
# all good!
msg = 'LinuxWacom: Set custom %s to %d' % (key, int(value))
Logger.info(msg)
Logger.info('LinuxWacom: mode is <%s>' % self.mode)
def start(self):
if self.input_fn is None:
return
self.uid = 0
self.queue = collections.deque()
self.thread = threading.Thread(
target=self._thread_run,
kwargs=dict(
queue=self.queue,
input_fn=self.input_fn,
device=self.device,
default_ranges=self.default_ranges))
self.thread.daemon = True
self.thread.start()
def _thread_run(self, **kwargs):
input_fn = kwargs.get('input_fn')
queue = kwargs.get('queue')
device = kwargs.get('device')
drs = kwargs.get('default_ranges').get
touches = {}
touches_sent = []
l_points = {}
# prepare some vars to get limit of some component
range_min_position_x = 0
range_max_position_x = 2048
range_min_position_y = 0
range_max_position_y = 2048
range_min_pressure = 0
range_max_pressure = 255
invert_x = int(bool(drs('invert_x', 0)))
invert_y = int(bool(drs('invert_y', 0)))
reset_touch = False
def process(points):
actives = list(points.keys())
for args in points.values():
tid = args['id']
try:
touch = touches[tid]
except KeyError:
touch = LinuxWacomMotionEvent(device, tid, args)
touches[touch.id] = touch
if touch.sx == args['x'] \
and touch.sy == args['y'] \
and tid in touches_sent:
continue
touch.move(args)
if tid not in touches_sent:
queue.append(('begin', touch))
touches_sent.append(tid)
queue.append(('update', touch))
for tid in list(touches.keys())[:]:
if tid not in actives:
touch = touches[tid]
if tid in touches_sent:
touch.update_time_end()
queue.append(('end', touch))
touches_sent.remove(tid)
del touches[tid]
def normalize(value, vmin, vmax):
return (value - vmin) / float(vmax - vmin)
# open the input
try:
fd = open(input_fn, 'rb')
except IOError:
Logger.exception('Unable to open %s' % input_fn)
return
# get the controller name (EVIOCGNAME)
device_name = fcntl.ioctl(fd, EVIOCGNAME + (256 << 16),
" " * 256).split('\x00')[0]
Logger.info('LinuxWacom: using <%s>' % device_name)
# get abs infos
bit = fcntl.ioctl(fd, EVIOCGBIT + (EV_MAX << 16), ' ' * sz_l)
bit, = struct.unpack('Q', bit)
for x in range(EV_MAX):
# preserve this, we may want other things than EV_ABS
if x != EV_ABS:
continue
# EV_ABS available for this device ?
if (bit & (1 << x)) == 0:
continue
# ask abs info keys to the devices
sbit = fcntl.ioctl(fd, EVIOCGBIT + x + (KEY_MAX << 16),
' ' * sz_l)
sbit, = struct.unpack('Q', sbit)
for y in range(KEY_MAX):
if (sbit & (1 << y)) == 0:
continue
absinfo = fcntl.ioctl(fd, EVIOCGABS + y +
(struct_input_absinfo_sz << 16),
' ' * struct_input_absinfo_sz)
abs_value, abs_min, abs_max, abs_fuzz, \
abs_flat, abs_res = struct.unpack('iiiiii', absinfo)
if y == ABS_X:
range_min_position_x = drs('min_position_x', abs_min)
range_max_position_x = drs('max_position_x', abs_max)
Logger.info('LinuxWacom: ' +
'<%s> range position X is %d - %d' % (
device_name, abs_min, abs_max))
elif y == ABS_Y:
range_min_position_y = drs('min_position_y', abs_min)
range_max_position_y = drs('max_position_y', abs_max)
Logger.info('LinuxWacom: ' +
'<%s> range position Y is %d - %d' % (
device_name, abs_min, abs_max))
elif y == ABS_PRESSURE:
range_min_pressure = drs('min_pressure', abs_min)
range_max_pressure = drs('max_pressure', abs_max)
Logger.info('LinuxWacom: ' +
'<%s> range pressure is %d - %d' % (
device_name, abs_min, abs_max))
# read until the end
changed = False
touch_id = 0
touch_x = 0
touch_y = 0
touch_pressure = 0
while fd:
data = fd.read(struct_input_event_sz)
if len(data) < struct_input_event_sz:
break
# extract each event
for i in range(len(data) / struct_input_event_sz):
ev = data[i * struct_input_event_sz:]
# extract timeval + event infos
tv_sec, tv_usec, ev_type, ev_code, ev_value = \
struct.unpack('LLHHi', ev[:struct_input_event_sz])
if ev_type == EV_SYN and ev_code == SYN_REPORT:
if touch_id in l_points:
p = l_points[touch_id]
else:
p = dict()
l_points[touch_id] = p
p['id'] = touch_id
if not reset_touch:
p['x'] = touch_x
p['y'] = touch_y
p['pressure'] = touch_pressure
if self.mode == 'pen' \
and touch_pressure == 0 \
and not reset_touch:
del l_points[touch_id]
if changed:
if 'x' not in p:
reset_touch = False
continue
process(l_points)
changed = False
if reset_touch:
l_points.clear()
reset_touch = False
process(l_points)
elif ev_type == EV_MSC and ev_code == MSC_SERIAL:
touch_id = ev_value
elif ev_type == EV_ABS and ev_code == ABS_X:
val = normalize(ev_value,
range_min_position_x,
range_max_position_x)
if invert_x:
val = 1. - val
touch_x = val
changed = True
elif ev_type == EV_ABS and ev_code == ABS_Y:
val = 1. - normalize(ev_value,
range_min_position_y,
range_max_position_y)
if invert_y:
val = 1. - val
touch_y = val
changed = True
elif ev_type == EV_ABS and ev_code == ABS_PRESSURE:
touch_pressure = normalize(ev_value,
range_min_pressure,
range_max_pressure)
changed = True
elif ev_type == EV_ABS and ev_code == ABS_MISC:
if ev_value == 0:
reset_touch = True
def update(self, dispatch_fn):
# dispatch all event from threads
try:
while True:
event_type, touch = self.queue.popleft()
dispatch_fn(event_type, touch)
except:
pass
MotionEventFactory.register('linuxwacom', LinuxWacomMotionEventProvider)
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] |
section-3-13-mini_project-solution.py | gopal-amlekar/Jumpstart-IOT-Exercise | 1 | 75991 | import RPi.GPIO as GPIO
import smbus
import time
SHT21_ADD = 0x40
SHT21_STATUS_MASK = 0xFFFC
SHT21_MEASURE_TEMP_CMD = 0xF3
SHT21_MEASURE_HUM_CMD = 0xF5
MAX_TEMPERATURE = 120
MAX_HUMIDITY = 100
temperatureBarGraph = [26,23,20,17,14,11,8,5]
humidityBarGraph = [27,24,21,18,15,12,9,6]
unusedLEDs = [16,13,10,7]
temperatureButton = 25
humidityButton = 22
def get_temperature():
bus.write_byte(SHT21_ADD, SHT21_MEASURE_TEMP_CMD)
time.sleep(0.1)
temp_msb = bus.read_byte (SHT21_ADD)
#print temp_msb
temp_lsb = bus.read_byte (SHT21_ADD)
#print temp_lsb
temp_chksm = bus.read_byte (SHT21_ADD)
#print temp_chksm
temperature = (temp_msb << 8) + temp_lsb
#print temperature
temperature &= SHT21_STATUS_MASK
#print temperature
temperature *= 175.72
#print temperature
temperature /= 1<<16
#print temperature
temperature -= 46.85
return temperature
def get_humidity():
bus.write_byte(SHT21_ADD, SHT21_MEASURE_HUM_CMD)
time.sleep(0.1)
hum_msb = bus.read_byte (SHT21_ADD)
#print temp_msb
hum_lsb = bus.read_byte (SHT21_ADD)
#print temp_lsb
hum_chksm = bus.read_byte (SHT21_ADD)
#print temp_chksm
humidity = (hum_msb << 8) + hum_lsb
#print temperature
humidity &= SHT21_STATUS_MASK
#print temperature
humidity *= 125.0
#print temperature
humidity /= 1<<16
#print temperature
humidity -= 6
return humidity
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)
for led_tmp in temperatureBarGraph:
GPIO.setup(led_tmp, GPIO.OUT)
for led_hum in humidityBarGraph:
GPIO.setup(led_hum, GPIO.OUT)
for led_tmp in temperatureBarGraph:
GPIO.output(led_tmp, GPIO.LOW)
for led_hum in humidityBarGraph:
GPIO.output(led_hum, GPIO.LOW)
for led_unused in unusedLEDs:
GPIO.setup(led_unused, GPIO.OUT)
GPIO.output(led_unused, GPIO.LOW)
GPIO.setup(temperatureButton, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.setup(humidityButton, GPIO.IN, pull_up_down=GPIO.PUD_UP)
temperatureLEDs = len(temperatureBarGraph)
humidityLEDs = len(humidityBarGraph)
temperatureDiv = int(MAX_TEMPERATURE / temperatureLEDs) - 1
humidityDiv = int(MAX_HUMIDITY / humidityLEDs)
try:
bus = smbus.SMBus(1)
while True:
time.sleep(2)
temperature = get_temperature()
humidity = get_humidity()
print "Temperature is ", temperature
print "Humidity is ", humidity
if (GPIO.input(temperatureButton)):
bar_level = int(temperature/temperatureDiv)
for led_temp in range(0, bar_level):
#print led_temp
GPIO.output(temperatureBarGraph[led_temp], GPIO.HIGH)
for led_temp in range(bar_level, len(temperatureBarGraph)):
GPIO.output(temperatureBarGraph[led_temp], GPIO.LOW)
else:
for led_tmp in temperatureBarGraph:
GPIO.output(led_tmp, GPIO.LOW)
if (GPIO.input(humidityButton)):
bar_level = int(humidity/humidityDiv)
for led_temp in range(0, bar_level):
#print led_temp
GPIO.output(humidityBarGraph[led_temp], GPIO.HIGH)
for led_temp in range(bar_level, len(humidityBarGraph)):
GPIO.output(humidityBarGraph[led_temp], GPIO.LOW)
else:
for led_tmp in humidityBarGraph:
GPIO.output(led_tmp, GPIO.LOW)
except IOError as (errno, strerror):
print "I/O error({0}): {1}. Try changing I2C device number in code".format(errno, strerror)
except KeyboardInterrupt:
print "Keyboard Interrupt Received"
except:
print "Unexpected error:"
raise
finally:
GPIO.cleanup()
print "Exiting Program"
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2
] |
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