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regform/apps.py | elishaking/django-form | 0 | 151678 | from django.apps import AppConfig
class RegformConfig(AppConfig):
name = 'regform'
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env/lib/python3.6/site-packages/gunicorn/workers/base.py | anthowen/duplify | 7 | 1610655 | # -*- coding: utf-8 -
#
# This file is part of gunicorn released under the MIT license.
# See the NOTICE for more information.
from datetime import datetime
import os
from random import randint
import signal
from ssl import SSLError
import sys
import time
import traceback
from gunicorn import six
from gunicorn import util
from gunicorn.workers.workertmp import WorkerTmp
from gunicorn.reloader import reloader_engines
from gunicorn.http.errors import (
InvalidHeader, InvalidHeaderName, InvalidRequestLine, InvalidRequestMethod,
InvalidHTTPVersion, LimitRequestLine, LimitRequestHeaders,
)
from gunicorn.http.errors import InvalidProxyLine, ForbiddenProxyRequest
from gunicorn.http.wsgi import default_environ, Response
from gunicorn.six import MAXSIZE
class Worker(object):
SIGNALS = [getattr(signal, "SIG%s" % x)
for x in "ABRT HUP QUIT INT TERM USR1 USR2 WINCH CHLD".split()]
PIPE = []
def __init__(self, age, ppid, sockets, app, timeout, cfg, log):
"""\
This is called pre-fork so it shouldn't do anything to the
current process. If there's a need to make process wide
changes you'll want to do that in ``self.init_process()``.
"""
self.age = age
self.pid = "[booting]"
self.ppid = ppid
self.sockets = sockets
self.app = app
self.timeout = timeout
self.cfg = cfg
self.booted = False
self.aborted = False
self.reloader = None
self.nr = 0
jitter = randint(0, cfg.max_requests_jitter)
self.max_requests = cfg.max_requests + jitter or MAXSIZE
self.alive = True
self.log = log
self.tmp = WorkerTmp(cfg)
def __str__(self):
return "<Worker %s>" % self.pid
def notify(self):
"""\
Your worker subclass must arrange to have this method called
once every ``self.timeout`` seconds. If you fail in accomplishing
this task, the master process will murder your workers.
"""
self.tmp.notify()
def run(self):
"""\
This is the mainloop of a worker process. You should override
this method in a subclass to provide the intended behaviour
for your particular evil schemes.
"""
raise NotImplementedError()
def init_process(self):
"""\
If you override this method in a subclass, the last statement
in the function should be to call this method with
super(MyWorkerClass, self).init_process() so that the ``run()``
loop is initiated.
"""
# set environment' variables
if self.cfg.env:
for k, v in self.cfg.env.items():
os.environ[k] = v
util.set_owner_process(self.cfg.uid, self.cfg.gid,
initgroups=self.cfg.initgroups)
# Reseed the random number generator
util.seed()
# For waking ourselves up
self.PIPE = os.pipe()
for p in self.PIPE:
util.set_non_blocking(p)
util.close_on_exec(p)
# Prevent fd inheritance
[util.close_on_exec(s) for s in self.sockets]
util.close_on_exec(self.tmp.fileno())
self.wait_fds = self.sockets + [self.PIPE[0]]
self.log.close_on_exec()
self.init_signals()
# start the reloader
if self.cfg.reload:
def changed(fname):
self.log.info("Worker reloading: %s modified", fname)
self.alive = False
self.cfg.worker_int(self)
time.sleep(0.1)
sys.exit(0)
reloader_cls = reloader_engines[self.cfg.reload_engine]
self.reloader = reloader_cls(callback=changed)
self.reloader.start()
self.load_wsgi()
self.cfg.post_worker_init(self)
# Enter main run loop
self.booted = True
self.run()
def load_wsgi(self):
try:
self.wsgi = self.app.wsgi()
except SyntaxError as e:
if self.cfg.reload == 'off':
raise
self.log.exception(e)
# fix from PR #1228
# storing the traceback into exc_tb will create a circular reference.
# per https://docs.python.org/2/library/sys.html#sys.exc_info warning,
# delete the traceback after use.
try:
exc_type, exc_val, exc_tb = sys.exc_info()
self.reloader.add_extra_file(exc_val.filename)
tb_string = six.StringIO()
traceback.print_tb(exc_tb, file=tb_string)
self.wsgi = util.make_fail_app(tb_string.getvalue())
finally:
del exc_tb
def init_signals(self):
# reset signaling
[signal.signal(s, signal.SIG_DFL) for s in self.SIGNALS]
# init new signaling
signal.signal(signal.SIGQUIT, self.handle_quit)
signal.signal(signal.SIGTERM, self.handle_exit)
signal.signal(signal.SIGINT, self.handle_quit)
signal.signal(signal.SIGWINCH, self.handle_winch)
signal.signal(signal.SIGUSR1, self.handle_usr1)
signal.signal(signal.SIGABRT, self.handle_abort)
# Don't let SIGTERM and SIGUSR1 disturb active requests
# by interrupting system calls
if hasattr(signal, 'siginterrupt'): # python >= 2.6
signal.siginterrupt(signal.SIGTERM, False)
signal.siginterrupt(signal.SIGUSR1, False)
if hasattr(signal, 'set_wakeup_fd'):
signal.set_wakeup_fd(self.PIPE[1])
def handle_usr1(self, sig, frame):
self.log.reopen_files()
def handle_exit(self, sig, frame):
self.alive = False
def handle_quit(self, sig, frame):
self.alive = False
# worker_int callback
self.cfg.worker_int(self)
time.sleep(0.1)
sys.exit(0)
def handle_abort(self, sig, frame):
self.alive = False
self.cfg.worker_abort(self)
sys.exit(1)
def handle_error(self, req, client, addr, exc):
request_start = datetime.now()
addr = addr or ('', -1) # unix socket case
if isinstance(exc, (InvalidRequestLine, InvalidRequestMethod,
InvalidHTTPVersion, InvalidHeader, InvalidHeaderName,
LimitRequestLine, LimitRequestHeaders,
InvalidProxyLine, ForbiddenProxyRequest,
SSLError)):
status_int = 400
reason = "Bad Request"
if isinstance(exc, InvalidRequestLine):
mesg = "Invalid Request Line '%s'" % str(exc)
elif isinstance(exc, InvalidRequestMethod):
mesg = "Invalid Method '%s'" % str(exc)
elif isinstance(exc, InvalidHTTPVersion):
mesg = "Invalid HTTP Version '%s'" % str(exc)
elif isinstance(exc, (InvalidHeaderName, InvalidHeader,)):
mesg = "%s" % str(exc)
if not req and hasattr(exc, "req"):
req = exc.req # for access log
elif isinstance(exc, LimitRequestLine):
mesg = "%s" % str(exc)
elif isinstance(exc, LimitRequestHeaders):
mesg = "Error parsing headers: '%s'" % str(exc)
elif isinstance(exc, InvalidProxyLine):
mesg = "'%s'" % str(exc)
elif isinstance(exc, ForbiddenProxyRequest):
reason = "Forbidden"
mesg = "Request forbidden"
status_int = 403
elif isinstance(exc, SSLError):
reason = "Forbidden"
mesg = "'%s'" % str(exc)
status_int = 403
msg = "Invalid request from ip={ip}: {error}"
self.log.debug(msg.format(ip=addr[0], error=str(exc)))
else:
if hasattr(req, "uri"):
self.log.exception("Error handling request %s", req.uri)
status_int = 500
reason = "Internal Server Error"
mesg = ""
if req is not None:
request_time = datetime.now() - request_start
environ = default_environ(req, client, self.cfg)
environ['REMOTE_ADDR'] = addr[0]
environ['REMOTE_PORT'] = str(addr[1])
resp = Response(req, client, self.cfg)
resp.status = "%s %s" % (status_int, reason)
resp.response_length = len(mesg)
self.log.access(resp, req, environ, request_time)
try:
util.write_error(client, status_int, reason, mesg)
except:
self.log.debug("Failed to send error message.")
def handle_winch(self, sig, fname):
# Ignore SIGWINCH in worker. Fixes a crash on OpenBSD.
self.log.debug("worker: SIGWINCH ignored.")
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2
] |
library/docker/docker.py | Kafkamorph/shutit | 0 | 46719 | <filename>library/docker/docker.py
#Copyright (C) 2014 OpenBet Limited
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is furnished
#to do so, subject to the following conditions:
#
#The above copyright notice and this permission notice shall be included in all
#copies or substantial portions of the Software.
#
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
#FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
#COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
#IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
#CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
from shutit_module import ShutItModule
class docker(ShutItModule):
def build(self,shutit):
shutit.send('echo deb http://archive.ubuntu.com/ubuntu precise universe > /etc/apt/sources.list.d/universe.list')
shutit.send('apt-get update -qq')
shutit.install('iptables')
shutit.install('ca-certificates')
shutit.install('lxc')
shutit.install('curl')
shutit.install('aufs-tools')
shutit.send('pushd /usr/bin')
shutit.send('curl https://get.docker.io/builds/Linux/x86_64/docker-latest > docker')
shutit.send('chmod +x docker')
wrapdocker = """cat > /usr/bin/wrapdocker << 'END'
#!/bin/bash
# First, make sure that cgroups are mounted correctly.
CGROUP=/sys/fs/cgroup
[ -d $CGROUP ] ||
mkdir $CGROUP
mountpoint -q $CGROUP ||
mount -n -t tmpfs -o uid=0,gid=0,mode=0755 cgroup $CGROUP || {
echo "Could not make a tmpfs mount. Did you use -privileged?"
exit 1
}
if [ -d /sys/kernel/security ] && ! mountpoint -q /sys/kernel/security
then
mount -t securityfs none /sys/kernel/security || {
echo "Could not mount /sys/kernel/security."
echo "AppArmor detection and -privileged mode might break."
}
fi
# Mount the cgroup hierarchies exactly as they are in the parent system.
for SUBSYS in $(cut -d: -f2 /proc/1/cgroup)
do
[ -d $CGROUP/$SUBSYS ] || mkdir $CGROUP/$SUBSYS
mountpoint -q $CGROUP/$SUBSYS ||
mount -n -t cgroup -o $SUBSYS cgroup $CGROUP/$SUBSYS
# The two following sections address a bug which manifests itself
# by a cryptic "lxc-start: no ns_cgroup option specified" when
# trying to start containers withina container.
# The bug seems to appear when the cgroup hierarchies are not
# mounted on the exact same directories in the host, and in the
# container.
# Named, control-less cgroups are mounted with "-o name=foo"
# (and appear as such under /proc/<pid>/cgroup) but are usually
# mounted on a directory named "foo" (without the "name=" prefix).
# Systemd and OpenRC (and possibly others) both create such a
# cgroup. To avoid the aforementioned bug, we symlink "foo" to
# "name=foo". This shouldn't have any adverse effect.
echo $SUBSYS | grep -q ^name= && {
NAME=$(echo $SUBSYS | sed s/^name=//)
ln -s $SUBSYS $CGROUP/$NAME
}
# Likewise, on at least one system, it has been reported that
# systemd would mount the CPU and CPU accounting controllers
# (respectively "cpu" and "cpuacct") with "-o cpuacct,cpu"
# but on a directory called "cpu,cpuacct" (note the inversion
# in the order of the groups). This tries to work around it.
[ $SUBSYS = cpuacct,cpu ] && ln -s $SUBSYS $CGROUP/cpu,cpuacct
done
# Note: as I write those lines, the LXC userland tools cannot setup
# a "sub-container" properly if the "devices" cgroup is not in its
# own hierarchy. Let's detect this and issue a warning.
grep -q :devices: /proc/1/cgroup ||
echo "WARNING: the 'devices' cgroup should be in its own hierarchy."
grep -qw devices /proc/1/cgroup ||
echo "WARNING: it looks like the 'devices' cgroup is not mounted."
# Now, close extraneous file descriptors.
pushd /proc/self/fd >/dev/null
for FD in *
do
case "$FD" in
# Keep stdin/stdout/stderr
[012])
;;
# Nuke everything else
*)
eval exec "$FD>&-"
;;
esac
done
popd >/dev/null
# If a pidfile is still around (for example after a container restart),
# delete it so that docker can start.
rm -rf /var/run/docker.pid
# If we were given a PORT environment variable, start as a simple daemon;
# otherwise, spawn a shell as well
if [ "$PORT" ]
then
exec docker -d -H 0.0.0.0:$PORT
else
docker -d &
exec bash
fi
END"""
shutit.send(wrapdocker)
shutit.send('chmod +x /usr/bin/wrapdocker')
start_docker = """cat > /root/start_docker.sh << 'END'
#!/bin/bash
/root/start_ssh_server.sh
docker -d &
/usr/bin/wrapdocker
echo "SSH Server up"
echo "Docker daemon running"
END"""
shutit.send(start_docker)
shutit.send('chmod +x /root/start_docker.sh')
shutit.send('popd')
return True
def is_installed(self,shutit):
config_dict = shutit.cfg
return False
def module():
return docker(
'shutit.tk.docker.docker', 0.396,
description='docker server (communicates with host\'s docker daemon)',
depends=['shutit.tk.setup', 'shutit.tk.ssh_server.ssh_server']
)
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] |
mosqito/sound_level_meter/noct_spectrum/_nominal_frequency.py | MitchellAcoustics/MoSQITo | 1 | 197837 | from numpy import array
NOMINAL_OCTAVE_CENTER_FREQUENCIES = array(
[
31.5,
63.0,
125.0,
250.0,
500.0,
1000.0,
2000.0,
4000.0,
8000.0,
16000.0,
]
)
NOMINAL_THIRD_OCTAVE_CENTER_FREQUENCIES = array(
[
25.0,
31.5,
40.0,
50.0,
63.0,
80.0,
100.0,
125.0,
160.0,
200.0,
250.0,
315.0,
400.0,
500.0,
630.0,
800.0,
1000.0,
1250.0,
1600.0,
2000.0,
2500.0,
3150.0,
4000.0,
5000.0,
6300.0,
8000.0,
10000.0,
12500.0,
16000.0,
20000.0,
]
)
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29900,
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13,
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13,
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29900,
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29897,
13,
2
] |
chess/__main__.py | quadratic-bit/pygame-chess | 3 | 35477 | <filename>chess/__main__.py
from sys import exit
from typing import Optional, Final
import pygame
from rich.traceback import install
from chess.board import Chessboard, Move, PieceType, PieceColour
from chess.bot import ChessBot
from chess.const import GameState
from chess.utils import load_image, load_sound, load_font
install(show_locals=True)
def terminate() -> None:
pygame.quit()
exit(0)
def main():
# Game setup
# Pygame stuff
pygame.init()
SCREEN_W, SCREEN_H = SCREEN_SHAPE = 1200, 800 # type: Final
screen = pygame.display.set_mode(SCREEN_SHAPE)
pygame.display.set_caption("Chess")
# Colours
colour_bg = pygame.Color("#443742")
colour_contrast_bg = pygame.Color("#8D80AD")
# FPS handler
clock = pygame.time.Clock()
FPS = 60
# Creating a board using FEN
# Start position: rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1
# Advanced: 1r1q3r/3b2bk/p5pp/2QB4/5p2/P5nP/1PP5/2KRR3 b - - 6 12
# Pawn promotion: 8/6P1/2Q5/4p3/3qP3/5Q2/1q3PK1/qk6 w - - 36 73
# Checkmate: 3rkbnr/1p1bp3/1q1p3p/p5p1/3n4/PPR2Q2/5PPP/6K1 w - - 1 2
fen_game_state = "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1"
board = Chessboard.from_fen(fen_game_state)
# Sounds
sound_common = load_sound("common.ogg")
sound_check = load_sound("check.ogg")
# Fonts
font_header = load_font("ubuntumono/UbuntuMono-R.ttf", 90)
font_option = load_font("ubuntumono/UbuntuMono-B.ttf", 55)
# Defining variables to interact with player
grabbing: Optional[tuple[int, int]] = None
hovering = False
# Render Flag
last_move: Optional[Move] = None
# Defining game loop functions
def start_screen() -> None:
"""Start screen"""
nonlocal screen, font_header, font_option
bg_start_img = pygame.transform.scale(load_image('bg_start.png'), SCREEN_SHAPE)
title = font_header.render("Шахматы", True, pygame.Color("white"))
option = font_option.render("Нажмите любую клавишу...", True, pygame.Color("white"))
screen.blit(bg_start_img, (0, 0))
screen.blit(title, (420, 200))
screen.blit(option, (270, 650))
pygame.display.flip()
while True:
for event_ in pygame.event.get():
if event_.type == pygame.QUIT:
terminate()
elif event_.type == pygame.KEYDOWN or event_.type == pygame.MOUSEBUTTONDOWN:
return
clock.tick(FPS)
def choose_game_mode_screen() -> bool:
"""Game mode screen ( True if vs AI, False otherwise )"""
nonlocal screen
screen.fill(colour_bg)
pygame.draw.rect(screen, colour_contrast_bg, (300, 352, 600, 96))
pygame.draw.rect(screen, colour_contrast_bg, (340, 576, 520, 96))
button_vs_ai = font_header.render("Выберите режим игры", True, "white")
button_vs_player = font_option.render("Против компьютера", True, "black")
screen.blit(button_vs_ai, (170, 131))
screen.blit(button_vs_player, (400, 368))
screen.blit(font_option.render("Против игрока", True, "black"), (460, 593))
# Icons by Font Awesome!
# License: https://fontawesome.com/license/free
screen.blit(pygame.transform.scale(load_image('desktop-solid.png'), (80, 80)), (308, 360))
screen.blit(pygame.transform.scale(load_image('chess-solid.png'), (80, 80)), (348, 584))
pygame.display.flip()
button_rects = [pygame.Rect(300, 352, 600, 96), pygame.Rect(340, 576, 520, 96)]
def is_colliding(m_pos: tuple[int, int]) -> bool:
return any(map(lambda b: b.x < m_pos[0] < b.x + b.w and b.y < m_pos[1] < b.y + b.h, button_rects))
while True:
for event_ in pygame.event.get():
if event_.type == pygame.QUIT:
terminate()
elif event_.type == pygame.MOUSEBUTTONDOWN:
if is_colliding(event_.pos):
if button_rects[0].x < event_.pos[0] < button_rects[0].x + button_rects[0].w and \
button_rects[0].y < event_.pos[1] < button_rects[0].y + button_rects[0].h:
return True
return False
elif event_.type == pygame.MOUSEMOTION:
if is_colliding(event_.pos):
pygame.mouse.set_cursor(pygame.SYSTEM_CURSOR_HAND)
else:
pygame.mouse.set_cursor(pygame.SYSTEM_CURSOR_ARROW)
clock.tick(FPS)
def end_screen(state: GameState, board_state: tuple) -> None:
board.render(screen, last_move, game_info=board_state)
scaffold = pygame.Surface(SCREEN_SHAPE)
pygame.draw.rect(scaffold, pygame.Color("black"),
(0, 0, SCREEN_W, SCREEN_H))
scaffold.set_alpha(0)
screen.blit(scaffold, (0, 0))
end_font = load_font("ubuntumono/UbuntuMono-R.ttf", 90)
end_font_colour = pygame.Color("white")
mate = end_font.render(
"Мат!"
if state == GameState.Checkmate
else "Пат!", True, end_font_colour)
score = end_font.render(
"0-1"
if board.active_colour == PieceColour.White
else "1-0", True, end_font_colour)
bg = pygame.Surface((600, 400))
bg.fill(pygame.Color("black"))
bg.set_alpha(180)
mate.set_alpha(255)
score.set_alpha(255)
mate_rect = mate.get_rect()
bg_rect = bg.get_rect()
score_rect = score.get_rect()
mdx = (bg_rect.w - mate_rect.w) // 2
mdy = (bg_rect.h - mate_rect.h) // 3
sdx = (bg_rect.w - score_rect.w) // 2
sdy = (bg_rect.h - score_rect.h) // 1.5
screen.blit(bg, (300, 200))
screen.blit(mate, (300 + mdx, 200 + mdy))
screen.blit(score, (300 + sdx, 200 + sdy))
pygame.display.flip()
pygame.mouse.set_cursor(pygame.SYSTEM_CURSOR_ARROW)
while True:
for e in pygame.event.get():
if e.type == pygame.QUIT:
terminate()
elif e.type == pygame.KEYDOWN or e.type == pygame.MOUSEBUTTONDOWN:
return
def toggle_state(board_state: tuple) -> bool:
"""Check and toggle game state (for endgames especially)"""
state = board.toggle_state()
if state == GameState.Continue:
return False
else:
end_screen(state, board_state)
return True
def game_loop(vs_ai: bool) -> None:
"""Main game loop"""
nonlocal grabbing, hovering, last_move
# My linter can't handle unsigned variables like these
bot = last_move_uncaught = None
if vs_ai:
# Initialising the AI
bot = ChessBot()
# Bot Flag
last_move_uncaught = False
# Toggle flag
board_info: Optional[tuple] = None
# Initial rendering
board.render(screen)
pygame.display.flip()
# Starting main loop
while True:
for event in pygame.event.get():
# Quitting game
if event.type == pygame.QUIT:
terminate()
# LMB while hovering above a piece (grab a piece)
if event.type == pygame.MOUSEBUTTONDOWN and \
event.button == pygame.BUTTON_LEFT and hovering:
# Find grabbed piece
grabbing = (event.pos[0] // 100, event.pos[1] // 100)
if board.at(*grabbing).Colour != board.active_colour:
# Wrong colour!
grabbing = None
else:
# Render a board with that grabbed piece being grabbed
board.render(screen, last_move, grabbing, event.pos,
game_info=board_info)
pygame.display.flip()
# Releasing LMB
elif event.type == pygame.MOUSEBUTTONUP and \
event.button == pygame.BUTTON_LEFT:
# Get position where player dropped the piece
released = (event.pos[0] // 100, event.pos[1] // 100)
if pygame.mouse.get_focused() and grabbing is not None and \
released != grabbing and event.pos[0] <= 800 and \
event.pos[1] <= 800:
# Trying to make move
x, y = grabbing[0] + grabbing[1] * 8, \
released[0] + released[1] * 8
move = Move(x, y, board.at(*released))
# If we can make move -> let the bot make the next one
move = board.can_make(move)
if move is not None:
board.make_move(move)
if board.king_is_safe(board.passive_colour):
sound_common.play()
else:
sound_check.play()
last_move = move
last_move_uncaught = True
if toggle_state(board_info):
return
# Stop grabbing
grabbing = None
# Rendering board after releasing piece
board.render(screen, last_move, game_info=board_info)
pygame.display.flip()
if vs_ai and last_move_uncaught is not None and bot is not None:
# Bot's turn
if last_move is not None and last_move_uncaught:
last_move, board_info = bot.get_move(board, last_move)
board.make_move(last_move)
if board.king_is_safe(board.passive_colour):
sound_common.play()
else:
sound_check.play()
if toggle_state(board_info):
return
# Updating flag
last_move_uncaught = False
# Rendering board after bot's turn
board.render(screen, last_move, game_info=board_info)
pygame.display.flip()
# Handling mouse and cursor
if pygame.mouse.get_focused():
pos = pygame.mouse.get_pos()
# Changing cursor state
if board.at(pos[0] // 100, pos[1] // 100).Type != PieceType.Empty:
pygame.mouse.set_cursor(pygame.SYSTEM_CURSOR_HAND)
hovering = True
else:
pygame.mouse.set_cursor(pygame.SYSTEM_CURSOR_ARROW)
hovering = False
# Rendering board and a hovering piece
if grabbing:
board.render(screen, last_move, grabbing, pos,
game_info=board_info)
pygame.display.flip()
else:
# Mouse is out of window -> stop grabbing
grabbing = None
board.render(screen, last_move, game_info=board_info)
pygame.display.flip()
hovering = False
clock.tick(FPS)
# Starting the game
start_screen()
while True:
# Main loop
game_loop(choose_game_mode_screen())
# Resetting the board
board = Chessboard.from_fen(fen_game_state)
if __name__ == "__main__":
main()
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test/agenda_test.py | chalothon/CLIPS_1 | 0 | 52297 | <gh_stars>0
import unittest
from clips import Environment, CLIPSError, Strategy, SalienceEvaluation
DEFTEMPLATE = """(deftemplate template-fact
(slot template-slot))
"""
DEFRULE = """(defrule MAIN::rule-name
(declare (salience 10))
(implied-fact implied-value)
=>
(assert (rule-fired)))
"""
DEFTEMPLATERULE = """(defrule MAIN::rule-name
(implied-fact implied-value)
(template-fact (template-slot template-value))
=>
(assert (rule-fired)))
"""
DEFOTHERRULE = """(defrule MAIN::other-rule-name
(declare (salience 20))
(implied-fact implied-value)
=>
(assert (rule-fired)))
"""
class TestAgenda(unittest.TestCase):
def setUp(self):
self.env = Environment()
self.env.build(DEFTEMPLATE)
self.env.build(DEFRULE)
def test_agenda_strategy(self):
"""Agenda strategy getting/setting."""
for strategy in Strategy:
self.env.strategy = strategy
self.assertEqual(self.env.strategy, strategy)
def test_agenda_salience_evaluation(self):
"""Agenda salience_evaluation getting/setting."""
for salience_evaluation in SalienceEvaluation:
self.env.salience_evaluation = salience_evaluation
self.assertEqual(
self.env.salience_evaluation, salience_evaluation)
def test_agenda_activation(self):
"""Agenda activation test."""
self.env.assert_string('(implied-fact implied-value)')
self.assertTrue(self.env.agenda_changed)
activation = tuple(self.env.activations())[0]
self.assertEqual(activation.name, 'rule-name')
self.assertEqual(activation.salience, 10)
self.assertEqual(str(activation), '10 rule-name: f-1')
self.assertEqual(repr(activation), 'Activation: 10 rule-name: f-1')
activation.delete()
self.assertFalse(activation in self.env.activations())
def test_agenda_run(self):
"""Agenda rules are fired on run."""
self.env.assert_string('(implied-fact implied-value)')
self.env.run()
fact_names = (f.template.name for f in self.env.facts())
self.assertTrue('rule-fired' in fact_names)
def test_agenda_activation_order(self):
"""Agenda activations order change if salience or strategy change."""
self.env.build(DEFOTHERRULE)
self.env.assert_string('(implied-fact implied-value)')
self.assertTrue(self.env.agenda_changed)
activations = tuple(self.env.activations())
self.assertEqual(tuple(a.name for a in activations),
(u'other-rule-name', u'rule-name'))
activations[1].salience = 30
self.assertFalse(self.env.agenda_changed)
self.env.reorder()
self.assertTrue(self.env.agenda_changed)
activations = tuple(self.env.activations())
self.assertEqual(tuple(a.name for a in activations),
(u'rule-name', u'other-rule-name'))
self.env.refresh()
self.assertTrue(self.env.agenda_changed)
self.env.clear()
activations = tuple(self.env.activations())
self.assertEqual(len(activations), 0)
class TestRules(unittest.TestCase):
def setUp(self):
self.env = Environment()
self.env.build(DEFTEMPLATE)
self.env.build(DEFTEMPLATERULE)
def test_rule_build(self):
"""Simple Rule build."""
rule = self.env.find_rule('rule-name')
self.assertTrue(rule in self.env.rules())
self.assertEqual(rule.module.name, 'MAIN')
self.assertTrue(rule.deletable)
self.assertEqual(str(rule), DEFTEMPLATERULE)
self.assertEqual(repr(rule), "Rule: %s" % DEFTEMPLATERULE)
self.assertFalse(rule.watch_firings)
rule.watch_firings = True
self.assertTrue(rule.watch_firings)
self.assertFalse(rule.watch_activations)
rule.watch_activations = True
self.assertTrue(rule.watch_activations)
rule.undefine()
with self.assertRaises(LookupError):
self.env.find_rule('rule-name')
with self.assertRaises(TypeError):
rule.name
def test_rule_matches(self):
"""Partial rule matches."""
rule = self.env.find_rule('rule-name')
self.env.assert_string('(implied-fact implied-value)')
self.assertEqual(rule.matches(), (1, 0, 0))
rule.undefine()
def test_rule_activation(self):
"""Rule activation."""
rule = self.env.find_rule('rule-name')
self.env.assert_string('(implied-fact implied-value)')
self.env.assert_string(
'(template-fact (template-slot template-value))')
self.assertEqual(rule.matches(), (2, 1, 1))
self.env.run()
rule.refresh()
fact_names = (f.template.name for f in self.env.facts())
self.assertTrue('rule-fired' in fact_names)
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demo.py | PAV-Laboratory/cryptoblotter | 1 | 34928 | #!/usr/bin/env python3
from cryptofeed import FeedHandler
from cryptofeed.defines import TRADES
from cryptoblotter.exchanges import CoinbaseBlotter
from cryptoblotter.trades import SequentialIntegerTradeCallback, ThreshCallback
from cryptoblotter.trades.constants import VOLUME
async def trades(trade):
print(trade)
if __name__ == "__main__":
fh = FeedHandler()
fh.add_feed(
CoinbaseBlotter(
symbols=["BTC-USD"],
channels=[TRADES],
callbacks={
TRADES: SequentialIntegerTradeCallback(
ThreshCallback(
trades,
thresh_attr=VOLUME,
thresh_value=1000,
window_seconds=60,
)
)
},
)
)
fh.run()
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scripts/python/download_os_images.py | rbrud/power-up | 0 | 134263 | #!/usr/bin/env python
# Copyright 2018 IBM Corp.
#
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import nested_scopes, generators, division, absolute_import, \
with_statement, print_function, unicode_literals
import argparse
import sys
import yaml
from orderedattrdict.yamlutils import AttrDictYAMLLoader
import os.path
import wget
import hashlib
import lib.logger as logger
from lib.config import Config
from lib.genesis import check_os_profile, get_os_images_path, GEN_PATH
from lib.exception import UserException
OS_IMAGES_URLS_FILENAME = 'os-image-urls.yml'
def _sha1sum(file_path):
sha1sum = hashlib.sha1()
with open(file_path, 'rb') as file_object:
for block in iter(lambda: file_object.read(sha1sum.block_size), b''):
sha1sum.update(block)
return sha1sum.hexdigest()
def download_os_images(config_path=None):
"""Download OS installation images"""
log = logger.getlogger()
os_images_path = get_os_images_path() + "/"
os_image_urls_yaml_path = os_images_path + OS_IMAGES_URLS_FILENAME
cfg = Config(config_path)
os_image_urls = yaml.load(open(os_image_urls_yaml_path),
Loader=AttrDictYAMLLoader).os_image_urls
for os_profile in cfg.yield_ntmpl_os_profile():
for os_image_url in os_image_urls:
if check_os_profile(os_profile) in os_image_url.name:
for image in os_image_url.images:
dest = os_images_path
if 'filename' in image:
dest += image.filename
else:
dest += image.url.split("/")[-1]
if not os.path.isfile(dest):
log.info('Downloading OS image: %s' % image.url)
wget.download(image.url, out=dest)
print('')
sys.stdout.flush()
log.info('Verifying OS image sha1sum: %s' % dest)
sha1sum = _sha1sum(dest)
if image.sha1sum != sha1sum:
msg = ('OS image sha1sum verification failed: %s' %
dest)
log.error(msg)
raise UserException(msg)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('config_path', default='config.yml',
help='Config file path. Absolute path or relative '
'to power-up/')
parser.add_argument('--print', '-p', dest='log_lvl_print',
help='print log level', default='info')
parser.add_argument('--file', '-f', dest='log_lvl_file',
help='file log level', default='info')
args = parser.parse_args()
if not os.path.isfile(args.config_path):
args.config_path = GEN_PATH + args.config_path
print('Using config path: {}'.format(args.config_path))
if not os.path.isfile(args.config_path):
sys.exit('{} does not exist'.format(args.config_path))
logger.create(args.log_lvl_print, args.log_lvl_file)
download_os_images(args.config_path)
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2/dogcat.py | JonahY/AE_NN_Cls | 3 | 86779 | <filename>2/dogcat.py
# coding=utf-8
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import Dataset
from torchvision import transforms, datasets, models
import shutil
# 随机种子设置
random_state = 42
np.random.seed(random_state)
# kaggle原始数据集地址
original_dataset_dir = r'D:\data\archive\training_set\training_set'
total_num = 4000
random_idx = np.array(range(total_num))
np.random.shuffle(random_idx)
# 待处理的数据集地址
base_dir = r'D:\data\archive\data2'
if not os.path.exists(base_dir):
os.mkdir(base_dir)
# 训练集、测试集的划分
sub_dirs = ['train', 'test']
animals = ['cats', 'dogs']
train_idx = random_idx[:int(total_num * 0.9)]
test_idx = random_idx[int(total_num * 0.9):]
numbers = [train_idx, test_idx]
for idx, sub_dir in enumerate(sub_dirs):
dir = os.path.join(base_dir, sub_dir)
if not os.path.exists(dir):
os.mkdir(dir)
for animal in animals:
animal_dir = os.path.join(dir, animal) #
if not os.path.exists(animal_dir):
os.mkdir(animal_dir)
fnames = [animal[:-1] + '.{}.jpg'.format(i+1) for i in numbers[idx]]
for fname in fnames:
src = os.path.join(original_dataset_dir, animal, fname)
dst = os.path.join(animal_dir, fname)
shutil.copyfile(src, dst)
# 验证训练集、验证集、测试集的划分的照片数目
print(animal_dir + ' total images : %d' % (len(os.listdir(animal_dir))))
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run_pipy.py | johnrcruzado/adatasets | 3 | 152927 | <filename>run_pipy.py
# pylint: disable=C0321,C0103,C0301,E1305,E1121,C0302,C0330,C0111,W0613,W0611,R1705
# -*- coding: utf-8 -*-
"""
Pypi Uploader
python pypi.py
1) Create the package in one folder
2)
A file called .pypirc that has to contain login credentials.
$ ~ YOURTEXTEDITOR ~/.pypirc
Open a file and paste this to in it:
In Github Secrets
[pypi]
username = _token_ #token is <PASSWORD>
password = <PASSWORD>
"""
import os,re, sys, subprocess
import os.path as op
curdir = op.abspath(op.curdir)
setup_file = op.join(curdir, 'setup.py')
class Version(object):
pattern = re.compile(r"(version\s*=\s*['\"]\s*(\d+)\s*\.\s*(\d+)\s*\.\s*(\d+)\s*['\"])")
def __init__(self, major, minor, patch):
self.major = major
self.minor = minor
self.patch = patch
def __str__(self):
return f'Version({self.stringify()})'
def __repr__(self):
return self.__str__()
def stringify(self):
return f'\'{self.major}.{self.minor}.{self.patch}\''
def new_version(self, orig):
return '='.join([orig.split('=')[0], self.stringify()])
@classmethod
def parse(cls, string):
re_result = re.findall(cls.pattern, string)
if len(re_result) == 0:
return Exception('Program was not able to parse version string, please check your setup.py file.')
return re_result[0][0], cls(*re_result[0][1:])
def get_current_githash():
import subprocess
label = subprocess.check_output(["git", "describe", "--always"]).strip();
label = label.decode('utf-8')
return label
def update_version(path, n=1):
content = open(path, 'r').read()
orig, version = Version.parse(content)
print (f'Current version: {version}')
version.minor = int(version.minor) + int(n)
import time
version.patch = int(time.time()*0.01) ### unique ID
# version.patch = get_current_githash()
print (f'New Version: {version}')
with open(path, 'w') as file:
file.write(content.replace(orig, version.new_version(orig)))
############################################################################################################
def git_commit(message):
if not ask(f'About to git commit {message}, are you sure: '):
exit()
os.system(f'git commit -am "{message}"')
if not ask('About to git push, are you sure: '):
exit()
os.system('git push')
def ask(question, ans='yes'):
return input(question).lower() == ans.lower()
def pypi_upload():
"""
It requires credential in .pypirc files
__token__
or in github SECRETS
"""
os.system('python setup.py sdist bdist_wheel')
### Github
os.system('twine upload --repository-url https://upload.pypi.org/legacy/ dist/* --username ${{ secrets.PYPI_USERNAME }} --password ${{ secrets.PYPI_PASSWORD }} --verbose ')
print("Upload files")
print("Deleting build files")
for item in os.listdir(op.abspath(op.join(setup_file, '..'))):
if item.endswith('.egg-info') or item in ['dist', 'build']:
os.system(f'rm -rf {item}')
############################################################################################################
def main(*args):
print ('Program Started')
update_version(setup_file, 1)
# git_commit(*sys.argv[1:3])
# pypi_upload()
print ('Upload success')
if __name__ == '__main__':
main()
#if len(sys.argv) == 1:
# print (f'Usage: python {sys.argv[0]} "commmit message"'); sys.exit()
#
#main(*sys.argv[1:3])
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] |
app.py | Lunga001/pmg-cms-2 | 2 | 158501 | from pmg import app
from pmg.tasks import StartScheduler
from pmg.models.soundcloud_track import SoundcloudTrack
from flask_script import Server, Manager
from flask_migrate import MigrateCommand
app.debug = True
manager = Manager(app)
manager.add_command("runserver", Server(port=5000, threaded=True, host="0.0.0.0"))
manager.add_command("db", MigrateCommand)
manager.add_command("start_scheduler", StartScheduler)
@manager.command
def sync_soundcloud():
SoundcloudTrack.sync()
if __name__ == "__main__":
manager.run()
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] |
project_e/dealers/forms.py | ElectricFleming/project-e | 0 | 94975 | <filename>project_e/dealers/forms.py<gh_stars>0
from django import forms
class DealerCreationForm(forms.Form):
name = forms.CharField()
address = forms.CharField() #Address
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wok/core/base.py | atugushev/wok | 0 | 149702 | <gh_stars>0
import functools
import pathlib
import typing
import click
import pygit2
from . import context
def is_clean(repo: pygit2.Repository) -> bool:
return not any(
code
for code in repo.status().values()
if (
code
^ (code & pygit2.GIT_STATUS_WT_NEW)
^ (code & pygit2.GIT_STATUS_IGNORED)
)
)
def require_clean(func: typing.Callable) -> typing.Callable:
@functools.wraps(func)
def wrapper(
*args: typing.Any, ctx: context.Context, **kwargs: typing.Any
) -> typing.Any:
if not is_clean(repo=ctx.root_repo):
print(ctx.root_repo.status())
raise ValueError(ctx.root_repo.workdir)
for repo_conf in ctx.conf.repos:
repo = pygit2.Repository(str(repo_conf.path))
if not is_clean(repo=repo):
raise ValueError(repo.workdir)
return func(*args, ctx=ctx, **kwargs)
return wrapper
def switch(repo: pygit2.Repository, ref: pygit2.Reference) -> None:
stashed = False
if not is_clean(repo=repo):
repo.stash(stasher=repo.default_signature)
stashed = True
repo.checkout(refname=ref)
if stashed:
repo.stash_pop()
def commit(
repo: pygit2.Repository,
message: str,
pathspecs: typing.Optional[typing.List[typing.Union[str, pathlib.Path]]] = None,
) -> None:
if pathspecs is None:
pathspecs = []
pathspecs = [
(
str(
pathspec.relative_to(repo.workdir)
if pathspec.is_absolute()
else pathspec
)
if isinstance(pathspec, pathlib.Path)
else pathspec
)
for pathspec in pathspecs
]
repo.index.add_all(pathspecs=pathspecs)
repo.index.write()
tree = repo.index.write_tree()
try:
parent, ref = repo.resolve_refish(refish=repo.head.name)
except pygit2.GitError:
parents = []
ref_name = 'refs/heads/master'
else:
parents = [parent.oid]
ref_name = ref.name
repo.create_commit(
ref_name, repo.default_signature, repo.default_signature, message, tree, parents
)
def push(repo: pygit2.Repository, branch_name: str) -> None:
branch = repo.branches.local[branch_name]
if not branch.is_head():
raise ValueError(branch)
try:
remote = repo.remotes['origin']
except KeyError:
return
remote.push(specs=[f'{branch.name}'], callbacks=RemoteCallbacks())
upstream_branch = repo.branches.get(f'{remote.name}/{branch.shorthand}')
if upstream_branch is not None:
branch.upstream = upstream_branch
def sync(repo: pygit2.Repository, branch_name: str) -> None:
"""
Tries to update the `branch_name` branch of the `repo` repo to the latest
upstream branch state.
If the branch is up to date, does nothing.
If the branch can be fast-forwarded, resets to the upstream.
Otherwise, fails with an error.
"""
branch = repo.branches.local[branch_name]
if not branch.is_head():
raise ValueError(branch)
try:
remote = repo.remotes['origin']
except KeyError:
return
remote.fetch(callbacks=RemoteCallbacks())
upstream_branch = branch.upstream
if not upstream_branch:
return
merge_state, _ = repo.merge_analysis(upstream_branch.target, branch.name)
if merge_state & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
return
if not (merge_state & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD):
raise ValueError(branch)
repo.reset(upstream_branch.target, pygit2.GIT_RESET_HARD)
repo.checkout(refname=branch)
def finish(repo: pygit2.Repository, branch_name: str, message: str) -> None:
master = repo.branches.local['master']
branch = repo.branches.local[branch_name]
if not branch.is_head():
raise ValueError(branch)
merge_squash(repo=repo, ours_branch=master, theirs_branch=branch, message=message)
repo.checkout(refname=master)
branch.delete()
def merge_squash(
repo: pygit2.Repository,
ours_branch: pygit2.Branch,
theirs_branch: pygit2.Branch,
message: str,
) -> None:
"""
Performs a merge of the `theirs_branch` into `ours_branch` sqaushing the commits
"""
merge_state, _ = repo.merge_analysis(theirs_branch.target, ours_branch.name)
if merge_state & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
return
if not (merge_state & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD):
raise ValueError(theirs_branch)
index: pygit2.Index = repo.merge_trees(
ancestor=ours_branch, ours=ours_branch, theirs=theirs_branch
)
tree = index.write_tree(repo=repo)
repo.create_commit(
ours_branch.name,
repo.default_signature,
repo.default_signature,
message,
tree,
[ours_branch.target],
)
def tag(repo: pygit2.Repository, tag_name: str) -> None:
repo.references.create(name=f'refs/tags/{tag_name}', target=repo.head.target)
def clone(url: str, path: pathlib.Path, **kwargs: typing.Any) -> pygit2.Repository:
return pygit2.clone_repository(
url=url, path=str(path), callbacks=RemoteCallbacks(), **kwargs
)
class RemoteCallbacks(pygit2.RemoteCallbacks):
"""
Overrides the credentials callback.
"""
def credentials(
self, url: str, username_from_url: typing.Optional[str], allowed_types: int
) -> typing.Optional[
typing.Union[pygit2.Keypair, pygit2.UserPass, pygit2.Username]
]:
"""
If the remote server requires authentication, this function will be called and
its return value used for authentication.
"""
if allowed_types & pygit2.credentials.GIT_CREDTYPE_SSH_KEY:
return pygit2.KeypairFromAgent(username_from_url)
elif allowed_types & pygit2.credentials.GIT_CREDTYPE_USERPASS_PLAINTEXT:
username = username_from_url or click.prompt("Username")
password = click.prompt("Password", hide_input=True)
return pygit2.UserPass(username=username, password=password)
elif allowed_types & pygit2.credentials.GIT_CREDTYPE_USERNAME:
return pygit2.Username(username_from_url)
else:
return None
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setup.py | SforAiDl/max | 5 | 66507 | <filename>setup.py
import codecs
import os
from setuptools import find_packages, setup
NAME = "Jeta"
DESCRIPTION = "A JAX based meta learning library"
with open("README.md", "r") as f:
LONG_DESCRIPTION = f.read()
PROJECT = os.path.abspath(os.path.dirname(__file__))
EXCLUDE = ()
VERSION = "0.1.0"
AUTHOR = "Society for Artificial Intelligence and Deep Learning"
EMAIL = "<EMAIL>"
LICENSE = "MIT"
REPO_URL = "https://github.com/SforAiDl/jeta"
PROJECT_URLS = {"Source": REPO_URL}
CLASSIFIERS = (
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
)
def read(*parts):
"""
returns contents of file
"""
with codecs.open(os.path.join(PROJECT, *parts), "rb", "utf-8") as file:
return file.read()
def get_requires(path):
"""
generates requirements from file path given as REQUIRE_PATH
"""
for line in read(path).splitlines():
line = line.strip()
if line and not line.startswith("#"):
yield line
INSTALL_REQUIRES = list(get_requires("requirements.txt"))
CONFIG = {
"name": NAME,
"description": DESCRIPTION,
"long_description": LONG_DESCRIPTION,
"long_description_content_type": "text/markdown",
"packages": find_packages(where=PROJECT, exclude=EXCLUDE),
"version": VERSION,
"author": AUTHOR,
"author_email": EMAIL,
"license": LICENSE,
"url": REPO_URL,
"project_urls": PROJECT_URLS,
"classifiers": CLASSIFIERS,
"install_requires": INSTALL_REQUIRES,
"python_requires": ">=3.6",
}
if __name__ == "__main__":
setup(**CONFIG)
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data_process.py | liliangbin/unet-match | 1 | 1603287 | # -*- coding:utf-8 -*-
import SimpleITK as sitk
import cv2
from keras.preprocessing.image import img_to_array, load_img
def to_hu(image):
MIN_BLOOD = -10
MAX_BLOOD = 400
print(MAX_BLOOD, "===", MIN_BLOOD)
image = (image - MIN_BLOOD) / (MAX_BLOOD - MIN_BLOOD)
image[image > 1] = 1.
image[image < 0] = 0.
return image
image = sitk.ReadImage("20001.dcm")
image_array = sitk.GetArrayFromImage(image) # z, y, x
print(image_array)
print(image_array.shape)
image_array = image_array.swapaxes(0, 2)
image_array = image_array.swapaxes(0, 1)
# 可以使用 transpose
# 维数变化
print("image_array==shape==>", image_array.shape)
image_array[image_array == -2000] = 0
# np.savetxt("test.txt", image_array)
image_array = to_hu(image_array)*2
image_array = image_array * 255
#image_array[image_array < 40] = 0
print(image_array[255][255])
print(image_array[160][160])
cv2.imwrite("info.png", image_array)
# np.savetxt("test2.txt", image_array * 255)
# images = np.squeeze(image_array)
#
# plt.imshow(images, cmap="gray")
# plt.axis("off")
# plt.savefig("test.png")
# plt.show()
print("image_array done")
img = load_img("20001_mask.png", color_mode="grayscale")
# img = img_to_array(img)
label = img_to_array(img)
print("label==shape ==", label.shape)
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experiments/_play.py | Chen-Cai-OSU/GeometryofEmbedding | 2 | 85904 | import matplotlib
matplotlib.use('tkagg')
import os
import subprocess
import sys
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
import tensorflow as tf
from ampligraph.common.aux import rel_rank_stat, load_data, eigengap
from ampligraph.common.aux_play import get_model, viz_distm
from ampligraph.evaluation import evaluate_performance, mrr_score, hits_at_n_score
from sacred import Experiment
from sacred.observers import MongoObserver
import numpy as np
import warnings
from pymongo import MongoClient
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
tf.logging.set_verbosity(tf.logging.ERROR) # https://stackoverflow.com/questions/48608776/how-to-suppress-tensorflow-warning-displayed-in-result
parser = ArgumentParser("scoring", formatter_class=ArgumentDefaultsHelpFormatter, conflict_handler='resolve')
parser.add_argument("--depth", default=8, type=int, help='method')
parser.add_argument('--rb', action='store_true')
parser.add_argument('--verbose', action='store_true')
parser.add_argument('--fix_rel', action='store_true')
parser.add_argument("--viz", action='store_true')
parser.add_argument('--data', type = str, default='single_fam_tree')
parser.add_argument('--n_epoch', type=int, default=200)
parser.add_argument('--prob', type=float, default=0)
parser.add_argument("--seed", default=42, type=int, help='random seed')
parser.add_argument("--add", default=1, type=int, help='additional relation')
parser.add_argument("--extra_rel", default=30, type=int, help='extra relation')
parser.add_argument("--noise_rel", default=1, type=int, help='noise relation (half noise)')
parser.add_argument("--n_node", default=1000, type=int, help='n_nodes')
parser.add_argument("--model", default='ComplEx', type=str, help='model name')
parser.add_argument("--period", default=3, type=int, help='the period of relations')
client = MongoClient('localhost', 27017)
EXPERIMENT_NAME = 'KG_corrupt'
YOUR_CPU = None
DATABASE_NAME = 'KG_corrupt'
ex = Experiment(EXPERIMENT_NAME)
ex.observers.append(MongoObserver.create(url=YOUR_CPU, db_name=DATABASE_NAME))
warnings.filterwarnings("ignore", category=DeprecationWarning)
from ampligraph.common.aux import timefunction
# @timefunction
def topk_tails(model, hr=np.array(['101', '1']), top_k=5, print_f = False):
"""
:param model:
:param hr: h and r
:param top_k:
:return:
"""
# hr = np.array([['101', '1']])
hr = hr.reshape(1, 2)
C = np.array([np.append(hr, x) for x in list(model.ent_to_idx.keys())])
tmp = np.array(model.predict(C)).reshape(C.shape[0], 1) # np.array of shape (n,1)
df = pd.DataFrame(np.hstack([C, tmp]), columns=['head', 'relation', 'tail_', 'score'])
def filter_score(row):
if float(row.score) < 1e-4:
return 0
else:
return float(row.score)
df['score_filter'] = df.apply(filter_score, axis=1)
df = df.sort_values('score_filter', ascending=False, inplace=False)
if print_f:
print('Top %s solutions for query (%s, %s, ?)' % (top_k, hr[0, 0], hr[0, 1]))
print(df[:top_k])
print('-' * 50)
# print(df.tail_)
best_tails = list(df.tail_) # return the best tail
return int(best_tails[0]), int(best_tails[1])
def ranks_summary(ranks):
mrr = mrr_score(ranks)
hits_1, hits_3, hits_10, hits_20, hits_50 = hits_at_n_score(ranks, n=1), hits_at_n_score(ranks,
n=3), hits_at_n_score(
ranks, n=10), hits_at_n_score(ranks, n=20), hits_at_n_score(ranks, n=50)
print("MRR: %f, Hits@1: %f, Hits@3: %f, Hits@10: %f, Hits@20: %f, Hits@50: %f" % (
mrr, hits_1, hits_3, hits_10, hits_20, hits_50))
print('-' * 150)
def eval_corrution(model, filter, args, x, noise = 1, noise_loc = 't'):
corrupt_test = corrupt(x, period=args.n_node, noise=noise, corrput_location= noise_loc)
args_ = {'strict':False, 'model': model, 'filter_triples': filter, 'verbose': args.verbose, 'use_default_protocol': True}
ranks_corrupt = evaluate_performance(corrupt_test, **args_)
for i in range(10):
print('noise ' + str(noise) + ' at ' + noise_loc, corrupt_test[i], ranks_corrupt[2*i], ranks_corrupt[2*i+1])
ranks_summary(ranks_corrupt)
print('-'*150)
def corrupt(x, period = 1000, noise = 1, corrput_location = 'h'):
# array of shape (n, 3)
n, d = x.shape
assert d == 3
res = []
for i in range(n):
h, r, t = x[i]
if corrput_location == 't':
t = str((int(t) + noise)%period) # 1 is noise here
elif corrput_location == 'r':
# todo make sure no new relation is introduced
r = str((int(r) + noise)%period)
elif corrput_location == 'h':
h = str((int(h) + noise)%period)
else:
raise Exception('No such corruption location %s'%corrput_location)
res.append([h, r, t])
return np.array(res)
def traj_graph(lis1, lis2, name='', viz= False, lis1_only = False):
# lis = range(2, 20)
n = len(lis1)
assert len(lis1) == len(lis2)
edges = [(lis1[0], lis1[1])]
for i in range(1, n-1):
edge1 = (lis1[i], lis1[i + 1])
edges.append(edge1)
if not lis1_only:
edge2 = (lis1[i], lis2[i + 1])
edges.append(edge2)
g = nx.Graph()
g.add_edges_from(edges)
pos = nx.spring_layout(g)
# pos = nx.circular_layout(g)
nx.draw(g, pos, node_color='b', node_size=5, with_labels=True, width=1)
try:
os.makedirs('./img')
except FileExistsError:
pass
if viz: plt.show()
plt.savefig('./img/traj_graph' + name)
# lis1 = [2, 3, 4, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 35, 36, 37, 38, 39, 40, 41, 42]
# lis2 = [34, 74, 35, 89, 30, 14, 37, 15, 32, 40, 61, 0, 1, 16, 61, 25, 95, 88, 30, 31, 29, 79, 58, 68, 41, 64, 85, 36, 70, 68, 30, 14, 37, 15, 32, 40, 61, 0, 1, 16, 61, 25, 95, 88, 30, 31, 29, 79, 58, 68, 41, 64, 85, 36, 70, 68, 30, 14, 37, 15, 32, 40, 61, 0, 1, 16, 61, 25, 95, 88, 30, 31, 29, 79, 58, 68, 41, 64, 85, 36, 70, 68, 30, 14, 37, 15, 32, 40, 61, 0, 1, 16, 61, 25, 95, 88, 30, 31, 29, 79]
# traj_graph(lis1, lis2, name='', viz=True, lis1_only=True)
# sys.exit()
@ex.config
def get_config():
# unused params
depth = 8
rb = True
# param
verbose = True
data = 'single_fam_tree'
seed = 42
noise_rel = 8
n_node = 1000
model = 'ComplEx'
rels = '1 2 3 4 5 6 7 8 9 10 11 12'#'1 2 3 4 5 6 7 8 9 10 11 12' #'1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16'
# other param
period = 3
@ex.main
def main(model, n_node, noise_rel, seed, data, verbose, rels, period):
sys.argv = []
args = parser.parse_args()
args.model = model
args.n_node = n_node
args.noise_rel = noise_rel
args.seed = seed
args.data = data
args.verbose = verbose
args.rels = rels
args.period = period
print(args)
if args.data == 'single_fam_tree':
py_server = ['/home/cai.507/anaconda3/envs/ampligraph/bin/python']
py_local = ['~/anaconda3/bin/python']
tmp_command = ['../ampligraph/datasets/single_fam_tree.py',
'--seed', str(args.seed), '--add', str(args.add),
'--extra_rel', str(args.extra_rel),
'--noise_rel', str(args.noise_rel),
'--rels', str(args.rels),
'--n_node', str(args.n_node)]
command_server = py_server + tmp_command
command_local = py_local + tmp_command
else:
raise ('No such data %s'%args.data)
try:
print(command_server)
print(subprocess.check_output(command_server))
except FileNotFoundError:
print(command_local)
print(subprocess.check_output(command_local))
X, _ = load_data(args)
for key, val in X.items():
print(key, len(val),)
# get model
model = get_model(args)
# Fit the model on training and validation set
filter = np.concatenate((X['train'], X['valid'], X['test']))
print(model,'\n')
model.fit(X['train'], early_stopping=True,
early_stopping_params= \
{ 'x_valid': X['valid'], # validation set
'criteria': 'hits10', # Uses hits10 criteria for early stopping
'burn_in': 100, # early stopping kicks in after 100 epochs
'check_interval': 20, # validates every 20th epoch
'stop_interval': 5, # stops if 5 successive validation checks are bad.
'x_filter': filter, # Use filter for filtering out positives
'corruption_entities': 'all', # corrupt using all entities
'corrupt_side': 's+o' # corrupt subject and object (but not at once)
})
# Run the evaluation procedure on the test set (with filtering). Usually, we corrupt subject and object sides separately and compute ranks
# ranks = evaluate_performance(X['test'], model=model, filter_triples=filter, verbose=args.verbose, use_default_protocol=True) # corrupt subj and obj separately while evaluating
eval_corrution(model, filter, args, X['test'], noise=0, noise_loc='t')
eval_corrution(model, filter, args, X['test'], noise=1, noise_loc='t')
# eval_corrution(model, filter, args, X['test'], noise=1, noise_loc='h')
queries = []
best_tails, second_best_tails = [], []
step = 2
edges = []
for i in range(n_node):
query = [str(i), str(step)]
best_tail, second_tail = topk_tails(model, hr = np.array(query), top_k=3, print_f=True)
edge = (i, second_tail)
edges.append(edge)
print(edges)
g = nx.Graph()
g.add_edges_from(edges)
# pos = nx.spring_layout(g, seed=42)
pos = nx.circular_layout(g)
nx.draw(g, pos, node_color='b', node_size=5, with_labels=True, width=1)
name = './img/circular_layout_second/rel_' + str(step) + '_' + str(n_node) + '_' + str(rels) # TODO change circular layout
title = f'{n_node} nodes. Rels {rels}. relation step {step}'
plt.title(title)
plt.savefig(name)
sys.exit()
for i in range(n_step):
query = [str(h), str(step)]
print('iter %s: head %s'%(i, query[0]))
queries.append(query)
best_tail, second_best_tail = topk_tails(model, hr=np.array(query), top_k=5, print_f=False)
best_tails.append(best_tail)
second_best_tails.append(second_best_tail)
h = best_tail
print(best_tails)
print(second_best_tails)
traj_graph(best_tails, second_best_tails, name='_' + str(n_step))
if __name__ == "__main__":
# main('ComplEx', 1000, 1, 42, 'single_fam_tree', True)
ex.run_commandline() # SACRED: this allows you to run Sacred not only from your terminal,
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Challenges-5/delStartEven.py | chandrakant100/Python-Project | 0 | 84620 | # Challenge 3 : Write a function called delete_starting_evens() that has a parameter named lst.
# The function should remove elements from the front of lst until the front of the list is not even.
# The function should then return lst.
# Date : Sun 07 Jun 2020 07:21:17 AM IST
def delete_starting_evens(lst):
while len(lst) != 0 and lst[0] % 2 == 0:
del lst[0]
return lst
print(delete_starting_evens([4, 8, 10, 11, 12, 15]))
print(delete_starting_evens([4, 8, 10]))
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] |
tests/unit/transformations/test_histedges.py | gnafit/gna | 5 | 109739 | #!/usr/bin/env python
from matplotlib import pyplot as P
import numpy as N
from load import ROOT as R
import gna.constructors as C
import pytest
@pytest.mark.parametrize('edges', [N.linspace(0.0, 10.0, 11), N.geomspace(0.1, 1000.0, 5)])
def test_histedges_v01(edges):
centers = 0.5*(edges[1:]+edges[:-1])
widths = edges[1:]-edges[:-1]
data = N.arange(edges.size-1)
hist = C.Histogram(edges, data)
h2e = R.HistEdges()
h2e.histedges.hist(hist.hist.hist)
out_edges = h2e.histedges.edges.data()
out_centers = h2e.histedges.centers.data()
out_widths = h2e.histedges.widths.data()
print( 'Input:' )
print( edges )
print( 'Output:' )
print( 'Edges', out_edges )
print( 'Centers', out_centers )
print( 'Widths', out_widths )
assert (edges==out_edges).all()
assert (centers==out_centers).all()
assert (widths==out_widths).all()
if __name__ == "__main__":
test_histedges()
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FSWDF-Project/main.py | chrischism8063/Mongo-py | 0 | 192152 | # main entry to the system
from application import app
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setup.py | SteveLTN/iex-api-python | 0 | 12113 | <reponame>SteveLTN/iex-api-python<filename>setup.py
import setuptools
import glob
import os
required = [
"requests",
"pandas",
"arrow",
"socketIO-client-nexus"
]
setuptools.setup(name='iex-api-python',
version="0.0.5",
description='Fetch data from the IEX API',
long_description=open('README.md').read().strip(),
author='<NAME>',
author_email='<EMAIL>',
url='http://www.github.com/danielecook/iex-api-python',
packages=['iex'],
install_requires=required,
keywords=['finance', 'stock', 'market', 'market-data', 'IEX', 'API'],
license='MIT License',
zip_safe=False)
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practice/practice_1.1/binary_search.py | Electro98/aads | 7 | 106989 | """Шаблон бинарного поиска"""
def search(sequence, item):
"""Бинарный поиск"""
raise NotImplementedError()
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cloudmesh/shell/shellutil.py | JulienPalard/cloudmesh | 0 | 170554 | '''some useful functions while working with shell'''
from __future__ import print_function
from cloudmesh.user.cm_user import cm_user
import json
from cloudmesh_common.tables import array_dict_table_printer, dict_key_list_table_printer
from cloudmesh_base.util import banner
import csv
from cmd3.console import Console
import hostlist
from cloudmesh.cm_mongo import cm_mongo
from cloudmesh.util.naming import server_name_analyzer
ALLOWED_PRINT_FORMAT = ['table', 'json', 'csv']
def get_default_print_format(username):
'''
the funtion will try to find the default printing form from db_defaults
if db_defaults has no item 'shell_print_format', the function will
set 'shell_print_format' = table
'''
format_type = None
user_obj = cm_user()
userdata = user_obj.info(username)
try:
format_type = userdata['defaults']['shell_print_format']
except:
pass
if format_type in [None, 'none']:
userdata['defaults']['shell_print_format'] = "table"
user_obj.set_defaults(username, userdata['defaults'])
userdata = user_obj.info(username)
format_type = userdata['defaults']['shell_print_format']
return format_type
def shell_commands_dict_output(username,
d,
# choose format manually
print_format=None,
# choose table format if needed
table_format=None,
# more specific table arguments
# --------------------------
# for table format: "key_list"
indexed=False,
# --------------------------
firstheader=None,
header=None,
oneitem=False,
vertical_table=False,
title=None,
count=False):
'''
some shell commands output a dict or table, this function should be
called when commands are printing in such way.
if none of parameters json and table is given, the funtion will try
to find the default printing form from db_defaults
if db_defaults has no item 'shell_print_format', the function will
set 'shell_print_format' = table
param username:: user id
param d:: data to print
param print_format:: print format: table, json, csv
param table_format:: choose table format if needed, DESCRIPTIONS:
type1: key_list:
accept a dict in the form:
{key1: [list1],
key2: [list2],
.......
=>
| key1 | key2 |
| l
| i
| s
| t
acceptable args: indexed: provide index
param firstheader:: designed for table, provide a attribute name for the
first item of each row, since the dict doesn't provide
it
param header:: designed for table(and used to filter input dict), a list of lists,
provides column order, e.g.
[[a,b], [c, d], ...
where 'a' is the printing column name and 'b' is the attribute name
in the dict
if you don't want to change the header name but want to keep the
header order or filter the items to list, for each item in the list
you may provide a string or a list with one item instead of a list of
two items
param oneitem:: designed for table, normally the input dict should be in such
form:
{a: {...}, b:{...}}, where each subitem is a row
if there is only one item, input dict is the {...} of the subitem
above
param title: provide a title for the table
param count: provide count info at the end of the table
'''
format_type = None
if print_format:
format_type = print_format
else:
format_type = get_default_print_format(username)
if format_type not in ALLOWED_PRINT_FORMAT:
Console.error("wrong print format: {0}. (allowed print format: {1})".format(format_type,
", ".join(ALLOWED_PRINT_FORMAT)))
return False
headers = None
order = None
if header:
headers = []
order = []
for i in header:
if isinstance(i, basestring):
headers.append(i)
order.append(i)
elif isinstance(i, list):
if len(i) == 1:
headers.append(i[0])
order.append(i[0])
else:
headers.append(i[0])
order.append(i[1])
else:
print("ERROR: header info is not correct")
return False
# --------------------------------------------------------------------------
# filter the input dict
# --------------------------------------------------------------------------
if order:
new_d = {}
if oneitem:
for item in order:
if item in d:
new_d[item] = d[item]
else:
for i in d:
new_d[i] = {}
for item in order:
if item in d[i]:
new_d[i][item] = d[i][item]
d = new_d
# --------------------------------------------------------------------------
if format_type == "json":
if title:
banner(title)
print(json.dumps(d, indent=4))
elif format_type == "csv":
with open(".temp.csv", "wb") as f:
w = csv.DictWriter(f, d.keys())
w.writeheader()
w.writerow(d)
elif format_type == "table":
if table_format == "key_list":
print(dict_key_list_table_printer(d, indexed=indexed))
return
if title:
print("+" + "-" * (len(title) - 2) + "+")
print(title)
print_data = []
if oneitem:
print_data = [d]
else:
for k in sorted(d):
d[k][' '] = k
print_data.append(d[k])
if header:
if firstheader:
headers = [firstheader] + headers
order = [' '] + order
if vertical_table:
print(array_dict_table_printer(print_data,
order=order,
header=headers,
vertical=True))
else:
print(array_dict_table_printer(print_data, order=order, header=headers))
if count:
c = len(print_data)
sentence = "count: {0}".format(c)
print(sentence)
print("+" + "-" * (len(sentence) - 2) + "+")
def get_command_list_refresh_default_setting(username):
'''
value to define the default behaviour of command list, if True, then refresh
before list as default
'''
try:
user_obj = cm_user()
except:
Console.error("There is a problem with "
"cm_user object initialization")
return False
defaults_data = user_obj.info(username)['defaults']
if "shell_command_list_refresh_default_setting" not in defaults_data or \
defaults_data["shell_command_list_refresh_default_setting"] in [None, 'none']:
defaults_data["shell_command_list_refresh_default_setting"] = True
user_obj.set_defaults(username, defaults_data)
return defaults_data["shell_command_list_refresh_default_setting"]
else:
return defaults_data["shell_command_list_refresh_default_setting"]
def get_vms_look_for(username,
cloudname,
servername=None,
serverid=None,
groupname=None,
prefix=None,
hostls=None,
getAll=False,
refresh=False):
'''
work as a filter to find the VMs you are looking for. Input the seaching conditions,
and returns a list of server ids that meet the condition
you cannot provide servername and serverid at the same time
you cannot provide prefix and hostlist at the same time
param hostls:: e.g. sample[1-3,18] => ['sample1', 'sample2', 'sample3', 'sample18']
param refresh:: refresh before filtering
param getAll:: if True, the function consider all VMs are selected before filtering.
if False, then none are selected before filtering
'''
# GroupManagement automatically loads mongodb connection.
# To avoid creating db connection by import, we moved this import inside of
# the function. This way allows us to use GroupManagement class only in this
# function. Other functions in this file won't load db connection by import.
# <NAME> - 12/1/2014
from cloudmesh.experiment.group import GroupManagement
# input checking
if servername and serverid:
Console.error("you cannot provide servername and serverid at the same time")
return False
if prefix and hostls:
Console.error("you cannot provide prefix and hostlist at the same time")
return False
if hostls:
try:
hostls_list = hostlist.expand_hostlist(hostls)
except:
Console.error("please check your hostlist input, right format e.g. sample[1-9,18]")
return False
# get server data
try:
mongo = cm_mongo()
except:
Console.error("There is a problem with the mongo server")
return False
if refresh:
mongo.activate(cm_user_id=username, names=[cloudname])
mongo.refresh(cm_user_id=username,
names=[cloudname],
types=['servers'])
if groupname:
vms_in_group_list = []
GroupManage = GroupManagement(username)
groups_list = GroupManage.get_groups_names_list()
if groupname not in groups_list:
return []
else:
vms_in_group_list = GroupManage.list_items_of_group(groupname, _type="VM")["VM"]
servers_dict = mongo.servers(
clouds=[cloudname], cm_user_id=username)[cloudname]
# search for qualified vms for each critera
res_d = {}
if servername:
res_d['servername'] = []
if serverid:
res_d['serverid'] = []
if groupname:
res_d['groupname'] = []
if prefix:
res_d['prefix'] = []
if hostls:
res_d['hostls'] = []
if getAll:
res_d['getAll'] = []
for k, v in servers_dict.iteritems():
if servername and servername == v['name']:
res_d['servername'].append(k)
if serverid and serverid == k:
res_d['serverid'].append(k)
if groupname:
if v['name'] in vms_in_group_list:
res_d['groupname'].append(k)
if prefix:
nametemp = server_name_analyzer(v['name'])
if prefix and prefix == nametemp[0]:
res_d['prefix'].append(k)
if hostls and v['name'] in hostls_list:
res_d['hostls'].append(k)
if getAll and v['cm_cloud'] == cloudname:
res_d['getAll'].append(k)
# -------------------------
# intersect the results
ls = res_d.values()
l = len(ls)
if l == 0:
res = []
elif l == 1:
res = ls[0]
else:
res = ls[0]
del ls[0]
for i in ls:
res = set(res) & set(i)
res = list(res)
return res
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565,
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322,
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333,
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13,
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2704,
703,
6293,
2609,
3867,
1923,
978,
322,
1923,
333,
472,
278,
1021,
931,
1159,
13,
4706,
736,
7700,
13,
1678,
565,
10944,
322,
3495,
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29901,
13,
4706,
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703,
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2609,
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10944,
322,
3495,
1761,
472,
278,
1021,
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1159,
13,
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7700,
13,
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565,
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13,
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1018,
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13,
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353,
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13,
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2704,
703,
552,
559,
1423,
596,
3495,
1761,
1881,
29892,
1492,
3402,
321,
29889,
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29889,
4559,
29961,
29896,
29899,
29929,
29892,
29896,
29947,
29962,
1159,
13,
9651,
736,
7700,
13,
13,
1678,
396,
679,
1923,
848,
13,
1678,
1018,
29901,
13,
4706,
19476,
353,
7477,
29918,
29885,
7443,
580,
13,
1678,
5174,
29901,
13,
4706,
9405,
29889,
2704,
703,
8439,
338,
263,
1108,
411,
278,
19476,
1923,
1159,
13,
4706,
736,
7700,
13,
13,
1678,
565,
11086,
29901,
13,
4706,
19476,
29889,
11236,
403,
29898,
4912,
29918,
1792,
29918,
333,
29922,
6786,
29892,
2983,
11759,
9274,
978,
2314,
13,
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19476,
29889,
22379,
29898,
4912,
29918,
1792,
29918,
333,
29922,
6786,
29892,
13,
462,
418,
2983,
11759,
9274,
978,
1402,
13,
462,
418,
4072,
29922,
1839,
643,
874,
11287,
13,
13,
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325,
1516,
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262,
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2972,
29918,
1761,
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13,
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1402,
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1792,
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333,
29922,
6786,
9601,
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978,
29962,
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13,
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396,
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363,
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325,
1516,
363,
1269,
3994,
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13,
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620,
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13,
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322,
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6869,
300,
3451,
29961,
29900,
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13,
9651,
620,
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325,
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325,
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29895,
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396,
448,
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13,
1678,
396,
25869,
278,
2582,
13,
1678,
19375,
353,
620,
29918,
29881,
29889,
5975,
580,
13,
1678,
301,
353,
7431,
29898,
3137,
29897,
13,
1678,
565,
301,
1275,
29871,
29900,
29901,
13,
4706,
620,
353,
5159,
13,
1678,
25342,
301,
1275,
29871,
29896,
29901,
13,
4706,
620,
353,
19375,
29961,
29900,
29962,
13,
1678,
1683,
29901,
13,
4706,
620,
353,
19375,
29961,
29900,
29962,
13,
4706,
628,
19375,
29961,
29900,
29962,
13,
4706,
363,
474,
297,
19375,
29901,
13,
9651,
620,
353,
731,
29898,
690,
29897,
669,
731,
29898,
29875,
29897,
13,
4706,
620,
353,
1051,
29898,
690,
29897,
13,
13,
1678,
736,
620,
13,
268,
13,
2
] |
exercicio27.py | monabrisa/-infosatc-lp-avaliativo-01 | 0 | 106631 | <filename>exercicio27.py
hectares = float(input("Digite o valor da área em hectares: "))
metros = hectares * 10000
print("{} hectares são {} m²".format(hectares, metros)) | [
1,
529,
9507,
29958,
735,
6269,
11088,
29906,
29955,
29889,
2272,
13,
354,
312,
5114,
353,
5785,
29898,
2080,
703,
14991,
568,
288,
16497,
1146,
17335,
953,
28834,
5114,
29901,
376,
876,
13,
28204,
353,
28834,
5114,
334,
29871,
29896,
29900,
29900,
29900,
29900,
13,
2158,
703,
8875,
28834,
5114,
12777,
6571,
286,
30088,
1642,
4830,
29898,
354,
312,
5114,
29892,
24086,
876,
2
] |
sppas/sppas/src/anndata/hierarchy.py | mirfan899/MTTS | 0 | 181746 | <reponame>mirfan899/MTTS
# -*- coding: UTF-8 -*-
"""
..
---------------------------------------------------------------------
___ __ __ __ ___
/ | \ | \ | \ / the automatic
\__ |__/ |__/ |___| \__ annotation and
\ | | | | \ analysis
___/ | | | | ___/ of speech
http://www.sppas.org/
Use of this software is governed by the GNU Public License, version 3.
SPPAS is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
SPPAS is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with SPPAS. If not, see <http://www.gnu.org/licenses/>.
This banner notice must not be removed.
---------------------------------------------------------------------
anndata.hierarchy.py
~~~~~~~~~~~~~~~~~~~~
"""
from collections import OrderedDict
from .anndataexc import AnnDataTypeError
from .anndataexc import HierarchyAlignmentError
from .anndataexc import HierarchyAssociationError
from .anndataexc import HierarchyParentTierError
from .anndataexc import HierarchyChildTierError
from .anndataexc import HierarchyAncestorTierError
# ----------------------------------------------------------------------------
class sppasHierarchy(object):
"""Generic representation of a hierarchy between tiers.
:author: <NAME>
:organization: Laboratoire Parole et Langage, Aix-en-Provence, France
:contact: <EMAIL>
:license: GPL, v3
:copyright: Copyright (C) 2011-2018 <NAME>
Two types of hierarchy are considered:
- TimeAssociation:
the points of a child tier are all equals to the points of
a reference tier, as for example:
| parent: Words | l' | âne | est | là |
| child: Lemmas | le | âne | être | là |
- TimeAlignment:
the points of a child tier are all included in the set of
points of a reference tier, as for example:
| parent: Phonemes | l | a | n | e | l | a |
| child: Words | l' | âne | est | là |
|
| parent: Phonemes | l | a | n | e | l | a |
| child: Syllables | l.a | n.e | l.a |
In that example, notice that there's no hierarchy link between
"Tokens" and "Syllables" and notice that "Phonemes" is the
grand-parent of "Lemmas".
And the following obvious rules are applied:
- A child can have ONLY ONE parent!
- A parent can have as many children as wanted.
- A hierarchy is a tree, not a graph.
Todo is to consider a time association that is not fully completed:
| parent: Tokens | l' | âne | euh | euh | est | là | @ |
| child: Lemmas | le | âne | | être | là |
"""
types = {"TimeAssociation", "TimeAlignment"}
def __init__(self):
"""Create a new sppasHierarchy instance."""
super(sppasHierarchy, self).__init__()
# key = child tier ; value = tuple(parent, link_type)
self.__hierarchy = OrderedDict()
# -----------------------------------------------------------------------
# Getters
# -----------------------------------------------------------------------
def get_parent(self, child_tier):
"""Return the parent tier for a given child tier.
:param child_tier: (sppasTier) The child tier to found
"""
if child_tier not in self.__hierarchy.keys():
return None
parent, link = self.__hierarchy[child_tier]
return parent
# -----------------------------------------------------------------------
def get_hierarchy_type(self, child_tier):
"""Return the hierarchy type between a child tier and its parent.
:returns: (str) one of the hierarchy type
"""
if child_tier not in self.__hierarchy.keys():
return ""
parent, link = self.__hierarchy[child_tier]
return link
# -----------------------------------------------------------------------
def get_children(self, parent_tier, link_type=None):
"""Return the list of children of a tier, for a given type.
:param parent_tier: (sppasTier) The child tier to found
:param link_type: (str) The type of hierarchy
:returns: List of tiers
"""
if link_type is not None:
if link_type not in sppasHierarchy.types:
raise AnnDataTypeError(link_type,
"TimeAssociation, TimeAlignment")
children = []
for child_tier in self.__hierarchy.keys():
parent, link = self.__hierarchy[child_tier]
if parent is parent_tier:
if link_type is None or link_type == link:
children.append(child_tier)
return children
# -----------------------------------------------------------------------
def get_ancestors(self, child_tier):
"""Return all the direct ancestors of a tier.
:param child_tier: (sppasTier)
:returns: List of tiers with parent, grand-parent, grand-grand-parent...
"""
if child_tier not in self.__hierarchy.keys():
return []
ancestors = []
parent = self.get_parent(child_tier)
while parent is not None:
ancestors.append(parent)
parent = self.get_parent(parent)
return ancestors
# -----------------------------------------------------------------------
# Validators
# -----------------------------------------------------------------------
@staticmethod
def validate_time_alignment(parent_tier, child_tier):
"""Validate a time alignment hierarchy link between 2 tiers.
:param parent_tier: (sppasTier) The parent tier
:param child_tier: (sppasTier) The child tier to be linked to parent
:raises: HierarchyAlignmentError
"""
if parent_tier.is_superset(child_tier) is False:
raise HierarchyAlignmentError(parent_tier.get_name(),
child_tier.get_name())
# -----------------------------------------------------------------------
@staticmethod
def validate_time_association(parent_tier, child_tier):
"""Validate a time association hierarchy link between 2 tiers.
:param parent_tier: (sppasTier) The parent tier
:param child_tier: (sppasTier) The child tier to be linked to the parent
:raises: HierarchyAssociationError
"""
if parent_tier.is_superset(child_tier) is False and \
child_tier.is_superset(parent_tier) is False:
raise HierarchyAssociationError(parent_tier.get_name(),
child_tier.get_name())
# -----------------------------------------------------------------------
def validate_link(self, link_type, parent_tier, child_tier):
"""Validate a hierarchy link between 2 tiers.
:param link_type: (constant) One of the hierarchy types
:param parent_tier: (sppasTier) The parent tier
:param child_tier: (sppasTier) The child tier to be linked to parent
:raises: AnnDataTypeError, HierarchyParentTierError, \
HierarchyChildTierError, HierarchyAncestorTierError, \
HierarchyAlignmentError, HierarchyAssociationError
"""
if link_type not in sppasHierarchy.types:
raise AnnDataTypeError(link_type, "TimeAssociation, TimeAlignment")
# A child has only one parent
if child_tier in self.__hierarchy.keys():
parent, link = self.__hierarchy[child_tier]
raise HierarchyParentTierError(child_tier.get_name(),
link,
parent.get_name())
# A tier can't be its own child/parent
if parent_tier == child_tier:
raise HierarchyChildTierError(child_tier.get_name())
# Check for TimeAlignment
if link_type == "TimeAlignment":
sppasHierarchy.validate_time_alignment(parent_tier,
child_tier)
# Check for TimeAssociation
if link_type == "TimeAssociation":
sppasHierarchy.validate_time_association(parent_tier,
child_tier)
# No circular hierarchy allowed.
ancestors = self.get_ancestors(parent_tier)
family = []
for ancestor in ancestors:
uncles = self.get_children(ancestor)
family.extend(uncles)
family.extend(ancestors)
if child_tier in family:
raise HierarchyAncestorTierError(child_tier.get_name(),
parent_tier.get_name())
# -----------------------------------------------------------------------
# Setters
# -----------------------------------------------------------------------
def add_link(self, link_type, parent_tier, child_tier):
"""Validate and add a hierarchy link between 2 tiers.
:param link_type: (constant) One of the hierarchy types
:param parent_tier: (sppasTier) The parent tier
:param child_tier: (sppasTier) The child tier to be linked to parent
"""
self.validate_link(link_type, parent_tier, child_tier)
self.__hierarchy[child_tier] = (parent_tier, link_type)
# -----------------------------------------------------------------------
def remove_child(self, child_tier):
"""Remove a hierarchy link between a parent and a child.
:param child_tier: (sppasTier) The tier linked to a reference
"""
if child_tier in self.__hierarchy.keys():
del self.__hierarchy[child_tier]
# -----------------------------------------------------------------------
def remove_parent(self, parent_tier):
"""Remove all hierarchy links between a parent and its children.
:param parent_tier: (sppasTier) The parent tier
"""
to_remove = []
for child_tier in self.__hierarchy.keys():
parent, link = self.__hierarchy[child_tier]
if parent is parent_tier:
to_remove.append(child_tier)
for child_tier in to_remove:
del self.__hierarchy[child_tier]
# -----------------------------------------------------------------------
def remove_tier(self, tier):
"""Remove all occurrences of a tier inside the hierarchy.
:param tier: (sppasTier) The tier to remove as parent or child.
"""
to_remove = []
for child in self.__hierarchy.keys():
parent, link = self.__hierarchy[child]
if parent is tier or child is tier:
to_remove.append(child)
for child_tier in to_remove:
del self.__hierarchy[child_tier]
# -----------------------------------------------------------------------
def copy(self):
"""Return a deep copy of the hierarchy."""
h = sppasHierarchy()
for child_tier in self.__hierarchy:
parent_tier = self.__hierarchy[child_tier][0]
link_type = self.__hierarchy[child_tier][1]
h.add_link(link_type, parent_tier, child_tier)
return h
# -----------------------------------------------------------------------
# Automatic hierarchy
# -----------------------------------------------------------------------
@staticmethod
def infer_hierarchy_type(tier1, tier2):
"""Test if tier1 can be a parent tier for tier2.
:returns: One of hierarchy types or an empty string
"""
if tier1.is_superset(tier2) is False:
return ""
if tier2.is_superset(tier1) is True:
return "TimeAssociation"
return "TimeAlignment"
# ------------------------------------------------------------------------
# Overloads
# ------------------------------------------------------------------------
def __len__(self):
return len(self.__hierarchy)
def __iter__(self):
for a in self.__hierarchy:
yield a
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263,
13,
2
] |
Training/MOOC Tensorflow 2.0/BeiDa/class1/p22_random.uniform.py | church06/Pythons | 177 | 116321 | import tensorflow as tf
f = tf.random.uniform([2, 2], minval=0, maxval=1)
print("f:", f)
| [
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2
] |
practice.py | precognition2018/SimpleFaceAPI | 0 | 79009 | import tensorflow as tf
x = tf.placeholder(tf.float32, [None,3])
x_data = [[1,2,3],[4,5,6]]
w = tf.Variable(tf.random_normal([3,2]))
b = tf.Variable(tf.random_normal([2,1]))
expr = tf.matmul(x,w) + b
sess = tf.Session()
sess.run(tf.global_variables_initializer())
print(x_data)
print(sess.run(w))
print(sess.run(b))
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2
] |
kge/ui/ui_manager.py | Fredkiss3/kge | 4 | 59616 | from typing import Callable, Set, Union, Type
import kge.inputs.keys as Keys
from kge.core import events
# from kge.core.component_system import ComponentSystem
# from kge.ui.canvas_renderer import CanvasRenderer
from kge.core.system import System
from kge.utils.vector import Vector
from kge.ui.canvas import Canvas, Clear, ClickDown, ClickUp, Hover, CanvasEvent
from kge.utils.spatial_hash import Box
class UIManager(System):
"""
A system that dispatch events to canvas
FIXME : CANVAS SHOULD NOT BE AFFECTED BY ZOOM
"""
_stated = set() # type: Set[Canvas]
_searchSize = Vector.Unit() / 32
def __enter__(self):
pass
def on_key_down(self, ev: events.KeyDown, dispatch: Callable):
# TODO : SHOULD DO SOMETHIN' (?)
if ev.key is Keys.Up:
pass
elif ev.key is Keys.Down:
pass
def on_key_up(self):
# TODO : SHOULD DO SOMETHIN' (?)
pass
def dispatch(self, ev:Union[events.MouseMotion, events.MouseDown, events.MouseUp], e: Union[Type[ClickUp], Type[ClickDown], Type[Hover]]):
# FIXME : IS THIS PERFORMANT ?
self.clear_canvases()
canvases = ev.scene.spatial_hash.search(ev.position, self._searchSize, Canvas)
# For testing if canvas collide with mouse
box = Box(ev.position, self._searchSize)
# Clear all canvases
for canvas in canvases: # type: Canvas
cbox = Box(canvas.position, canvas.scale)
if box.overlaps(cbox) and canvas.visible:
canvas.dispatch(e(zone=box))
self._stated.add(canvas)
def on_mouse_motion(self, ev: events.MouseMotion, dispatch: Callable):
self.dispatch(ev, Hover)
def clear_canvases(self):
for c in self._stated:
c.dispatch(Clear())
self._stated = set()
def on_mouse_down(self, ev: events.MouseDown, dispatch: Callable):
self.dispatch(ev, ClickDown)
def on_mouse_up(self, ev: events.MouseUp, dispatch: Callable):
self.dispatch(ev, ClickUp)
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] |
code/tools/prepare_instance.py | santomon/taskonomy | 789 | 45333 | import subprocess
import os
def download_task_model(task):
m_path = os.path.join('/home/ubuntu/s3', "model_log_final", task,
"logs/model.permanent-ckpt")
dirs, fname = os.path.split(m_path)
dst_dir = dirs.replace('/home/ubuntu/s3', "s3://taskonomy-unpacked-oregon")
tmp_path = "/home/ubuntu/temp/{}".format(task)
subprocess.call('mkdir -p {}'.format(tmp_path), shell=True)
tmp_fname = os.path.join(tmp_path, fname)
aws_cp_command = "aws s3 cp {}.data-00000-of-00001 {}".format(os.path.join(dst_dir, fname), tmp_path)
subprocess.call(aws_cp_command, shell=True)
aws_cp_command = "aws s3 cp {}.meta {}".format(os.path.join(dst_dir, fname), tmp_path)
subprocess.call(aws_cp_command, shell=True)
aws_cp_command = "aws s3 cp {}.index {}".format(os.path.join(dst_dir, fname), tmp_path)
subprocess.call(aws_cp_command, shell=True)
list_of_tasks = 'autoencoder curvature denoise edge2d edge3d \
keypoint2d keypoint3d colorization jigsaw \
reshade rgb2depth rgb2mist rgb2sfnorm \
room_layout segment25d segment2d vanishing_point_well_defined \
segmentsemantic_rb class_1000 class_places impainting_whole'
list_of_tasks = 'impainting_whole'
list_of_tasks = list_of_tasks.split()
for t in list_of_tasks:
download_task_model(t)
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textprivacy/__init__.py | KennethEnevoldsen/DaAnonymization | 12 | 95417 | """Top-level package for TextAnonymization."""
__author__ = """<NAME>"""
__email__ = "<EMAIL>"
__version__ = "0.1.0"
from textprivacy import utils
from textprivacy.textanonymization import TextAnonymizer
from textprivacy.textpseudonymization import TextPseudonymizer
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annotations.py | jtextor/immunet | 1 | 170518 | import json
import gzip
from pathlib import Path
# JSON KEYS
DATASET_ID_JSON_KEY = "ds_id"
DATASET_TYPE_JSON_KEY = "type"
SLIDE_ID_JSON_KEY = "slide_id"
TILE_ID_JSON_KEY = "tile_id"
ANNOT_TYPE_JSON_KEY = "type"
ANNOT_X_JSON_KEY = "x"
ANNOT_Y_JSON_KEY = "y"
ANNOT_POSITIVITY_JSON_KEY = "positivity"
SLIDES_JSON_KEY = "slides"
TILES_JSON_KEY = "tiles"
ANNOTATIONS_JSON_KEY = "annotations"
class Dataset:
def __init__(self, dict):
self.id = dict[DATASET_ID_JSON_KEY]
self.type = dict[DATASET_TYPE_JSON_KEY] if DATASET_TYPE_JSON_KEY in dict else None
self.slides = []
if SLIDES_JSON_KEY in dict:
slide_dicts = dict[SLIDES_JSON_KEY]
for slide_dict in slide_dicts:
self.add_slide(Slide(slide_dict))
def add_slide(self, slide):
slide.dataset_id = self.id
for tile in slide.tiles:
tile.dataset_id = self.id
self.slides.append(slide)
@property
def tiles(self):
_tiles = []
for slide in self.slides:
_tiles += slide.tiles
return _tiles
class Slide:
def __init__(self, dict):
self.id = dict[SLIDE_ID_JSON_KEY]
self.dataset_id = None
self.tiles = []
if TILES_JSON_KEY in dict:
tile_dicts = dict[TILES_JSON_KEY]
for tile_dict in tile_dicts:
self.add_tile(Tile(tile_dict))
def add_tile(self, tile):
tile.slice_id = self.id
self.tiles.append(tile)
class Tile:
def __init__(self, dict):
self.id = dict[TILE_ID_JSON_KEY]
self.dataset_id = None
self.slice_id = None
self.annotations = []
if ANNOTATIONS_JSON_KEY in dict:
self.annotations = dict[ANNOTATIONS_JSON_KEY]
def build_path(self, relative_path, file_name="components.tiff"):
if self.dataset_id is None or self.slice_id is None:
return None
return relative_path / self.dataset_id / self.slice_id / self.id / file_name
def load_tile_annotations(annotations_path):
with gzip.open(annotations_path) as f:
annotations = json.loads(f.read())
datasets = [Dataset(dataset_dict) for dataset_dict in annotations]
tiles = []
for dataset in datasets:
tiles += dataset.tiles
return tiles
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] |
ImageProcessingScripts/Image Contours Co-ordinates/image_contours_co-ordinates.py | sumitsoni0907/Awesome_Python_Scripts | 1 | 117357 | <reponame>sumitsoni0907/Awesome_Python_Scripts
# Image Contours Co-ordinates
# imported necessary library
import tkinter
from tkinter import *
import tkinter as tk
import tkinter.messagebox as mbox
from tkinter import ttk
from tkinter import filedialog
from PIL import ImageTk, Image
import cv2
import numpy as np
# Main Window & Configuration
window = tk.Tk() # created a tkinter gui window frame
window.title("Image Contours Co-ordinates") # title given is "DICTIONARY"
window.geometry('1000x700')
# top label
start1 = tk.Label(text = "Image Contours Co-ordinates", font=("Arial", 50), fg="magenta") # same way bg
start1.place(x = 50, y = 10)
def start_fun():
window.destroy()
# start button created
startb = Button(window, text="START",command=start_fun,font=("Arial", 25), bg = "orange", fg = "blue", borderwidth=3, relief="raised")
startb.place(x =155 , y =580 )
# image on the main window
path = "Images/front.jpg"
# Creates a Tkinter-compatible photo image, which can be used everywhere Tkinter expects an image object.
img1 = ImageTk.PhotoImage(Image.open(path))
# The Label widget is a standard Tkinter widget used to display a text or image on the screen.
panel = tk.Label(window, image = img1)
panel.place(x = 125, y = 120)
# function created for exiting
def exit_win():
if mbox.askokcancel("Exit", "Do you want to exit?"):
window.destroy()
# exit button created
exitb = Button(window, text="EXIT",command=exit_win,font=("Arial", 25), bg = "red", fg = "blue", borderwidth=3, relief="raised")
exitb.place(x =720 , y = 580 )
window.protocol("WM_DELETE_WINDOW", exit_win)
window.mainloop()
# Main Window & Configuration
window1 = tk.Tk() # created a tkinter gui window frame
window1.title("Image Contours Co-ordinates")
window1.geometry('1000x700')
# function to open file
def open_file():
global filename
filename = filedialog.askopenfilename(title="Select file")
# print(filename)
path_text.delete("1.0", "end")
path_text.insert(END, filename)
# function to show original video
def original_fun():
global filename
img = cv2.imread(filename, 1)
cv2.imshow("Original Image", img)
# function to show grayscale video
def contours_fun():
global filename
# Reading image
font = cv2.FONT_HERSHEY_TRIPLEX
img2 = cv2.imread(filename, cv2.IMREAD_COLOR)
# Reading same image in another variable and converting to gray scale.
img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
# Converting image to a binary image ( black and white only image).
_, threshold = cv2.threshold(img, 110, 255, cv2.THRESH_BINARY)
# Detecting contours in image.
contours, _ = cv2.findContours(threshold, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# Going through every contours found in the image.
for cnt in contours:
approx = cv2.approxPolyDP(cnt, 0.009 * cv2.arcLength(cnt, True), True)
# draws boundary of contours.
cv2.drawContours(img2, [approx], 0, (0, 0, 255), 2)
# Used to flatted the array containing the co-ordinates of the vertices.
n = approx.ravel()
i = 0
for j in n:
if (i % 2 == 0):
x = n[i]
y = n[i + 1]
# String containing the co-ordinates.
string = str(x) + " " + str(y)
if (i == 0):
# text on topmost co-ordinate.
cv2.putText(img2, "TOP", (x, y),font, 0.5, (255, 0, 0))
else:
# text on remaining co-ordinates.
cv2.putText(img2, string, (x, y),font, 0.5, (0, 255, 0))
i = i + 1
# Showing the final image.
cv2.imshow('Image with Contours Co-ordinates', img2)
# Exiting the window if 'q' is pressed on the keyboard.
if cv2.waitKey(0) & 0xFF == ord('q'):
cv2.destroyAllWindows()
# top label
start1 = tk.Label(text = "Image Contours Co-ordinates", font=("Arial", 50), fg="magenta") # same way bg
start1.place(x =50, y = 10)
lbl1 = tk.Label(text="Select any image with\nobject & get contours co-ordinates", font=("Arial", 40),fg="green") # same way bg
lbl1.place(x=90, y=110)
lbl2 = tk.Label(text="Selected Image", font=("Arial", 30),fg="brown") # same way bg
lbl2.place(x=80, y=280)
path_text = tk.Text(window1, height=1, width=37, font=("Arial", 30), bg="light yellow", fg="orange",borderwidth=2, relief="solid")
path_text.place(x=80, y = 330)
# Select Button
selectb=Button(window1, text="SELECT",command=open_file, font=("Arial", 25), bg = "light green", fg = "blue")
selectb.place(x = 120, y = 580)
# original image Button
getb=Button(window1, text="ORIGINAL IMAGE",command=original_fun, font=("Arial", 25), bg = "orange", fg = "blue")
getb.place(x = 80, y = 450)
# contour Button
getb=Button(window1, text="CONTOURS CO-ORD.",command=contours_fun, font=("Arial", 25), bg = "orange", fg = "blue")
getb.place(x = 530, y = 450)
# function for exiting
def exit_win1():
if mbox.askokcancel("Exit", "Do you want to exit?"):
window1.destroy()
# Get Images Button
getb=Button(window1, text="EXIT",command=exit_win1, font=("Arial", 25), bg = "red", fg = "blue")
getb.place(x = 750, y = 580)
window1.protocol("WM_DELETE_WINDOW", exit_win1)
window1.mainloop()
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] |
appengine/cr-buildbucket/errors.py | mithro/chromium-infra | 0 | 91020 | # Copyright 2014 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import re
BUCKET_NAME_REGEX = re.compile(r'^[0-9a-z_\.\-/]{1,100}$')
class Error(Exception):
pass
class BuildNotFoundError(Error):
pass
class BuildIsCompletedError(Error):
"""Build is complete and cannot be changed."""
class InvalidInputError(Error):
"""Raised when service method argument value is invalid."""
class LeaseExpiredError(Error):
"""Raised when provided lease_key does not match the current one."""
def validate_bucket_name(bucket):
"""Raises InvalidInputError if bucket name is invalid."""
if not bucket:
raise InvalidInputError('Bucket not specified')
if not isinstance(bucket, basestring):
raise InvalidInputError(
'Bucket must be a string. It is %s.' % type(bucket).__name__)
if not BUCKET_NAME_REGEX.match(bucket):
raise InvalidInputError(
'Bucket name "%s" does not match regular expression %s' %
(bucket, BUCKET_NAME_REGEX.pattern))
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] |
src/__init__.py | MishaCatskill/lykos | 0 | 73660 | <filename>src/__init__.py
import argparse
import datetime
import time
import botconfig
import src.settings as var
from src import logger
from src.logger import stream, stream_handler, debuglog, errlog, plog
from src import db
# Import the user-defined game modes
# These are not required, so failing to import it doesn't matter
# The file then imports our game modes
# Fall back to importing our game modes if theirs fail
# Do the same with roles
try:
import gamemodes # type: ignore
except ImportError:
import src.gamemodes
try:
import roles # type: ignore
roles.CUSTOM_ROLES_DEFINED
except (ImportError, AttributeError):
import src.roles
# Handle launch parameters
# Argument --debug means start in debug mode
# --verbose means to print a lot of stuff (when not in debug mode)
# --normal means to override the above and use nothing
# Settings can be defined in the config, but launch argumentss override it
debug_mode = False
verbose = False
normal = False
lagcheck = False
# Carry over settings from botconfig into settings.py
for setting, value in botconfig.__dict__.items():
if not setting.isupper():
continue # Not a setting
if setting == "DEBUG_MODE":
debug_mode = value
if setting == "VERBOSE_MODE":
verbose = value
if setting == "NORMAL_MODE":
normal = value
if not setting in var.__dict__.keys():
continue # Don't carry over config-only settings
# If we got that far, it's valid
setattr(var, setting, value)
parser = argparse.ArgumentParser()
parser.add_argument('--debug', action='store_true')
parser.add_argument('--verbose', action='store_true')
parser.add_argument('--normal', action='store_true')
parser.add_argument('--lagcheck', action='store_true')
args = parser.parse_args()
if args.debug: debug_mode = True
if args.verbose: verbose = True
if args.normal: normal = True
if args.lagcheck: lagcheck = True
botconfig.DEBUG_MODE = debug_mode if not normal else False
botconfig.VERBOSE_MODE = verbose if not normal else False
# vim: set sw=4 expandtab:
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Searching and Sorting/playlist.py | mishrakeshav/CSES-Problem-Set | 0 | 37610 |
def solve():
n = int(input())
k = list(map(int,input().split()))
hashmap = dict()
j = 0
ans = 0
c = 0
for i in range(n):
if k[i] in hashmap and hashmap[k[i]] > 0:
while i > j and k[i] in hashmap and hashmap[k[i]] > 0:
hashmap[k[j]] -= 1
j += 1
c -= 1
hashmap[k[i]] = 1
c += 1
ans = max(ans,c)
print(ans)
if __name__ == '__main__':
solve() | [
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pada/assemble/__init__.py | eleveyuan/kada | 4 | 1603524 | # comment code to fix partially/circular import
# from pada.assemble.locates import url
# from pada.assemble.assembles import assemble
# from pada.assemble.pipes import pipe
#
#
# __all__ = ['url', 'assemble', 'pipe'] | [
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source_optics/scanner/commits.py | heemayl/source_optics | 1 | 137172 | # Copyright 2018-2019 SourceOptics Project Contributors
#
# 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 fnmatch
import os
import re
from django.utils.dateparse import parse_datetime
from ..models import Author, Commit, File, FileChange
from . import commands
# we use git --log with a special one-line format string to capture certain fields
# we regex across those fields with a custom delimiter to make it easy to find them
DEL = '&DEL&>'
# Fields recorded (in order)
# commit hash %H
# author_name %an
# author_date %ad
# commit_date %cd
# author_email %ae
# subject %f
PRETTY_STRING = f"'{DEL}%H{DEL}%an{DEL}%ad{DEL}%cd{DEL}%ae{DEL}%f{DEL}'"
# the regex to match the string, which must watch the log format PRETTY_STRING
PARSER_RE_STRING = f"{DEL}(?P<commit>.*){DEL}(?P<author_name>.*){DEL}(?P<author_date>.*){DEL}(?P<commit_date>.*){DEL}(?P<author_email>.*){DEL}(?P<subject>.*){DEL}"
PARSER_RE = re.compile(PARSER_RE_STRING, re.VERBOSE)
FILES_HACK_REPO = None
FILES_HACK = dict()
# Regex for handling arbitrary file path renames;
# `(/)?`: First captured group that matches `/` optionally
# `(?:\{[^}=]+=>)`: non-captured group to match until `=>`
# `([^}]+)`: matches the portion upto next `}` (`\}`)
# In the replacement, only the captured groups are used.
FILE_PATH_RENAME_RE = re.compile(r'(/)?(?:\{[^}=]+=>)([^}]+)\}')
class Commits:
"""
This class clones a repository (git) using a provided URL and credential
and proceeds to execute git log on it to scan its data
"""
@classmethod
def get_file(cls, repo, path, filename):
"""
provide a lookup of File objects for repo/path/filename tuples to
prevent excessive database access. This cache is only kept around
for the current repository
"""
global FILES_HACK_REPO
global FILES_HACK
if FILES_HACK_REPO != repo.name:
FILES_HACK = dict()
FILES_HACK_REPO = repo.name
files = File.objects.filter(repo=repo).all()
for fobj in files:
assert fobj is not None
key = os.path.join(fobj.path, fobj.name)
FILES_HACK[key] = fobj
original = os.path.join(path, filename)
result = FILES_HACK[original]
assert result is not None
return result
@classmethod
def bulk_create(cls, total_commits, total_files, total_file_changes):
"""
we keep a list of the three types of objects and only create them periodically,
to prevent from doing too many database transactions. The batch size here is fairly
arbitrary.
"""
# by not ignoring conflicts, we can test whether our scanner "overwork" code is correct
# use -F to try a full test from scratch
if len(total_commits):
Commit.objects.bulk_create(total_commits, 100, ignore_conflicts=True)
del total_commits[:]
if len(total_files):
File.objects.bulk_create(total_files, 100, ignore_conflicts=True)
del total_files[:]
if len(total_file_changes):
FileChange.objects.bulk_create(total_file_changes, 100, ignore_conflicts=True)
del total_file_changes[:]
@classmethod
def process_commits(cls, repo, repo_dir, mode='Commit'):
"""
Uses git log to gather the commit data for a repository. This is run three times in three different
modes over the same git log output. See usage in processor.py.
"""
cmd_string = 'git rev-list --all --count'
commit_total = commands.execute_command(repo, cmd_string, log=False, timeout=600, chdir=repo_dir, capture=True)
try:
commit_total = int(commit_total)
except TypeError:
print("no commits yet")
return
cmd_string = ('git log --all --numstat --date=iso-strict-local --pretty=format:'
+ PRETTY_STRING)
last_commit = None
count = 0
total_commits = []
total_file_changes = []
total_files = []
global GLITCH_COUNT
def handler(line):
"""
this code processes every line from the output
"""
nonlocal last_commit
nonlocal count
if count % 200 == 0:
print("scanning (repo:%s) (mode:%s): %s/%s" % (repo, mode, count, commit_total))
if count % 2000 == 0:
cls.bulk_create(total_commits, total_files, total_file_changes)
if not line or line == "\n":
#print("F1")
return True # continue
elif line.startswith(DEL):
commit = cls.handle_diff_information(repo, line, mode)
if last_commit != commit:
count = count + 1
last_commit = commit
total_commits.append(commit)
return True
elif "does not have any commits yet" in line:
#print("skipping, no commits yet")
return False
else:
if mode != 'Commit':
assert last_commit is not None
cls.handle_file_information(repo, line, last_commit, mode, total_files, total_file_changes)
return True
commands.execute_command(repo, cmd_string, log=False, timeout=1200, chdir=repo_dir, handler=handler)
cls.bulk_create(total_commits, total_files, total_file_changes)
return True
@classmethod
def create_file(cls, full_path, commit, la, lr, binary, mode, total_files, total_file_changes):
"""
After we have recorded commits, this function creates either Files or FileChange objects
depending on what scanner pass we are running through.
"""
assert commit is not None
assert mode in [ 'File', 'FileChange' ]
fname = os.path.basename(full_path)
# find the extension
(_, ext) = os.path.splitext(full_path)
path = os.path.dirname(full_path)
if mode == 'File':
# update the global file object with the line counts
total_files.append(File(
repo=commit.repo,
path=path,
name=fname,
ext=ext,
binary=binary
))
# BOOKMARK
elif mode == 'FileChange':
file = cls.get_file(commit.repo, path, fname)
if file is None:
# this shouldn't happen, but if we get here the parser has a bug.
raise Exception("FATAL, MISSING FILE RECORD, SHOULDN'T BE HERE!")
total_file_changes.append(FileChange(
commit=commit,
lines_added=la,
lines_removed=lr,
file=file
))
@classmethod
def matches(self, needle, haystack, exact=False, trim_dot=False):
"""
This function is used by the source code filtering feature to see if a file path
matches an expression or not.
"""
# user input may be inconsistent about trailing slashes so be flexible
if haystack.endswith("/"):
haystack = haystack[:-1]
if needle.endswith("/"):
needle = needle[:-1]
if trim_dot:
# for extension checking, do not require the user input to be ".mp4" to mean "mp4"
haystack = haystack.replace(".","")
needle = needle.replace(".", "")
if "?" in needle or "*" in needle or "[" in needle:
# this looks like a fnmatch pattern
return fnmatch.fnmatch(haystack, needle)
elif exact:
# we are processing an extension, require an exact match
return haystack == needle
else:
# we are processing paths, not extensions, so just require it to start with the substring
return haystack.startswith(needle)
@classmethod
def has_matches(cls, needles, haystack, exact=False, trim_dot=False):
"""
tests whether a file pattern has any one of multiple matches
"""
for needle in needles:
if cls.matches(needle, haystack, exact=exact, trim_dot=trim_dot):
return True
return False
@classmethod
def has_no_matches(cls, needles, haystack, exact=False, trim_dot=False):
return not cls.has_matches(needles, haystack, exact=exact, trim_dot=trim_dot)
@classmethod
def repair_move_path(cls, path):
"""
handles details about moves in git log by fixing path elements like /{org=>com}/
to just log the file in the final path. This will possibly give users credit for
aspects of a move but this something we can explore later. Not sure if it does - MPD.
"""
return FILE_PATH_RENAME_RE.sub(r'\1\2', path)
@classmethod
def should_process_path(cls, repo, path):
"""
Repository configuration supports filtering based on path, to decide to index or not-index
certain files. This might be used to only index a 'src/' directory or otherwise not
index a directory called 'docs/', and is off by default. This function handles
a decision on whether to process a path.
"""
org = repo.organization
directory_allow = repo.scanner_directory_allow_list or org.scanner_directory_allow_list
directory_deny = repo.scanner_directory_deny_list or org.scanner_directory_deny_list
extension_allow = repo.scanner_extension_allow_list or org.scanner_extension_allow_list
extension_deny = repo.scanner_extension_deny_list or org.scanner_extension_deny_list
dirname = os.path.dirname(path)
split_ext = os.path.splitext(path)
extension = None
if len(split_ext) > 1:
extension = split_ext[-1]
if directory_allow:
directory_allow = directory_allow.split("\n")
if directory_deny:
directory_deny = directory_deny.split("\n")
if extension_allow:
extension_allow = extension_allow.split("\n")
if extension_deny:
extension_deny = extension_deny.split("\n")
if directory_allow and cls.has_no_matches(directory_allow, dirname):
return False
if directory_deny and cls.has_matches(directory_deny, dirname):
return False
if extension:
if extension_allow and cls.has_no_matches(extension_allow, extension, exact=True, trim_dot=True):
return False
if extension_deny and cls.has_matches(extension_deny, extension, exact=True, trim_dot=True):
return False
return True
@classmethod
def handle_file_information(cls, repo, line, last_commit, mode, total_files, total_file_changes):
"""
process the list of file changes in this commit
"""
tokens = line.split()
(added, removed, path) = (tokens[0], tokens[1], ''.join(tokens[2:]))
# binary files will have '-' in their field changes. Set these to 0
binary = False
if added == '-':
binary = True
added = 0
if removed == '-':
binary = True
removed = 0
# FIXME: when scanning one repo, the added string containted
try:
added = int(added)
removed = int(removed)
except:
# FIXME:
# I found one instance in one repo where the 'added' text returns "warning: inexact" and in this case
# we might as well keep going, we probably need to parse the line differently in this instance.
# example found in kubernetes/kubernetes on github. This reference is not an endorsement.
added = 0
removed = 0
path = cls.repair_move_path(path)
if not cls.should_process_path(repo, path):
return None
cls.create_file(path, last_commit, added, removed, binary, mode, total_files, total_file_changes)
@classmethod
def handle_diff_information(cls, repo, line, mode):
"""
process the amount of lines changed in this commit
"""
# FIXME: give all these fields names
match = PARSER_RE.match(line)
if not match:
raise Exception("DOESN'T MATCH? %s" % line)
data = match.groupdict()
if mode != 'Commit':
# running back through the logs to set up the file changes
commit = Commit.objects.get(sha=data['commit'])
return commit
email = data['author_email']
author, created = Author.objects.get_or_create(email=email)
commit_date = parse_datetime(data['commit_date'])
author_date = parse_datetime(data['author_date'])
# will pass on to bulk_create
return Commit(
sha=data['commit'],
subject=data['subject'],
repo=repo,
author=author,
author_date=author_date,
commit_date=commit_date
)
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] |
tests/test_integration_features.py | daviesje/21cmFAST | 28 | 126268 | """
A set of large-scale tests which test code updates against previously-run "golden"
results.
The idea here is that any new updates (except for major versions) should be non-breaking;
firstly, they should not break the API, so that the tests should run without crashing without
being changed.
Secondly, the actual results of running the basic functions should remain the same for
the same input code, except for potential bug-fixes. In these cases, these tests should
pick these changes up. The test data should then be changed to reflect the new gold
standard, and if applicable, a new test should be written that reflects the previous
broken code.
Thirdly, it enforces that new features, where possible, are added in such a way as to
keep the default behaviour constant. That is, the tests here should *not* run the added
feature, and therefore should continue to produce the same test results regardless of
the new feature added. The new feature should be accompanied by its own tests, whether
in this or another test module. If a new feature *must* be included by default, then
it must be implemented in a new major version of the code, at which point the test data
is able to be updated.
Comparison tests here are meant to be as small as possible while attempting to form
a reasonable test: they should be of reduced data such as power spectra or global xHI
measurements, and they should be generated with small simulations.
"""
import pytest
import h5py
import logging
import matplotlib as mpl
import numpy as np
from py21cmfast import config, global_params
from . import produce_integration_test_data as prd
logger = logging.getLogger("21cmFAST")
logger.setLevel(logging.INFO)
options = list(prd.OPTIONS.keys())
options_pt = list(prd.OPTIONS_PT.keys())
options_halo = list(prd.OPTIONS_HALO.keys())
@pytest.mark.parametrize("name", options)
def test_power_spectra_coeval(name, module_direc, plt):
redshift, kwargs = prd.OPTIONS[name]
print(f"Options used for the test at z={redshift}: ", kwargs)
# First get pre-made data
with h5py.File(prd.get_filename("power_spectra", name), "r") as fl:
true_powers = {
"_".join(key.split("_")[1:]): value[...]
for key, value in fl["coeval"].items()
if key.startswith("power_")
}
# Now compute the Coeval object
with config.use(direc=module_direc, regenerate=False, write=True):
with global_params.use(zprime_step_factor=prd.DEFAULT_ZPRIME_STEP_FACTOR):
# Note that if zprime_step_factor is set in kwargs, it will over-ride this.
test_k, test_powers, _ = prd.produce_coeval_power_spectra(
redshift, **kwargs
)
if plt == mpl.pyplot:
make_coeval_comparison_plot(test_k, true_powers, test_powers, plt)
for key, value in true_powers.items():
print(f"Testing {key}")
assert np.sum(~np.isclose(value, test_powers[key], atol=0, rtol=1e-2)) < 10
np.testing.assert_allclose(value, test_powers[key], atol=0, rtol=1e-1)
@pytest.mark.parametrize("name", options)
def test_power_spectra_lightcone(name, module_direc, plt):
redshift, kwargs = prd.OPTIONS[name]
print(f"Options used for the test at z={redshift}: ", kwargs)
# First get pre-made data
with h5py.File(prd.get_filename("power_spectra", name), "r") as fl:
true_powers = {}
true_global = {}
for key in fl["lightcone"].keys():
if key.startswith("power_"):
true_powers["_".join(key.split("_")[1:])] = fl["lightcone"][key][...]
elif key.startswith("global_"):
true_global[key] = fl["lightcone"][key][...]
# Now compute the lightcone
with config.use(direc=module_direc, regenerate=False, write=True):
with global_params.use(zprime_step_factor=prd.DEFAULT_ZPRIME_STEP_FACTOR):
# Note that if zprime_step_factor is set in kwargs, it will over-ride this.
test_k, test_powers, lc = prd.produce_lc_power_spectra(redshift, **kwargs)
if plt == mpl.pyplot:
make_lightcone_comparison_plot(
test_k, lc.node_redshifts, true_powers, true_global, test_powers, lc, plt
)
for key, value in true_powers.items():
print(f"Testing {key}")
# Ensure all but 10 of the values is within 1%, and none of the values
# is outside 10%
assert np.sum(~np.isclose(value, test_powers[key], atol=0, rtol=1e-2)) < 10
assert np.allclose(value, test_powers[key], atol=0, rtol=1e-1)
for key, value in true_global.items():
print(f"Testing Global {key}")
assert np.allclose(value, getattr(lc, key), atol=0, rtol=1e-3)
def make_lightcone_comparison_plot(
k, z, true_powers, true_global, test_powers, lc, plt
):
n = len(true_global) + len(true_powers)
fig, ax = plt.subplots(2, n, figsize=(3 * n, 5))
for i, (key, val) in enumerate(true_powers.items()):
make_comparison_plot(
k, val, test_powers[key], ax[:, i], xlab="k", ylab=f"{key} Power"
)
for i, (key, val) in enumerate(true_global.items(), start=i + 1):
make_comparison_plot(
z, val, getattr(lc, key), ax[:, i], xlab="z", ylab=f"{key}"
)
def make_coeval_comparison_plot(k, true_powers, test_powers, plt):
fig, ax = plt.subplots(
2, len(true_powers), figsize=(3 * len(true_powers), 6), sharex=True
)
for i, (key, val) in enumerate(true_powers.items()):
make_comparison_plot(
k, val, test_powers[key], ax[:, i], xlab="k", ylab=f"{key} Power"
)
def make_comparison_plot(x, true, test, ax, logx=True, logy=True, xlab=None, ylab=None):
ax[0].plot(x, true, label="True")
ax[0].plot(x, test, label="Test")
if logx:
ax[0].set_xscale("log")
if logy:
ax[0].set_yscale("log")
if xlab:
ax[0].set_xlabel(xlab)
if ylab:
ax[0].set_ylabel(ylab)
ax[0].legend()
ax[1].plot(x, (test - true) / true)
ax[1].set_ylabel("Fractional Difference")
@pytest.mark.parametrize("name", options_pt)
def test_perturb_field_data(name):
redshift, kwargs = prd.OPTIONS_PT[name]
print("Options used for the test: ", kwargs)
# First get pre-made data
with h5py.File(prd.get_filename("perturb_field_data", name), "r") as f:
power_dens = f["power_dens"][...]
power_vel = f["power_vel"][...]
pdf_dens = f["pdf_dens"][...]
pdf_vel = f["pdf_vel"][...]
with global_params.use(zprime_step_factor=prd.DEFAULT_ZPRIME_STEP_FACTOR):
# Note that if zprime_step_factor is set in kwargs, it will over-ride this.
(
k_dens,
p_dens,
k_vel,
p_vel,
x_dens,
y_dens,
x_vel,
y_vel,
ic,
) = prd.produce_perturb_field_data(redshift, **kwargs)
assert np.allclose(power_dens, p_dens, atol=5e-3, rtol=1e-3)
assert np.allclose(power_vel, p_vel, atol=5e-3, rtol=1e-3)
assert np.allclose(pdf_dens, y_dens, atol=5e-3, rtol=1e-3)
assert np.allclose(pdf_vel, y_vel, atol=5e-3, rtol=1e-3)
@pytest.mark.parametrize("name", options_halo)
def test_halo_field_data(name):
redshift, kwargs = prd.OPTIONS_HALO[name]
print("Options used for the test: ", kwargs)
# First get pre-made data
with h5py.File(prd.get_filename("halo_field_data", name), "r") as f:
n_pt_halos = f["n_pt_halos"][...]
pt_halo_masses = f["pt_halo_masses"][...]
with global_params.use(zprime_step_factor=prd.DEFAULT_ZPRIME_STEP_FACTOR):
# Note that if zprime_step_factor is set in kwargs, it will over-ride this.
pt_halos = prd.produce_halo_field_data(redshift, **kwargs)
assert np.allclose(n_pt_halos, pt_halos.n_halos, atol=5e-3, rtol=1e-3)
assert np.allclose(
np.sum(pt_halo_masses), np.sum(pt_halos.halo_masses), atol=5e-3, rtol=1e-3
)
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] |
test/client/device/common/test_thread_base.py | redmic-project/device-oag-buoy-buoy-client | 0 | 107126 | <filename>test/client/device/common/test_thread_base.py
import unittest
from nose.tools import eq_
from buoy.client.device.common.base import BaseThread
from buoy.client.notification.client.common import NoticePriorityQueue
class TestThreadBase(unittest.TestCase):
def setUp(self):
queue_notice = NoticePriorityQueue()
self.thread = BaseThread(queue_notice=queue_notice)
def test_is_aliveReturnFalse_when_createThread(self):
eq_(self.thread.is_alive(), False)
def test_is_aliveReturnFalse_when_stopThread(self):
self.thread.active = True
self.thread.stop()
eq_(self.thread.is_alive(), False)
if __name__ == '__main__':
unittest.main()
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1678,
443,
27958,
29889,
3396,
580,
13,
2
] |
chap05/list0507.py | ytianjin/GitTest | 0 | 110822 | # 打印输出位的逻辑与运算的结果、逻辑或运算的结果、逻辑异或运算的结果以及取反的结果
a = int(input('正整数a：'))
b = int(input('正整数b：'))
w = int(input('表示位数：'))
f = '{{:0{}b}}'.format(w)
m = 2 ** w - 1 # w位都相当于二进制数的1
print('a = ' + f.format(a))
print('b = ' + f.format(b))
print('a & b = ' + f.format(a & b))
print('a | b = ' + f.format(a | b))
print('a ^ b = ' + f.format(a ^ b))
print('~a = ' + f.format(~a & m))
print('~b = ' + f.format(~b & m)) | [
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2
] |
src/firebolt/client/client.py | ptiurin/firebolt-python-sdk | 0 | 1617803 | <filename>src/firebolt/client/client.py
from typing import Any, Optional
from async_property import async_cached_property # type: ignore
from httpx import AsyncClient as HttpxAsyncClient
from httpx import Client as HttpxClient
from httpx import _types
from httpx import codes as HttpxCodes
from httpx._types import AuthTypes
from firebolt.client.auth import Auth
from firebolt.client.constants import DEFAULT_API_URL
from firebolt.common.exception import AccountNotFoundError
from firebolt.common.urls import ACCOUNT_BY_NAME_URL, ACCOUNT_URL
from firebolt.common.util import cached_property, fix_url_schema, mixin_for
FireboltClientMixinBase = mixin_for(HttpxClient) # type: Any
class FireboltClientMixin(FireboltClientMixinBase):
def __init__(
self,
*args: Any,
account_name: Optional[str] = None,
api_endpoint: str = DEFAULT_API_URL,
auth: AuthTypes = None,
**kwargs: Any,
):
self.account_name = account_name
self._api_endpoint = fix_url_schema(api_endpoint)
super().__init__(*args, auth=auth, **kwargs)
def _build_auth(self, auth: _types.AuthTypes) -> Optional[Auth]:
if auth is None or isinstance(auth, Auth):
return auth
elif isinstance(auth, tuple):
return Auth(
username=str(auth[0]),
password=str(auth[1]),
api_endpoint=self._api_endpoint,
)
else:
raise TypeError(f'Invalid "auth" argument: {auth!r}')
class Client(FireboltClientMixin, HttpxClient):
"""
An http client, based on httpx.Client, that handles the authentication
for Firebolt database.
Authentication can be passed through auth keyword as a tuple or as a
FireboltAuth instance
httpx.Client:
+ (HttpxClient.__doc__ or "")
"""
@cached_property
def account_id(self) -> str:
if self.account_name:
response = self.get(
url=ACCOUNT_BY_NAME_URL, params={"account_name": self.account_name}
)
if response.status_code == HttpxCodes.NOT_FOUND:
raise AccountNotFoundError(self.account_name)
# process all other status codes
response.raise_for_status()
return response.json()["account_id"]
else: # account_name isn't set, use the default account.
return self.get(url=ACCOUNT_URL).json()["account"]["id"]
class AsyncClient(FireboltClientMixin, HttpxAsyncClient):
"""
An http client, based on httpx.AsyncClient, that asyncronously handles
authentication for Firebolt database.
Authentication can be passed through auth keyword as a tuple or as a
FireboltAuth instance
httpx.AsyncClient:
+ (HttpxAsyncClient.__doc__ or "")
"""
@async_cached_property
async def account_id(self) -> str:
if self.account_name:
response = await self.get(
url=ACCOUNT_BY_NAME_URL, params={"account_name": self.account_name}
)
if response.status_code == HttpxCodes.NOT_FOUND:
raise AccountNotFoundError(self.account_name)
# process all other status codes
response.raise_for_status()
return response.json()["account_id"]
else: # account_name isn't set; use the default account.
return (await self.get(url=ACCOUNT_URL)).json()["account"]["id"]
| [
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2477,
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29918,
4219,
8106,
3126,
580,
3366,
10149,
3108,
3366,
333,
3108,
13,
2
] |
attack/attacker.py | smallflyingpig/adversarial_attack_and_defense | 0 | 51726 | <filename>attack/attacker.py
import torch
from torch import nn
from abc import ABC, abstractmethod
class AdditiveAttacker(ABC):
def __init__(self, eps, data_min=-1, data_max=1):
self.eps = eps
self.data_min = data_min
self.data_max = data_max
@abstractmethod
def _get_perturbation(self, x, y=None):
pass
def generate(self, x, y=None):
delta = self._get_perturbation(x, y)
x = x + delta * self.eps
x[x<self.data_min] = self.data_min
x[x>self.data_max] = self.data_max
return x
| [
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] |
app/src/main/python/doupai.py | One-PZ/lmgp | 0 | 174655 | # -*- coding:utf-8 -*-
import re
import json
import requests
"""
目标APP：逗拍
目标url：APP视频分享链接
爬取思路：
1. 通过APP里的分享获取视频url
2. 对https://v2.doupai.cc/topic/XXXXXX.json发送post请求，获取json数据
"""
class DouPai(object):
def __init__(self, url):
self.url = url
self.session = requests.Session()
def get_video(self):
try:
# 处理url，获取视频id
pattern = re.compile('(http[s]?://[^\s]+)', re.S)
deal_url = re.findall(pattern, self.url)[0]
headers = {
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/85.0.4183.102 Safari/537.36",
"origin": "https://p.doupai.cc"
}
# 获取vid
vid = re.findall("topic/(.*?).html", deal_url, re.S)[0]
base_url = "https://v2.doupai.cc/topic/{}.json".format(vid)
result = self.session.get(url=base_url, headers=headers, timeout=10)
if result.status_code == 200:
try:
doc = result.json()
url = doc["data"]["videoUrl"]
title = doc["data"]["name"]
cover = doc["data"]["imageUrl"]
info = {
"title": title,
"cover": cover,
"video_url": url
}
return json.dumps(info, ensure_ascii=False)
except Exception as e:
return json.dumps({"info": "暂无相关数据，请检查相关数据：" + str(e)}, ensure_ascii=False)
else:
return json.dumps({"info": "暂无相关数据，请检查相关数据："}, ensure_ascii=False)
except Exception as e:
return json.dumps({"info": "暂无相关数据，请检查相关数据：" + str(e)}, ensure_ascii=False)
if __name__ == '__main__':
dou_pai = DouPai("出国证 https://p.doupai.cc/#/topic/5fa20d8c8f71d10031f27abb.html")
print(dou_pai.get_video()) | [
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] |
api-v2/config_helper.py | MikeChurvis/mikechurvis.github.io | 1 | 54113 | from django.core.exceptions import ImproperlyConfigured
def validate_settings(settings, required_settings: set[str]):
required_settings = {
'CONTACTFORM_NAME_MAX_LENGTH',
'CONTACTFORM_ORGANIZATION_MAX_LENGTH',
'CONTACTFORM_EMAIL_MAX_LENGTH',
'CONTACTFORM_EMAIL_REGULAR_EXPRESSION',
'CONTACTFORM_MESSAGE_MIN_LENGTH',
'CONTACTFORM_MESSAGE_MAX_LENGTH',
}
missing_settings = set(filter(lambda attr: not hasattr(settings, attr), required_settings))
if missing_settings:
raise ImproperlyConfigured(
'The following settings are required and are missing:\n{}'.format("\n".join(missing_settings))
)
| [
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] |
icon-downloader/isko.py | vpa1/vetrani-bez-jedu | 0 | 85629 | <gh_stars>0
import requests,psycopg2,logging
try:
aqdata=requests.get('http://portal.chmi.cz/files/portal/docs/uoco/web_generator/actual_hour_data_cze.json').json()
conn = psycopg2.connect(user = "wforecast_app", password = "<PASSWORD>",
host = "db",
port = "5432",
database = "wforecast")
curr = conn.cursor()
hour = aqdata["States"][0]["DateToUTC"]
for station in aqdata["States"][0]["Regions"][13]["Stations"]:
statcode = station["Code"]
for component in station["Components"]:
pollutant = component["Code"]
interval = component.get("Int")
val = component.get("Val")
if (val!=None and val!=''):
curr.execute("insert into isko.isko_data (observation_hour,station,pollutant,interval,val) VALUES(date_trunc('hour',%s::timestamp),%s,%s,%s,%s)",[hour,statcode,pollutant,interval,val.replace(' ','')])
conn.commit()
conn.close()
logging.info("ISKO download successful")
except(Exception):
logging.exception("Download failed with exception ") | [
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] |
tests/serializer.py | asellappen/python-junit-xml | 95 | 129029 | import codecs
import os
import tempfile
from xml.dom import minidom
from six import PY2
from junit_xml import to_xml_report_file, to_xml_report_string
def serialize_and_read(test_suites, to_file=False, prettyprint=False, encoding=None):
"""writes the test suite to an XML string and then re-reads it using minidom,
returning => (test suite element, list of test case elements)"""
try:
iter(test_suites)
except TypeError:
test_suites = [test_suites]
if to_file:
fd, filename = tempfile.mkstemp(text=True)
os.close(fd)
with codecs.open(filename, mode="w", encoding=encoding) as f:
to_xml_report_file(f, test_suites, prettyprint=prettyprint, encoding=encoding)
print("Serialized XML to temp file [%s]" % filename)
xmldoc = minidom.parse(filename)
os.remove(filename)
else:
xml_string = to_xml_report_string(test_suites, prettyprint=prettyprint, encoding=encoding)
if PY2:
assert isinstance(xml_string, unicode) # noqa: F821
print("Serialized XML to string:\n%s" % xml_string)
if encoding:
xml_string = xml_string.encode(encoding)
xmldoc = minidom.parseString(xml_string)
def remove_blanks(node):
for x in node.childNodes:
if x.nodeType == minidom.Node.TEXT_NODE:
if x.nodeValue:
x.nodeValue = x.nodeValue.strip()
elif x.nodeType == minidom.Node.ELEMENT_NODE:
remove_blanks(x)
remove_blanks(xmldoc)
xmldoc.normalize()
ret = []
suites = xmldoc.getElementsByTagName("testsuites")[0]
for suite in suites.getElementsByTagName("testsuite"):
cases = suite.getElementsByTagName("testcase")
ret.append((suite, cases))
return ret
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] |
python_sample_app/publisher/publisher.py | open-edge-insights/eii-samples | 1 | 76455 | <gh_stars>1-10
# Copyright (c) 2020 Intel Corporation.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""EII Message Bus publisher example
"""
import time
import eii.msgbus as mb
import os
import json
from distutils.util import strtobool
import cfgmgr.config_manager as cfg
from util.util import Util
def start_publisher():
publisher = None
try:
ctx = cfg.ConfigMgr()
if ctx.get_num_publishers() == -1:
raise "No publisher instances found, exiting..."
pub_ctx = ctx.get_publisher_by_index(0)
msgbus_cfg = pub_ctx.get_msgbus_config()
print('[INFO] Initializing message bus context')
msgbus_pub = mb.MsgbusContext(msgbus_cfg)
topics = pub_ctx.get_topics()
print(f'[INFO] Initializing publisher for topic \'{topics[0]}\'')
publisher = msgbus_pub.new_publisher(topics[0])
app_cfg = ctx.get_app_config()
print(f'App Config is \'{app_cfg}\'')
print('[INFO] Running...')
while True:
blob = b'\x22' * 10
meta = {
'integer': 123,
'floating': 55.5,
'string': 'test',
'boolean': True,
'empty': None,
'obj': {'test': {'test2': 'hello'}, 'test3': 'world'},
'arr': ['test', 123]
}
publisher.publish((meta, blob,))
print(f'[INFO] Msg published by publisher : \'{meta}\'')
time.sleep(int(app_cfg["loop_interval"]))
except KeyboardInterrupt:
print('[INFO] Quitting...')
finally:
if publisher is not None:
publisher.close()
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] |
tests/test_users.py | benranderson/demo | 1 | 172274 | <reponame>benranderson/demo
import json
from tests.utils import add_user, recreate_db
class TestUsersList:
def test_add_user(self, client, test_db):
response = client.post(
"/users",
data=json.dumps({"username": "ben", "email": "<EMAIL>"}),
content_type="application/json",
)
assert response.status_code == 201
assert "<EMAIL> was added!" in response.json["message"]
assert "success" in response.json["status"]
def test_add_user_invalid_json(self, client, test_db):
resp = client.post(
"/users", data=json.dumps({}), content_type="application/json"
)
data = json.loads(resp.data.decode())
assert resp.status_code == 400
assert "Invalid payload." in data["message"]
assert "fail" in data["status"]
def test_add_user_invalid_json_keys(self, client, test_db):
resp = client.post(
"/users",
data=json.dumps({"email": "<EMAIL>"}),
content_type="application/json",
)
data = json.loads(resp.data.decode())
assert resp.status_code == 400
assert "Invalid payload." in data["message"]
assert "fail" in data["status"]
def test_add_user_duplicate_email(self, client, test_db):
client.post(
"/users",
data=json.dumps({"username": "ben", "email": "<EMAIL>"}),
content_type="application/json",
)
resp = client.post(
"/users",
data=json.dumps({"username": "ben", "email": "<EMAIL>"}),
content_type="application/json",
)
data = json.loads(resp.data.decode())
assert resp.status_code == 400
assert "Sorry. That email already exists." in data["message"]
assert "fail" in data["status"]
class TestUsers:
def test_single_user(self, client, test_db):
user = add_user("tom", "<EMAIL>")
resp = client.get(f"/users/{user.id}")
data = json.loads(resp.data.decode())
assert resp.status_code == 200
assert "tom" in data["data"]["username"]
assert "<EMAIL>" in data["data"]["email"]
assert "success" in data["status"]
def test_single_user_no_id(self, client, test_db):
resp = client.get("/users/blah")
data = json.loads(resp.data.decode())
assert resp.status_code == 404
assert "User does not exist" in data["message"]
assert "fail" in data["status"]
def test_single_user_incorrect_id(self, client, test_db):
resp = client.get("/users/999")
data = json.loads(resp.data.decode())
assert resp.status_code == 404
assert "User does not exist" in data["message"]
assert "fail" in data["status"]
def test_all_users(self, client, test_db):
recreate_db()
add_user("michael", "<EMAIL>")
add_user("fletcher", "<EMAIL>")
resp = client.get("/users")
data = json.loads(resp.data.decode())
assert resp.status_code == 200
assert len(data["data"]["users"]) == 2
assert "michael" in data["data"]["users"][0]["username"]
assert "<EMAIL>" in data["data"]["users"][0]["email"]
assert "fletcher" in data["data"]["users"][1]["username"]
assert "<EMAIL>" in data["data"]["users"][1]["email"]
assert "success" in data["status"]
def test_remove_user(self, client, test_db):
recreate_db()
user = add_user("user-to-be-removed", "<EMAIL>")
resp_one = client.get("/users")
data = json.loads(resp_one.data.decode())
assert resp_one.status_code == 200
assert len(data["data"]["users"]) == 1
resp_two = client.delete(f"/users/{user.id}")
data = json.loads(resp_two.data.decode())
assert resp_two.status_code == 200
assert "<EMAIL> was removed!" in data["message"]
assert "success" in data["status"]
resp_three = client.get("/users")
data = json.loads(resp_three.data.decode())
assert resp_three.status_code == 200
assert len(data["data"]["users"]) == 0
def test_remove_user_incorrect_id(self, client, test_db):
resp = client.delete("/users/999")
data = json.loads(resp.data.decode())
assert resp.status_code == 404
assert "User does not exist" in data["message"]
assert "fail" in data["status"]
def test_update_user(self, client, test_db):
user = add_user("user-to-be-updated", "<EMAIL>")
resp_one = client.put(
f"/users/{user.id}",
data=json.dumps({"username": "me", "email": "<EMAIL>"}),
content_type="application/json",
)
data = json.loads(resp_one.data.decode())
assert resp_one.status_code == 200
assert f"{user.id} was updated!" in data["message"]
assert "success" in data["status"]
resp_two = client.get(f"/users/{user.id}")
data = json.loads(resp_two.data.decode())
assert resp_two.status_code == 200
assert "me" in data["data"]["username"]
assert "<EMAIL>" in data["data"]["email"]
assert "success" in data["status"]
def test_update_user_invalid_json(self, client, test_db):
resp = client.put(
"/users/1", data=json.dumps({}), content_type="application/json"
)
data = json.loads(resp.data.decode())
assert resp.status_code == 400
assert "Invalid payload." in data["message"]
assert "fail" in data["status"]
def test_update_user_invalid_json_keys(self, client, test_db):
resp = client.put(
"/users/1",
data=json.dumps({"email": "<EMAIL>"}),
content_type="application/json",
)
data = json.loads(resp.data.decode())
assert resp.status_code == 400
assert "Invalid payload." in data["message"]
assert "fail" in data["status"]
def test_update_user_does_not_exist(self, client, test_db):
resp = client.put(
"/users/999",
data=json.dumps({"username": "me", "email": "<EMAIL>"}),
content_type="application/json",
)
data = json.loads(resp.data.decode())
assert resp.status_code == 404
assert "User does not exist" in data["message"]
assert "fail" in data["status"]
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setup.py | okanlv/coco-caption | 11 | 1608187 | <gh_stars>10-100
import sys
import os
import subprocess
from setuptools import setup, find_packages
import get_stanford_models
get_stanford_models.main()
packages = [p for p in find_packages() if "pycocoevalcap" in p]
setup(
name="pycocoevalcap",
packages=packages,
package_dir={"pycocoevalcap": "pycocoevalcap"},
install_requires=['pycocotools'],
version="0.0",
zip_safe=False,
include_package_data=True,
)
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] |
gans/models/gans/gan.py | tlatkowski/gans-2.0 | 78 | 176423 | from abc import ABC
from abc import abstractmethod
class GAN(ABC):
@property
@abstractmethod
def generators(self):
raise NotImplementedError
@property
@abstractmethod
def discriminators(self):
raise NotImplementedError
@abstractmethod
def predict(self, inputs):
raise NotImplementedError
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tkinter_examples/draw_chess_board.py | DazEB2/SimplePyScripts | 117 | 9520 | <filename>tkinter_examples/draw_chess_board.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = 'ipetrash'
from tkinter import *
root = Tk()
root.title('Chess board')
canvas = Canvas(root, width=700, height=700, bg='#fff')
canvas.pack()
fill = '#fff'
outline = '#000'
size = 88
for i in range(8):
for j in range(8):
x1, y1, x2, y2 = i * size, j * size, i * size + size, j * size + size
canvas.create_rectangle(x1, y1, x2, y2, fill=fill, outline=outline)
fill, outline = outline, fill
fill, outline = outline, fill
root.mainloop()
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] |
restfulpy/tests/test_orderable_mixin.py | maryayi/restfulpy | 1 | 101749 | <reponame>maryayi/restfulpy
import unittest
from sqlalchemy import Unicode
from nanohttp import settings
from restfulpy.orm import DeclarativeBase, DBSession, Field, OrderableMixin
from restfulpy.testing import WebAppTestCase
from restfulpy.tests.helpers import MockupApplication
class OrderableCheckingModel(OrderableMixin, DeclarativeBase):
__tablename__ = 'orderable_checking_model'
title = Field(Unicode(50), primary_key=True)
class OrderableCheckingModelTestCase(WebAppTestCase):
application = MockupApplication('MockupApplication', None)
__configuration__ = '''
db:
url: sqlite:// # In memory DB
echo: false
'''
@classmethod
def configure_app(cls):
cls.application.configure(force=True)
settings.merge(cls.__configuration__)
def test_orderable_mixin(self):
for i in range(3):
# noinspection PyArgumentList
instance = OrderableCheckingModel(
title='test title %s' % i,
order=i
)
DBSession.add(instance)
DBSession.commit()
instances = OrderableCheckingModel.apply_default_sort().all()
self.assertEqual(instances[0].order, 0)
self.assertEqual(instances[2].order, 2)
if __name__ == '__main__': # pragma: no cover
unittest.main()
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tfidf.py | ezhouyang/class | 1 | 86191 | <reponame>ezhouyang/class
#coding:utf-8
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.linear_model import SGDClassifier
from sklearn import cross_validation
import csv
from scipy import sparse
def read_file():
print "read_train"
f = open("train.tsv","U")
reader = csv.reader(f,delimiter='\t')
#用来存放训练集的文章
text_train = []
#用来存放训练集的标签
label = []
#用来存放测试集的文章
text_test = []
#用来存放urlid
urlid = []
extra_train = []
extra_test = []
g = lambda x : x.isalpha or x == ' '
a = 0
print "read train file begin"
for row in reader:
if a == 0:
a = a + 1
else:
#标签为最后一项
label.append(int(row[len(row)-1]))
#选择第二项作为训练文章
#text_train.append(row[2]+" "+row[0]+" "+row[3])
text_train.append(row[2]+" "+row[0])
#处理一下其他feature
#extra_train.append([float(i)/100.0 for i in row[5:len(row)-1]])
#extra_train.append([float(row[13]),float(row[6])/10.0,float(row[7])/10.0,float(row[8])/10.0,float(row[9])/10.0])
extra_train.append([float(row[7])])
f.close()
print "read test"
f = open("test.tsv","U")
reader = csv.reader(f,delimiter='\t')
a = 0
for row in reader:
if a == 0:
a = a + 1
else:
urlid.append(row[1])
#text_test.append(row[2]+" "+row[0]+" "+row[3])
text_test.append(row[2]+" "+row[0])
#extra_test.append([float(i)/100.0 for i in row[5:len(row)]])
#extra_test.append([float(row[13]),float(row[6])/10.0,float(row[7])/10.0,float(row[8])/10.0,float(row[9])/10.0])
extra_test.append([float(row[7])])
return text_train,label,text_test,urlid,extra_train,extra_test
def remain(answer):
"""
Arguments:
- `answer`:
"""
for i in range(len(answer)):
if answer[i] > 0.9725:
answer[i] = 1.0
return answer
if __name__ == "__main__":
train,label,test,urlid,extra_train1,extra_test1 = read_file()
print "train length",len(train)
print "test length",len(test)
vectorizer = TfidfVectorizer(sublinear_tf=True,min_df = 3,ngram_range=(1,2),smooth_idf=True,token_pattern=r'\w{1,}',use_idf=1,analyzer='word',strip_accents='unicode')
print "transform train to tf matrix"
print "transform test to tf matrix"
length_train = len(train)
x_all = train + test
x_all = vectorizer.fit_transform(x_all)
x = x_all[:length_train]
t = x_all[length_train:]
extra_train,extra_test = [],[]
print "读topic"
f1 = open("topic_train.txt")
for line in f1.readlines():
sp = line.split()
sp = [float(j) for j in sp]
extra_train.append(sp)
f2 = open("topic_test.txt")
for line in f2.readlines():
sp = line.split()
sp = [float(j) for j in sp]
extra_test.append(sp)
extra_train = np.array(extra_train)
extra_test = np.array(extra_test)
print "topic num",extra_train.shape
print "合并特征"
x = sparse.hstack((x,extra_train)).tocsr()
t = sparse.hstack((t,extra_test)).tocsr()
#x = sparse.hstack((x,extra_train1)).tocsr()
#t = sparse.hstack((t,extra_test1)).tocsr()
label = np.array(label)
clf = LogisticRegression(penalty='l1',C=30,tol=1e-9)
x = clf.fit_transform(x,label)
t = clf.transform(t)
print "x shape",x.shape
print "t.shape",t.shape
#clf = svm.SVC(kernel='sigmoid',degree=9,gamma=10)
#clf = svm.SVC(degree=9,gamma=0.001)
#clf = KNeighborsClassifier(n_neighbors=1)
#
#clf = SGDClassifier(loss="log",n_iter=300, penalty="l2",alpha=0.0003)
clf = LogisticRegression(penalty='l2',dual=True,fit_intercept=False,C=3.2,tol=1e-9,class_weight=None, random_state=None, intercept_scaling=1.0)
print "交叉验证"
print np.mean(cross_validation.cross_val_score(clf,x,label,cv=20,scoring='roc_auc'))
clf.fit(x,label)
#验一下自己的结果
print "训练自己",clf.score(x,label)
answer = clf.predict_proba(t)[:,1]
#answer = remain(answer)
f = open("hand_answer.csv","w")
f.write('urlid,label\n')
for i in xrange(len(test)):
f.write("%s,%s\n"%(urlid[i],answer[i]))
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tweet-fanclub/handler.py | alexellis/faas-twitter-fanclub | 22 | 124312 | import requests
import json
import base64
import redis
import os
def handle(st):
# parse Github event
req = json.loads(st)
minutes = 1
minutes_val = os.getenv("cache-minutes")
if minutes_val != None:
minutes = int(minutes_val)
loginName = req["sender"]["login"]
try:
redis_client = redis.StrictRedis("redis")
redis_key = "tweet-" + loginName
cached = redis_client.get(redis_key)
if cached != None:
print(loginName + " attempted to trigger event again before cache expired. Extending cache timeout.")
redis_client.setex(redis_key, 60 * minutes, "1")
return
redis_client.setex(redis_key, 60 * minutes, "1")
except Exception:
print("Redis may be down or errored")
# download the avatar binary using getavatar function
r = requests.post("http://gateway:8080/function/get-avatar", json=req)
res = r.json()
# Figure out the correct extension for the avatar.
ext = ".jpg"
if res["contentType"] == "image/png":
ext = ".png"
# Take the encoded image and turn into binary bytes
imageData = base64.standard_b64decode(res["content"])
put_url = "http://minio-shim:8080/put/" + loginName + ext
# Store in the fan-club photo gallery
r1 = requests.post(put_url, data= imageData)
gazer = {}
gazer["login"] = loginName
gazer["filename"] = loginName + ext
r2 = requests.post("http://gateway:8080/function/tweetstargazer", json.dumps(gazer))
club_res = {}
club_res["put_url"] = put_url
club_res["tweet_result"] = r2.text
club_res["status"] = "success"
club_res["username"] = req["sender"]["login"]
club_res["bytes"] = len(imageData)
# Useful for logging, GitHub's invoker will receive this string
print(json.dumps(club_res))
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class work/Jacob_ledbetter_102215_pygmaedemo/Jacob_ledbetter_102215_pygmaedemo.py | jll123567/freshmen-code | 0 | 173554 | #------------------------------------------
#press f5
#arows to move
#get hearts avoid green thing
#if you die try again
#good luck
#_______----------------------------
#import
import pygame
import random
import time
#init
pygame.init()
#surface size
display_width=1000
display_height=600
#color def
black= (0,0,0)
white=(255,255,255)
red=(255,0,0)
cyan=(65,255,243)
#more surface info
gameDisplay = pygame.display.set_mode((display_width,display_height))
pygame.display.set_caption('demo')
#clock
clock = pygame.time.Clock()
#the refrence imiges
faceImg = pygame.image.load('face.png')
hrtImg=pygame.image.load('hrt_sml.png')
badImg=pygame.image.load('bad.png')
#runing the elements
def bad(x,y):
gameDisplay.blit(badImg,(x,y))
def face(x,y):
gameDisplay.blit(faceImg,(x,y))
def heart(x,y):
gameDisplay.blit(hrtImg,(x,y))
#dif
def dif():
global hrt_count
if hrt_count==10:
bad(badx-100,bady+10)
def score(count):
font = pygame.font.SysFont('./fontp',32)
text= font.render(str(count),True,black)
gameDisplay.blit(text,(0,0))
#start pos
facex=(display_width*0.68)
facey=(display_height*0.4)
hrtx=(display_width*0.34)
hrty=facey
badx=random.randint(20,950)
bady=random.randint(20,550)
#hrtcounter
hrt_count=0
#the change vars
facex_change=0
facey_change=0
badx_chng=5
bady_chng=5
#crash var(old)
crashed = False
#game loop
while not crashed:
for event in pygame.event.get():
if event.type == pygame.QUIT:
crashed = True
quit()
#border fix
if facex > display_width:
facex -= 150
elif facex < 0:
facex += 150
elif facey > display_height:
facey -= 50
elif facey < 0:
facey += 50
#movement
if event.type == pygame.KEYDOWN:
if event.key==pygame.K_LEFT:
facex_change= -5
elif event.key==pygame.K_RIGHT:
facex_change=5
elif event.key==pygame.K_UP:
facey_change= -5
elif event.key==pygame.K_DOWN:
facey_change= 5
if event.type==pygame.KEYUP:
if event.key==pygame.K_RIGHT or event.key == pygame.K_LEFT:
facex_change=0
elif event.key==pygame.K_UP or event.key==pygame.K_DOWN:
facey_change=0
facex += facex_change
facey += facey_change
#dif change
dif()
#bad movement
if bady > 580:
bady_chng=bady_chng*-1
bady_chng-=1
elif bady < 20:
bady_chng=bady_chng**1
bady_chng+=1
elif badx > 980:
badx_chng=badx_chng*-1
badx_chng-=1
elif badx < 20:
badx_chng=badx_chng**1
badx_chng +=1
badx +=badx_chng
bady += bady_chng
#hrt colection
if facex >= hrtx - 50 and facex <= hrtx +50:
if facey >= hrty-50 and facey<=hrty+50:
hrt_count += 1
hrtx=random.randint(0,950)
hrty=random.randint(0,550)
#bad colision detection
if facex >= badx- 50 and facex <= badx +50:
if facey >= bady-50 and facey<=bady+50:
print('you ded')
print('score:',hrt_count)
time.sleep(2)
pygame.quit()
time.sleep(2
)
quit()
#background
gameDisplay.fill(cyan)
heart(hrtx,hrty)
face(facex,facey)
bad(badx,bady)
score(hrt_count)
#update
pygame.display.update()
clock.tick(30)
#quit
pygame.quit()
quit()
#complete!!!
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] |
matcher.py | timsliu/nimble-notifier | 0 | 125043 | <reponame>timsliu/nimble-notifier
# matcher.py
#
# This program opens the list of active users in each state and matches with
# the list of available sites
import os
import json
import util
from haversine import haversine, Unit
import copy
import defs
def users_this_tick(user_dict, tick):
'''filters a user dictionary for users that should be updated this tick
given the refresh period and random offset'''
# no tick indicates all users should be updated
if tick is None:
return list(user_dict.keys())
tick_list = []
# list of users to update this tick
for email in user_dict.keys():
user = user_dict[email]
# number of ticks between updates
update_freq_ticks = max(1, int(user["refresh_interval"]/defs.TICK_TIME))
# offset for which tick to update
rand_offset_wrapped = user["rand_offset"] % update_freq_ticks
if tick % update_freq_ticks == rand_offset_wrapped:
tick_list.append(email)
return tick_list
def match_state(user_data, loc_data, state, tick_list):
'''match the people in a state with the available locations
inputs: users - dictionary of users in a state
locs - dictionary of availble locations in a state
state - string state being matched
tick_list - list of emails to update this tick
output: list of users with nearby locations'''
match_list = []
# iterate through users, which are email addresses
for user in tick_list:
user_coor = util.zip_to_coors(user_data[user]["zip"])
matched_user = {}
matched_user["email"] = user
matched_user["avail"] = []
# for given user, search through all location looking for availability
for loc in loc_data:
distance = haversine(user_coor, loc["coordinates"], unit=Unit.MILES)
if distance < user_data[user]["search_radius"]:
matched_loc = copy.deepcopy(loc) # deep copy the location
matched_loc["distance"] = distance # set the distance
matched_user["avail"].append(matched_loc)
# availability found for this user
if len(matched_user["avail"]) > 0:
# last availability emailed to user
last_available = user_data[user]["last_avail"]
new_available = [loc["name"] for loc in matched_user["avail"]]
# only mark a match and update the lastest availability if the
# new list is not a subset of the last availability sent out
if not set(new_available).issubset(set(last_available)):
matched_user["state"] = state
matched_user["rand_offset"] = user_data[user]["rand_offset"]
# copy thread_id and msg_id from user_data to be used in
# sending the emails
if "thread_id" in user_data[user].keys():
matched_user["thread_id"] = user_data[user]["thread_id"]
matched_user["msg_id"] = user_data[user]["msg_id"]
else:
matched_user["thread_id"] = None
matched_user["msg_id"] = None
match_list.append(matched_user)
# record latest availability in the user dict saved to json
user_data[user]["last_avail"] = new_available
return match_list, user_data
def match_all(tick=None):
'''loop through all states, opening JSON and matching with the available
sites in that state
inputs: tick (optional) - integer index of the current tick; used to
determine which users to update
output: list of emails and nearby locations'''
all_matches = [] # list of all users w/ matches
user_files = os.listdir(os.path.join(os.getcwd(), "data/user"))
user_files = filter(lambda x: "_users.json" in x, user_files)
for state_user in user_files:
state = state_user[0:2] # parse the state abbreviation
# paths to the state location and state users
state_loc = os.path.join("data/location", "{}_location.json".format(state))
state_user_path = os.path.join("data/user", state_user)
# load data for users and locations in the state
with open(state_loc, "r") as f:
loc_data = json.load(f)
with open(state_user_path, "r") as f:
# open user file, find matches, and update last availability
user_dict = json.load(f)
# create list of users to update this tick
tick_list = users_this_tick(user_dict, tick)
# update list of all matches and dump the updated user data back
matches, user_dict = match_state(user_dict, loc_data, state, tick_list)
all_matches += matches
user_data = json.dumps(user_dict, indent=4)
with open(state_user_path, "w") as f:
f.write(user_data)
return all_matches
if __name__ == "__main__":
match_all()
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HADOOP/SPOILER_mapper.py | Athan-asios/HADOOP-AND-SPARK-USAGE-FOR-BIG-DATA-ANALYSIS | 0 | 149240 | #!/usr/bin/env python3
# ref: http://open.blogs.nytimes.com/2014/07/10/emr-streaming-in-go/
import sys
import json
def main():
# loop through each line of stdin
for line in sys.stdin:
try:
# parse the incoming json
j = json.loads(line.strip())
# initialize output structure
output = dict()
# grab an identifier
output = j["authors"]
if(len(output) > 1):
#print(*output, sep = " 1\n", end = " ")
for i in range(len(output)):
#separator = " {}\n".format(i)
#print(output[i] + " => " + i)
print(output[i], end = "")
print(" {}\t1".format(i+1))
else:
print(*output, end = " 1\t1")
except Exception as e:
sys.stderr.write("unable to read log: %s" % e)
continue
try:
# generate json output
output_json = json.dumps(output)
#output_json = output
# write the key and json to stdout
#print("{}\n".format(output_json))
except Exception as e:
sys.stderr.write("unable to write mapper output: %s" % e)
continue
if __name__ == "__main__":
main()
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grr/client/client_plugins.py | ethicalhackeragnidhra/Grr | 1 | 134216 | #!/usr/bin/env python
"""Centralized import point for client plugins.
This acts as a centralized point for modules that need to be loaded for
the client components so that the client_startup.Init() function will find and
register them.
This also acts as a sensible single place to add deployment specific plugin
modules that have been customized for your deployment.
"""
import sys
# pylint: disable=g-import-not-at-top,unused-import,g-bad-import-order
# Load the os specific modules.
if sys.platform == "win32":
from grr.client import windows
elif sys.platform == "darwin":
from grr.client import osx
elif "linux" in sys.platform:
from grr.client import linux
from grr.client import client_actions
from grr.client import comms
from grr.client import local
from grr.client import vfs_handlers
from grr.lib import log
# pylint: enable=g-import-not-at-top,unused-import,g-bad-import-order
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test.py | sasha42/c3lingo-stats | 2 | 35769 | #!/usr/bin/env python3
import unittest
from textwrap import dedent
from datetime import timedelta
from parse import parse_block, TranslationShift
class TestTranslationShift(unittest.TestCase):
def test_eq(self):
self.assertEqual(TranslationShift('name', 'lang'), TranslationShift('name', 'lang'))
self.assertNotEqual(TranslationShift('name', 'lang'), TranslationShift('anonther_name', 'lang'))
self.assertNotEqual(TranslationShift('name', 'lang'), TranslationShift('name', 'anonther_lang'))
class TestParseBlock(unittest.TestCase):
def test_simple(self):
result = parse_block(dedent("""
#1
[de] 11:00 +00:30, Adams
Opening Event
rufus, rixx
Fahrplan: https://fahrplan.events.ccc.de/congress/2018/Fahrplan/events/9985.html
Slides (if available): https://speakers.c3lingo.org/talks/15f4e5c5-40e1-4c73-8da0-4cc2a773ab13/
→ en: waffle, simplysaym, sirenensang
→ fr: informancer, ironic, yann0u
"""))
self.assertEqual(result.language, 'de')
self.assertEqual(result.room, 'Adams')
self.assertEqual(result.duration, timedelta(hours=0, minutes=30))
self.assertEqual(result.title, 'Opening Event')
self.assertEqual(result.speakers, ['rufus', 'rixx'])
self.assertEqual(result.fahrplan, 'https://fahrplan.events.ccc.de/congress/2018/Fahrplan/events/9985.html')
self.assertEqual(result.translation_shifts, [
TranslationShift('waffle', 'en', result),
TranslationShift('simplysaym', 'en', result),
TranslationShift('sirenensang', 'en', result),
TranslationShift('informancer', 'fr', result),
TranslationShift('ironic', 'fr', result),
TranslationShift('yann0u', 'fr', result),
])
def test_notes(self):
"""
Test that notes and parenthetical stuff inside the shift assignments is stripped out
as much as possible
"""
result = parse_block(dedent("""
#31
[de] 18:50 +01:00, Borg
"Das ist mir nicht erinnerlich." − Der NSU-Komplex heute
<NAME> (NSU-Watch)
Fahrplan: https://fahrplan.events.ccc.de/congress/2018/Fahrplan/events/9766.html
Slides (if available): https://speakers.c3lingo.org/talks/a12d17e9-3758-4fa0-b612-0c6ba22ea773/
→ en: tr1 (note), (foo) tr2
→ fr: tr3 – yay!
→ gsw: (reservation), (another one) , (never mind me)
"""))
self.assertEqual(result.translation_shifts, [
TranslationShift('tr1', 'en', result),
TranslationShift('tr2', 'en', result),
TranslationShift('tr3', 'fr', result),
])
def test_trailing_comma(self):
"""
Test that trailing commas don't cause trouble
"""
result = parse_block(dedent("""
#31
[de] 18:50 +01:00, Borg
"Das ist mir nicht erinnerlich." − Der NSU-Komplex heute
<NAME> (NSU-Watch)
Fahrplan: https://fahrplan.events.ccc.de/congress/2018/Fahrplan/events/9766.html
Slides (if available): https://speakers.c3lingo.org/talks/a12d17e9-3758-4fa0-b612-0c6ba22ea773/
→ en: tr1, tr2,
"""))
self.assertEqual(result.translation_shifts, [
TranslationShift('tr1', 'en', result),
TranslationShift('tr2', 'en', result),
])
if __name__ == '__main__':
unittest.main()
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] |
main.py | BL-Lac149597870/drugVQA | 0 | 18713 | <filename>main.py
'''
Author: QHGG
Date: 2021-02-27 13:42:43
LastEditTime: 2021-03-01 23:26:38
LastEditors: QHGG
Description:
FilePath: /drugVQA/main.py
'''
import torch
from sklearn import metrics
import warnings
warnings.filterwarnings("ignore")
torch.cuda.set_device(0)
print('cuda size == 1')
from trainAndTest import *
import time
def timeLable():
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
def main():
"""
Parsing command line parameters, reading data, fitting and scoring a SEAL-CI model.
"""
losses,accs,testResults = train(trainArgs)
with open("logs/"+ timeLable() +"losses.txt", "w") as f:
f.writelines([str(log) + '\n' for log in losses])
with open("logs/"+ timeLable() +"accs.txt", "w") as f:
f.writelines([str(log) + '\n' for log in accs])
with open("logs/"+ timeLable() +"testResults.txt", "w") as f:
f.writelines([str(log) + '\n' for log in testResults])
if __name__ == "__main__":
main()
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Chapter08/qt08_winBkground01.py | csy1993/PythonQt | 0 | 178799 | <filename>Chapter08/qt08_winBkground01.py<gh_stars>0
# -*- coding: utf-8 -*-
'''
【简介】
界面背景图片设置
'''
import sys
from PyQt5.QtWidgets import QMainWindow , QApplication
app = QApplication(sys.argv)
win = QMainWindow()
win.setWindowTitle("界面背景图片设置")
win.resize(350, 250)
win.setObjectName("MainWindow")
win.setStyleSheet("#MainWindow{border-image:url(./images/python.jpg);}")
#win.setStyleSheet("#MainWindow{background-color: yellow}")
win.show()
sys.exit(app.exec_())
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igibson/metrics/agent.py | Nick-AhSen/iGibson | 0 | 8291 | import copy
import numpy as np
import pybullet as p
from igibson.metrics.metric_base import MetricBase
class BehaviorRobotMetric(MetricBase):
def __init__(self):
self.initialized = False
self.state_cache = {}
self.next_state_cache = {}
self.agent_pos = {part: [] for part in ["left_hand", "right_hand", "body"]}
self.agent_grasping = {part: [] for part in ["left_hand", "right_hand"]}
self.agent_local_pos = {part: [] for part in ["left_hand", "right_hand"]}
self.agent_reset = {part: [] for part in ["left_hand", "right_hand", "body"]}
self.delta_agent_work = {part: [] for part in ["left_hand", "right_hand", "body"]}
self.delta_agent_distance = {part: [] for part in ["left_hand", "right_hand", "body"]}
self.delta_agent_grasp_distance = {part: [] for part in ["left_hand", "right_hand"]}
self.clip = 0.2
def step_callback(self, igbhvr_act_inst, _):
robot = igbhvr_act_inst.simulator.robots[0]
agent_work = {part: 0 for part in ["left_hand", "right_hand", "body"]}
agent_distance = {part: 0 for part in ["left_hand", "right_hand", "body"]}
for part in ["left_hand", "right_hand", "body"]:
self.next_state_cache[part] = {
"position": np.array(p.getBasePositionAndOrientation(robot.parts[part].get_body_id())[0]),
}
if not self.initialized:
self.state_cache = copy.deepcopy(self.next_state_cache)
self.initialized = True
if robot.action[19] > 0 and robot.action[27] > 0:
self.agent_reset["left_hand"].append(True)
self.agent_reset["right_hand"].append(True)
self.agent_reset["body"].append(True)
if robot.action[19] > 0:
self.agent_reset["left_hand"].append(True)
self.agent_reset["right_hand"].append(False)
self.agent_reset["body"].append(True)
elif robot.action[27] > 0:
self.agent_reset["left_hand"].append(False)
self.agent_reset["right_hand"].append(True)
self.agent_reset["body"].append(True)
else:
self.agent_reset["left_hand"].append(False)
self.agent_reset["right_hand"].append(False)
self.agent_reset["body"].append(False)
for part in self.state_cache:
delta_pos = np.linalg.norm(self.next_state_cache[part]["position"] - self.state_cache[part]["position"])
self.agent_pos[part].append(list(self.state_cache[part]["position"]))
# Exclude agent teleports
delta_pos = np.clip(delta_pos, -self.clip, self.clip)
if robot.parts[part].movement_cid is None:
force = 0
work = 0
else:
force = p.getConstraintState(robot.parts[part].movement_cid)
work = np.abs((delta_pos * np.linalg.norm(force)))
distance = np.abs(delta_pos)
if part in ["left_hand", "right_hand"]:
self.agent_local_pos[part].append(list(robot.parts[part].get_local_position_orientation()[0]))
if part in ["left_hand", "right_hand"] and (
len(p.getContactPoints(robot.parts[part].get_body_id())) > 0
or robot.parts[part].object_in_hand is not None
):
self.delta_agent_grasp_distance[part].append(distance)
self.agent_grasping[part].append(True)
elif part in ["left_hand", "right_hand"]:
self.delta_agent_grasp_distance[part].append(0)
self.agent_grasping[part].append(False)
agent_work[part] = work
agent_distance[part] = distance
self.delta_agent_work[part].append(work)
self.delta_agent_distance[part].append(distance)
self.state_cache = copy.deepcopy(self.next_state_cache)
def gather_results(self):
return {
"agent_distance": {
"timestep": self.delta_agent_distance,
},
"grasp_distance": {
"timestep": self.delta_agent_grasp_distance,
},
"work": {
"timestep": self.delta_agent_work,
},
"pos": {
"timestep": self.agent_pos,
},
"local_pos": {
"timestep": self.agent_local_pos,
},
"grasping": {
"timestep": self.agent_grasping,
},
"reset": {
"timestep": self.agent_reset,
},
}
class FetchRobotMetric(MetricBase):
def __init__(self):
self.initialized = False
self.state_cache = {}
self.next_state_cache = {}
self.agent_pos = {part: [] for part in ["gripper", "body"]}
self.agent_grasping = {part: [] for part in ["gripper"]}
self.agent_local_pos = {part: [] for part in ["gripper"]}
self.delta_agent_distance = {part: [] for part in ["gripper", "body"]}
self.delta_agent_grasp_distance = {part: [] for part in ["gripper"]}
self.clip = 0.2
def step_callback(self, igbhvr_act_inst, _):
robot = igbhvr_act_inst.simulator.robots[0]
agent_distance = {part: 0 for part in self.agent_pos}
self.next_state_cache = {
"gripper": {"position": robot.get_end_effector_position()},
"body": {"position": robot.get_position()},
}
if not self.initialized:
self.state_cache = copy.deepcopy(self.next_state_cache)
self.initialized = True
self.agent_pos["body"].append(list(self.state_cache["body"]["position"]))
delta_pos = np.linalg.norm(
np.array(self.next_state_cache["body"]["position"]) - self.state_cache["body"]["position"]
)
distance = np.abs(delta_pos)
self.delta_agent_distance["body"].append(distance)
self.agent_pos["gripper"].append(list(self.state_cache["gripper"]["position"]))
delta_pos = np.linalg.norm(
self.next_state_cache["gripper"]["position"] - self.state_cache["gripper"]["position"]
)
gripper_distance = np.abs(delta_pos)
self.delta_agent_distance["gripper"].append(gripper_distance)
self.agent_local_pos["gripper"].append(list(robot.get_relative_eef_position()))
contacts = p.getContactPoints(bodyA=robot.robot_ids[0], linkIndexA=robot.eef_link_id)
if len(contacts) > 0:
self.delta_agent_grasp_distance["gripper"].append(gripper_distance)
self.agent_grasping["gripper"].append(True)
else:
self.delta_agent_grasp_distance["gripper"].append(0)
self.agent_grasping["gripper"].append(False)
self.state_cache = copy.deepcopy(self.next_state_cache)
def gather_results(self):
return {
"agent_distance": {
"timestep": self.delta_agent_distance,
},
"grasp_distance": {
"timestep": self.delta_agent_grasp_distance,
},
"pos": {
"timestep": self.agent_pos,
},
"local_pos": {
"timestep": self.agent_local_pos,
},
"grasping": {
"timestep": self.agent_grasping,
},
}
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ProjectEuler.Problem.013.py | jihunroh/ProjectEuler-Python | 0 | 7323 | from ProjectEulerCommons.Base import *
numbers_list = """37107287533902102798797998220837590246510135740250
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Answer(
str(sum([int(line) for line in numbers_list]))[0:10]
)
"""
------------------------------------------------
ProjectEuler.Problem.013.py
The Answer is: 5537376230
Time Elasped: 0.005984783172607422sec
------------------------------------------------
"""
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test/functional/test_framework/tachacoin.py | tachacoin/tachacoin | 0 | 129607 | from .address import *
from .script import *
from .mininode import *
from .util import *
from .tachacoinconfig import *
from .blocktools import *
from .key import *
from .segwit_addr import *
import io
def make_transaction(node, vin, vout):
tx = CTransaction()
tx.vin = vin
tx.vout = vout
tx.rehash()
unsigned_raw_tx = bytes_to_hex_str(tx.serialize_without_witness())
signed_raw_tx = node.signrawtransactionwithwallet(unsigned_raw_tx)['hex']
return signed_raw_tx
def make_vin(node, value):
addr = node.getnewaddress()
txid_hex = node.sendtoaddress(addr, value/COIN)
txid = int(txid_hex, 16)
node.generate(1)
raw_tx = node.decoderawtransaction(node.gettransaction(txid_hex)['hex'])
for vout_index, txout in enumerate(raw_tx['vout']):
if txout['scriptPubKey']['addresses'] == [addr]:
break
else:
assert False
return CTxIn(COutPoint(txid, vout_index), nSequence=0)
def make_op_create_output(node, value, version, gas_limit, gas_price, data):
scriptPubKey = CScript()
scriptPubKey += version
scriptPubKey += gas_limit
scriptPubKey += gas_price
scriptPubKey += data
scriptPubKey += OP_CREATE
return CTxOut(value, scriptPubKey)
def make_op_call_output(value, version, gas_limit, gas_price, data, contract):
scriptPubKey = CScript()
scriptPubKey += version
scriptPubKey += gas_limit
scriptPubKey += gas_price
scriptPubKey += data
scriptPubKey += contract
scriptPubKey += OP_CALL
return CTxOut(value, scriptPubKey)
def convert_btc_address_to_tachacoin(addr, main=False):
version, hsh, checksum = base58_to_byte(addr, 25)
if version == 111:
return keyhash_to_p2pkh(binascii.unhexlify(hsh), main)
if version == 196:
return scripthash_to_p2sh(binascii.unhexlify(hsh), main)
assert(False)
def convert_btc_bech32_address_to_tachacoin(addr, main=False):
hdr, data = bech32_decode(addr)
return bech32_encode('qcrt', data)
def p2pkh_to_hex_hash(address):
return str(base58_to_byte(address, 25)[1])[2:-1]
def hex_hash_to_p2pkh(hex_hash):
return keyhash_to_p2pkh(hex_str_to_bytes(hex_hash))
def assert_vin(tx, expected_vin):
assert_equal(len(tx['vin']), len(expected_vin))
matches = []
for expected in expected_vin:
for i in range(len(tx['vin'])):
if i not in matches and expected[0] == tx['vin'][i]['scriptSig']['asm']:
matches.append(i)
break
assert_equal(len(matches), len(expected_vin))
def assert_vout(tx, expected_vout):
assert_equal(len(tx['vout']), len(expected_vout))
matches = []
for expected in expected_vout:
for i in range(len(tx['vout'])):
if i not in matches and expected[0] == tx['vout'][i]['value'] and expected[1] == tx['vout'][i]['scriptPubKey']['type']:
matches.append(i)
break
assert_equal(len(matches), len(expected_vout))
def rpc_sign_transaction(node, tx):
ret = node.signrawtransactionwithwallet(bytes_to_hex_str(tx.serialize()))
assert(ret['complete'])
tx_signed_raw_hex = ret['hex']
f = io.BytesIO(hex_str_to_bytes(tx_signed_raw_hex))
tx_signed = CTransaction()
tx_signed.deserialize(f)
return tx_signed
def make_vin_from_unspent(node, unspents=None, value=2000000000000, address=None):
if not unspents:
unspents = node.listunspent()
for i in range(len(unspents)):
unspent = unspents[i]
if unspent['amount'] == value/COIN and (not address or address == unspent['address']):
unspents.pop(i)
return CTxIn(COutPoint(int(unspent['txid'], 16), unspent['vout']), nSequence=0)
return None
def read_evm_array(node, address, abi, ignore_nulls=True):
arr = []
index = 0
ret = node.callcontract(address, abi + hex(index)[2:].zfill(64))
while ret['executionResult']['excepted'] == 'None':
if int(ret['executionResult']['output'], 16) != 0 or not ignore_nulls:
arr.append(ret['executionResult']['output'])
index += 1
ret = node.callcontract(address, abi + hex(index)[2:].zfill(64))
return arr
class DGPState:
def __init__(self, node, contract_address):
self.last_state_assert_block_height = 0
self.node = node
self.contract_address = contract_address
self.param_count = 0
self.gov_keys = []
self.admin_keys = []
self.params_for_block = []
self.param_address_at_indices = []
self.required_votes = [0, 0, 0]
self.current_on_vote_statuses = [
[False, False, False],
[False, False, False],
[False, False]
]
self.current_on_vote_address_proposals = [
["0", "0", "0"],
["0", "0"]
]
self.abiAddAddressProposal = "bf5f1e83" #addAddressProposal(address,uint256)
self.abiRemoveAddressProposal = "4cc0e2bc" # removeAddressProposal(address,uint256)
self.abiChangeValueProposal = "19971cbd" # changeValueProposal(uint256,uint256)
self.abiAlreadyVoted = "e9944a81" # alreadyVoted(address,address[])
self.abiGetAddressesList = "850d9758" # getAddressesList(uint256)
self.abiGetArrayNonNullLength = "9b216626" # getArrayNonNullLenght(address[])
self.abiGetCurrentOnVoteAddressProposal = "0c83ebac" # getCurrentOnVoteAddressProposal(uint256,uint256)
self.abiGetCurrentOnVoteStatus = "5f302e8b" # getCurrentOnVoteStatus(uint256,uint256)
self.abiGetCurrentOnVoteValueProposal = "4364725c" # getCurrentOnVoteValueProposal(uint256)
self.abiGetCurrentOnVoteVotes = "f9f51401" # getCurrentOnVoteVotes(uint256,uint256)
self.abiGetParamAddressAtIndex = "15341747" # getParamAddressAtIndex(uint256)
self.abiGetParamCount = "27e35746" # getParamCount()
self.abiGetParamHeightAtIndex = "8a5a9d07" # getParamHeightAtIndex(uint256)
self.abiGetParamsForBlock = "f769ac48" # getParamsForBlock(uint256)
self.abiGetRequiredVotes = "1ec28e0f" # getRequiredVotes(uint256)
self.abiIsAdminKey = "<KEY>" # isAdminKey(address)
self.abiIsGovKey = "<KEY>" # isGovKey(address)
self.abiSetInitialAdmin = "6fb81cbb" # setInitialAdmin()
self.abiTallyAdminVotes = "bec171e5" # tallyAdminVotes(address[])
self.abiTallyGovVotes = "4afb4f11" # tallyGovVotes(address[])
self.abiGovKeys = "30a79873" # govKeys(uint256)
self.abiAdminKeys = "aff125f6" # adminKeys(uint256)
def send_set_initial_admin(self, sender):
self.node.sendtoaddress(sender, 1)
self.node.sendtocontract(self.contract_address, self.abiSetInitialAdmin, 0, 2000000, TACHACOIN_MIN_GAS_PRICE_STR, sender)
def send_add_address_proposal(self, proposal_address, type1, sender):
self.node.sendtoaddress(sender, 1)
self.node.sendtocontract(self.contract_address, self.abiAddAddressProposal + proposal_address.zfill(64) + hex(type1)[2:].zfill(64), 0, 2000000, TACHACOIN_MIN_GAS_PRICE_STR, sender)
def send_remove_address_proposal(self, proposal_address, type1, sender):
self.node.sendtoaddress(sender, 1)
self.node.sendtocontract(self.contract_address, self.abiRemoveAddressProposal + proposal_address.zfill(64) + hex(type1)[2:].zfill(64), 0, 2000000, TACHACOIN_MIN_GAS_PRICE_STR, sender)
def send_change_value_proposal(self, uint_proposal, type1, sender):
self.node.sendtoaddress(sender, 1)
self.node.sendtocontract(self.contract_address, self.abiChangeValueProposal + hex(uint_proposal)[2:].zfill(64) + hex(type1)[2:].zfill(64), 0, 2000000, TACHACOIN_MIN_GAS_PRICE_STR, sender)
def assert_state(self):
# This assertion is only to catch potential errors in the test code (if we forget to add a generate after an evm call)
assert(self.last_state_assert_block_height < self.node.getblockcount())
self.last_state_assert_block_height = self.node.getblockcount()
self._assert_param_count(self.param_count)
self._assert_gov_keys_equal(self.gov_keys)
self._assert_admin_keys_equal(self.admin_keys)
for block_height, param_for_block in self.params_for_block:
self._assert_params_for_block(block_height, param_for_block)
# Make sure that there are no subsequent params for blocks
if self.params_for_block:
ret = self.node.callcontract(self.contract_address, self.abiGetParamsForBlock + hex(0x2fff)[2:].zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), int(param_for_block, 16))
else:
ret = self.node.callcontract(self.contract_address, self.abiGetParamsForBlock + hex(0x2fff)[2:].zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), 0)
for index, param_address_at_index in enumerate(self.param_address_at_indices):
self._assert_param_address_at_index(index, param_address_at_index)
# Make sure that there are no subsequent params at the next index
if self.param_address_at_indices:
ret = self.node.callcontract(self.contract_address, self.abiGetParamAddressAtIndex + hex(index+1)[2:].zfill(64))
assert(ret['executionResult']['excepted'] != 'None')
else:
ret = self.node.callcontract(self.contract_address, self.abiGetParamAddressAtIndex + hex(0x0)[2:].zfill(64))
assert(ret['executionResult']['excepted'] != 'None')
for type1, required_votes in enumerate(self.required_votes):
self._assert_required_votes(type1, required_votes)
for type1, arr1 in enumerate(self.current_on_vote_statuses):
for type2, current_on_vote_status in enumerate(arr1):
self._assert_current_on_vote_status(type1, type2, current_on_vote_status)
for type1, arr1 in enumerate(self.current_on_vote_address_proposals):
for type2, current_on_vote_address_proposal in enumerate(arr1):
self._assert_current_on_vote_address_proposal(type1, type2, current_on_vote_address_proposal)
"""
function getRequiredVotes(uint _type) constant returns (uint val){
// type 0: adminVotesForParams
// type 1: govVotesForParams
// type 2: adminVotesForManagement
if(_type>2) throw; // invalid type
if(_type==0)return activeVotesRequired.adminVotesForParams;
if(_type==1)return activeVotesRequired.govVotesForParams;
if(_type==2)return activeVotesRequired.adminVotesForManagement;
}
"""
def _assert_required_votes(self, type1, expected_required_votes):
ret = self.node.callcontract(self.contract_address, self.abiGetRequiredVotes + str(type1).zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), expected_required_votes)
"""
function getCurrentOnVoteStatus(uint _type, uint _type2) constant returns (bool val){
// type 0: addAddress
// type 1: changeValue
// type 2: removeAddress
// type2 0: adminKey
// type2 1: govKey
// type2 2: paramsAddress
if(_type>2 || _type2>2) throw; // invalid type
if(_type==0)return currentProposals.keys[_type2].onVote;
if(_type==1)return currentProposals.uints[_type2].onVote;
if(_type==2)return currentProposals.removeKeys[_type2].onVote;
}
"""
def _assert_current_on_vote_status(self, type1, type2, expected_current_on_vote_status):
ret = self.node.callcontract(self.contract_address, self.abiGetCurrentOnVoteStatus + str(type1).zfill(64) + str(type2).zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), expected_current_on_vote_status)
"""
function getCurrentOnVoteAddressProposal(uint _type, uint _type2) constant returns (address val){
// type 0: addAddress
// type 1: removeAddress
// type2 0: adminKey
// type2 1: govKey
// type2 2: paramsAddress
if(_type>1 || _type2>2) throw; // invalid type
if(_type==0)return currentProposals.keys[_type2].proposal;
if(_type==1)return currentProposals.removeKeys[_type2].proposal;
}
"""
def _assert_current_on_vote_address_proposal(self, type1, type2, expected_address):
ret = self.node.callcontract(self.contract_address, self.abiGetCurrentOnVoteAddressProposal + hex(type1)[2:].zfill(64) + hex(type2)[2:].zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), int(expected_address, 16))
"""
function getCurrentOnVoteValueProposal(uint _type) constant returns (uint val){
// type 0: adminVotesForParams
// type 1: govVotesForParams
// type 2: adminVotesForManagement
if(_type>2) throw; // invalid type
return currentProposals.uints[_type].proposal;
}
"""
def _assert_current_on_vote_value_proposal(self, type1, expected_proposal):
ret = self.node.callcontract(self.contract_address, self.abiGetCurrentOnVoteValueProposal + hex(type1)[2:].zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), expected_proposal)
"""
function getParamsForBlock(uint _reqBlockHeight) constant returns (address paramsAddress){
uint i;
for(i=paramsHistory.length-1;i>0;i--){
if(paramsHistory[i].blockHeight<=_reqBlockHeight)return paramsHistory[i].paramsAddress;
}
if(paramsHistory[0].blockHeight<=_reqBlockHeight)return paramsHistory[0].paramsAddress;
return 0;
}
"""
def _assert_params_for_block(self, required_block_height, expected_param_address):
ret = self.node.callcontract(self.contract_address, self.abiGetParamsForBlock + hex(required_block_height)[2:].zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), int(expected_param_address, 16))
"""
function getParamAddressAtIndex(uint _paramIndex) constant returns (address paramsAddress){
return paramsHistory[_paramIndex].paramsAddress;
}
"""
def _assert_param_address_at_index(self, param_index, expected_param_address):
ret = self.node.callcontract(self.contract_address, self.abiGetParamAddressAtIndex + hex(param_index)[2:].zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), int(expected_param_address, 16))
"""
function getParamHeightAtIndex(uint _paramIndex) constant returns (uint paramsHeight){
return paramsHistory[_paramIndex].blockHeight;
}
"""
def _assert_param_block_height_at_index(self, param_index, expected_block_height):
ret = self.node.callcontract(self.contract_address, self.abiGetParamHeightAtIndex + hex(param_index)[2:].zfill(64))
assert_equal(int(ret['executionResult']['output'], 16), expected_block_height)
"""
function getParamCount() constant returns (uint paramsCount){
return paramsHistory.length;
}
"""
def _assert_param_count(self, expected_param_count):
ret = self.node.callcontract(self.contract_address, self.abiGetParamCount)
assert_equal(int(ret['executionResult']['output'], 16), expected_param_count)
def _assert_gov_keys_equal(self, expected_gov_keys):
real_gov_keys = read_evm_array(self.node, self.contract_address, self.abiGovKeys)
assert_equal(len(real_gov_keys), len(expected_gov_keys))
for real, expected in zip(real_gov_keys, expected_gov_keys):
assert_equal(int(real, 16), int(expected, 16))
def _assert_admin_keys_equal(self, expected_admin_keys):
real_admin_keys = read_evm_array(self.node, self.contract_address, self.abiAdminKeys)
assert_equal(len(real_admin_keys), len(expected_admin_keys))
for real, expected in zip(real_admin_keys, expected_admin_keys):
assert_equal(int(real, 16), int(expected, 16))
def collect_prevouts(node, amount=None, address=None):
blocks = []
for block_no in range(1, node.getblockcount()+1):
blocks.append(node.getblock(node.getblockhash(block_no)))
staking_prevouts = []
for unspent in node.listunspent():
for block in blocks:
if unspent['txid'] in block['tx']:
tx_block_time = block['time']
break
else:
assert(False)
if unspent['confirmations'] > COINBASE_MATURITY and (not amount or amount == unspent['amount']) and (not address or address == unspent['address']):
staking_prevouts.append((COutPoint(int(unspent['txid'], 16), unspent['vout']), int(unspent['amount']*COIN), tx_block_time))
return staking_prevouts
def create_unsigned_pos_block(node, staking_prevouts, nTime=None):
tip = node.getblock(node.getbestblockhash())
if not nTime:
current_time = int(time.time()) + 16
nTime = current_time & 0xfffffff0
parent_block_stake_modifier = int(tip['modifier'], 16)
coinbase = create_coinbase(tip['height']+1)
coinbase.vout[0].nValue = 0
coinbase.vout[0].scriptPubKey = b""
coinbase.rehash()
block = create_block(int(tip['hash'], 16), coinbase, nTime)
block.hashStateRoot = int(tip['hashStateRoot'], 16)
block.hashUTXORoot = int(tip['hashUTXORoot'], 16)
if not block.solve_stake(parent_block_stake_modifier, staking_prevouts):
return None
txout = node.gettxout(hex(block.prevoutStake.hash)[2:].zfill(64), block.prevoutStake.n)
# input value + block reward
out_value = int((float(str(txout['value'])) + INITIAL_BLOCK_REWARD) * COIN) // 2
# create a new private key used for block signing.
block_sig_key = ECKey()
block_sig_key.set(hash256(struct.pack('<I', 0)), False)
pubkey = block_sig_key.get_pubkey().get_bytes()
scriptPubKey = CScript([pubkey, OP_CHECKSIG])
stake_tx_unsigned = CTransaction()
stake_tx_unsigned.vin.append(CTxIn(block.prevoutStake))
stake_tx_unsigned.vout.append(CTxOut())
# Split the output value into two separate txs
stake_tx_unsigned.vout.append(CTxOut(int(out_value), scriptPubKey))
stake_tx_unsigned.vout.append(CTxOut(int(out_value), scriptPubKey))
stake_tx_signed_raw_hex = node.signrawtransactionwithwallet(bytes_to_hex_str(stake_tx_unsigned.serialize()))['hex']
f = io.BytesIO(hex_str_to_bytes(stake_tx_signed_raw_hex))
stake_tx_signed = CTransaction()
stake_tx_signed.deserialize(f)
block.vtx.append(stake_tx_signed)
block.hashMerkleRoot = block.calc_merkle_root()
return (block, block_sig_key)
def create_unsigned_mpos_block(node, staking_prevouts, nTime=None, block_fees=0):
mpos_block, block_sig_key = create_unsigned_pos_block(node, staking_prevouts, nTime)
tip = node.getblock(node.getbestblockhash())
# The block reward is constant for regtest
stake_per_participant = int(INITIAL_BLOCK_REWARD*COIN+block_fees) // MPOS_PARTICIPANTS
for i in range(MPOS_PARTICIPANTS-1):
partipant_block = node.getblock(node.getblockhash(tip['height']-500-i))
participant_tx = node.decoderawtransaction(node.gettransaction(partipant_block['tx'][1])['hex'])
participant_pubkey = hex_str_to_bytes(participant_tx['vout'][1]['scriptPubKey']['asm'].split(' ')[0])
mpos_block.vtx[1].vout.append(CTxOut(stake_per_participant, CScript([OP_DUP, OP_HASH160, hash160(participant_pubkey), OP_EQUALVERIFY, OP_CHECKSIG])))
# the input value
txout = node.gettxout(hex(mpos_block.prevoutStake.hash)[2:], mpos_block.prevoutStake.n)
# Reward per output
main_staker_reward = (int(float(str(txout['value']))*COIN) + stake_per_participant)
mpos_block.vtx[1].vout[1].nValue = main_staker_reward // 2
mpos_block.vtx[1].vout[2].nValue = main_staker_reward // 2
stake_tx_signed_raw_hex = node.signrawtransactionwithwallet(bytes_to_hex_str(mpos_block.vtx[1].serialize()))['hex']
f = io.BytesIO(hex_str_to_bytes(stake_tx_signed_raw_hex))
stake_tx_signed = CTransaction()
stake_tx_signed.deserialize(f)
mpos_block.vtx[1] = stake_tx_signed
mpos_block.hashMerkleRoot = mpos_block.calc_merkle_root()
return mpos_block, block_sig_key
# Generates 4490 - blockheight PoW blocks + 510 PoS blocks,
# i.e. block height afterwards will be 5000 and we will have valid MPoS participants.
def activate_mpos(node, use_cache=True):
if not node.getblockcount():
node.setmocktime(int(time.time()) - 1000000)
node.generatetoaddress(4490-node.getblockcount(), "qSrM9K6FMhZ29Vkp8Rdk8Jp66bbfpjFETq")
staking_prevouts = collect_prevouts(node, address="qSrM9K6FMhZ29Vkp8Rdk8Jp66bbfpjFETq")
for i in range(510):
time.sleep(0.05)
nTime = (node.getblock(node.getbestblockhash())['time']+45) & 0xfffffff0
node.setmocktime(nTime)
block, block_sig_key = create_unsigned_pos_block(node, staking_prevouts, nTime=nTime)
block.sign_block(block_sig_key)
block.rehash()
block_count = node.getblockcount()
assert_equal(node.submitblock(bytes_to_hex_str(block.serialize())), None)
assert_equal(node.getblockcount(), block_count+1)
# Remove the staking prevout so we don't accidently reuse it
for j in range(len(staking_prevouts)):
prevout = staking_prevouts[j]
if prevout[0].serialize() == block.prevoutStake.serialize():
staking_prevouts.pop(j)
break
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{{cookiecutter.project_slug}}/app/api/helpers/__init__.py | khanh41/fastapi-mongodb-base-project | 3 | 120680 | <reponame>khanh41/fastapi-mongodb-base-project
import ast
import os
import zipfile
import wget
def base_file_download(path_check, url_download):
if not os.path.isfile(path_check):
wget.download(url_download, path_check)
def base_zip_download(path_check, url_download):
if not os.path.isdir(path_check):
os.makedirs(path_check)
if len(os.listdir(path_check)) <= 0:
zip_path = wget.download(url_download, path_check + ".zip")
with zipfile.ZipFile(zip_path, "r") as f:
f.extractall(os.path.dirname(zip_path))
def base_zip_folder_download(path_check, url_download):
if not os.path.isdir(path_check):
zip_path = wget.download(url_download, path_check + ".zip")
with zipfile.ZipFile(zip_path, "r") as f:
f.extractall(os.path.dirname(zip_path))
def convert_string_to_list(coordinates_text: str):
coordinates_text = coordinates_text.replace(" ", "")
try:
return ast.literal_eval(coordinates_text)
except:
raise ValueError("coordinates_text is string of list, example: \"[[1,2,3,4],[1,2,3,4]]\"")
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] |
sensor/Csv_Manip.py | utagsa2021/GolfSwingAnalysis | 0 | 114354 | import csv
import numpy as np
import pandas as pd
# Removes first few rows of data that don't have actual sensor readings
def trim_csv(in_file_name, out_file_name):
with open(in_file_name, "r") as inp, open(out_file_name, "w") as out:
writer = csv.writer(out)
for row in csv.reader(inp):
if float(row[14]) != 0:
writer.writerow(row)
# Reads csv file into numpy array
def read_csv_data(file_name):
file_1 = pd.read_csv(file_name)
return file_1.values | [
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ambari-server/src/test/python/stacks/1.3.2/MAPREDUCE/test_mapreduce_client.py | vsosrc/ambari | 0 | 102084 | <reponame>vsosrc/ambari
#!/usr/bin/env python
'''
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
'''
from mock.mock import MagicMock, call, patch
import tempfile
import tarfile
import contextlib
from stacks.utils.RMFTestCase import *
import os
origin_exists = os.path.exists
@patch.object(os.path, "exists", new=MagicMock(
side_effect=lambda *args: origin_exists(args[0])
if args[0][-2:] == "j2" else True))
class TestMapreduceClient(RMFTestCase):
def test_configure_default(self):
self.executeScript("1.3.2/services/MAPREDUCE/package/scripts/client.py",
classname = "Client",
command = "configure",
config_file="default.json"
)
self.assertResourceCalled('Directory', '/var/run/hadoop/mapred',
owner = 'mapred',
group = 'hadoop',
recursive = True,
)
self.assertResourceCalled('Directory', '/var/log/hadoop/mapred',
owner = 'mapred',
group = 'hadoop',
recursive = True,
)
self.assertResourceCalled('Directory', '/var/log/hadoop/mapred/userlogs',
mode = 01777,
recursive = True,
)
self.assertResourceCalled('Directory', '/hadoop/mapred',
owner = 'mapred',
recursive = True,
mode = 0755,
ignore_failures=True,
)
self.assertResourceCalled('Directory', '/hadoop/mapred1',
owner = 'mapred',
recursive = True,
mode = 0755,
ignore_failures=True,
)
self.assertResourceCalled('File', '/etc/hadoop/conf/mapred.exclude',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('File', '/etc/hadoop/conf/mapred.include',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('File', '/etc/hadoop/conf/taskcontroller.cfg',
content = Template('taskcontroller.cfg.j2'),
owner = 'hdfs',
)
self.assertResourceCalled('File', '/etc/hadoop/conf/mapred-queue-acls.xml',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('XmlConfig', 'mapred-site.xml',
owner = 'mapred',
group = 'hadoop',
conf_dir = '/etc/hadoop/conf',
configurations = self.getConfig()['configurations']['mapred-site'],
configuration_attributes = self.getConfig()['configuration_attributes']['mapred-site']
)
self.assertResourceCalled('File', '/etc/hadoop/conf/fair-scheduler.xml',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('File', '/etc/hadoop/conf/ssl-client.xml.example',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('File', '/etc/hadoop/conf/ssl-server.xml.example',
owner = 'mapred',
group = 'hadoop',
)
self.assertNoMoreResources()
def test_configure_secured(self):
self.executeScript("1.3.2/services/MAPREDUCE/package/scripts/client.py",
classname = "Client",
command = "configure",
config_file="secured.json"
)
self.assertResourceCalled('Directory', '/var/run/hadoop/mapred',
owner = 'mapred',
group = 'hadoop',
recursive = True,
)
self.assertResourceCalled('Directory', '/var/log/hadoop/mapred',
owner = 'mapred',
group = 'hadoop',
recursive = True,
)
self.assertResourceCalled('Directory', '/var/log/hadoop/mapred/userlogs',
mode = 01777,
recursive = True,
)
self.assertResourceCalled('Directory', '/hadoop/mapred',
owner = 'mapred',
recursive = True,
mode = 0755,
ignore_failures=True,
)
self.assertResourceCalled('File', '/etc/hadoop/conf/mapred.exclude',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('File', '/etc/hadoop/conf/mapred.include',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('File', '/usr/lib/hadoop/bin/task-controller',
owner = 'root',
group = 'hadoop',
mode = 06050,
)
self.assertResourceCalled('File', '/etc/hadoop/conf/taskcontroller.cfg',
content = Template('taskcontroller.cfg.j2'),
owner = 'root',
group = 'hadoop',
mode = 0644,
)
self.assertResourceCalled('File', '/etc/hadoop/conf/mapred-queue-acls.xml',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('XmlConfig', 'mapred-site.xml',
owner = 'mapred',
group = 'hadoop',
conf_dir = '/etc/hadoop/conf',
configurations = self.getConfig()['configurations']['mapred-site'],
configuration_attributes = self.getConfig()['configuration_attributes']['mapred-site']
)
self.assertResourceCalled('File', '/etc/hadoop/conf/fair-scheduler.xml',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('File', '/etc/hadoop/conf/ssl-client.xml.example',
owner = 'mapred',
group = 'hadoop',
)
self.assertResourceCalled('File', '/etc/hadoop/conf/ssl-server.xml.example',
owner = 'mapred',
group = 'hadoop',
)
self.assertNoMoreResources()
@patch.object(tarfile,"open", new = MagicMock())
@patch.object(tempfile,"mkdtemp", new = MagicMock(return_value='/tmp/123'))
@patch.object(contextlib,"closing", new = MagicMock())
@patch("os.path.exists", new = MagicMock(return_value=True))
def test_generate_configs_default(self):
self.executeScript("1.3.2/services/MAPREDUCE/package/scripts/client.py",
classname = "Client",
command = "generate_configs",
config_file="default.json"
)
self.assertResourceCalled('Directory', '/tmp',
recursive = True,
)
self.assertResourceCalled('XmlConfig', 'core-site.xml',
conf_dir = '/tmp/123',
configuration_attributes = self.getConfig()['configuration_attributes']['core-site'],
configurations = self.getConfig()['configurations']['core-site'],
)
self.assertResourceCalled('XmlConfig', 'mapred-site.xml',
conf_dir = '/tmp/123',
configuration_attributes = self.getConfig()['configuration_attributes']['mapred-site'],
configurations = self.getConfig()['configurations']['mapred-site'],
)
self.assertResourceCalled('File', '/tmp/123/log4j.properties',
content = InlineTemplate("log4jproperties\nline2log4jproperties\nline2\nambari.jobhistory.database=jdbc:postgresql://c6401.ambari.apache.org/ambarirca\nambari.jobhistory.driver=org.postgresql.Driver\nambari.jobhistory.user=mapred\nambari.jobhistory.password=mapred\nambari.jobhistory.logger=${hadoop.root.logger}\n\nlog4j.appender.JHA=org.apache.ambari.log4j.hadoop.mapreduce.jobhistory.JobHistoryAppender\nlog4j.appender.JHA.database=jdbc:postgresql://c6401.ambari.apache.org/ambarirca\nlog4j.appender.JHA.driver=org.postgresql.Driver\nlog4j.appender.JHA.user=mapred\nlog4j.appender.JHA.password=<PASSWORD>.org.apache.hadoop.mapred.JobHistory$JobHistoryLogger=DEBUG,JHA\nlog4j.additivity.org.apache.hadoop.mapred.JobHistory$JobHistoryLogger=true\n\n"),
)
self.assertResourceCalled('Directory', '/tmp/123',
action = ['delete'],
)
self.assertNoMoreResources()
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singleAlgoritm/ContinuousEnvMlpInput/SAC.py | NLS-SJTU/BatcRL | 3 | 102329 | import os
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class ReplayBuffer:
def __init__(self, state_dim, action_dim,max_size=1e6, device = torch.device('cpu')):
self.state_dim = state_dim
self.action_dim = action_dim
self.max_size = max_size
self.device = device
self.state_buf = torch.empty((max_size, state_dim), dtype=torch.float32, device=device)
self.other_buf = torch.empty((max_size, action_dim + 2), dtype=torch.float32, device=device)
self.index = 0
self.total_len = 0
def append(self, state, other):
#other: reward, done, action
self.index = self.index % self.max_size
self.state_buf[self.index] = torch.as_tensor(state, dtype=torch.float32, device=self.device)
self.other_buf[self.index] = torch.as_tensor(other, dtype=torch.float32, device=self.device)
self.index = self.index+1
self.total_len = min(self.max_size, max(self.index, self.total_len))
def sample_batch(self, batch_size):
self.idx = np.random.randint(0, self.total_len-1, batch_size)
state = self.state_buf[self.idx]
action = self.other_buf[self.idx, 2:].long()
reward = self.other_buf[self.idx, 0].unsqueeze(1)
mask = self.other_buf[self.idx, 1].unsqueeze(1)
next_state = self.state_buf[self.idx + 1]
return state, action, reward, mask, next_state
class ActorSAC(nn.Module):
def __init__(self, state_dim, action_dim, mid_dim=256):
super(ActorSAC, self).__init__()
self.net = nn.Sequential(
nn.Linear(state_dim, mid_dim), nn.ReLU(),
nn.Linear(mid_dim, mid_dim), nn.ReLU(),
nn.Linear(mid_dim, mid_dim), nn.ReLU(),
)
self.avg = nn.Linear(mid_dim, action_dim)
self.log_std = nn.Linear(mid_dim, action_dim)
self.log_sqrt_2pi = np.log(np.sqrt(2 * np.pi))
def forward(self, state):
x = self.net(state)
return self.avg(x).tanh()
def get_actions(self, state):
x = self.net(state)
avg = self.avg(x)
std = self.log_std(x).clamp(-20, 2).exp()
action = avg + torch.rand_like(avg) * std
return action.tanh()
def get_actions_logprob(self, state):
x =self.net(state)
avg = self.avg(x)
log_std = self.log_std(x).clamp(-20, 2)
noise = torch.rand_like(avg, requires_grad=True)
action_tanh = (avg + noise * log_std.exp()).tanh()
log_prob = log_std + self.log_sqrt_2pi + noise.pow(2).__mul__(0.5)
log_prob = log_prob + (1. - action_tanh.pow(2)).log()
return action_tanh, log_prob.sum(1,keepdim=True)
class CriticSAC(nn.Module):
def __init__(self, state_dim, action_dim, mid_dim=256):
super(CriticSAC, self).__init__()
self.net = nn.Sequential(
nn.Linear(state_dim+action_dim, mid_dim), nn.ReLU(),
nn.Linear(mid_dim, mid_dim), nn.ReLU()
)
self.q1 = nn.Linear(mid_dim, 1) # optimal Q value
self.q2 = nn.Linear(mid_dim, 1)
def forward(self, state, action):
x = torch.cat((state, action), dim=1)
x = self.net(x)
q1 = self.q1(x)
q2 = self.q2(x)
return q1, q2
class SACAgent:
def __init__(self, state_dim, action_dim):
self.learning_rate = 1e-4
self.batch_size = 128
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.alpha = 0.1
self.max_memo = int(1e6)
self.target_step = 1024
self.gamma = 0.99
self.mid_dim = 256
self.actor = ActorSAC(state_dim, action_dim, self.mid_dim).to(self.device)
self.critic = CriticSAC(state_dim, action_dim,self.mid_dim).to(self.device)
self.actor_optim = torch.optim.Adam(self.actor.parameters(), lr=self.learning_rate)
self.critic_optim = torch.optim.Adam(self.critic.parameters(), lr=self.learning_rate)
self.replay_buffer = ReplayBuffer(state_dim, action_dim, self.max_memo, self.device)
self.repect_time = 32
self.state = None
def select_action(self, states):
states = torch.as_tensor((states, ), dtype=torch.float32, device=self.device)
action = self.actor.get_actions(states)
return action.cpu().detach().numpy()[0]
def explore_env(self, env):
state = self.state if self.state is not None else env.reset()
for __ in range(self.target_step):
action = self.select_action(state)
state_, reward, done, _ = env.step(action)
other = (reward, (1 - done) * self.gamma, *action)
self.replay_buffer.append(state, other)
state = state_ if not done else env.reset()
self.state = state
return self.target_step
def update(self):
for i in range(int(self.target_step * self.repect_time / self.batch_size)):
batch_state, batch_action, batch_reward, batch_mask, batch_next_state = self.replay_buffer.sample_batch(self.batch_size)
with torch.no_grad():
next_action, next_log_prob = self.actor.get_actions_logprob(batch_next_state)
next_q = torch.min(*self.critic(batch_next_state, next_action))
q_label = batch_reward + batch_mask * (next_q + self.alpha * next_log_prob)
# critic optim
q1, q2 = self.critic(batch_state, batch_action)
critic_loss = F.mse_loss(q1, q_label) + F.mse_loss(q2, q_label)
self.optim_update(self.critic_optim, critic_loss)
# actor optim
action_pg, log_prob = self.actor.get_actions_logprob(batch_state)
actor_obj = -torch.mean(torch.min(*self.critic(batch_state, action_pg))+ self.alpha * log_prob)
self.optim_update(self.actor_optim, actor_obj)
@staticmethod
def optim_update(optimizer, objective):
optimizer.zero_grad()
objective.backward()
optimizer.step()
@torch.no_grad()
def evaluate(self, env, render=False):
epochs = 20
res = np.zeros((epochs,))
obs = env.reset()
index = 0
while index < epochs:
if render: env.render()
obs = torch.as_tensor((obs,), dtype=torch.float32, device=self.device)
action = self.actor(obs)
action = action.detach().cpu().numpy()[0]
s_, reward, done, _ = env.step(action)
res[index] += reward
if done:
index += 1
obs = env.reset()
else:
obs = s_
return res.mean(), res.std()
def load_and_save_weight(self, path, mode='load'):
actor_path = os.path.join(path, 'actor.pth')
critic_path = os.path.join(path, 'critic.pth')
if mode == 'load':
if os.path.exists(actor_path) and os.path.exists(critic_path):
self.actor.load_state_dict(torch.load(actor_path))
self.critic.load_state_dict(torch.load(critic_path))
else:
if not os.path.exists(path):
os.makedirs(path)
torch.save(self.actor.state_dict(), actor_path)
torch.save(self.critic.state_dict(), critic_path)
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ckanext/federgob/FDG/merge_metadata.py | ontoquercus/ckanext-federgob | 1 | 99660 | <reponame>ontoquercus/ckanext-federgob
#!/usr/bin/python
# -*- coding: utf-8 -*-
#Script by: <NAME>
#Date: 28-10-2014
#Script to process the user metadata about the catalog.
#Get all the fields from fields.conf
fields_conf_file = open('fields.conf','r')
fields_lines = fields_conf_file.readlines()
fields_conf_file.close()
for line in fields_lines :
if '{-URL-CATALOG-} : ' in line : url_catalog = line.replace('{-URL-CATALOG-} : ','').replace('\n','')
elif '{-URL-DATASET-} : ' in line : url_dataset = line.replace('{-URL-DATASET-} : ','').replace('\n','')
elif '{-LANGUAGE-} : ' in line : language = line.replace('{-LANGUAGE-} : ','').replace('\n','')
elif '{-TITLE-} : ' in line : title = line.replace('{-TITLE-} : ','').replace('\n','')
elif '{-DESCRIPTION-} : ' in line : description = line.replace('{-DESCRIPTION-} : ','').replace('\n','')
elif '{-ISSUED-} : ' in line : issued = line.replace('{-ISSUED-} : ','').replace('\n','')
elif '{-URL-PUBLISHER-} : ' in line : url_publisher = line.replace('{-URL-PUBLISHER-} : ','').replace('\n','')
elif '{-URL-LICENSE-} : ' in line : url_license = line.replace('{-URL-LICENSE-} : ','').replace('\n','')
#Save the metadata in the base catalog.
fIn = open('base_catalog_template.rdf','r')
filedata = fIn.read()
fIn.close()
newdata = filedata.replace('{-URL-CATALOG-}',url_catalog)
newdata = newdata.replace('{-URL-DATASET-}',url_dataset)
newdata = newdata.replace('{-LANGUAGE-}',language)
newdata = newdata.replace('{-TITLE-}',title)
newdata = newdata.replace('{-DESCRIPTION-}',description)
newdata = newdata.replace('{-ISSUED-}',issued)
newdata = newdata.replace('{-URL-PUBLISHER-}',url_publisher)
newdata = newdata.replace('{-URL-LICENSE-}',url_license)
fOut = open('base_catalog.rdf','w')
fOut.write(newdata)
fOut.close()
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2
] |
tests/test_topo.py | marrow-legacy/pulp.dependancy | 0 | 72329 | # encoding: utf-8
from unittest import TestCase
from pulp.wsgi.graph.registry import register, graph
from pulp.wsgi.graph.exception import *
from pulp.wsgi.graph.topo import resolve, leveled
@register
class A(object):
provides = ['foo']
@register
class B(object):
uses = ['foo', 'bar']
@register
class C(object):
after = '*'
@register
class D(object):
provides = ['bar']
needs = ['A']
class TestErrorConditions(TestCase):
pass
class TestResolve(TestCase):
def test_basic(self):
self.assertEquals(resolve([(1,2), (3,4), (5,6), (1,3), (1,5), (1,6), (2,5)]), [1, 2, 3, 5, 4, 6])
self.assertEquals(resolve([(1,2), (1,3), (2,4), (3,4), (5,6), (4,5)] ), [1, 2, 3, 4, 5, 6])
def test_errors(self):
self.assertRaises(CyclicDependancyError, lambda: resolve([(1,2), (2,3), (3,2)]))
class TestLeveledSort(TestCase):
def test_basic(self):
dependency_pairs = [(1,2), (3,4), (5,6), (1,3), (1,5), (1,6), (2,5)]
dependency_graph = [i for i in leveled(dependency_pairs)]
self.assertEquals(dependency_graph, [[1], [2, 3], [4, 5], [6]])
dependency_pairs = [(1,2), (1,3), (2,4), (3,4), (5,6), (4,5)]
dependency_graph = [i for i in leveled(dependency_pairs)]
self.assertEquals(dependency_graph, [[1], [2, 3], [4], [5], [6]])
def test_errors(self):
dependency_pairs = [(1,2), (2,3), (3,4), (4, 3)]
self.assertRaises(CyclicDependancyError, lambda: [i for i in leveled(dependency_pairs)])
class TestMiddlewareGraph(TestCase):
def test_classes(self):
self.assertEquals(resolve(graph()), [B, D, A, C])
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518,
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360,
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319,
29892,
315,
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2
] |
aoc-2021/day5.py | flanggut/advent-of-code | 0 | 54036 | <gh_stars>0
import aoc
import numpy as np
def drawlinetogrid(grid, begin, end):
diff = end - begin
numpoints = np.max(np.abs(diff))
if numpoints == 0:
return
diff = diff / numpoints
for i in range(0, numpoints + 1):
coord = (begin + i * diff).astype(int)
grid[coord[0], coord[1]] += 1
data = aoc.get_data(5)[0]
board = np.zeros(shape=(1000, 1000))
for line in data:
begin, end = line.split(" -> ")
begin = np.array(begin.split(",")).astype(int)
end = np.array(end.split(",")).astype(int)
drawlinetogrid(board, begin, end)
print(np.count_nonzero(board >= 2))
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2
] |
data-integration/getPANGAEA.py | enermaps/enermaps | 5 | 133336 | #!/usr/bin/env python3
"""
Pipeline for PANGAEA data, with custom NETCDF reading.
This script allows for data updates.
@author: giuseppeperonato
"""
import json
import logging
import os
import shutil
import sys
import frictionless
import numpy as np
import pandas as pd
import requests
import utilities
import xarray
from pyproj import CRS
# Constants
logging.basicConfig(level=logging.INFO)
Z = None
DT = 720
SEL = "MON"
EPSG = 4326 # source
# Settings for the query metadata
# these are the fields that are used to construct a query
QUERY_FIELDS = ["scale"] # empty list means all; None means do not use query fields.
# these are parameters that added to those automatically generated by the pipeline
QUERY_PARAMETERS = {
"temporal_granularity": "month",
"is_tiled": False,
"is_raster": True,
}
DB_URL = utilities.DB_URL
def prepareNETCDF(
df: pd.DataFrame,
crs: CRS = CRS.from_epsg(3035),
delete_orig: bool = False,
):
"""
Convert NetCDF into EnerMaps rasters (single band, GeoTiff, EPSG:3035).
Adapted to multi-dimensional NetCDF files as the ones from PANGAEA.
Parameters
----------
df : DataFrame.
Results of API extraction.
crs : pyproj.crs.CRS.
coordinate reference system.
delete_orig : bool, optional.
Set to True to delete original downloaded file (e.g. NetCDF).
Returns
-------
df : DataFrame
Results with schema for EnerMaps data table
"""
dicts = []
for i, row in df.iterrows():
filename_orig = row["value"]
logging.info(filename_orig)
xds = xarray.open_dataset(filename_orig)
if "crs" not in df.columns:
raise ValueError("Missing crs")
if "variable" not in df.columns:
raise ValueError("Missing variable")
xds.rio.write_crs(row["crs"].to_string(), inplace=True)
variable = row.variable
dims = list(xds[variable].dims)
if "lat" in dims:
dims.remove("lat")
if "lon" in dims:
dims.remove("lon")
def np_encoder(object):
"""Source: https://stackoverflow.com/a/65151218."""
if isinstance(object, np.generic):
return object.item()
def prepareFile(tmp_filename, dest_filename, my_dict, filename_orig):
"""Export raster."""
# Change day to 1st of the month, to be consistent across datasets
my_dict["start_at"] = my_dict["start_at"].replace(day=1)
if not os.path.exists(tmp_filename):
reprojected.rio.to_raster(tmp_filename)
dicts.append(my_dict)
# Compress
os.system( # nosec
"gdal_translate {filename} {dest_filename} -of GTIFF --config"
" GDAL_PAM_ENABLED NO -co COMPRESS=DEFLATE -co BIGTIFF=YES".format(
filename=tmp_filename, dest_filename=dest_filename
)
)
os.remove(tmp_filename)
return dicts
if len(dims) == 2:
for d0 in range(xds[variable][dims[0]].shape[0]):
for d1 in range(xds[variable][dims[1]].shape[0]):
my_dict = {}
dest_filename = "{}_{}_{}.tif".format(
filename_orig.split(".")[0], d0, d1
)
tmp_filename = "{}_{}_{}_tmp.tif".format(
filename_orig.split(".")[0], d0, d1
)
my_dict["fid"] = os.path.basename(dest_filename)
my_dict["variable"] = xds[variable][d0][d1].attrs["long_name"]
my_dict["unit"] = xds[variable][d0].attrs.get("units")
# Add extra fields
my_dict["fields"] = {
**xds.attrs, # at the dataset level
**xds[variable][d0][d1].attrs,
} # at the dimension level
for dim in dims:
if dim != "time": # add information about extra dimensions
my_dict["fields"][dim] = str(
xds[variable][d0][d1][dim].values
)
my_dict["fields"] = json.dumps(
my_dict["fields"], default=np_encoder
)
my_dict["israster"] = True
my_dict["start_at"] = pd.to_datetime(
xds[variable][d0][d1].time.values
)
date_future = my_dict["start_at"] + pd.DateOffset(months=1)
my_dict["dt"] = (
date_future - my_dict["start_at"]
).total_seconds() / 3600
# reproj
reprojected = xds[variable][d0][d1].rio.reproject(crs.to_string())
dicts = prepareFile(
tmp_filename, dest_filename, my_dict, filename_orig
)
elif len(dims) == 1:
for d0 in range(xds[variable][dims[0]].shape[0]):
my_dict = {}
dest_filename = "{}_{}.tif".format(filename_orig.split(".")[0], d0)
tmp_filename = "{}_{}_tmp.tif".format(filename_orig.split(".")[0], d0)
my_dict["fid"] = os.path.basename(dest_filename)
my_dict["variable"] = xds[variable][d0].attrs["long_name"]
my_dict["unit"] = xds[variable][d0].attrs.get("units")
# Add extra fields
my_dict["fields"] = {
**xds.attrs, # at the dataset level
**xds[variable][d0].attrs,
} # at the dimension level
my_dict["fields"] = json.dumps(my_dict["fields"], default=np_encoder)
my_dict["israster"] = True
my_dict["start_at"] = pd.to_datetime(xds[variable][d0].time.values)
date_future = my_dict["start_at"] + pd.DateOffset(months=1)
my_dict["dt"] = (
date_future - my_dict["start_at"]
).total_seconds() / 3600
# Reproject
if xds[variable][d0].dtype == "<m8[ns]":
# otherwise an error is thrown
reprojected = (
xds[variable][d0]
.astype(np.float32)
.rio.reproject(crs.to_string())
)
else:
reprojected = xds[variable][d0].rio.reproject(crs.to_string())
dicts = prepareFile(tmp_filename, dest_filename, my_dict, filename_orig)
else:
raise ValueError("Too many dimensions")
if delete_orig:
os.remove(filename_orig)
data = pd.DataFrame(
dicts,
columns=[
"start_at",
"fields",
"variable",
"value",
"ds_id",
"fid",
"dt",
"z",
"unit",
"israster",
],
)
return data
def prepare(dp: frictionless.package.Package):
"""
Prepare data in EnerMaps format.
Parameters
----------
dp : frictionless.package.Package
Valid datapackage
Returns
-------
DataFrame
Data in EnerMaps format.
"""
if not os.path.exists("tmp"):
os.mkdir("tmp")
for resource_idx, resource in enumerate(dp["resources"]):
file_list = resource["path"]
r = requests.get(file_list, stream=True)
lines = [line for line in r.iter_lines()]
skiprows = [ind for ind, i in enumerate(lines) if i.startswith(b"*/")][0]
files = pd.read_csv(file_list, skiprows=skiprows + 1, delimiter="\t")
files = files.loc[files["File name"].str.contains(SEL), :]
# Prepare df containing paths to rasters
rasters = []
for r, row in files.iterrows():
if not os.path.exists(os.path.join("tmp", row["File name"])):
logging.info("Downloading {}".format(row["File name"]))
utilities.download_url(
row["URL file"], os.path.join("tmp", row["File name"])
)
raster = {
"value": os.path.join("tmp", row["File name"]),
"start_at": pd.to_datetime(row["File name"].split("_")[6]),
"z": None,
"unit": None,
"dt": DT,
"crs": CRS.from_epsg(EPSG),
"variable": row["File name"].split("_")[0],
}
rasters.append(raster)
rasters = pd.DataFrame(rasters)
data_enermaps = prepareNETCDF(rasters, delete_orig=True)
return data_enermaps
def get(url: str, dp: frictionless.package.Package, force: bool = False):
"""
Retrieve data and check update.
Parameters
----------
url : str
URL to retrieve the data from.
dp : frictionless.package.Package
Datapackage against which validating the data.
force : Boolean, optional
If True, new data will be uploaded even if the same as in the db. The default is False.
Returns
-------
DataFrame
Data in EnerMaps format.
GeoDataFrame
Spatial data in EnerMaps format.
frictionless.package.Package
Pakage descring the data.
"""
ld = utilities.get_ld_json(url)
for resource in ld["distribution"]:
if resource["@type"] == "DataDownload":
if (
resource["encodingFormat"] == "CSV"
or resource["encodingFormat"] == "text/tab-separated-values"
):
file = resource["contentUrl"]
datePublished = ld["datePublished"]
# Inferring and completing metadata
logging.info("Creating datapackage for input data")
new_dp = frictionless.describe_package(
file,
stats=True,
) # Add stats
# Add date
new_dp["datePublished"] = datePublished
# Logic for update
if dp is not None: # Existing dataset
# check stats
isChangedStats = dp["resources"][0]["stats"] != new_dp["resources"][0]["stats"]
isChangedDate = dp["datePublished"] != new_dp["datePublished"]
if (
isChangedStats or isChangedDate
): # Data integration will continue, regardless of force argument
logging.info("Data has changed")
if utilities.isDFvalid(dp, new_dp):
enermaps_data = prepare(new_dp)
elif force: # Data integration will continue, even if data has not changed
logging.info("Forced update")
if utilities.isDFvalid(dp, new_dp):
enermaps_data = prepare(new_dp)
else: # Data integration will stop here, returning Nones
logging.info("Data has not changed. Use --force if you want to reupload.")
return None, None, None
else: # New dataset
dp = new_dp # this is just for the sake of the schema control
if utilities.isDFvalid(dp, new_dp):
enermaps_data = prepare(new_dp)
return enermaps_data, new_dp
if __name__ == "__main__":
datasets = pd.read_csv("datasets.csv", index_col=[0])
script_name = os.path.basename(sys.argv[0])
ds_ids, isForced = utilities.parser(script_name, datasets)
for ds_id in ds_ids:
logging.info("Retrieving Dataset {}".format(ds_id))
dp = utilities.getDataPackage(
ds_id,
DB_URL,
)
data, dp = get(datasets.loc[ds_id, "di_URL"], dp, isForced)
# Move rasters into the data directory
if not os.path.exists("data"):
os.mkdir("data")
if not os.path.exists(os.path.join("data", str(ds_id))):
os.mkdir(os.path.join("data", str(ds_id)))
for i, row in data.iterrows():
try:
shutil.move(
os.path.join("tmp", row.fid),
os.path.join("data", str(ds_id), row.fid),
)
except FileNotFoundError:
logging.error("File not found; {}".format(row.fid))
if isinstance(data, pd.DataFrame):
if utilities.datasetExists(
ds_id,
DB_URL,
):
utilities.removeDataset(ds_id, DB_URL)
logging.info("Removed existing dataset")
# Create dataset table
metadata = datasets.loc[ds_id].fillna("").to_dict()
metadata["datapackage"] = dp
# Add parameters as metadata
(
metadata["parameters"],
metadata["default_parameters"],
) = utilities.get_query_metadata(data, QUERY_FIELDS, QUERY_PARAMETERS)
metadata["datapackage"] = dp
metadata = json.dumps(metadata)
dataset = pd.DataFrame(
[
{
"ds_id": ds_id,
"metadata": metadata,
"shared_id": datasets.loc[ds_id, "shared_id"],
}
]
)
utilities.toPostgreSQL(
dataset,
DB_URL,
schema="datasets",
)
# Create data table
data["ds_id"] = ds_id
utilities.toPostgreSQL(
data,
DB_URL,
schema="data",
)
# Create empty spatial table
spatial = pd.DataFrame()
spatial[["fid", "ds_id"]] = data[["fid", "ds_id"]]
utilities.toPostgreSQL(
spatial,
DB_URL,
schema="spatial",
)
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] |
examples/assessing_frontier/zt_loader.py | OscarDeGar/py_grama | 13 | 37678 | ## Data Loader: TE-CCA zT Dataset
# <NAME> (<EMAIL>) 2021-03-12
#
from citrination_client import CitrinationClient, PifSystemReturningQuery
from citrination_client import DataQuery, DatasetQuery, Filter
from matminer.featurizers.base import MultipleFeaturizer
from matminer.featurizers import composition as cf
from pymatgen import Composition
from sl_utils import pifs2df, setResDir
import pandas as pd
import numpy as np
import os
import time
prefix = "zT"
file_responses = prefix + "_responses.csv"
file_features = prefix + "_features.csv"
## Helper functions
def get_compostion(c):
"""Attempt to parse composition, return None if failed"""
try:
return Composition(c)
except:
return None
def load_data_zT():
results_dir = setResDir()
## Metadata
keys_response = [
'Seebeck coefficient; squared',
'Electrical resistivity',
'Thermal conductivity'
]
sign = np.array([
+1, # Seebeck
-1, # Electric resistivity
-1 # Thermal conductivity
])
## Load data, if possible
# --------------------------------------------------
try:
df_X_all = pd.read_csv(results_dir + file_features)
X_all = df_X_all.drop(df_X_all.columns[0], axis = 1).values
df_Y_all = pd.read_csv(results_dir + file_responses)
Y_all = df_Y_all.drop(df_Y_all.columns[0], axis = 1).values
print("Cached data loaded.")
except FileNotFoundError:
## Data Import
# --------------------------------------------------
# Initialize client
print("Accessing data from Citrination...")
site = 'https://citrination.com' # Citrination
client = CitrinationClient(api_key=os.environ['CITRINATION_API_KEY'], site=site)
search_client = client.search
# Aluminum dataset
dataset_id = 178480 # ucsb_te_roomtemp_seebeck
system_query = PifSystemReturningQuery(
size=1000,
query=DataQuery(
dataset=DatasetQuery(id=Filter(equal=str(dataset_id)))
)
)
query_result = search_client.pif_search(system_query)
print(" Found {} PIFs in dataset {}.".format(
query_result.total_num_hits,
dataset_id
))
## Wrangle
# --------------------------------------------------
pifs = [x.system for x in query_result.hits]
# Utility function will tabularize PIFs
df_response = pifs2df(pifs)
# Down-select columns to play well with to_numeric
df_response = df_response[
['Seebeck coefficient', 'Electrical resistivity', 'Thermal conductivity']
]
df_response = df_response.apply(pd.to_numeric)
# Parse chemical compositions
formulas = [pif.chemical_formula for pif in pifs]
df_comp = pd.DataFrame(
columns = ['chemical_formula'],
data = formulas
)
# Join
df_data = pd.concat([df_comp, df_response], axis = 1)
print(" Accessed data.")
# Featurize
print("Featurizing data...")
df_data['composition'] = df_data['chemical_formula'].apply(get_compostion)
f = MultipleFeaturizer([
cf.Stoichiometry(),
cf.ElementProperty.from_preset("magpie"),
cf.ValenceOrbital(props=['avg']),
cf.IonProperty(fast=True)
])
X = np.array(f.featurize_many(df_data['composition']))
# Find valid response values
keys_original = [
'Seebeck coefficient',
'Electrical resistivity',
'Thermal conductivity'
]
index_valid_response = {
key: df_data[key].dropna().index.values for key in keys_original
}
index_valid_all = df_data[keys_original].dropna().index.values
X_all = X[index_valid_all, :]
Y_all = df_data[keys_original].iloc[index_valid_all].values
# Manipulate columns for proper objective values
Y_all[:, 0] = Y_all[:, 0] ** 2 # Squared seebeck
print(" Data prepared; {0:} valid observations.".format(X_all.shape[0]))
# Cache data
pd.DataFrame(data = X_all).to_csv(results_dir + file_features)
pd.DataFrame(
data = Y_all,
columns = keys_response
).to_csv(results_dir + file_responses)
print("Data cached in results directory.")
return X_all, Y_all, sign, keys_response, prefix
if __name__ == "__main__":
X_all, Y_all, sign, keys_response, prefix = load_data_zT()
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] |
test.py | Yanrs/PokemonGoInBrazil | 0 | 130365 | <reponame>Yanrs/PokemonGoInBrazil
import sys
import unittest
from unittest.mock import patch, Mock
import urllib.request
import urllib.error
import http.client
import main
# Pokemon Go in Brazil Test Suite
# --------------------------------
# For the HTML Parser tests, unicode strings
# are encoded to utf-8 in bytes, then passed into
# the feed methods as string decoded into the system's
# default encoding. This is done because the parser expects
# a string that is formatted to the system's default encoding
# so it can then encode that to the system's default encoding
# in bytes, then decode to utf-8.
class TestingPokemonGoMethods(unittest.TestCase):
def test_pokemonHTMLParser(self):
parser = main.PokemonHTMLParser()
myBytes = "<html><meta content=\"Pokémon GO on the App Store\"></meta></html>".encode("utf-8")
parser.feed(myBytes.decode(sys.stdout.encoding))
self.assertEqual(parser.didFindGo(), True)
def test_pokemonHTMLParser_pokemonGoFind(self):
parser = main.PokemonHTMLParser()
myBytes = "<html><meta content=\"fgfdsdfgsdfgdfsgdfPokémon GOsdfgdsfgfdgdfsgdfg\"></meta></html>".encode("utf-8")
parser.feed(myBytes.decode(sys.stdout.encoding))
self.assertEqual(parser.didFindGo(), True)
def test_pokemonHTMLParser_noContent(self):
parser = main.PokemonHTMLParser()
myBytes = "<html></html>".encode("utf-8")
parser.feed(myBytes.decode(sys.stdout.encoding))
self.assertEqual(parser.didFindGo(), False)
def test_pokemonHTMLParser_noPokemonGo(self):
parser = main.PokemonHTMLParser()
parser.feed("<html><meta content=\"Digimon NO\"></meta></html>")
self.assertEqual(parser.didFindGo(), False)
@patch("urllib.request.urlopen")
def test_checkForPokemonGo(self, urlopen_mock):
urlopen_mock.return_value = Mock(spec=http.client.HTTPResponse)
urlopen_mock.return_value.read.return_value = "<html><meta content=\"Pokémon GO on the App Store\"></meta></html>".encode("utf-8")
self.assertEqual(main.checkForPokemonGo("us"), True) # those bastards!
@patch("urllib.request.urlopen")
def test_checkForPokemonGoErrorHandling(self, urlopen_mock):
urlopen_mock.side_effect = urllib.error.URLError("Error retrieving webpage")
with self.assertRaises(main.PokemonGoError):
main.checkForPokemonGo("us")
@patch("main.checkForPokemonGo")
def test_PokemonGoBrazilCall(self, checkForPokemonGo_mock):
main.isPokemonGoInBrazil()
main.checkForPokemonGo.assert_called_once_with("br")
if __name__ == "__main__":
unittest.main()
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src/scene/common.py | Speterius/ray_tracing | 3 | 148052 | <gh_stars>1-10
from typing import Union, List, Tuple
import numpy as np
# Typing aliases:
Vector3D = Union[np.ndarray, list, Tuple[float, float, float]]
Color = Union[List[int], Tuple[int, int, int]]
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utils.py | plotly/sample_dash_livy_spark | 6 | 189429 | <filename>utils.py<gh_stars>1-10
from constants import JobStates, SparkStates
import json
import requests
def prettify_json(json_dict):
return json.dumps(json_dict, indent=4, sort_keys=True)
def parse_json(json_text):
return json.loads(json_text)
job_states = JobStates()
spark_states = SparkStates()
class LivyRequests:
livy_host = "http://localhost:8998"
data = {"kind": "pyspark", "executorMemory": "512m"}
headers = {"Content-Type": "application/json"}
def list_sessions(self):
resp = requests.get(self.livy_host + "/sessions", headers=self.headers)
payload = resp.json()
return payload["sessions"]
def kill_sessions(self):
sessions = self.list_sessions()
payloads = []
for session in sessions:
session_url = self.livy_host + "/sessions/{}".format(session["id"])
resp = requests.delete(session_url, headers=self.headers)
payload = resp.json()
payloads.append(payload)
if len(payloads) == 0:
return None
else:
return payloads
def run_session(self):
resp = requests.post(
self.livy_host + "/sessions",
data=json.dumps(self.data),
headers=self.headers,
)
payload = resp.json()
return {
"id": payload["id"],
"state": payload["state"],
"session-url": self.livy_host + resp.headers["location"],
}
def session_info(self, session_url):
resp = requests.get(session_url, headers=self.headers)
payload = resp.json()
return {
"id": payload["id"],
"state": payload["state"],
"session-url": session_url,
}
def run_job(self, session_url, job):
resp = requests.post(
session_url + "/statements", data=json.dumps(job), headers=self.headers
)
payload = resp.json()
return {
"id": payload["id"],
"state": payload["state"],
"output": payload["output"],
"statement-url": self.livy_host + resp.headers["location"],
}
def job_info(self, statement_url):
try:
resp = requests.get(statement_url, headers=self.headers)
payload = resp.json()
return {
"id": payload["id"],
"state": payload["state"],
"output": payload["output"],
"statement-url": statement_url,
}
except Exception as e:
return {"state": job_states.ERROR, "error": str(e)}
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Panalyzer/TraceParser/csv2np_buffered.py | dashazhangdake/gem5test | 0 | 47675 | import csv
import numpy as np
import time
from pathlib import Path
from Panalyzer.utils.wr_extractor import wr_extractor
from Panalyzer.TraceParser.logic_masking import *
def arm32buffered_csv2np(fcsv, buffersize, num_reg):
detailded_info = {'wr': None, 'regval': None, 'tick': None, 'masking': None, 'src1': None, 'src2': None,
'op': None}
tick_list = np.zeros([buffersize], dtype=np.int64)
wr_list = np.full([num_reg, 2, buffersize], False, dtype=bool)
reg_val_table = np.zeros([num_reg, buffersize], dtype=np.int64)
op_list = []
src1_list = []
src2_list = []
with open(fcsv, mode='r') as infocsv:
info_reader = csv.reader(infocsv)
buffer_idx = 0
chunk_counter = 0
for idx, row in enumerate(info_reader):
if idx % buffersize == 0:
buffer_idx = 0
chunk_counter = chunk_counter + 1
print(chunk_counter)
tick_list = np.zeros([buffersize], dtype=np.int64)
wr_list = np.full([num_reg, 2, buffersize], False, dtype=bool)
reg_val_table = np.zeros([num_reg, buffersize], dtype=np.int64)
op_list = []
src1_list = []
src2_list = []
else:
buffer_idx = buffer_idx + 1
tick_list[buffer_idx] = row[0] # Tick number list: an 1 x line_number np array
op_id = row[3]
op_list.append(op_id) # Opname is just a simple list of strings
# Variables required for utility.wr_extractor, feed into the function, then abstract the required
# data structure
op_dst1 = row[4]
op_dst2 = row[5]
op_src1 = row[6]
op_src2 = row[7]
src1_list.append(op_src1)
src2_list.append(op_src2)
data = row[-1]
for k in range(num_reg): # kth register
val_prev = reg_val_table[k, buffer_idx - 1]
reg_name = 'r' + str(k) # fp, lr, sp ,pc are renamed, simply
wr_list[k, 0, buffer_idx] = \
wr_extractor(reg_name, op_dst1, op_dst2, op_src1, op_src2, op_id, data, val_prev)[0]
wr_list[k, 1, buffer_idx] = \
wr_extractor(reg_name, op_dst1, op_dst2, op_src1, op_src2, op_id, data, val_prev)[1]
reg_val_table[k, buffer_idx] = \
wr_extractor(reg_name, op_dst1, op_dst2, op_src1, op_src2, op_id, data, val_prev)[2]
return tick_list
if __name__ == "__main__":
project_dir = Path(__file__).resolve().parent.parent
csv_dir = project_dir.joinpath('tempcsv')
fname = "fftbaseline.csv"
start_time = time.perf_counter() # Time counter starts
T = arm32buffered_csv2np(csv_dir / fname, 10000, 16)
elapsed_time_pandas = time.perf_counter() - start_time # Stop point of the timer
# tickexample = T['tick']
# wrexample = T['wr']
# regvalexample = T['regval']
# masking_table = T['masking']
# ops_list = T['op']
#
# # print('tick \n', tickexample, '\n wr: \n', wrexample, '\n regval:\n', regvalexample)
# print(ops_list) | [
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2
] |
nngen/operator/pad.py | hirayamy/nngen | 0 | 96145 | <reponame>hirayamy/nngen
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from nngen.operator.pool import _pool
class pad(_pool):
def __sub_str__(self):
if hasattr(self, 'pad_col_left') and self.padding == 'SAME':
padding = ("'%s'-(%d, %d, %d, %d)" %
(self.padding,
self.pad_col_left_value, self.pad_col_right_value,
self.pad_row_top_value, self.pad_row_bottom_value))
else:
padding = self.padding
par = ' par:%d' % self.par if self.par > 1 else ''
value_ram_size = (' value_ram_size:%d' % self.value_ram_size
if self.value_ram_size is not None else '')
out_ram_size = (' out_ram_size:%d' % self.out_ram_size
if self.out_ram_size is not None else '')
return (' padding:%s%s%s%s' %
(padding, par, value_ram_size, out_ram_size))
def __init__(self, value, padding,
dtype=None, name=None, par=1,
value_ram_size=None, out_ram_size=None):
ksize = (1, 1, 1, 1)
strides = (1, 1, 1, 1)
_pool.__init__(self, value, ksize, strides,
padding, dtype, name, par,
value_ram_size, out_ram_size)
def get_pad_value(self, strm):
return strm.Int(0)
def pool_op(self, strm, index, *vars):
return vars[0]
def eval(self, memo, input_dict, **kwargs):
if id(self) in memo:
return memo[id(self)]
import nngen.verify as verify
name = self.__class__.__name__
args = [arg.eval(memo, input_dict)
for arg in self.args]
value = args[0]
kwargs['padding'] = self.padding
kwargs['dtype'] = self.dtype
kwargs['name'] = self.name
kwargs['par'] = self.par
method = self.get_eval_method()
ret = method(value, **kwargs)
memo[id(self)] = ret
return ret
| [
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] |
models/eval_rels.py | IIGROUP/PUM | 12 | 176408 | import os
from dataloaders.visual_genome import VG, vg_collate
from lib.utils import define_model, load_ckpt, do_test
from config import cfg
from torch.utils.data import DataLoader
test_data = VG(cfg.test_data_name, num_val_im=5000, filter_duplicate_rels=True,
use_proposals=cfg.use_proposals, filter_non_overlap=cfg.mode == 'sgdet',
num_im=cfg.num_im)
test_loader = DataLoader(
dataset=test_data,
batch_size=cfg.num_gpus,
shuffle=False,
num_workers=cfg.num_workers,
collate_fn=lambda x: vg_collate(x, mode='rel', num_gpus=cfg.num_gpus, is_train=True if cfg.test_data_name == 'train' else False),
drop_last=True,
pin_memory=True,
)
if cfg.cache is not None and os.path.exists(cfg.cache):
# No need to load model
detector = None
else:
detector = define_model(cfg, test_data.ind_to_classes, test_data.ind_to_predicates)
load_ckpt(detector, cfg.ckpt)
detector.cuda()
do_test(detector, test_data, test_loader)
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] |
scripts/train_template.py | mibaumgartner/hackathon_health | 0 | 75684 | from dataset import CovidImageDataset
from argparse import ArgumentParser
import torch
import torch.nn as nn
from model import VGG
import numpy as np
import os
from pytorch_lightning.utilities.seed import seed_everything
import random
def seed_worker(worker_id):
'''
https://pytorch.org/docs/stable/notes/randomness.html#dataloader
to fix https://tanelp.github.io/posts/a-bug-that-plagues-thousands-of-open-source-ml-projects/
ensures different random numbers each batch with each worker every epoch while keeping reproducibility
'''
worker_seed = torch.initial_seed() % 2 ** 32
np.random.seed(worker_seed)
random.seed(worker_seed)
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
train_loss = 0
correct = 0
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = nn.CrossEntropyLoss()(output, target)
loss.backward()
train_loss += loss
_, predicted = torch.max(output.data, 1)
correct += (predicted == target).sum().item()
optimizer.step()
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def val(model, device, val_loader):
model.eval()
val_loss = 0
correct = 0
with torch.no_grad():
for data, target in val_loader:
data, target = data.to(device), target.to(device)
output = model(data)
val_loss += nn.CrossEntropyLoss()(output, target)
_, predicted = torch.max(output.data, 1)
correct += (predicted == target).sum().item()
print('\nValidation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
val_loss / len(val_loader), correct, len(val_loader.dataset),
100. * correct / len(val_loader.dataset)))
def main():
parser = ArgumentParser()
parser.add_argument("--data_dir", type=str, default='/hkfs/work/workspace/scratch/im9193-health_challenge/data')
parser.add_argument("--num_epochs", type=int, default=250)
parser.add_argument("--augment", type=str, default='resize_rotate_crop')
parser.add_argument("--seed", type=int, default=42)
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save_model', action='store_true', help='saves the trained model')
parser.add_argument('--model_name', type=str, help='model file name', default='vgg_baseline')
args = parser.parse_args()
device = torch.device("cuda")
# the following 3 lines are only needed to make the training fully reproducible, you can remove them
seed_everything(args.seed)
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':16:8'
torch.use_deterministic_algorithms(True)
data_base = args.data_dir
trainset = CovidImageDataset(
os.path.join(data_base, 'train.csv'),
os.path.join(data_base, 'imgs'),
transform=args.augment)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=128,
worker_init_fn=seed_worker)
valset = CovidImageDataset(
os.path.join(data_base, 'valid.csv'),
os.path.join(data_base, 'imgs'),
transform=None)
valloader = torch.utils.data.DataLoader(valset, batch_size=64, shuffle=False, num_workers=128,
worker_init_fn=seed_worker)
model = VGG('VGG19').cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, nesterov=True, momentum=0.9)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.num_epochs)
print('CUDA available:', torch.cuda.is_available())
for epoch in range(1, args.num_epochs + 1):
train(args, model, device, trainloader, optimizer, epoch)
val(model, device, valloader)
scheduler.step()
if args.save_model:
model_dir = '/hkfs/work/workspace/scratch/im9193-health_challenge_baseline/saved_models' # TODO adapt to your group workspace
os.makedirs(model_dir, exist_ok=True)
torch.save(model.state_dict(), os.path.join(model_dir, "{}.pt".format(args.model_name)))
if __name__ == '__main__':
main()
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avrofilter/avrofilter.py | CAIDA/hermes-avrofilter | 0 | 55368 | # Copyright 2019 The Regents of the University of California.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
#
# Original version written by <NAME> <<EMAIL>>
from swift.common.utils import get_logger, split_path, list_from_csv
from swift.common.swob import Request, Response, wsgify
from swift.common.constraints import valid_api_version
from swift.common.request_helpers import get_param
from swift.proxy.controllers.base import get_container_info, get_object_info
from swift.common.swob import wsgify
from avro_streamer.avro_streamer import GenericStrippingAvroParser
class AvroFilterMiddleware(object):
"""
Swift middleware for removing certain fields from downloaded Avro
objects, depending on a user's role.
Essentially, this allows the Avro objects to be selectively censored
for different classes of user -- for instance, there may be sensitive
data that is being collected that should only be made available to
privileged users.
See attached README.md file for instructions on how to configure this
middleware appropriately.
Stripping is only applied to objects that have a Content-Type of
'application/vnd.caida.<datatype>.avro'.
Requires: python-avro-streamer (https://github.com/CAIDA/python-avro-streamer)
"""
def __init__(self, app, conf, logger=None):
self.app = app
if logger:
self.logger = logger
else:
self.logger = get_logger(conf, log_route='avrofilter')
# Any roles specified as "nostrip_roles" will always receive the
# full uncensored Avro data
if 'nostrip_roles' in conf:
self.nostrip_roles = set([x.strip() \
for x in conf['nostrip_roles'].split(',')])
else:
self.nostrip_roles = set()
# admin should always be a nostrip role
self.nostrip_roles.add('admin')
self.defaultstrip = {}
self.dontstrip = {}
# Any field mentioned in a "retain_keys" option will be stripped
# by default, unless the user matches a role where that field is
# explicitly listed as being retained
# In other words: defaultstrip is the union of all of the fields that
# are explicitly configured as retainable. Any "public" fields should
# NOT be listed as a retained field for any role.
for k,v in conf.iteritems():
# The role that this option applies to is specified in the
# prefix of the configuration option name
# e.g. "swiftro_retain_keys" -> role = "swiftro"
if not k.endswith("_retain_keys"):
continue
role = k[:-12]
if role in self.dontstrip:
self.logger.info("Warning: role '%s' appears multiple times in AvroFilterMiddleware configuration" % (role))
# TODO only warn once per duplicate role
continue
self.dontstrip[role] = {}
for ts in list_from_csv(v):
ts = ts.strip()
if len(ts) == 0:
continue
# fields are listed using <datatype>:<fieldname> format, e.g.
# "flowtuple:netacq_country"
ts = ts.split(':')
if len(ts) != 2:
self.logger.info("Invalid 'retain_keys' parameter format, should be <data type>:<field name> (not %s)" % (ts))
continue
if ts[0] not in self.dontstrip[role]:
self.dontstrip[role][ts[0]] = set()
if ts[0] not in self.defaultstrip:
self.defaultstrip[ts[0]] = set()
self.dontstrip[role][ts[0]].add(ts[1])
self.defaultstrip[ts[0]].add(ts[1])
@wsgify
def __call__(self, req):
try:
(version, account, container, obj) = \
split_path(req.path_info, 4, 4, True)
except ValueError:
return req.get_response(self.app)
# Only worry about data fetches, not uploads.
if not valid_api_version(version) or req.method not in ('GET', 'HEAD'):
return req.get_response(self.app)
# Get all roles that apply to the user making the request
roles = set()
if (req.environ.get('HTTP_X_IDENTITY_STATUS') == 'Confirmed' or \
req.environ.get('HTTP_X_SERVICE_IDENTITY_STATUS') in \
(None, "Confirmed")):
roles = set(list_from_csv(req.environ.get('HTTP_X_ROLES', '')))
# If we have one of the "nostrip" roles, then don't do any stripping
if roles.intersection(self.nostrip_roles):
return req.get_response(self.app)
# Perform the request and grab a response object that we can work
# with
resp = req.get_response(self.app)
# Check that the requested object is actually a CAIDA avro file
conttype = resp.headers.get("Content-Type", None)
if conttype is None:
return resp
if not conttype.startswith("application/vnd.caida."):
return resp
if not conttype.endswith(".avro"):
return resp
dtype = conttype.replace("application/vnd.caida.", "", 1)[:-5]
if dtype not in self.defaultstrip:
return resp
# Start by planning to strip all fields for this datatype that have
# been explicitly appeared in the config file. Then for each role that
# the user has, remove any fields from the strip set that should be
# retained for that role.
tostrip = self.defaultstrip[dtype]
for r in roles:
if r not in self.dontstrip:
# No specified config for this role, so leave strip set as is
continue
if dtype not in self.dontstrip[r]:
continue
tostrip = tostrip - self.dontstrip[r][dtype]
# Remove the Etag because otherwise swift clients get very upset
# about the md5sum of the response body not matching the md5sum
# in the Etag header :/
if 'Etag' in resp.headers:
del(resp.headers['Etag'])
# If we are going to be stripping fields, replace our response
# iterable with one that will parse the received Avro and remove
# the desired fields. The swift proxy should handle the rest.
x = GenericStrippingAvroParser(resp.app_iter, resp.body, tostrip)
resp.app_iter = x
return resp
def filter_factory(global_conf, **local_conf):
"""Returns a WSGI filter app for use with paste.deploy."""
conf = global_conf.copy()
conf.update(local_conf)
def avro_strip(app):
return AvroFilterMiddleware(app, conf)
return avro_strip
# vim: set sw=4 tabstop=4 softtabstop=4 expandtab :
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] |
src/model/operationsbn.py | guoyongcs/HNAS | 60 | 113826 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import sys
sys.path.append("..")
from option import args
OPS = {
'none': lambda C, stride, affine: Zero(stride),
'avg_pool_3x3': lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
'max_pool_3x3': lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
'skip_connect': lambda C, stride, affine: Identity(),
'sep_conv_3x3': lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
'sep_conv_5x5': lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
'sep_conv_7x7': lambda C, stride, affine: SepConv(C, C, 7, stride, 3, affine=affine),
'dil_conv_3x3': lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),
'dil_conv_5x5': lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),
'conv_7x1_1x7': lambda C, stride, affine: nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C, C, (1, 7), stride=(1, stride), padding=(0, 3), bias=False),
nn.Conv2d(C, C, (7, 1), stride=(stride, 1), padding=(3, 0), bias=False),
nn.BatchNorm2d(C, affine=affine)
),
'conv_3x3': lambda C, stride, affine: ReLUConvBN(C, C, 3, stride, 1, affine=affine),
'conv_3x3_no_relu': lambda C, stride, affine: ConvBN(C, C, 3, stride, 1, affine=affine),
'conv_3x3_no_relu_no_bn': lambda C, stride, affine: Conv(C, C, 3, stride, 1, affine=affine),
'conv_3x3_no_bn': lambda C, stride, affine: ReLUConv(C, C, 3, stride, 1, affine=affine),
'rcab': lambda C, stride, affine: RCAB( C, 3, 3,bias=True, bn=True, act=nn.ReLU(True), res_scale=1),
#first 3 is kernel-size,the second 3 is the number of feature map
'up_and_down': lambda C, stride, affine: UPANDDOWN( C, scale_factor=args.scale[0]),
#upsampling cell:
'sub_pixel':lambda C, stride, affine: SUBPIXEL(C, scale_factor=stride),
'manual_sub_pixel':lambda C, stride, affine: SUBPIXEL(C, scale_factor=2),
'deconvolution':lambda C, stride, affine: Deconvolution(C,stride),
'bilinear':lambda C, stride, affine: Bilinear(stride),
'nearest':lambda C, stride, affine: Nearest(stride),
'linear':lambda C, stride, affine: Linear(stride),
'area':lambda C, stride, affine: Area(stride),
'upproject': lambda C, stride, affine: Upproject(C, scale_factor=2),
'downproject': lambda C, stride, affine: Downproject(C, scale_factor=2),
'upprojectnone': lambda C, stride, affine: UpprojectNone(C, scale_factor=2),
}
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias)
class ReLUConv(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(ReLUConv, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
# nn.BatchNorm2d(C_out, affine=affine)
)
def forward(self, x):
return self.op(x)
class Downproject(nn.Module):
def __init__(
self, base_filter, scale_factor):
super(Downproject, self).__init__()
if scale_factor == 2:
kernel = 6
stride = 2
padding = 2
elif scale_factor == 4:
kernel = 8
stride = 4
padding = 2
elif scale_factor == 8:
kernel = 12
stride = 8
padding = 2
# self.up1 = UpBlock(base_filter, kernel, stride, padding)
self.down1 = DownBlock(base_filter, kernel, stride, padding)
def forward(self, x):
x = self.down1(x)
return x
class UpprojectNone(nn.Module):
def __init__(
self, base_filter, scale_factor):
super(UpprojectNone, self).__init__()
if scale_factor == 2:
kernel = 6
stride = 2
padding = 2
elif scale_factor == 4:
kernel = 8
stride = 4
padding = 2
elif scale_factor == 8:
kernel = 12
stride = 8
padding = 2
self.up1 = UpBlock(base_filter, kernel, stride, padding)
# self.down1 = DownBlock(base_filter, kernel, stride, padding)
def forward(self, x):
x = self.up1(x)
return x.mul(0.)
class Upproject(nn.Module):
def __init__(
self, base_filter, scale_factor):
super(Upproject, self).__init__()
if scale_factor == 2:
kernel = 6
stride = 2
padding = 2
elif scale_factor == 4:
kernel = 8
stride = 4
padding = 2
elif scale_factor == 8:
kernel = 12
stride = 8
padding = 2
self.up1 = UpBlock(base_filter, kernel, stride, padding)
# self.down1 = DownBlock(base_filter, kernel, stride, padding)
def forward(self, x):
x = self.up1(x)
return x
class Conv(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(Conv, self).__init__()
self.op = nn.Sequential(
# nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
# nn.BatchNorm2d(C_out, affine=affine)
)
def forward(self, x):
return self.op(x)
class ConvBN(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(ConvBN, self).__init__()
self.op = nn.Sequential(
# nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
nn.BatchNorm2d(C_out, affine=affine)
)
def forward(self, x):
return self.op(x)
class Bilinear(nn.Module):
def __init__(self, stride):
super(Bilinear, self).__init__()
self.scale=stride
def forward(self, x):
return F.interpolate(x, scale_factor=self.scale, mode='bilinear')
class Linear(nn.Module):
def __init__(self, stride):
super(Linear, self).__init__()
self.scale=stride
def forward(self, x):
return F.interpolate(x, scale_factor=self.scale, mode='linear')
class Area(nn.Module):
def __init__(self, stride):
super(Area, self).__init__()
self.scale=stride
def forward(self, x):
return F.interpolate(x, scale_factor=self.scale, mode='area')
class Nearest(nn.Module):
def __init__(self, stride):
super(Nearest, self).__init__()
self.scale=stride
def forward(self, x):
return F.interpolate(x, scale_factor=self.scale, mode='nearest')
class Deconvolution(nn.Module):
def __init__(self, C, stride):
super(Deconvolution, self).__init__()
if stride==2:
kernel_size=3
output_padding=1
elif stride==4:
kernel_size=5
output_padding = 1
else:
kernel_size=3
output_padding = 0
self.deconv=nn.ConvTranspose2d(C, C,kernel_size=kernel_size,stride=stride, padding=1,output_padding=output_padding)
def forward(self, x):
return self.deconv(x)
class SUBPIXEL(nn.Module):
def __init__(self, C, scale_factor,conv=default_conv):
super(SUBPIXEL, self).__init__()
self.upsample=Upsampler(conv, scale_factor, C, act=False)
def forward(self, x):
return self.upsample(x)
class Upsampler(nn.Sequential):
def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True):
m = []
if (scale & (scale - 1)) == 0: # Is scale = 2^n?
for _ in range(int(math.log(scale, 2))):
m.append(conv(n_feats, 4 * n_feats, 3, bias))
m.append(nn.PixelShuffle(2))
if bn:
m.append(nn.BatchNorm2d(n_feats))
if act == 'relu':
m.append(nn.ReLU(True))
elif act == 'prelu':
m.append(nn.PReLU(n_feats))
elif scale == 3:
m.append(conv(n_feats, 9 * n_feats, 3, bias))
m.append(nn.PixelShuffle(3))
if bn:
m.append(nn.BatchNorm2d(n_feats))
if act == 'relu':
m.append(nn.ReLU(True))
elif act == 'prelu':
m.append(nn.PReLU(n_feats))
else:
raise NotImplementedError
super(Upsampler, self).__init__(*m)
class ReLUConvBN(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(ReLUConvBN, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
nn.BatchNorm2d(C_out, affine=affine)
)
def forward(self, x):
return self.op(x)
class DilConv(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
super(DilConv, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=C_in, bias=False),
nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
nn.BatchNorm2d(C_out, affine=affine),
)
def forward(self, x):
return self.op(x)
class SepConv(nn.Module):
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
super(SepConv, self).__init__()
self.op = nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False),
nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
nn.BatchNorm2d(C_in, affine=affine),
nn.ReLU(inplace=False),
nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, padding=padding, groups=C_in, bias=False),
nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
nn.BatchNorm2d(C_out, affine=affine),
)
def forward(self, x):
return self.op(x)
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class Zero(nn.Module):
def __init__(self, stride):
super(Zero, self).__init__()
self.stride = stride
def forward(self, x):
return x.mul(0.)
class FactorizedReduce(nn.Module):
def __init__(self, C_in, C_out, affine=True):
super(FactorizedReduce, self).__init__()
assert C_out % 2 == 0
self.relu = nn.ReLU(inplace=False)
self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
self.bn = nn.BatchNorm2d(C_out, affine=affine)
def forward(self, x):
x = self.relu(x)
if x.size(2)%2!=0:
x = F.pad(x, (1,0,1,0), "constant", 0)
out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1)
out = self.bn(out)
return out
class ThinConv2d(nn.Conv2d):
"""
custom convolutional layers for thin convolution
"""
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, groups=1, bias=True):
super(ThinConv2d, self).__init__(in_channels=in_channels, out_channels=out_channels,
kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
def _thin_weight(self, input, index=None):
n, c, h, w = input.size()
# print(index.size())
# print(index)
num_nodes = index.size(0)
index = index.view(1, num_nodes, 1, 1)
final_index = index.expand(n, num_nodes, h, w)
thin_data = torch.gather(input, 1, final_index)
return thin_data
def forward(self, input, index=None):
if index is not None:
thin_weight = self._thin_weight(self.weight, index)
else:
thin_weight = self.weight
return F.conv2d(input, thin_weight, self.bias, self.stride,
self.padding, self.dilation, self.groups)
class FinalConv(nn.Module):
def __init__(self, C_in, C_out, affine=True):
super(FinalConv, self).__init__()
assert C_out % 2 == 0
self.relu = nn.ReLU(inplace=False)
self.thin_conv = ThinConv2d(C_in, C_out, 1, stride=1, padding=0, bias=False)
self.bn = nn.BatchNorm2d(C_out, affine=affine)
def forward(self, x, index):
x = self.relu(x)
out = self.thin_conv(x, index)
out = self.bn(out)
return out
## Channel Attention (CA) Layer
class CALayer(nn.Module):
def __init__(self, channel, reduction=16):
super(CALayer, self).__init__()
# global average pooling: feature --> point
self.avg_pool = nn.AdaptiveAvgPool2d(1)
# feature channel downscale and upscale --> channel weight
self.conv_du = nn.Sequential(
nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
nn.Sigmoid()
)
def forward(self, x):
y = self.avg_pool(x)
y = self.conv_du(y)
return x * y
## Residual Channel Attention Block (RCAB)
class RCAB(nn.Module):
def __init__(
self, n_feat, kernel_size, reduction,conv=default_conv,
bias=True, bn=False, act=nn.ReLU(True), res_scale=1):
super(RCAB, self).__init__()
modules_body = []
for i in range(2):
modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
if bn: modules_body.append(nn.BatchNorm2d(n_feat))
if i == 0: modules_body.append(act)
modules_body.append(CALayer(n_feat, reduction))
self.body = nn.Sequential(*modules_body)
self.res_scale = res_scale
def forward(self, x):
res = self.body(x)
#res = self.body(x).mul(self.res_scale)
res += x
return res
class UPANDDOWN(nn.Module):
def __init__(
self, base_filter, scale_factor):
super(UPANDDOWN, self).__init__()
if scale_factor == 2:
kernel = 6
stride = 2
padding = 2
elif scale_factor == 4:
kernel = 8
stride = 4
padding = 2
elif scale_factor == 8:
kernel = 12
stride = 8
padding = 2
self.up1 = UpBlock(base_filter, kernel, stride, padding)
self.down1 = DownBlock(base_filter, kernel, stride, padding)
def forward(self, x):
x = self.up1(x)
x = self.down1(x)
return x
class UpBlock(torch.nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, bias=True, activation='prelu', norm=None):
super(UpBlock, self).__init__()
self.up_conv1 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, activation, norm=None)
self.up_conv2 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, activation, norm=None)
self.up_conv3 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, activation, norm=None)
def forward(self, x):
h0 = self.up_conv1(x)
l0 = self.up_conv2(h0)
h1 = self.up_conv3(l0 - x)
return h1 + h0
class DownBlock(torch.nn.Module):
def __init__(self, num_filter, kernel_size=8, stride=4, padding=2, bias=True, activation='prelu', norm=None):
super(DownBlock, self).__init__()
self.down_conv1 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, activation, norm=None)
self.down_conv2 = DeconvBlock(num_filter, num_filter, kernel_size, stride, padding, activation, norm=None)
self.down_conv3 = ConvBlock(num_filter, num_filter, kernel_size, stride, padding, activation, norm=None)
def forward(self, x):
l0 = self.down_conv1(x)
h0 = self.down_conv2(l0)
l1 = self.down_conv3(h0 - x)
return l1 + l0
class DeconvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=4, stride=2, padding=1, bias=True, activation='prelu', norm=None):
super(DeconvBlock, self).__init__()
self.deconv = torch.nn.ConvTranspose2d(input_size, output_size, kernel_size, stride, padding, bias=bias)
self.norm = norm
if self.norm == 'batch':
self.bn = torch.nn.BatchNorm2d(output_size)
elif self.norm == 'instance':
self.bn = torch.nn.InstanceNorm2d(output_size)
self.activation = activation
if self.activation == 'relu':
self.act = torch.nn.ReLU(True)
elif self.activation == 'prelu':
self.act = torch.nn.PReLU()
elif self.activation == 'lrelu':
self.act = torch.nn.LeakyReLU(0.2, True)
elif self.activation == 'tanh':
self.act = torch.nn.Tanh()
elif self.activation == 'sigmoid':
self.act = torch.nn.Sigmoid()
def forward(self, x):
if self.norm is not None:
out = self.bn(self.deconv(x))
else:
out = self.deconv(x)
if self.activation is not None:
return self.act(out)
else:
return out
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None):
super(ConvBlock, self).__init__()
self.conv = torch.nn.Conv2d(input_size, output_size, kernel_size, stride, padding, bias=bias)
self.norm = norm
if self.norm =='batch':
self.bn = torch.nn.BatchNorm2d(output_size)
elif self.norm == 'instance':
self.bn = torch.nn.InstanceNorm2d(output_size)
self.activation = activation
if self.activation == 'relu':
self.act = torch.nn.ReLU(True)
elif self.activation == 'prelu':
self.act = torch.nn.PReLU()
elif self.activation == 'lrelu':
self.act = torch.nn.LeakyReLU(0.2, True)
elif self.activation == 'tanh':
self.act = torch.nn.Tanh()
elif self.activation == 'sigmoid':
self.act = torch.nn.Sigmoid()
def forward(self, x):
if self.norm is not None:
out = self.bn(self.conv(x))
else:
out = self.conv(x)
if self.activation is not None:
return self.act(out)
else:
return out | [
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3188,
29918,
4572,
29892,
8466,
29892,
380,
2426,
29892,
7164,
8443,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
921,
353,
1583,
29889,
786,
29896,
29898,
29916,
8443,
13,
4706,
736,
921,
30004,
13,
30004,
13,
1990,
1281,
29894,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
315,
29918,
262,
29892,
315,
29918,
449,
29892,
8466,
29918,
2311,
29892,
380,
2426,
29892,
7164,
29892,
2756,
457,
29922,
5574,
1125,
30004,
13,
4706,
2428,
29898,
1168,
29894,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
459,
353,
302,
29876,
29889,
16941,
2556,
29898,
30004,
13,
9651,
396,
302,
29876,
29889,
1123,
29931,
29965,
29898,
262,
6689,
29922,
8824,
511,
30004,
13,
9651,
302,
29876,
29889,
1168,
29894,
29906,
29881,
29898,
29907,
29918,
262,
29892,
315,
29918,
449,
29892,
8466,
29918,
2311,
29892,
380,
2426,
29922,
303,
2426,
29892,
7164,
29922,
12791,
29892,
24003,
29922,
8824,
511,
30004,
13,
9651,
396,
302,
29876,
29889,
23145,
29940,
555,
29906,
29881,
29898,
29907,
29918,
449,
29892,
2756,
457,
29922,
3470,
457,
8443,
13,
4706,
1723,
30004,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
1583,
29889,
459,
29898,
29916,
8443,
13,
30004,
13,
1990,
1281,
29894,
29933,
29940,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
315,
29918,
262,
29892,
315,
29918,
449,
29892,
8466,
29918,
2311,
29892,
380,
2426,
29892,
7164,
29892,
2756,
457,
29922,
5574,
1125,
30004,
13,
4706,
2428,
29898,
1168,
29894,
29933,
29940,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
459,
353,
302,
29876,
29889,
16941,
2556,
29898,
30004,
13,
9651,
396,
302,
29876,
29889,
1123,
29931,
29965,
29898,
262,
6689,
29922,
8824,
511,
30004,
13,
9651,
302,
29876,
29889,
1168,
29894,
29906,
29881,
29898,
29907,
29918,
262,
29892,
315,
29918,
449,
29892,
8466,
29918,
2311,
29892,
380,
2426,
29922,
303,
2426,
29892,
7164,
29922,
12791,
29892,
24003,
29922,
8824,
511,
30004,
13,
9651,
302,
29876,
29889,
23145,
29940,
555,
29906,
29881,
29898,
29907,
29918,
449,
29892,
2756,
457,
29922,
3470,
457,
8443,
13,
4706,
1723,
30004,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
1583,
29889,
459,
29898,
29916,
8443,
13,
30004,
13,
1990,
20347,
457,
279,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
380,
2426,
1125,
30004,
13,
4706,
2428,
29898,
29933,
309,
457,
279,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
7052,
29922,
303,
2426,
30004,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
383,
29889,
1639,
3733,
403,
29898,
29916,
29892,
6287,
29918,
19790,
29922,
1311,
29889,
7052,
29892,
4464,
2433,
18152,
457,
279,
1495,
30004,
13,
30004,
13,
1990,
22985,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
380,
2426,
1125,
30004,
13,
4706,
2428,
29898,
12697,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
7052,
29922,
303,
2426,
30004,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
383,
29889,
1639,
3733,
403,
29898,
29916,
29892,
6287,
29918,
19790,
29922,
1311,
29889,
7052,
29892,
4464,
2433,
10660,
1495,
30004,
13,
30004,
13,
1990,
18320,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
380,
2426,
1125,
30004,
13,
4706,
2428,
29898,
13799,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
7052,
29922,
303,
2426,
30004,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
383,
29889,
1639,
3733,
403,
29898,
29916,
29892,
6287,
29918,
19790,
29922,
1311,
29889,
7052,
29892,
4464,
2433,
6203,
1495,
30004,
13,
30004,
13,
1990,
26206,
342,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
380,
2426,
1125,
30004,
13,
4706,
2428,
29898,
29940,
799,
342,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
7052,
29922,
303,
2426,
30004,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
383,
29889,
1639,
3733,
403,
29898,
29916,
29892,
6287,
29918,
19790,
29922,
1311,
29889,
7052,
29892,
4464,
2433,
28502,
342,
1495,
30004,
13,
30004,
13,
1990,
897,
535,
4068,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
315,
29892,
380,
2426,
1125,
30004,
13,
4706,
2428,
29898,
2772,
535,
4068,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
565,
380,
2426,
1360,
29906,
29901,
30004,
13,
9651,
8466,
29918,
2311,
29922,
29941,
30004,
13,
9651,
1962,
29918,
12791,
29922,
29896,
30004,
13,
4706,
25342,
380,
2426,
1360,
29946,
29901,
30004,
13,
9651,
8466,
29918,
2311,
29922,
29945,
30004,
13,
9651,
1962,
29918,
12791,
353,
29871,
29896,
30004,
13,
4706,
1683,
29901,
30004,
13,
9651,
8466,
29918,
2311,
29922,
29941,
30004,
13,
9651,
1962,
29918,
12791,
353,
29871,
29900,
30004,
13,
4706,
1583,
29889,
311,
20580,
29922,
15755,
29889,
1168,
29894,
4300,
4220,
29906,
29881,
29898,
29907,
29892,
315,
29892,
17460,
29918,
2311,
29922,
17460,
29918,
2311,
29892,
303,
2426,
29922,
303,
2426,
29892,
7164,
29922,
29896,
29892,
4905,
29918,
12791,
29922,
4905,
29918,
12791,
8443,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
1583,
29889,
311,
20580,
29898,
29916,
8443,
13,
30004,
13,
1990,
27092,
2227,
29990,
6670,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
315,
29892,
6287,
29918,
19790,
29892,
20580,
29922,
4381,
29918,
20580,
1125,
30004,
13,
4706,
2428,
29898,
20633,
2227,
29990,
6670,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
14340,
981,
29922,
29965,
567,
314,
20069,
29898,
20580,
29892,
6287,
29918,
19790,
29892,
315,
29892,
1044,
29922,
8824,
8443,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
1583,
29889,
14340,
981,
29898,
29916,
8443,
13,
30004,
13,
1990,
501,
567,
314,
20069,
29898,
15755,
29889,
16941,
2556,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
7602,
29892,
6287,
29892,
302,
29918,
1725,
1446,
29892,
289,
29876,
29922,
8824,
29892,
1044,
29922,
8824,
29892,
24003,
29922,
5574,
1125,
30004,
13,
30004,
13,
4706,
286,
353,
5159,
30004,
13,
4706,
565,
313,
7052,
669,
313,
7052,
448,
29871,
29896,
876,
1275,
29871,
29900,
29901,
1678,
396,
1317,
6287,
353,
29871,
29906,
29985,
29876,
29973,
30004,
13,
9651,
363,
903,
297,
3464,
29898,
524,
29898,
755,
29889,
1188,
29898,
7052,
29892,
29871,
29906,
876,
1125,
30004,
13,
18884,
286,
29889,
4397,
29898,
20580,
29898,
29876,
29918,
1725,
1446,
29892,
29871,
29946,
334,
302,
29918,
1725,
1446,
29892,
29871,
29941,
29892,
24003,
876,
30004,
13,
18884,
286,
29889,
4397,
29898,
15755,
29889,
29637,
2713,
21897,
29898,
29906,
876,
30004,
13,
18884,
565,
289,
29876,
29901,
30004,
13,
462,
1678,
286,
29889,
4397,
29898,
15755,
29889,
23145,
29940,
555,
29906,
29881,
29898,
29876,
29918,
1725,
1446,
876,
30004,
13,
18884,
565,
1044,
1275,
525,
2674,
29884,
2396,
30004,
13,
462,
1678,
286,
29889,
4397,
29898,
15755,
29889,
1123,
29931,
29965,
29898,
5574,
876,
30004,
13,
18884,
25342,
1044,
1275,
525,
1457,
6092,
2396,
30004,
13,
462,
1678,
286,
29889,
4397,
29898,
15755,
29889,
29925,
1123,
29931,
29965,
29898,
29876,
29918,
1725,
1446,
876,
30004,
13,
30004,
13,
4706,
25342,
6287,
1275,
29871,
29941,
29901,
30004,
13,
9651,
286,
29889,
4397,
29898,
20580,
29898,
29876,
29918,
1725,
1446,
29892,
29871,
29929,
334,
302,
29918,
1725,
1446,
29892,
29871,
29941,
29892,
24003,
876,
30004,
13,
9651,
286,
29889,
4397,
29898,
15755,
29889,
29637,
2713,
21897,
29898,
29941,
876,
30004,
13,
9651,
565,
289,
29876,
29901,
30004,
13,
18884,
286,
29889,
4397,
29898,
15755,
29889,
23145,
29940,
555,
29906,
29881,
29898,
29876,
29918,
1725,
1446,
876,
30004,
13,
9651,
565,
1044,
1275,
525,
2674,
29884,
2396,
30004,
13,
18884,
286,
29889,
4397,
29898,
15755,
29889,
1123,
29931,
29965,
29898,
5574,
876,
30004,
13,
9651,
25342,
1044,
1275,
525,
1457,
6092,
2396,
30004,
13,
18884,
286,
29889,
4397,
29898,
15755,
29889,
29925,
1123,
29931,
29965,
29898,
29876,
29918,
1725,
1446,
876,
30004,
13,
4706,
1683,
29901,
30004,
13,
9651,
12020,
2216,
1888,
2037,
287,
2392,
30004,
13,
30004,
13,
4706,
2428,
29898,
29965,
567,
314,
20069,
29892,
1583,
467,
1649,
2344,
1649,
10456,
29885,
8443,
13,
30004,
13,
30004,
13,
30004,
13,
1990,
830,
29931,
29965,
1168,
29894,
29933,
29940,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
315,
29918,
262,
29892,
315,
29918,
449,
29892,
8466,
29918,
2311,
29892,
380,
2426,
29892,
7164,
29892,
2756,
457,
29922,
5574,
1125,
30004,
13,
4706,
2428,
29898,
1123,
29931,
29965,
1168,
29894,
29933,
29940,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
459,
353,
302,
29876,
29889,
16941,
2556,
29898,
30004,
13,
9651,
302,
29876,
29889,
1123,
29931,
29965,
29898,
262,
6689,
29922,
8824,
511,
30004,
13,
9651,
302,
29876,
29889,
1168,
29894,
29906,
29881,
29898,
29907,
29918,
262,
29892,
315,
29918,
449,
29892,
8466,
29918,
2311,
29892,
380,
2426,
29922,
303,
2426,
29892,
7164,
29922,
12791,
29892,
24003,
29922,
8824,
511,
30004,
13,
9651,
302,
29876,
29889,
23145,
29940,
555,
29906,
29881,
29898,
29907,
29918,
449,
29892,
2756,
457,
29922,
3470,
457,
8443,
13,
4706,
1723,
30004,
13,
30004,
13,
1678,
822,
6375,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
736,
1583,
29889,
459,
29898,
29916,
8443,
13,
30004,
13,
30004,
13,
1990,
360,
309,
1168,
29894,
29898,
15755,
29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
315,
29918,
262,
29892,
315,
29918,
449,
29892,
8466,
29918,
2311,
29892,
380,
2426,
29892,
7164,
29892,
270,
8634,
29892,
2756,
457,
29922,
5574,
1125,
30004,
13,
4706,
2428,
29898,
29928,
309,
1168,
29894,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
459,
353,
302,
29876,
29889,
16941,
2556,
29898,
30004,
13,
9651,
302,
29876,
29889,
1123,
29931,
29965,
29898,
262,
6689,
29922,
8824,
511,
30004,
13,
9651,
302,
29876,
29889,
1168,
29894,
29906,
29881,
29898,
29907,
29918,
262,
29892,
315,
29918,
262,
29892,
8466,
29918,
2311,
29922,
17460,
29918,
2311,
29892,
380,
2426,
29922,
303,
2426,
29892,
7164,
29922,
12791,
29892,
270,
8634,
29922,
29881,
8634,
11167,
13,
462,
418,
6471,
29922,
29907,
29918,
262,
29892,
24003,
29922,
8824,
511,
30004,
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16109,
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29900,
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13,
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13,
1990,
383,
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1891,
29934,
6085,
346,
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8634,
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6689,
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8824,
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2277,
17368,
6212,
2509,
313,
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365,
2747,
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315,
1964,
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334,
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13,
2277,
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333,
950,
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6212,
2509,
15658,
313,
10363,
2882,
8443,
13,
1990,
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2882,
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29889,
7355,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
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1583,
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302,
29918,
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8466,
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20376,
29892,
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29922,
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20580,
11167,
13,
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24003,
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5574,
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289,
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29922,
8824,
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1044,
29922,
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1123,
29931,
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5574,
511,
620,
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7052,
29922,
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1125,
30004,
13,
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2428,
29898,
10363,
2882,
29892,
1583,
467,
1649,
2344,
1649,
26471,
13,
4706,
10585,
29918,
2587,
353,
5159,
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13,
4706,
363,
474,
297,
3464,
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16941,
2556,
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7576,
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29918,
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4619,
921,
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13,
4706,
736,
620,
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13,
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13,
30004,
13,
1990,
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9468,
3970,
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1678,
822,
4770,
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29916,
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13,
4706,
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13,
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5020,
7445,
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305,
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1678,
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5574,
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2433,
1457,
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2
] |
config.py | StuartSul/SampyoNet | 0 | 32410 | ## Built-in packages
import getopt
import json
import os
import sys
## Third-party packages
from PIL import Image
import joblib
import numpy as np
import tqdm
## Tensorflow
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import GlobalMaxPool2D
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import MaxPool2D
from tensorflow.keras.models import Model
from tensorflow.keras.layers import SeparableConv2D
import tensorflow_addons as tfa
## Global variable declarations
global INPUT_WIDTH
global INPUT_HEIGHT
global FILTER_SIZE
global DENSE_UNITS
global DROPOUT
global OUTPUT_CLASS
## Global model parameters (DO NOT CHANGE)
INPUT_WIDTH = 1500
INPUT_HEIGHT = 850
FILTER_SIZE = 32
DENSE_UNITS = 1024
DROPOUT = 0.3
OUTPUT_CLASS = 3
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src/isa.py | plafl/helisim | 6 | 1610075 | # Atmosfera ISA
# Condiciones a nivel del mar
P0 = 101.3e3 # Pa
T0 = 288 # K
# Gravedad
g = 9.81 # m/s^2
# Ley de temperatura en la troposfera: T = T0 - L*h
L = 0.0065 # K/m
# Constante del gas
Ra = 287 # J/(Kg*K)
# h en metros, T en kelvins
def T(h):
return T0 - L*h
# h en metros, P en pascales
def P(h):
return P0*(T(h)/T0)**(g/L/Ra)
# Conversion
def inHg_to_Pa(x):
return 3.386389e3*x
def Pa_to_inHg(x):
return x/3.386389e3
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python/AlgoritmosPython/raizAlgarismos.py | jonfisik/Projects | 2 | 98158 | __author__ = 'JPaschoal'
__version__ = '1.0.1'
__email__ = '<EMAIL>'
__date__ = '08/05/2021'
'''
Qualquer número natural de quatro algarismos pode ser dividido
em duas dezenas formadas pelos seus dois primeiros e dois
últimos dígitos.
Exemplos:
1297: 12 e 97
5314: 53 e 14
Escreva um programa que imprime todos os milhares (4 algarismos), 1000 <= n < 10000, cuja raiz quadrada seja a soma das dezenas formadas pela divisão acima.
Exemplo: raiz de 9801 = 99 = 98 + 01
Portanto 9801 é um dos números a ser impresso.
'''
# 1000 --> 10 00 --> 10 + 00 = 10 --> 10**2 = 100 == 1000 [V/F] imprimir 1000
# 1001 --> 10 01 --> 10 + 01 = 11 --> 11**2 = 121 == 1001 [V/F] imprimir 1001
# .
# .
# .
# 9801 --> 98 01 --> 98 + 01 = 99 --> 99**2 = 9801 == 9801 [V/F] imprimir 9801
#--------------------------------------------
def traco():
return print('-----'*10)
print('')
print("TESTE MILHARES 1000 - 10 000")
traco()
# váriaveis
num = 1000
while num < 10000:
aux = num
dois_ultm = aux%100
aux //= 100
dois_prim = aux%100
if (dois_ultm + dois_prim)**2 == num:
print(f'>>> {num}')
num += 1
traco()
print('')
# END | [
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lesson 16/question 2.py | Kev-in123/ICS2O7 | 2 | 1609911 | <filename>lesson 16/question 2.py
import random
rolls = [random.randint(1, 6) + random.randint(1,6) for i in range(50)]
try:
interested_roll = int(input("What number are you interested in: "))
except ValueError:
print("Invalid value try again")
interested_roll = int(input("What number are you interested in: "))
while 2<interested_roll>12:
print("Invalid number try again")
try:
interested_roll = int(input("What number are you interested in: "))
except ValueError:
print("Invalid value try again")
interested_roll = int(input("What number are you interested in: "))
count_interested = rolls.count(interested_roll)
print(f"The number {interested_roll} appeared {count_interested} time(s)") | [
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] |
python/spinn/models/fat_classifier.py | stanfordnlp/spinn | 200 | 57337 | <reponame>stanfordnlp/spinn<gh_stars>100-1000
"""From the project root directory (containing data files), this can be run with:
Boolean logic evaluation:
python -m spinn.models.fat_classifier --training_data_path ../bl-data/pbl_train.tsv \
--eval_data_path ../bl-data/pbl_dev.tsv
SST sentiment (Demo only, model needs a full GloVe embeddings file to do well):
python -m spinn.models.fat_classifier --data_type sst --training_data_path sst-data/train.txt \
--eval_data_path sst-data/dev.txt --embedding_data_path spinn/tests/test_embedding_matrix.5d.txt \
--model_dim 10 --word_embedding_dim 5
SNLI entailment (Demo only, model needs a full GloVe embeddings file to do well):
python -m spinn.models.fat_classifier --data_type snli --training_data_path snli_1.0/snli_1.0_dev.jsonl \
--eval_data_path snli_1.0/snli_1.0_dev.jsonl --embedding_data_path spinn/tests/test_embedding_matrix.5d.txt \
--model_dim 10 --word_embedding_dim 5
Note: If you get an error starting with "TypeError: ('Wrong number of dimensions..." during development,
there may already be a saved checkpoint in ckpt_path that matches the name of the model you're developing.
Move or delete it as appropriate.
"""
from functools import partial
import os
import pprint
import sys
import gflags
from theano import tensor as T
import theano
import numpy as np
from spinn import afs_safe_logger
from spinn import util
from spinn.data.boolean import load_boolean_data
from spinn.data.sst import load_sst_data
from spinn.data.snli import load_snli_data
import spinn.fat_stack
import spinn.plain_rnn
import spinn.cbow
FLAGS = gflags.FLAGS
def build_sentence_model(cls, vocab_size, seq_length, tokens, transitions,
num_classes, training_mode, ground_truth_transitions_visible, vs,
initial_embeddings=None, project_embeddings=False, ss_mask_gen=None, ss_prob=0.0):
"""
Construct a classifier which makes use of some hard-stack model.
Args:
cls: Hard stack class to use (from e.g. `spinn.fat_stack`)
vocab_size:
seq_length: Length of each sequence provided to the stack model
tokens: Theano batch (integer matrix), `batch_size * seq_length`
transitions: Theano batch (integer matrix), `batch_size * seq_length`
num_classes: Number of output classes
training_mode: A Theano scalar indicating whether to act as a training model
with dropout (1.0) or to act as an eval model with rescaling (0.0).
ground_truth_transitions_visible: A Theano scalar. If set (1.0), allow the model access
to ground truth transitions. This can be disabled at evaluation time to force Model 1
(or 2S) to evaluate in the Model 2 style with predicted transitions. Has no effect on Model 0.
vs: Variable store.
"""
# Prepare layer which performs stack element composition.
if cls is spinn.plain_rnn.RNN:
if FLAGS.use_gru:
compose_network = partial(util.GRULayer,
initializer=util.HeKaimingInitializer())
else:
compose_network = partial(util.LSTMLayer,
initializer=util.HeKaimingInitializer())
embedding_projection_network = None
elif cls is spinn.cbow.CBOW:
compose_network = None
embedding_projection_network = None
else:
if FLAGS.lstm_composition:
if FLAGS.use_gru:
compose_network = partial(util.TreeGRULayer,
initializer=util.HeKaimingInitializer())
else:
compose_network = partial(util.TreeLSTMLayer,
initializer=util.HeKaimingInitializer())
else:
assert not FLAGS.connect_tracking_comp, "Can only connect tracking and composition unit while using TreeLSTM"
compose_network = partial(util.ReLULayer,
initializer=util.HeKaimingInitializer())
if project_embeddings:
embedding_projection_network = util.Linear
else:
assert FLAGS.word_embedding_dim == FLAGS.model_dim, \
"word_embedding_dim must equal model_dim unless a projection layer is used."
embedding_projection_network = util.IdentityLayer
# Build hard stack which scans over input sequence.
sentence_model = cls(
FLAGS.model_dim, FLAGS.word_embedding_dim, vocab_size, seq_length,
compose_network, embedding_projection_network, training_mode, ground_truth_transitions_visible, vs,
predict_use_cell=FLAGS.predict_use_cell,
use_tracking_lstm=FLAGS.use_tracking_lstm,
tracking_lstm_hidden_dim=FLAGS.tracking_lstm_hidden_dim,
X=tokens,
transitions=transitions,
initial_embeddings=initial_embeddings,
embedding_dropout_keep_rate=FLAGS.embedding_keep_rate,
ss_mask_gen=ss_mask_gen,
ss_prob=ss_prob,
connect_tracking_comp=FLAGS.connect_tracking_comp,
context_sensitive_shift=FLAGS.context_sensitive_shift,
context_sensitive_use_relu=FLAGS.context_sensitive_use_relu,
use_input_batch_norm=False)
# Extract top element of final stack timestep.
if FLAGS.lstm_composition or cls is spinn.plain_rnn.RNN:
sentence_vector = sentence_model.final_representations[:,:FLAGS.model_dim / 2].reshape((-1, FLAGS.model_dim / 2))
sentence_vector_dim = FLAGS.model_dim / 2
else:
sentence_vector = sentence_model.final_representations.reshape((-1, FLAGS.model_dim))
sentence_vector_dim = FLAGS.model_dim
sentence_vector = util.BatchNorm(sentence_vector, sentence_vector_dim, vs, "sentence_vector", training_mode)
sentence_vector = util.Dropout(sentence_vector, FLAGS.semantic_classifier_keep_rate, training_mode)
# Feed forward through a single output layer
logits = util.Linear(
sentence_vector, sentence_vector_dim, num_classes, vs,
name="semantic_classifier", use_bias=True)
return sentence_model.transitions_pred, logits
def build_sentence_pair_model(cls, vocab_size, seq_length, tokens, transitions,
num_classes, training_mode, ground_truth_transitions_visible, vs,
initial_embeddings=None, project_embeddings=False, ss_mask_gen=None, ss_prob=0.0):
"""
Construct a classifier which makes use of some hard-stack model.
Args:
cls: Hard stack class to use (from e.g. `spinn.fat_stack`)
vocab_size:
seq_length: Length of each sequence provided to the stack model
tokens: Theano batch (integer matrix), `batch_size * seq_length`
transitions: Theano batch (integer matrix), `batch_size * seq_length`
num_classes: Number of output classes
training_mode: A Theano scalar indicating whether to act as a training model
with dropout (1.0) or to act as an eval model with rescaling (0.0).
ground_truth_transitions_visible: A Theano scalar. If set (1.0), allow the model access
to ground truth transitions. This can be disabled at evaluation time to force Model 1
(or 2S) to evaluate in the Model 2 style with predicted transitions. Has no effect on Model 0.
vs: Variable store.
"""
# Prepare layer which performs stack element composition.
if cls is spinn.plain_rnn.RNN:
if FLAGS.use_gru:
compose_network = partial(util.GRULayer,
initializer=util.HeKaimingInitializer())
else:
compose_network = partial(util.LSTMLayer,
initializer=util.HeKaimingInitializer())
embedding_projection_network = None
elif cls is spinn.cbow.CBOW:
compose_network = None
embedding_projection_network = None
else:
if FLAGS.lstm_composition:
if FLAGS.use_gru:
compose_network = partial(util.TreeGRULayer,
initializer=util.HeKaimingInitializer())
else:
compose_network = partial(util.TreeLSTMLayer,
initializer=util.HeKaimingInitializer())
else:
assert not FLAGS.connect_tracking_comp, "Can only connect tracking and composition unit while using TreeLSTM"
compose_network = partial(util.ReLULayer,
initializer=util.HeKaimingInitializer())
if project_embeddings:
embedding_projection_network = util.Linear
else:
assert FLAGS.word_embedding_dim == FLAGS.model_dim, \
"word_embedding_dim must equal model_dim unless a projection layer is used."
embedding_projection_network = util.IdentityLayer
# Split the two sentences
premise_tokens = tokens[:, :, 0]
hypothesis_tokens = tokens[:, :, 1]
premise_transitions = transitions[:, :, 0]
hypothesis_transitions = transitions[:, :, 1]
# Build two hard stack models which scan over input sequences.
premise_model = cls(
FLAGS.model_dim, FLAGS.word_embedding_dim, vocab_size, seq_length,
compose_network, embedding_projection_network, training_mode, ground_truth_transitions_visible, vs,
predict_use_cell=FLAGS.predict_use_cell,
use_tracking_lstm=FLAGS.use_tracking_lstm,
tracking_lstm_hidden_dim=FLAGS.tracking_lstm_hidden_dim,
X=premise_tokens,
transitions=premise_transitions,
initial_embeddings=initial_embeddings,
embedding_dropout_keep_rate=FLAGS.embedding_keep_rate,
ss_mask_gen=ss_mask_gen,
ss_prob=ss_prob,
connect_tracking_comp=FLAGS.connect_tracking_comp,
context_sensitive_shift=FLAGS.context_sensitive_shift,
context_sensitive_use_relu=FLAGS.context_sensitive_use_relu,
use_attention=FLAGS.use_attention,
initialize_hyp_tracking_state=FLAGS.initialize_hyp_tracking_state)
premise_stack_tops = premise_model.stack_tops if FLAGS.use_attention != "None" else None
premise_tracking_c_state_final = premise_model.tracking_c_state_final if cls not in [spinn.plain_rnn.RNN,
spinn.cbow.CBOW] else None
hypothesis_model = cls(
FLAGS.model_dim, FLAGS.word_embedding_dim, vocab_size, seq_length,
compose_network, embedding_projection_network, training_mode, ground_truth_transitions_visible, vs,
predict_use_cell=FLAGS.predict_use_cell,
use_tracking_lstm=FLAGS.use_tracking_lstm,
tracking_lstm_hidden_dim=FLAGS.tracking_lstm_hidden_dim,
X=hypothesis_tokens,
transitions=hypothesis_transitions,
initial_embeddings=initial_embeddings,
embedding_dropout_keep_rate=FLAGS.embedding_keep_rate,
ss_mask_gen=ss_mask_gen,
ss_prob=ss_prob,
connect_tracking_comp=FLAGS.connect_tracking_comp,
context_sensitive_shift=FLAGS.context_sensitive_shift,
context_sensitive_use_relu=FLAGS.context_sensitive_use_relu,
use_attention=FLAGS.use_attention,
premise_stack_tops=premise_stack_tops,
is_hypothesis=True,
initialize_hyp_tracking_state=FLAGS.initialize_hyp_tracking_state,
premise_tracking_c_state_final=premise_tracking_c_state_final)
# Extract top element of final stack timestep.
if FLAGS.use_attention == "None" or FLAGS.use_difference_feature or FLAGS.use_product_feature:
premise_vector = premise_model.final_representations
hypothesis_vector = hypothesis_model.final_representations
if (FLAGS.lstm_composition and cls is not spinn.cbow.CBOW) or cls is spinn.plain_rnn.RNN:
premise_vector = premise_vector[:,:FLAGS.model_dim / 2].reshape((-1, FLAGS.model_dim / 2))
hypothesis_vector = hypothesis_vector[:,:FLAGS.model_dim / 2].reshape((-1, FLAGS.model_dim / 2))
sentence_vector_dim = FLAGS.model_dim / 2
else:
premise_vector = premise_vector.reshape((-1, FLAGS.model_dim))
hypothesis_vector = hypothesis_vector.reshape((-1, FLAGS.model_dim))
sentence_vector_dim = FLAGS.model_dim
if FLAGS.use_attention != "None":
# Use the attention weighted representation
h_dim = FLAGS.model_dim / 2
mlp_input = hypothesis_model.final_weighed_representation.reshape((-1, h_dim))
mlp_input_dim = h_dim
else:
# Create standard MLP features
mlp_input = T.concatenate([premise_vector, hypothesis_vector], axis=1)
mlp_input_dim = 2 * sentence_vector_dim
if FLAGS.use_difference_feature:
mlp_input = T.concatenate([mlp_input, premise_vector - hypothesis_vector], axis=1)
mlp_input_dim += sentence_vector_dim
if FLAGS.use_product_feature:
mlp_input = T.concatenate([mlp_input, premise_vector * hypothesis_vector], axis=1)
mlp_input_dim += sentence_vector_dim
mlp_input = util.BatchNorm(mlp_input, mlp_input_dim, vs, "sentence_vectors", training_mode)
mlp_input = util.Dropout(mlp_input, FLAGS.semantic_classifier_keep_rate, training_mode)
if FLAGS.classifier_type == "ResNet":
features = util.Linear(
mlp_input, mlp_input_dim, FLAGS.sentence_pair_combination_layer_dim, vs,
name="resnet/linear", use_bias=True)
features_dim = FLAGS.sentence_pair_combination_layer_dim
for layer in range(FLAGS.num_sentence_pair_combination_layers):
features = util.HeKaimingResidualLayerSet(features, features_dim, vs, training_mode, name="resnet/" + str(layer),
dropout_keep_rate=FLAGS.semantic_classifier_keep_rate, depth=FLAGS.resnet_unit_depth,
initializer=util.HeKaimingInitializer())
features = util.BatchNorm(features, features_dim, vs, "combining_mlp/" + str(layer), training_mode)
features = util.Dropout(features, FLAGS.semantic_classifier_keep_rate, training_mode)
elif FLAGS.classifier_type == "Highway":
features = util.Linear(
mlp_input, mlp_input_dim, FLAGS.sentence_pair_combination_layer_dim, vs,
name="resnet/linear", use_bias=True)
features_dim = FLAGS.sentence_pair_combination_layer_dim
for layer in range(FLAGS.num_sentence_pair_combination_layers):
features = util.HighwayLayer(features, features_dim, vs, training_mode, name="highway/" + str(layer),
dropout_keep_rate=FLAGS.semantic_classifier_keep_rate,
initializer=util.HeKaimingInitializer())
features = util.BatchNorm(features, features_dim, vs, "combining_mlp/" + str(layer), training_mode)
features = util.Dropout(features, FLAGS.semantic_classifier_keep_rate, training_mode)
else:
# Apply a combining MLP
features = mlp_input
features_dim = mlp_input_dim
for layer in range(FLAGS.num_sentence_pair_combination_layers):
features = util.ReLULayer(features, features_dim, FLAGS.sentence_pair_combination_layer_dim, vs,
name="combining_mlp/" + str(layer),
initializer=util.HeKaimingInitializer())
features_dim = FLAGS.sentence_pair_combination_layer_dim
features = util.BatchNorm(features, features_dim, vs, "combining_mlp/" + str(layer), training_mode)
features = util.Dropout(features, FLAGS.semantic_classifier_keep_rate, training_mode)
# Feed forward through a single output layer
logits = util.Linear(
features, features_dim, num_classes, vs,
name="semantic_classifier", use_bias=True)
return premise_model.transitions_pred, hypothesis_model.transitions_pred, logits
def build_cost(logits, targets):
"""
Build a classification cost function.
"""
# Clip gradients coming from the cost function.
logits = theano.gradient.grad_clip(
logits, -1. * FLAGS.clipping_max_value, FLAGS.clipping_max_value)
predicted_dist = T.nnet.softmax(logits)
costs = T.nnet.categorical_crossentropy(predicted_dist, targets)
cost = costs.mean()
pred = T.argmax(logits, axis=1)
acc = 1. - T.mean(T.cast(T.neq(pred, targets), theano.config.floatX))
return cost, acc
def build_transition_cost(logits, targets, num_transitions):
"""
Build a parse action prediction cost function.
"""
# swap seq_length dimension to front so that we can scan per timestep
logits = T.swapaxes(logits, 0, 1)
targets = targets.T
def cost_t(logits, tgt, num_transitions):
# TODO(jongauthier): Taper down xent cost as we proceed through
# sequence?
predicted_dist = T.nnet.softmax(logits)
cost = T.nnet.categorical_crossentropy(predicted_dist, tgt)
pred = T.argmax(logits, axis=1)
error = T.neq(pred, tgt)
return cost, error
results, _ = theano.scan(cost_t, [logits, targets], non_sequences=[num_transitions])
costs, errors = results
# Create a mask that selects only transitions that involve real data.
unrolling_length = T.shape(costs)[0]
padding = unrolling_length - num_transitions
padding = T.reshape(padding, (1, -1))
rng = T.arange(unrolling_length) + 1
rng = T.reshape(rng, (-1, 1))
mask = T.gt(rng, padding)
# Compute acc using the mask
acc = 1.0 - (T.sum(errors * mask, dtype=theano.config.floatX)
/ T.sum(num_transitions, dtype=theano.config.floatX))
# Compute cost directly, since we *do* want a cost incentive to get the padding
# transitions right.
cost = T.mean(costs)
return cost, acc
def evaluate(eval_fn, eval_set, logger, step):
# Evaluate
acc_accum = 0.0
action_acc_accum = 0.0
eval_batches = 0.0
for (eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch) in eval_set[1]:
acc_value, action_acc_value = eval_fn(
eval_X_batch, eval_transitions_batch,
eval_y_batch, eval_num_transitions_batch, 0.0, # Eval mode: Don't apply dropout.
int(FLAGS.allow_gt_transitions_in_eval), # Allow GT transitions to be used according to flag.
float(FLAGS.allow_gt_transitions_in_eval)) # If flag not set, used scheduled sampling
# p(ground truth) = 0.0,
# else SS p(ground truth) = 1.0
acc_accum += acc_value
action_acc_accum += action_acc_value
eval_batches += 1.0
logger.Log("Step: %i\tEval acc: %f\t %f\t%s" %
(step, acc_accum / eval_batches, action_acc_accum / eval_batches, eval_set[0]))
return acc_accum / eval_batches
def evaluate_expanded(eval_fn, eval_set, eval_path, logger, step, sentence_pair_data, ind_to_word, predict_transitions):
"""
Write the gold parses and predicted parses in the files <eval_out_path>.gld and <eval_out_path>.tst
respectively. These files can be given as inputs to Evalb to evaluate parsing performance -
evalb -p evalb_spinn.prm <eval_out_path>.gld <eval_out_path>.tst
TODO(SB): Set up for RNN and Model0 on non-sentence-pair data; port support to classifier.py.
"""
# TODO: Prune out redundant code, make usable on Model0 as well.
acc_accum = 0.0
action_acc_accum = 0.0
eval_batches = 0.0
eval_gold_path = eval_path + ".gld"
eval_out_path = eval_path + ".tst"
eval_lbl_path = eval_path + ".lbl"
with open(eval_gold_path, "w") as eval_gold, open(eval_out_path, "w") as eval_out:
if FLAGS.write_predicted_label:
label_out = open(eval_lbl_path, "w")
if sentence_pair_data:
for (eval_X_batch, eval_transitions_batch, eval_y_batch,
eval_num_transitions_batch) in eval_set[1]:
acc_value, action_acc_value, sem_logit_values, logits_pred_hyp, logits_pred_prem = eval_fn(
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch,
0.0, # Eval mode: Don't apply dropout.
int(FLAGS.allow_gt_transitions_in_eval), # Allow GT transitions to be used according to flag.
float(FLAGS.allow_gt_transitions_in_eval)) # adjust visibility of GT
acc_accum += acc_value
action_acc_accum += action_acc_value
eval_batches += 1.0
# write each predicted transition to file
for orig_transitions, pred_logit_hyp, pred_logit_prem, tokens, true_class, example_sem_logits \
in zip(eval_transitions_batch, logits_pred_hyp,
logits_pred_prem, eval_X_batch, eval_y_batch, sem_logit_values):
if predict_transitions:
orig_hyp_transitions, orig_prem_transitions = orig_transitions.T
pred_hyp_transitions = pred_logit_hyp.argmax(axis=1)
pred_prem_transitions = pred_logit_prem.argmax(axis=1)
else:
orig_hyp_transitions = orig_prem_transitions = pred_hyp_transitions = pred_prem_transitions = None
hyp_tokens, prem_tokens = tokens.T
hyp_words = [ind_to_word[t] for t in hyp_tokens]
prem_words = [ind_to_word[t] for t in prem_tokens]
eval_gold.write(util.TransitionsToParse(orig_hyp_transitions, hyp_words) + "\n")
eval_out.write(util.TransitionsToParse(pred_hyp_transitions, hyp_words) + "\n")
eval_gold.write(util.TransitionsToParse(orig_prem_transitions, prem_words) + "\n")
eval_out.write(util.TransitionsToParse(pred_prem_transitions, prem_words) + "\n")
predicted_class = np.argmax(example_sem_logits)
exp_logit_values = np.exp(example_sem_logits)
class_probs = exp_logit_values / np.sum(exp_logit_values)
class_probs_repr = "\t".join(map(lambda p : "%.8f" % (p,), class_probs))
if FLAGS.write_predicted_label:
label_out.write(str(true_class == predicted_class) + "\t" + str(true_class)
+ "\t" + str(predicted_class) + "\t" + class_probs_repr + "\n")
else:
for (eval_X_batch, eval_transitions_batch, eval_y_batch,
eval_num_transitions_batch) in eval_set[1]:
acc_value, action_acc_value, sem_logit_values, logits_pred = eval_fn(
eval_X_batch, eval_transitions_batch, eval_y_batch, eval_num_transitions_batch,
0.0, # Eval mode: Don't apply dropout.
int(FLAGS.allow_gt_transitions_in_eval), # Allow GT transitions to be used according to flag.
float(FLAGS.allow_gt_transitions_in_eval)) # adjust visibility of GT
acc_accum += acc_value
action_acc_accum += action_acc_value
eval_batches += 1.0
# write each predicted transition to file
for orig_transitions, pred_logit, tokens, true_class, example_sem_logits \
in zip(eval_transitions_batch, logits_pred, eval_X_batch, eval_y_batch, sem_logit_values):
words = [ind_to_word[t] for t in tokens]
eval_gold.write(util.TransitionsToParse(orig_transitions, words) + "\n")
eval_out.write(util.TransitionsToParse(pred_logit.argmax(axis=1), words) + "\n")
predicted_class = np.argmax(example_sem_logits)
exp_logit_values = np.exp(example_sem_logits)
class_probs = exp_logit_values / np.sum(exp_logit_values)
class_probs_repr = "\t".join(map(lambda p : "%.3f" % (p,), class_probs))
if FLAGS.write_predicted_label:
label_out.write(str(true_class == predicted_class) + "\t" + str(true_class)
+ "\t" + str(predicted_class) + "\t" + class_probs_repr + "\n")
logger.Log("Written gold parses in %s" % (eval_gold_path))
logger.Log("Written predicted parses in %s" % (eval_out_path))
if FLAGS.write_predicted_label:
logger.Log("Written predicted labels in %s" % (eval_lbl_path))
label_out.close()
logger.Log("Step: %i\tEval acc: %f\t %f\t%s" %
(step, acc_accum / eval_batches, action_acc_accum / eval_batches, eval_set[0]))
def run(only_forward=False):
logger = afs_safe_logger.Logger(os.path.join(FLAGS.log_path, FLAGS.experiment_name) + ".log")
if FLAGS.data_type == "bl":
data_manager = load_boolean_data
elif FLAGS.data_type == "sst":
data_manager = load_sst_data
elif FLAGS.data_type == "snli":
data_manager = load_snli_data
else:
logger.Log("Bad data type.")
return
pp = pprint.PrettyPrinter(indent=4)
logger.Log("Flag values:\n" + pp.pformat(FLAGS.FlagValuesDict()))
# Load the data.
raw_training_data, vocabulary = data_manager.load_data(
FLAGS.training_data_path)
# Load the eval data.
raw_eval_sets = []
if FLAGS.eval_data_path:
for eval_filename in FLAGS.eval_data_path.split(":"):
eval_data, _ = data_manager.load_data(eval_filename)
raw_eval_sets.append((eval_filename, eval_data))
# Prepare the vocabulary.
if not vocabulary:
logger.Log("In open vocabulary mode. Using loaded embeddings without fine-tuning.")
train_embeddings = False
vocabulary = util.BuildVocabulary(
raw_training_data, raw_eval_sets, FLAGS.embedding_data_path, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA)
else:
logger.Log("In fixed vocabulary mode. Training embeddings.")
train_embeddings = True
# Load pretrained embeddings.
if FLAGS.embedding_data_path:
logger.Log("Loading vocabulary with " + str(len(vocabulary))
+ " words from " + FLAGS.embedding_data_path)
initial_embeddings = util.LoadEmbeddingsFromASCII(
vocabulary, FLAGS.word_embedding_dim, FLAGS.embedding_data_path)
else:
initial_embeddings = None
# Trim dataset, convert token sequences to integer sequences, crop, and
# pad.
logger.Log("Preprocessing training data.")
training_data = util.PreprocessDataset(
raw_training_data, vocabulary, FLAGS.seq_length, data_manager, eval_mode=False, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA,
for_rnn=FLAGS.model_type == "RNN" or FLAGS.model_type == "CBOW")
training_data_iter = util.MakeTrainingIterator(
training_data, FLAGS.batch_size)
eval_iterators = []
for filename, raw_eval_set in raw_eval_sets:
logger.Log("Preprocessing eval data: " + filename)
e_X, e_transitions, e_y, e_num_transitions = util.PreprocessDataset(
raw_eval_set, vocabulary, FLAGS.seq_length, data_manager, eval_mode=True, logger=logger,
sentence_pair_data=data_manager.SENTENCE_PAIR_DATA,
for_rnn=FLAGS.model_type == "RNN" or FLAGS.model_type == "CBOW")
eval_iterators.append((filename,
util.MakeEvalIterator((e_X, e_transitions, e_y, e_num_transitions), FLAGS.batch_size)))
# Set up the placeholders.
y = T.vector("y", dtype="int32")
lr = T.scalar("lr")
training_mode = T.scalar("training_mode") # 1: Training with dropout, 0: Eval
ground_truth_transitions_visible = T.scalar("ground_truth_transitions_visible", dtype="int32")
logger.Log("Building model.")
vs = util.VariableStore(
default_initializer=util.UniformInitializer(FLAGS.init_range), logger=logger)
if FLAGS.model_type == "CBOW":
model_cls = spinn.cbow.CBOW
elif FLAGS.model_type == "RNN":
model_cls = spinn.plain_rnn.RNN
else:
model_cls = getattr(spinn.fat_stack, FLAGS.model_type)
# Generator of mask for scheduled sampling
numpy_random = np.random.RandomState(1234)
ss_mask_gen = T.shared_randomstreams.RandomStreams(numpy_random.randint(999999))
# Training step number
ss_prob = T.scalar("ss_prob")
if data_manager.SENTENCE_PAIR_DATA:
X = T.itensor3("X")
transitions = T.itensor3("transitions")
num_transitions = T.imatrix("num_transitions")
predicted_premise_transitions, predicted_hypothesis_transitions, logits = build_sentence_pair_model(
model_cls, len(vocabulary), FLAGS.seq_length,
X, transitions, len(data_manager.LABEL_MAP), training_mode, ground_truth_transitions_visible, vs,
initial_embeddings=initial_embeddings, project_embeddings=(not train_embeddings),
ss_mask_gen=ss_mask_gen,
ss_prob=ss_prob)
else:
X = T.matrix("X", dtype="int32")
transitions = T.imatrix("transitions")
num_transitions = T.vector("num_transitions", dtype="int32")
predicted_transitions, logits = build_sentence_model(
model_cls, len(vocabulary), FLAGS.seq_length,
X, transitions, len(data_manager.LABEL_MAP), training_mode, ground_truth_transitions_visible, vs,
initial_embeddings=initial_embeddings, project_embeddings=(not train_embeddings),
ss_mask_gen=ss_mask_gen,
ss_prob=ss_prob)
xent_cost, acc = build_cost(logits, y)
# Set up L2 regularization.
l2_cost = 0.0
for var in vs.trainable_vars:
l2_cost += FLAGS.l2_lambda * T.sum(T.sqr(vs.vars[var]))
# Compute cross-entropy cost on action predictions.
if (not data_manager.SENTENCE_PAIR_DATA) and FLAGS.model_type not in ["Model0", "RNN", "CBOW"]:
transition_cost, action_acc = build_transition_cost(predicted_transitions, transitions, num_transitions)
elif data_manager.SENTENCE_PAIR_DATA and FLAGS.model_type not in ["Model0", "RNN", "CBOW"]:
p_transition_cost, p_action_acc = build_transition_cost(predicted_premise_transitions, transitions[:, :, 0],
num_transitions[:, 0])
h_transition_cost, h_action_acc = build_transition_cost(predicted_hypothesis_transitions, transitions[:, :, 1],
num_transitions[:, 1])
transition_cost = p_transition_cost + h_transition_cost
action_acc = (p_action_acc + h_action_acc) / 2.0 # TODO(SB): Average over transitions, not words.
else:
transition_cost = T.constant(0.0)
action_acc = T.constant(0.0)
transition_cost = transition_cost * FLAGS.transition_cost_scale
total_cost = xent_cost + l2_cost + transition_cost
if ".ckpt" in FLAGS.ckpt_path:
checkpoint_path = FLAGS.ckpt_path
else:
checkpoint_path = os.path.join(FLAGS.ckpt_path, FLAGS.experiment_name + ".ckpt")
if os.path.isfile(checkpoint_path):
logger.Log("Found checkpoint, restoring.")
step, best_dev_error = vs.load_checkpoint(checkpoint_path, num_extra_vars=2,
skip_saved_unsavables=FLAGS.skip_saved_unsavables)
else:
assert not only_forward, "Can't run an eval-only run without a checkpoint. Supply a checkpoint."
step = 0
best_dev_error = 1.0
# Do an evaluation-only run.
if only_forward:
if FLAGS.eval_output_paths:
eval_output_paths = FLAGS.eval_output_paths.strip().split(":")
assert len(eval_output_paths) == len(eval_iterators), "Invalid no. of output paths."
else:
eval_output_paths = [FLAGS.experiment_name + "-" + os.path.split(eval_set[0])[1] + "-parse"
for eval_set in eval_iterators]
# Load model from checkpoint.
logger.Log("Checkpointed model was trained for %d steps." % (step,))
# Generate function for forward pass.
logger.Log("Building forward pass.")
if data_manager.SENTENCE_PAIR_DATA:
eval_fn = theano.function(
[X, transitions, y, num_transitions, training_mode, ground_truth_transitions_visible, ss_prob],
[acc, action_acc, logits, predicted_hypothesis_transitions, predicted_premise_transitions],
on_unused_input='ignore',
allow_input_downcast=True)
else:
eval_fn = theano.function(
[X, transitions, y, num_transitions, training_mode, ground_truth_transitions_visible, ss_prob],
[acc, action_acc, logits, predicted_transitions],
on_unused_input='ignore',
allow_input_downcast=True)
# Generate the inverse vocabulary lookup table.
ind_to_word = {v : k for k, v in vocabulary.iteritems()}
# Do a forward pass and write the output to disk.
for eval_set, eval_out_path in zip(eval_iterators, eval_output_paths):
logger.Log("Writing eval output for %s." % (eval_set[0],))
evaluate_expanded(eval_fn, eval_set, eval_out_path, logger, step,
data_manager.SENTENCE_PAIR_DATA, ind_to_word, FLAGS.model_type not in ["Model0", "RNN", "CBOW"])
else:
# Train
new_values = util.RMSprop(total_cost, vs.trainable_vars.values(), lr)
new_values += [(key, vs.nongradient_updates[key]) for key in vs.nongradient_updates]
# Training open-vocabulary embeddings is a questionable idea right now. Disabled:
# new_values.append(
# util.embedding_SGD(total_cost, embedding_params, embedding_lr))
# Create training and eval functions.
# Unused variable warnings are supressed so that num_transitions can be passed in when training Model 0,
# which ignores it. This yields more readable code that is very slightly slower.
logger.Log("Building update function.")
update_fn = theano.function(
[X, transitions, y, num_transitions, lr, training_mode, ground_truth_transitions_visible, ss_prob],
[total_cost, xent_cost, transition_cost, action_acc, l2_cost, acc],
updates=new_values,
on_unused_input='ignore',
allow_input_downcast=True)
logger.Log("Building eval function.")
eval_fn = theano.function([X, transitions, y, num_transitions, training_mode, ground_truth_transitions_visible, ss_prob], [acc, action_acc],
on_unused_input='ignore',
allow_input_downcast=True)
logger.Log("Training.")
# Main training loop.
for step in range(step, FLAGS.training_steps):
if step % FLAGS.eval_interval_steps == 0:
for index, eval_set in enumerate(eval_iterators):
acc = evaluate(eval_fn, eval_set, logger, step)
if FLAGS.ckpt_on_best_dev_error and index == 0 and (1 - acc) < 0.99 * best_dev_error and step > 1000:
best_dev_error = 1 - acc
logger.Log("Checkpointing with new best dev accuracy of %f" % acc)
vs.save_checkpoint(checkpoint_path + "_best", extra_vars=[step, best_dev_error])
X_batch, transitions_batch, y_batch, num_transitions_batch = training_data_iter.next()
learning_rate = FLAGS.learning_rate * (FLAGS.learning_rate_decay_per_10k_steps ** (step / 10000.0))
ret = update_fn(X_batch, transitions_batch, y_batch, num_transitions_batch,
learning_rate, 1.0, 1.0, np.exp(step*np.log(FLAGS.scheduled_sampling_exponent_base)))
total_cost_val, xent_cost_val, transition_cost_val, action_acc_val, l2_cost_val, acc_val = ret
if step % FLAGS.statistics_interval_steps == 0:
logger.Log(
"Step: %i\tAcc: %f\t%f\tCost: %5f %5f %5f %5f"
% (step, acc_val, action_acc_val, total_cost_val, xent_cost_val, transition_cost_val,
l2_cost_val))
if step % FLAGS.ckpt_interval_steps == 0 and step > 0:
vs.save_checkpoint(checkpoint_path, extra_vars=[step, best_dev_error])
if __name__ == '__main__':
# Experiment naming.
gflags.DEFINE_string("experiment_name", "experiment", "")
# Data types.
gflags.DEFINE_enum("data_type", "bl", ["bl", "sst", "snli"],
"Which data handler and classifier to use.")
# Where to store checkpoints
gflags.DEFINE_string("ckpt_path", ".", "Where to save/load checkpoints. Can be either "
"a filename or a directory. In the latter case, the experiment name serves as the "
"base for the filename.")
gflags.DEFINE_string("log_path", ".", "A directory in which to write logs.")
# Data settings.
gflags.DEFINE_string("training_data_path", None, "")
gflags.DEFINE_string("eval_data_path", None, "Can contain multiple file paths, separated "
"using ':' tokens. The first file should be the dev set, and is used for determining "
"when to save the early stopping 'best' checkpoints.")
gflags.DEFINE_integer("seq_length", 30, "")
gflags.DEFINE_integer("eval_seq_length", 30, "")
gflags.DEFINE_string("embedding_data_path", None,
"If set, load GloVe-formatted embeddings from here.")
# Model architecture settings.
gflags.DEFINE_enum("model_type", "Model0",
["CBOW", "RNN", "Model0", "Model1", "Model2", "Model2S"],
"")
gflags.DEFINE_boolean("allow_gt_transitions_in_eval", False,
"Whether to use ground truth transitions in evaluation when appropriate "
"(i.e., in Model 1 and Model 2S.)")
gflags.DEFINE_integer("model_dim", 8, "")
gflags.DEFINE_integer("word_embedding_dim", 8, "")
gflags.DEFINE_integer("tracking_lstm_hidden_dim", 4, "")
gflags.DEFINE_boolean("use_tracking_lstm", True,
"Whether to use LSTM in the tracking unit")
gflags.DEFINE_boolean("predict_use_cell", False,
"For models which predict parser actions, use "
"both the tracking LSTM hidden and cell values as "
"input to the prediction layer")
gflags.DEFINE_enum("use_attention", "None",
["None", "Rocktaschel", "WangJiang", "Thang", "TreeWangJiang", "TreeThang"],
"")
gflags.DEFINE_boolean("context_sensitive_shift", False,
"Use LSTM hidden state and word embedding to determine the vector to be pushed")
gflags.DEFINE_boolean("context_sensitive_use_relu", False,
"Use ReLU Layer to combine embedding and tracking unit hidden state")
gflags.DEFINE_float("semantic_classifier_keep_rate", 0.5,
"Used for dropout in the semantic task classifier.")
gflags.DEFINE_float("embedding_keep_rate", 0.5,
"Used for dropout on transformed embeddings.")
gflags.DEFINE_boolean("lstm_composition", True, "")
gflags.DEFINE_enum("classifier_type", "MLP", ["MLP", "Highway", "ResNet"], "")
gflags.DEFINE_integer("resnet_unit_depth", 2, "")
# gflags.DEFINE_integer("num_composition_layers", 1, "")
gflags.DEFINE_integer("num_sentence_pair_combination_layers", 2, "")
gflags.DEFINE_integer("sentence_pair_combination_layer_dim", 1024, "")
gflags.DEFINE_float("scheduled_sampling_exponent_base", 0.99,
"Used for scheduled sampling, with probability of Model 1 over Model 2 being base^#training_steps")
gflags.DEFINE_boolean("use_difference_feature", True,
"Supply the sentence pair classifier with sentence difference features.")
gflags.DEFINE_boolean("use_product_feature", True,
"Supply the sentence pair classifier with sentence product features.")
gflags.DEFINE_boolean("connect_tracking_comp", True,
"Connect tracking unit and composition unit. Can only be true if using LSTM in both units.")
gflags.DEFINE_boolean("initialize_hyp_tracking_state", False,
"Initialize the c state of the tracking unit of hypothesis model with the final"
"tracking unit c state of the premise model.")
gflags.DEFINE_boolean("use_gru", False,
"Use GRU units instead of LSTM units.")
# Optimization settings.
gflags.DEFINE_integer("training_steps", 500000, "Stop training after this point.")
gflags.DEFINE_integer("batch_size", 32, "SGD minibatch size.")
gflags.DEFINE_float("learning_rate", 0.001, "Used in RMSProp.")
gflags.DEFINE_float("learning_rate_decay_per_10k_steps", 0.75, "Used in RMSProp.")
gflags.DEFINE_float("clipping_max_value", 5.0, "")
gflags.DEFINE_float("l2_lambda", 1e-5, "")
gflags.DEFINE_float("init_range", 0.005, "Mainly used for softmax parameters. Range for uniform random init.")
gflags.DEFINE_float("transition_cost_scale", 1.0, "Multiplied by the transition cost.")
# Display settings.
gflags.DEFINE_integer("statistics_interval_steps", 100, "Print training set results at this interval.")
gflags.DEFINE_integer("eval_interval_steps", 100, "Evaluate at this interval.")
gflags.DEFINE_integer("ckpt_interval_steps", 5000, "Update the checkpoint on disk at this interval.")
gflags.DEFINE_boolean("ckpt_on_best_dev_error", True, "If error on the first eval set (the dev set) is "
"at most 0.99 of error at the previous checkpoint, save a special 'best' checkpoint.")
# Evaluation settings
gflags.DEFINE_boolean("expanded_eval_only_mode", False,
"If set, a checkpoint is loaded and a forward pass is done to get the predicted "
"transitions. The inferred parses are written to the supplied file(s) along with example-"
"by-example accuracy information. Requirements: Must specify checkpoint path.")
gflags.DEFINE_string("eval_output_paths", None,
"Used when expanded_eval_only_mode is set. The number of supplied paths should be same"
"as the number of eval sets.")
gflags.DEFINE_boolean("write_predicted_label", False,
"Write the predicted labels in a <eval_output_name>.lbl file.")
gflags.DEFINE_boolean("skip_saved_unsavables", False,
"Assume that variables marked as not savable will appear in checkpoints anyway, and "
"skip them when loading. This should be used only when loading old checkpoints.")
# Parse command line flags.
FLAGS(sys.argv)
run(only_forward=FLAGS.expanded_eval_only_mode)
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laia/common/saver.py | eivtho/PyLaia | 89 | 1600676 | <reponame>eivtho/PyLaia
import inspect
import os
from typing import Any, Callable
import torch
from laia.common.logging import get_logger
_logger = get_logger(__name__)
class Saver:
def __call__(self, *args: Any, **kwargs: Any):
return self.save(*args, **kwargs)
def save(self, *args: Any, **kwargs: Any):
raise NotImplementedError
class BasicSaver(Saver):
def save(self, obj: Any, filepath: str) -> str:
filepath = os.path.realpath(filepath)
dirname = os.path.dirname(filepath)
os.makedirs(dirname, exist_ok=True)
torch.save(obj, filepath)
return filepath
class ObjectSaver(Saver):
def __init__(self, filepath: str) -> None:
self._filepath = filepath
self._basic_saver = BasicSaver()
def save(self, func_or_class: Callable, *args: Any, **kwargs: Any) -> str:
return self._basic_saver.save(
{
"module": inspect.getmodule(func_or_class).__name__,
"name": func_or_class.__name__,
"args": args,
"kwargs": kwargs,
},
self._filepath,
)
class ModelSaver(ObjectSaver):
def __init__(self, save_path: str, filename: str = "model") -> None:
super().__init__(os.path.join(save_path, filename))
def save(self, func: Callable, *args: Any, **kwargs: Any) -> str:
path = super().save(func, *args, **kwargs)
_logger.debug("Saved model {}", path)
return path
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] |
fluid/image_classification/eval.py | Panxj/models-1 | 0 | 182868 | <gh_stars>0
import os
import sys
import numpy as np
import argparse
import functools
import paddle
import paddle.fluid as fluid
from utility import add_arguments, print_arguments
from se_resnext import SE_ResNeXt
import reader
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('batch_size', int, 32, "Minibatch size.")
add_arg('use_gpu', bool, True, "Whether to use GPU or not.")
add_arg('test_list', str, '', "The testing data lists.")
add_arg('num_layers', int, 50, "How many layers for SE-ResNeXt model.")
add_arg('model_dir', str, '', "The model path.")
# yapf: enable
def eval(args):
class_dim = 1000
image_shape = [3, 224, 224]
image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
out = SE_ResNeXt(input=image, class_dim=class_dim, layers=args.num_layers)
cost = fluid.layers.cross_entropy(input=out, label=label)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
avg_cost = fluid.layers.mean(x=cost)
inference_program = fluid.default_main_program().clone(for_test=True)
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
if not os.path.exists(args.model_dir):
raise ValueError("The model path [%s] does not exist." %
(args.model_dir))
if not os.path.exists(args.test_list):
raise ValueError("The test lists [%s] does not exist." %
(args.test_list))
def if_exist(var):
return os.path.exists(os.path.join(args.model_dir, var.name))
fluid.io.load_vars(exe, args.model_dir, predicate=if_exist)
test_reader = paddle.batch(
reader.test(args.test_list), batch_size=args.batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
fetch_list = [avg_cost, acc_top1, acc_top5]
test_info = [[], [], []]
for batch_id, data in enumerate(test_reader()):
loss, acc1, acc5 = exe.run(inference_program,
feed=feeder.feed(data),
fetch_list=fetch_list)
test_info[0].append(loss[0])
test_info[1].append(acc1[0])
test_info[2].append(acc5[0])
if batch_id % 1 == 0:
print("Test {0}, loss {1}, acc1 {2}, acc5 {3}"
.format(batch_id, loss[0], acc1[0], acc5[0]))
sys.stdout.flush()
test_loss = np.array(test_info[0]).mean()
test_acc1 = np.array(test_info[1]).mean()
test_acc5 = np.array(test_info[2]).mean()
print("Test loss {0}, acc1 {1}, acc5 {2}".format(test_loss, test_acc1,
test_acc5))
sys.stdout.flush()
if __name__ == '__main__':
args = parser.parse_args()
print_arguments(args)
eval(args)
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] |
tests/example/reflexes/example_reflex.py | FarhanAliRaza/django-sockpuppet | 371 | 139049 | <reponame>FarhanAliRaza/django-sockpuppet
from sockpuppet.reflex import Reflex
class ExampleReflex(Reflex):
def increment(self, step=1):
self.session['count'] = int(self.element.dataset['count']) + step
class DecrementReflex(Reflex):
def decrement(self, step=1):
self.session['otherCount'] = int(self.element.dataset['count']) - step
class ParamReflex(Reflex):
def change_word(self):
self.word = 'space'
self.success = True
class FormReflex(Reflex):
def submit(self):
self.text_output = self.request.POST['text-input']
class ErrorReflex(Reflex):
def increment(self, step=1):
raise Exception('error happened')
class UserReflex(Reflex):
def get_user(self):
context = self.get_context_data()
self.user_reveal = context['object']
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ops/multihead/__init__.py | ancientmooner/CCNet | 0 | 192043 | from .multihead_block import MultiheadBlock
from .multihead_spatial_block import MultiheadSpatialBlock
__all__ = [
'MultiheadBlock', 'MultiheadSpatialBlock',
]
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viper/cli.py | curtisma/conda-virtuoso | 2 | 171942 | """
Command line interface for viper
"""
import click
from .docs import docs as docs_internal
from .cdslib import add_library, include_cdslib
@click.group()
def virt():
"""
Cadence virtuoso command-line utilities
"""
pass
@virt.command()
def docs():
docs_internal()
@virt.group()
def cdslib():
"""Edit a *.cdslib file"""
pass
@cdslib.command()
@click.argument("cds_path")
@click.argument("library_name")
@click.argument("library_path")
def add():
"""
Add a library
"""
add_library(cds_path, library_name, library_path)
@cdslib.command()
@click.argument("cds_path")
@click.argument("include_file_path")
@click.option('-s','--soft')
def include():
"""
Include another cds.lib file in the current one.
:return:
"""
include_cdslib(cds_path, include_file_path, soft)
if __name__ == '__main__':
virt(auto_envvar_prefix='VIRT')
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] |
lmbl_weight.py | zhuruijie16/Scripts_CE | 0 | 199293 | <filename>lmbl_weight.py
import os
import pandas as pd
import numpy as np
import shutil
cwd = os.getcwd()
sysname = os.path.basename(cwd)
print(sysname)
if os.path.isdir(cwd+'/lmbl/'):
shutil.rmtree(cwd+'/lmbl/')
os.mkdir('lmbl')
coor = os.path.join(cwd+ '/coordinates/')
lmbl = os.path.join(cwd+ '/lmbl/')
os.chdir(lmbl)
for filename in os.listdir(coor):
other_name=[]
other_X = []
other_Y = []
other_Z = []
C_X = []
C_Y = []
C_Z = []
count_C = 0
pos_C = []
print(filename)
with open(os.path.join(coor+filename)) as f:
lines=f.readlines()
f.close()
#obtian the number of C atoms and the coordinates of other atoms
for i in range(1,len(lines)):
lines_ele = lines[i].strip().split(',')
ele = lines_ele[0]
if ele == sysname:
count_C+=1
pos_C.append(i)
continue
else:
other_name.append(lines_ele[0])
other_X.append(lines_ele[1])
other_Y.append(lines_ele[2])
other_Z.append(lines_ele[3])
len_others=len(lines)-1-count_C
#obtain coordinates of
for j in range(0,count_C):
lines_C = lines[pos_C[j]].strip().split(',')
C_X.append(lines_C[1])
C_Y.append(lines_C[2])
C_Z.append(lines_C[3])
#calculate dist between C and other atoms
#dist between first C and other atoms
print(C_X)
CX = C_X[0]
CY = C_Y[0]
CZ = C_Z[0]
pair=[]
dist=[]
for k in range(0,len_others):
Xdiff = float(other_X[k]) - float(CX)
Ydiff = float(other_Y[k]) - float(CY)
Zdiff = float(other_Z[k]) - float(CZ)
d = np.sqrt(np.square(Xdiff)+np.square(Ydiff)+np.square(Zdiff))
pair.append(sysname + '-' + other_name[k])
dist.append(d)
if count_C > 1:
for l in range(1,count_C):
Xdiff = float(C_X[l]) - float(CX)
Ydiff = float(C_Y[l]) - float(CY)
Zdiff = float(C_Z[l]) - float(CZ)
d = np.sqrt(np.square(Xdiff) + np.square(Ydiff) + np.square(Zdiff))
pair.append(sysname + '-' + sysname)
dist.append(d)
weight=[]
for dis in dist:
w = 1 / dis
weight.append(w)
weight_sum=sum(weight)
frac = [ w / weight_sum for w in weight]
df = pd.DataFrame({'pair':pair,'dist':dist,'weight':weight,'weight_sum':weight_sum,'frac':frac})
df.to_csv(filename,index=False)
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] |
znfTruth/renameNewick.py | joelarmstrong/treeBuildingEvaluation | 0 | 173168 | <reponame>joelarmstrong/treeBuildingEvaluation
#!/usr/bin/env python
import sys
from sonLib.bioio import fastaRead
from sonLib.nxnewick import NXNewick
from sonLib.nxtree import NXTree
import networkx as nx
def induceTreeOnLeaves(nxtree, leaves):
leaves = set(leaves)
dg = nxtree.nxDg
nodesToKeep = []
for node in dg.nodes():
succ = set([nxtree.getName(i) for i in nx.dfs_postorder_nodes(dg, node) if nxtree.hasName(i)])
if len(succ.intersection(leaves)) != 0:
nodesToKeep.append(node)
return NXTree(dg.subgraph(nodesToKeep))
renameFile = open(sys.argv[1])
newickFile = open(sys.argv[2])
translate = {}
curPastaID = None
curRealName = None
for i, line in enumerate(renameFile):
line = line.strip()
if i % 3 == 0:
curPastaID = line
elif i % 3 == 1:
curRealName = line
else:
translate[curPastaID] = curRealName.replace("...", ".-.").replace(".", "_").replace("__", "_")
s = newickFile.read()
for header1, header2 in translate.items():
s = s.replace(header1, header2)
tree = NXNewick().parseString(s)
inducedTree = induceTreeOnLeaves(tree, translate.values())
print NXNewick().writeString(inducedTree)
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9643,
29897,
13,
2
] |
Python/prova/fibro 2.py | marcelosilva7/Primeiro-repositorio | 0 | 84341 | t1 = 0
t2 = 1
print(f'{t1} -> {t2} -> ', end='')
for c in range(1, 21):
t3 = t1 + t2
print(f'{t3} -> ', end='')
t1 = t2
t2 = t3
print('FIM', end='') | [
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builder_engine/custom_components/__init__.py | DiablosWhisper/machine_learning_toolpack | 0 | 166672 | <reponame>DiablosWhisper/machine_learning_toolpack<filename>builder_engine/custom_components/__init__.py
from .optimizers import *
from .callbacks import *
from .metrics import *
from .losses import *
from .layers import * | [
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web/migrations/0007_auto_20180824_0925.py | zinaukarenku/zkr-platform | 2 | 7360 | <filename>web/migrations/0007_auto_20180824_0925.py<gh_stars>1-10
# Generated by Django 2.1 on 2018-08-24 09:25
from django.db import migrations, models
import web.models
class Migration(migrations.Migration):
dependencies = [
('web', '0006_organizationmember_user'),
]
operations = [
migrations.AlterField(
model_name='organizationpartner',
name='logo',
field=models.ImageField(upload_to=web.models.OrganizationPartner._organization_partner_logo_file),
),
]
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AsRoot/app/views.py | erin-hughes/qradar-sample-apps | 8 | 25383 | <reponame>erin-hughes/qradar-sample-apps
# Copyright 2020 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from flask import Blueprint
# pylint: disable=invalid-name
viewsbp = Blueprint('viewsbp', __name__, url_prefix='/')
# Simple endpoint that displays the contents of sudoers
@viewsbp.route('/index')
def index():
with open('/opt/app-root/sudoers', 'r') as file:
text_formatted = " "
for line in file:
text_formatted += line + "</br>"
file.close()
return text_formatted
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689,
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13,
2
] |
aztk/spark/models/plugins/spark_ui_proxy/configuration.py | Geims83/aztk | 161 | 52316 | import os
from aztk.models.plugins.plugin_configuration import PluginConfiguration, PluginPort, PluginTargetRole
from aztk.models.plugins.plugin_file import PluginFile
dir_path = os.path.dirname(os.path.realpath(__file__))
class SparkUIProxyPlugin(PluginConfiguration):
def __init__(self):
super().__init__(
name="spark_ui_proxy",
ports=[PluginPort(internal=9999, public=True)],
target_role=PluginTargetRole.Master,
execute="spark_ui_proxy.sh",
args=["localhost:8080", "9999"],
files=[
PluginFile("spark_ui_proxy.sh", os.path.join(dir_path, "spark_ui_proxy.sh")),
PluginFile("spark_ui_proxy.py", os.path.join(dir_path, "spark_ui_proxy.py")),
],
)
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] |
tests/test_fetcher.py | someshchaturvedi/reference-sequence-fetcher | 0 | 165218 | from reference_sequence_fetcher.fetcher import Fetcher, handle_error
import pytest
import json
class MockResponse(object):
'''
Mock object to test handle_error function in test_handle_error_function.
It mocks the functionality of Response object from requests library
'''
def __init__(self, status_code, text=None):
self.status_code = status_code
self.text = text
def get_metadata(seq):
'''Used to retrieve metadata of sequence object using checksum. Called from
check_complete_metdata_response
'''
response = {
"metadata": {
"md5": seq.md5,
"trunc512": seq.sha512,
"length": seq.size,
"aliases": []
}
}
return json.dumps(response)
def check_complete_metdata_response(metadata, seq, checksum):
'''check_complete_metdata_response is a utility function used by
test_metadata function to remove duplication of code. It takes
response seq object and checksum ID used to query as input parameter and
assert for reponse header, status code and content
'''
assert metadata == get_metadata(seq)
def test_getter_setter_base_url_Fetcher():
'''test all scenarios of the user putting in base_url along with str() on
Fetcher object
'''
fetcher = Fetcher('172.16.58.3')
assert fetcher.get_base_url() == 'http://172.16.58.3'
fetcher.set_base_url('http://localhost')
assert fetcher.get_base_url() == 'http://localhost'
fetcher.set_base_url('http://localhost/cran')
assert fetcher.get_base_url() == 'http://localhost/cran'
fetcher.set_base_url('http://localhost/cran/')
assert fetcher.get_base_url() == 'http://localhost/cran'
assert str(fetcher) == 'http://localhost/cran'
def test_fetch_complete_sequence_retrieval(data, server):
'''test the complete sequence retrieval
'''
fetcher = Fetcher(server)
seq = data[0]
assert fetcher.fetch_sequence(seq.md5) == seq.sequence
assert Fetcher.sequence(server, seq.md5) == seq.sequence
assert fetcher._Fetcher__cache is None
def test_fetch_sub_sequence_retrieval(data, server):
'''test the sub sequence retrieval using start and end when end > start
'''
fetcher = Fetcher(server)
seq = data[0]
assert fetcher.fetch_sequence(seq.md5, start=0, end=10) \
== seq.sequence[:10]
assert fetcher.fetch_sequence(seq.md5, start=0, end=1) \
== seq.sequence[:1]
assert fetcher._Fetcher__cache is None
def test_fetch_sub_sequence_retrieval_one_parameter(data, server):
'''test the sub sequence retrieval using start and end when only one of the
start and end is given
'''
fetcher = Fetcher(server)
seq = data[0]
assert fetcher.fetch_sequence(seq.md5, end=5) == 'CCACA'
assert fetcher._Fetcher__cache == \
json.loads(get_metadata(seq))
assert fetcher.fetch_sequence(seq.md5, start=0) == seq.sequence
def test_fetch_circular_sub_sequence_retrieval(data, server):
'''test the sub sequence retrieval using start and end when end <= start
'''
fetcher = Fetcher(server)
assert fetcher.fetch_sequence(data[2].md5, start=5374, end=5) == \
'ATCCAACCTGCAGAGTT'
assert fetcher._Fetcher__cache is None
@pytest.mark.parametrize("_input, _output", [
(400, 'Check the parameters provided i.e start, end, fbs and lbs.'),
(404, 'Checksum identifier provided can not be found.'),
(415, 'Encoding is not supported by the server.'),
(416, 'Range can not be satisfied.'),
(500, 'There maybe some internal server error.')
])
def test_handle_error_function_sequence(server, _input, _output):
'''Uses MockResponse object to test handle_error function
'''
with pytest.raises(Exception) as excinfo:
handle_error(MockResponse(_input), 'sequence')
assert str(excinfo.value) == _output
@pytest.mark.parametrize("_input, _output", [
(400, 'Bad Request.'),
(404, 'Path does not exsist.')
])
def test_handle_error_function_metadata_info(server, _input, _output):
'''Uses MockResponse object to test handle_error function
'''
with pytest.raises(Exception) as excinfo:
handle_error(MockResponse(_input), 'metadata')
assert str(excinfo.value) == _output
def test_warning_501_handle_error_function(server):
'''Uses MockResponse object to test warning issued in case of 501 from
handle_error function
'''
with pytest.warns(UserWarning, match='Circular support is not implemented in the server'):
handle_error(MockResponse(501), 'sequence')
def test_metadata(server, data):
'''Uses check_complete_metdata_response utility function to test metadata
retrieval from fetch_metadata and Fetch.metadata using a mock server.
'''
seq = data[0]
fetcher = Fetcher(server)
check_complete_metdata_response(
fetcher.fetch_metadata(seq.md5), seq, seq.md5)
check_complete_metdata_response(
Fetcher.metadata(server, seq.md5), seq, seq.md5)
def check_complete_info_response(info):
assert "circular_supported" in info
assert "algorithms" in info
assert "subsequence_limit" in info
assert "supported_api_versions" in info
def test_service_info(server):
'''Test info retrieval from fetch_info and Fetch.info using a mock server.
'''
fetcher = Fetcher(server)
check_complete_info_response(fetcher.fetch_info())
check_complete_info_response(Fetcher.info(server))
| [
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autotest/t056_test_pcg_fmt.py | ritchie46/flopy | 1 | 1608378 | <reponame>ritchie46/flopy
# -*- coding: utf-8 -*-
"""
This simple script tests that the pcg load function works for both free-
and fixed-format pcg files for mf2005 models.
Refer to pull request #311: "Except block to forgive old fixed format pcg
for post-mf2k model versions" for more details.
"""
import os
import flopy as fp
pcg_fname = os.path.join("..","examples", "data", "pcg_fmt_test", "fixfmt.pcg")
def pcg_fmt_test():
# mf2k container - this will pass
m2k = fp.modflow.Modflow(version='mf2k')
m2k.pcg = fp.modflow.ModflowPcg.load(model=m2k,
f=pcg_fname)
# mf2005 container
m05 = fp.modflow.Modflow(version='mf2005')
m05.pcg = fp.modflow.ModflowPcg.load(model=m05,
f=pcg_fname)
# this will exit with ValueError without the except block added in pull req
assert m2k.pcg.rclose == m05.pcg.rclose
assert m2k.pcg.damp == m05.pcg.damp
return
if __name__ == '__main__':
pcg_fmt_test()
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] |
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