content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
# Copyright (C) 2014-2015 LiuLang <gsushzhsosgsu@gmail.com>
# Use of this source code is governed by GPLv3 license that can be found
# in http://www.gnu.org/licenses/gpl-3.0.html
import hashlib
import os
import zlib
CHUNK = 2 ** 20
| [
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... | 2.712644 | 87 |
#!/usr/bin/python
# encoding: utf-8
'''a rich client
1. for one server (instead of multi like in libmc.Client)
2. encapsulate @, ?, gc ...
use is instead of libmc.Client
'''
import telnetlib
import logging
import libmc
import string
import urllib
import itertools
import warnings
from collections import defaultdict
from beansdbadmin.core.hint import parse_new_hint_body
from beansdbadmin.core.data import parse_records
from beansdbadmin.core.hash import get_khash64
def get_buckets_keys_count(store):
""" return dict: buckets -> count """
st = {}
try:
for line in (store.get('@') or '').split('\n'):
if line:
d, _, c = line.split(' ')
if not d.endswith('/'):
continue
st[int(d[0], 16)] = int(c)
return st
except IOError:
raise Exception("cannot get @ from %s" % (store))
def get_primary_buckets(store):
""" return possible primary buckets, might be wrong on temporary nodes,
result is list of buckets in integer
"""
ss = get_buckets_keys_count(store)
bucket_list = ss.items()
bucket_list = [x for x in bucket_list if x[1] > 0]
if not bucket_list:
return None
bucket_list.sort(lambda a, b: cmp(a[1], b[1]), reverse=True)
result = [bucket_list[0]]
for i in bucket_list[1:]:
if result[-1][1] / i[1] >= 2:
break
result.append(i)
return [x[0] for x in result]
def get_key_info_disk(store, key):
'''return ver, vhash, flag, vsz, ts, fid, pos'''
info = store.get('??' + key)
if info:
return [int(x) for x in info.split()]
def test_new(addr, bucket):
b = bucket
c = DBClient(addr)
print "stats:", c.stats()
print 'version:', c.get_server_version()
print "isold:", c.is_old()
print "dir root:", c.get_dir("@")
print "bucket key count:", c.get_bucket_keys_count(int(b))
print "item_count:", c.item_count()
print "primary_buckets", get_primary_buckets(c)
leaf = c.get_dir("@" + b + "000000")
print "a dir leaf:", leaf
khash_str = list(leaf)[0]
print "a khash_str", khash_str
r = c.get_records_by_khash(khash_str)[0]
k = r[0]
print "key, len(value), (flag, tstamp, ver):", k, r[1], r[3:]
print "key info mem:", c.get_key_info_mem(k)
print "key info disk(ver, vhash, flag, vsz, ts, fid, pos):", \
c.get_key_info_disk(k)
print "key version:", c.get_version(k)
print "collision_summary", c.get_collision_summary(int(b))
print "gc status:", c.get_gc_status()
if __name__ == '__main__':
test_new("rosa3a:7900", '3')
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220... | 2.271637 | 1,167 |
import matplotlib.pyplot
__author__ = 'xiongyi'
line1 = [(200, 100), (200, 400)]
line2 = [(190, 190), (210, 210)]
if __name__ == '__main__':
matplotlib.pyplot.plot((line1[0][0],line1[1][0]),(line1[0][1],line1[1][1]))
matplotlib.pyplot.hold(True)
matplotlib.pyplot.plot((line2[0][0],line2[1][0]),(line2[0][1],line2[1][1]))
print(overlap())
matplotlib.pyplot.show()
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21536... | 2.120879 | 182 |
from sqlalchemy import select
from sqlalchemy.schema import Column
from .declarative import Model
class ModelLoader(Loader):
class AliasLoader(ModelLoader):
class ColumnLoader(Loader):
class TupleLoader(Loader):
class CallableLoader(Loader):
class ValueLoader(Loader):
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7,
... | 3.582278 | 79 |
# Standard imports
import logging
import math
import json
from uuid import UUID
from datetime import datetime, timedelta
import time
# Our imports
from emission.core.get_database import get_trip_db, get_section_db
import emission.analysis.result.carbon as carbon
import emission.core.common as common
import emission.net.api.stats as stats
from emission.core.wrapper.user import User
from emission.clients.leaderboard import leaderboard
from emission.clients.gamified import gamified
from emission.clients.recommendation import recommendation
from emission.clients.commontrips import commontrips
from emission.clients.data import data
# TODO: Consider subclassing to provide client specific user functions
# These are copy/pasted from our first client, the carshare study
# TODO: Simplify this. runBackgroundTasks are currently only invoked from the
# result precomputation code. We could change that code to pass in the day, and
# remove this interface. Extra credit: should we pass in the day, or a date
# range? Passing in the date range could make it possible for us to run the
# scripts more than once a day...
| [
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2,
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13... | 3.947183 | 284 |
#!/usr/bin/env python
"""
HAR Formatter for REDbot.
"""
__author__ = "Jerome Renard <jerome.renard@gmail.com>"
__copyright__ = """\
Copyright (c) 2008-2010 Mark Nottingham
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.
"""
import operator
import nbhttp.error as nberr
import redbot.speak as rs
from redbot.formatter import Formatter
nl = u"\n"
# TODO: errors and status on stderr with CLI?
| [
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31,
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13,
78... | 3.576623 | 385 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2020-2021 by Murray Altheim. All rights reserved. This file is part
# of the Robot Operating System project, released under the MIT License. Please
# see the LICENSE file included as part of this package.
#
# author: Murray Altheim
# created: 2020-09-19
# modified: 2020-09-19
#
import sys, colorsys
import ioexpander as io
from colorama import init, Fore, Style
init()
from lib.logger import Logger
# ..............................................................................
# return (( self._out_max - self._out_min ) * ( self.get_value() - self._in_min ) / ( self._in_max - self._in_min )) + self._out_min
#EOF
| [
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318,
... | 3.212963 | 216 |
"""
Module: 'urequests' on esp32 1.12.0
"""
# MCU: (sysname='esp32', nodename='esp32', release='1.12.0', version='v1.12 on 2019-12-20', machine='ESP32 module (spiram) with ESP32')
# Stubber: 1.3.2
usocket = None
| [
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6,
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... | 2.393258 | 89 |
#!/usr/bin/env python
#-*- coding=utf-8 -*-
#
# Copyright 2012 Jike Inc. All Rights Reserved.
# Author: liwei@jike.com
import re
from urlparse import urlparse
parse1()
| [
2,
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2,
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198,
2,
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25,
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31,
73,
522,
13,
785,... | 2.725806 | 62 |
from kivy.uix.gridlayout import GridLayout
from kivy.uix.label import Label
from kivy.uix.textinput import TextInput
from kivy.garden.matplotlib.backend_kivyagg import FigureCanvasKivyAgg
from kivy.uix.anchorlayout import AnchorLayout
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.button import Button
import matplotlib.pyplot as plt
import matplotlib
import datetime
from TransactionBook.model.Filter import Filter
from datetime import datetime
from kivy.uix.popup import Popup
from kivy.properties import NumericProperty, ReferenceListProperty
from kivy.uix.checkbox import CheckBox
from kivy.core.window import Window
if __name__ == "__main__":
from kivy.base import runTouchApp
c = MultiSelectPopUp(title="Test", option_list=["Item1", "Item2", "Item3"], callback=cb, option_init=[True, False, True])
runTouchApp(c) | [
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88... | 2.985816 | 282 |
from dataclasses import dataclass, field
from typing import List
from Car2 import Car
| [
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628
] | 3.954545 | 22 |
import subprocess
import threading
import time
import errno
import socket
import urllib
import pathlib
from io import StringIO
from http.server import BaseHTTPRequestHandler, HTTPServer
import lib.stations as stations
import lib.epg2xml as epg2xml
import lib.channels_m3u as channels_m3u
from lib.templates import templates
# with help from https://www.acmesystems.it/python_http
# and https://stackoverflow.com/questions/21631799/how-can-i-pass-parameters-to-a-requesthandler
# mostly from https://github.com/ZeWaren/python-upnp-ssdp-example
# and https://stackoverflow.com/questions/46210672/python-2-7-streaming-http-server-supporting-multiple-connections-on-one-port
def start(config, locast, location):
serverSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
serverSocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
serverSocket.bind((config["main"]['bind_ip'], int(config["main"]['bind_port'])))
serverSocket.listen(int(config["main"]["concurrent_listeners"]))
print("Now listening for requests.")
for i in range(int(config["main"]["concurrent_listeners"])):
PlexHttpServer(serverSocket, config, locast, location)
| [
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9399,
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6738,
2638,
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15388,
1330,
7308,
40717,... | 2.942643 | 401 |
from django import template
from django.contrib.auth.decorators import login_required
from django.http import HttpResponse
from django.template import loader
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40213,... | 3.809524 | 42 |
import os
from pathlib import Path
import numpy as np
AUDIO_FILENAME_ENDINGS = (".aiff", ".flac", ".m4a", ".mp3", ".ogg", ".opus", ".wav")
def get_file_paths(
root_path, filename_endings=AUDIO_FILENAME_ENDINGS, traverse_subdirectories=True
):
"""Return a list of paths to all files with the given filename extensions in a directory.
Also traverses subdirectories by default.
"""
file_paths = []
for root, dirs, filenames in os.walk(root_path):
filenames = sorted(filenames)
for filename in filenames:
input_path = os.path.abspath(root)
file_path = os.path.join(input_path, filename)
if filename.lower().endswith(filename_endings):
file_paths.append(Path(file_path))
if not traverse_subdirectories:
# prevent descending into subfolders
break
return file_paths
def calculate_rms(samples):
"""Given a numpy array of audio samples, return its Root Mean Square (RMS)."""
return np.sqrt(np.mean(np.square(samples), axis=-1))
def calculate_desired_noise_rms(clean_rms, snr):
"""
Given the Root Mean Square (RMS) of a clean sound and a desired signal-to-noise ratio (SNR),
calculate the desired RMS of a noise sound to be mixed in.
Based on https://github.com/Sato-Kunihiko/audio-SNR/blob/8d2c933b6c0afe6f1203251f4877e7a1068a6130/create_mixed_audio_file.py#L20
:param clean_rms: Root Mean Square (RMS) - a value between 0.0 and 1.0
:param snr: Signal-to-Noise (SNR) Ratio in dB - typically somewhere between -20 and 60
:return:
"""
a = float(snr) / 20
noise_rms = clean_rms / (10 ** a)
return noise_rms
def is_waveform_multichannel(samples):
"""
Return bool that answers the question: Is the given ndarray a multichannel waveform or not?
:param samples: numpy ndarray
:return:
"""
return len(samples.shape) > 1
def is_spectrogram_multichannel(spectrogram):
"""
Return bool that answers the question: Is the given ndarray a multichannel spectrogram?
:param samples: numpy ndarray
:return:
"""
return len(spectrogram.shape) > 2 and spectrogram.shape[-1] > 1
def convert_float_samples_to_int16(y):
"""Convert floating-point numpy array of audio samples to int16."""
if not issubclass(y.dtype.type, np.floating):
raise ValueError("input samples not floating-point")
return (y * np.iinfo(np.int16).max).astype(np.int16)
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1600,
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... | 2.547325 | 972 |
s = "([}}])"
stack = []
if len(s) % 2 == 1:
print(False)
exit()
for i in s:
if i == "(":
stack.append("(")
elif i == "[":
stack.append("[")
elif i == "{":
stack.append("{")
elif i == ")":
if len(stack) < 1:
print(False)
exit()
if stack[-1] == "(":
stack.pop()
else:
print(False)
exit()
elif i == "]":
if len(stack) < 1:
print(False)
exit()
if stack[-1] == "[":
stack.pop()
else:
print(False)
exit()
elif i == "}":
if len(stack) < 1:
print(False)
exit()
if stack[-1] == "{":
stack.pop()
else:
print(False)
exit()
if len(stack) == 0:
print(True)
else:
print(False)
| [
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366,
26933,
11709,
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1,
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198,
25558,
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1640,
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287,
264,
... | 1.647388 | 536 |
import random
import string
import os
from IPython.display import display, HTML
from .utils import html_loader
from .utils import get_content
from jinja2 import Template
| [
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17,
... | 3.909091 | 44 |
from decimal import Decimal
from fixtures import * # noqa: F401,F403
from fixtures import TEST_NETWORK
from flaky import flaky # noqa: F401
from pyln.client import RpcError, Millisatoshi
from utils import (
only_one, wait_for, sync_blockheight, EXPERIMENTAL_FEATURES, COMPAT,
VALGRIND
)
import os
import pytest
import subprocess
import time
import unittest
def test_minconf_withdraw(node_factory, bitcoind):
"""Issue 2518: ensure that ridiculous confirmation levels don't overflow
The number of confirmations is used to compute a maximum height that is to
be accepted. If the current height is smaller than the number of
confirmations we wrap around and just select everything. The fix is to
clamp the maxheight parameter to a positive small number.
"""
amount = 1000000
# Don't get any funds from previous runs.
l1 = node_factory.get_node(random_hsm=True)
addr = l1.rpc.newaddr()['bech32']
# Add some funds to withdraw later
for i in range(10):
l1.bitcoin.rpc.sendtoaddress(addr, amount / 10**8 + 0.01)
bitcoind.generate_block(1)
wait_for(lambda: len(l1.rpc.listfunds()['outputs']) == 10)
with pytest.raises(RpcError):
l1.rpc.withdraw(destination=addr, satoshi=10000, feerate='normal', minconf=9999999)
def test_addfunds_from_block(node_factory, bitcoind):
"""Send funds to the daemon without telling it explicitly
"""
# Previous runs with same bitcoind can leave funds!
l1 = node_factory.get_node(random_hsm=True)
addr = l1.rpc.newaddr()['bech32']
bitcoind.rpc.sendtoaddress(addr, 0.1)
bitcoind.generate_block(1)
wait_for(lambda: len(l1.rpc.listfunds()['outputs']) == 1)
outputs = l1.db_query('SELECT value FROM outputs WHERE status=0;')
assert only_one(outputs)['value'] == 10000000
# The address we detect must match what was paid to.
output = only_one(l1.rpc.listfunds()['outputs'])
assert output['address'] == addr
# Send all our money to a P2WPKH address this time.
addr = l1.rpc.newaddr("bech32")['bech32']
l1.rpc.withdraw(addr, "all")
bitcoind.generate_block(1)
time.sleep(1)
# The address we detect must match what was paid to.
output = only_one(l1.rpc.listfunds()['outputs'])
assert output['address'] == addr
# this test does a 'listtransactions' on a yet unconfirmed channel
def test_fundchannel_listtransaction(node_factory, bitcoind):
l1, l2 = node_factory.get_nodes(2)
l1.fundwallet(10**6)
l1.connect(l2)
txid = l1.rpc.fundchannel(l2.info['id'], 10**5)['txid']
# next call warned about SQL Accessing a null column
# and crashed the daemon for accessing random memory or null
txs = l1.rpc.listtransactions()['transactions']
tx = [t for t in txs if t['hash'] == txid][0]
assert tx['blockheight'] == 0
def test_withdraw_nlocktime(node_factory):
"""
Test that we don't set the nLockTime to 0 for withdrawal transactions.
"""
l1 = node_factory.get_node(1)
l1.fundwallet(10**4)
addr = l1.rpc.newaddr()["bech32"]
tx = l1.rpc.withdraw(addr, 10**3)["tx"]
nlocktime = node_factory.bitcoind.rpc.decoderawtransaction(tx)["locktime"]
tip = node_factory.bitcoind.rpc.getblockcount()
assert nlocktime > 0 and nlocktime <= tip
| [
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11,
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198,
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781,
15492,
1330,
781,
15492,
220,
1303,
645,
20402,
25,
376,... | 2.637161 | 1,254 |
"""Provides plots of mutations for Isolates and Lines."""
from microbepy.common import constants as cn
from microbepy.common.dataframe_sorter import DataframeSorter
from microbepy.common.isolate import Isolate
from microbepy.common import util
from microbepy.correlation import genome_correlation
from microbepy.data.model_data_provider import ModelDataProvider
from microbepy.data import util_data
from microbepy.plot.mutation_cofraction import MutationCofraction
from microbepy.plot.util_plot import PlotParms
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
COLORS = ['red', 'green', 'blue']
SPECIES = {cn.SPECIES_MIX_DVH: "DVH",
cn.SPECIES_MIX_MMP: "MMP",
None: "both"}
FONTSIZE_TITLE = 16
FONTSIZE_LABEL = 8
MAX_LINES = 9
MIN_FRACTION = 0.25
THRESHOLD_FRAC = 0.2
MAX_SIGLVL = 0.01
COLORBAR_MIN = 1.0
COLORBAR_MAX = 4.0
| [
37811,
15946,
1460,
21528,
286,
220,
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329,
1148,
349,
689,
290,
26299,
526,
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198,
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65,
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88,
13,
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1330,
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355,
269,
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198,
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4580,
65,
538,
88,
13,
11321,
13,
7890,
14535,
62,
82,
4337... | 2.656627 | 332 |
# Copyright (c) 2017 Sony Corporation. 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.
import numpy as np
import os
from scipy.misc import imread
from args import get_args
import matplotlib.pyplot as plt
def encode_label(label):
'''
Converting pixel values to corresponding class numbers. Assuming that the input label in 3-dim(h,w,c) and in BGR fromat read from cv2
'''
h, w, c = label.shape
new_label = np.zeros((h, w, 1), dtype=np.int32)
cls_to_clr_map = get_color()
for i in range(cls_to_clr_map.shape[0]):
#new_label[(label == cls_to_clr_map[i])[:,:,0]] = i
#new_label[np.argwhere((label.astype(np.int32) == cls_to_clr_map[i]).all(axis=2))]=i
print(np.where((label.astype(np.int32) == [120, 0, 128]).all(axis=2)))
if i == 21:
new_label[np.where(
(label.astype(np.int32) == cls_to_clr_map[i]).all(axis=2))] = 255
else:
new_label[np.where(
(label.astype(np.int32) == cls_to_clr_map[i]).all(axis=2))] = i
return new_label
# this method should generate train-image.txt and train-label.txt
def main():
'''
Arguments:
train-file = txt file containing randomly selected image filenames to be taken as training set.
val-file = txt file containing randomly selected image filenames to be taken as validation set.
data-dir = dataset directory
Usage: python dataset_utils.py --train-file="" --val-file="" --data_dir=""
'''
args = get_args()
data_dir = args.data_dir
if not os.path.exists(data_dir+'SegmentationClass/' + 'encoded/'):
os.makedirs(data_dir+'SegmentationClass/' + 'encoded/')
for filename in os.listdir(data_dir+'SegmentationClass/'):
if os.path.isdir(data_dir+'SegmentationClass/' + filename):
continue
label = imread(data_dir+'SegmentationClass/' +
filename).astype('float32')
label = encode_label(label)
np.save(data_dir+'SegmentationClass/' + 'encoded/' +
filename.split('.')[0] + '.npy', label)
generate_path_files(args.data_dir, args.train_file, args.val_file)
if __name__ == '__main__':
main()
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743,
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11846,
351... | 2.506925 | 1,083 |
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
#
import collections
import os
import torch
import math
from fairseq import bleu, data, options, utils
from fairseq.meters import AverageMeter, StopwatchMeter, TimeMeter
from fairseq.multiprocessing_trainer import MultiprocessingTrainer
from fairseq.progress_bar import progress_bar
from fairseq.sequence_generator import SequenceGenerator
def train(args, epoch, batch_offset, trainer, criterion, dataset, num_gpus):
"""Train the model for one epoch."""
itr = dataset.dataloader(args.train_subset, batch_size=args.batch_size,
test_batch_size=args.test_batch_size,
valid_batch_size=args.valid_batch_size,
num_workers=args.workers,
max_tokens=args.max_tokens, seed=args.seed, epoch=epoch,
max_positions=args.max_positions,
sample_without_replacement=args.sample_without_replacement)
loss_meter = AverageMeter()
bsz_meter = AverageMeter() # sentences per batch
wpb_meter = AverageMeter() # words per batch
wps_meter = TimeMeter() # words per second
clip_meter = AverageMeter() # % of updates clipped
gnorm_meter = AverageMeter() # gradient norm
desc = '| epoch {:03d}'.format(epoch)
lr = trainer.get_lr()
with progress_bar(itr, desc, leave=False) as t:
for i, sample in data.skip_group_enumerator(t, num_gpus, batch_offset):
loss, grad_norm = trainer.train_step(sample, criterion)
ntokens = sum(s['ntokens'] for s in sample)
src_size = sum(s['src_tokens'].size(0) for s in sample)
loss_meter.update(loss, ntokens)
bsz_meter.update(src_size)
wpb_meter.update(ntokens)
wps_meter.update(ntokens)
clip_meter.update(1 if grad_norm > args.clip_norm else 0)
gnorm_meter.update(grad_norm)
t.set_postfix(collections.OrderedDict([
('loss', '{:.2f} ({:.2f})'.format(loss, loss_meter.avg)),
('wps', '{:5d}'.format(round(wps_meter.avg))),
('wpb', '{:5d}'.format(round(wpb_meter.avg))),
('bsz', '{:5d}'.format(round(bsz_meter.avg))),
('lr', lr),
('clip', '{:3.0f}%'.format(clip_meter.avg * 100)),
('gnorm', '{:.4f}'.format(gnorm_meter.avg)),
]))
if i == 0:
# ignore the first mini-batch in words-per-second calculation
wps_meter.reset()
if args.save_interval > 0 and (i + 1) % args.save_interval == 0:
trainer.save_checkpoint(args, epoch, i + 1)
fmt = desc + ' | train loss {:2.2f} | train ppl {:3.2f}'
fmt += ' | s/checkpoint {:7d} | words/s {:6d} | words/batch {:6d}'
fmt += ' | bsz {:5d} | lr {:0.6f} | clip {:3.0f}% | gnorm {:.4f}'
t.write(fmt.format(loss_meter.avg, math.pow(2, loss_meter.avg),
round(wps_meter.elapsed_time),
round(wps_meter.avg),
round(wpb_meter.avg),
round(bsz_meter.avg),
lr, clip_meter.avg * 100,
gnorm_meter.avg))
def validate(args, epoch, trainer, criterion, dataset, subset, ngpus):
"""Evaluate the model on the validation set and return the average loss."""
itr = dataset.dataloader(subset, batch_size=None,
max_tokens=args.max_tokens,
max_positions=args.max_positions)
loss_meter = AverageMeter()
desc = '| epoch {:03d} | valid on \'{}\' subset'.format(epoch, subset)
with progress_bar(itr, desc, leave=False) as t:
for _, sample in data.skip_group_enumerator(t, ngpus):
ntokens = sum(s['ntokens'] for s in sample)
loss = trainer.valid_step(sample, criterion)
loss_meter.update(loss, ntokens)
t.set_postfix(loss='{:.2f}'.format(loss_meter.avg))
val_loss = loss_meter.avg
t.write(desc + ' | valid loss {:2.2f} | valid ppl {:3.2f}'
.format(val_loss, math.pow(2, val_loss)))
# update and return the learning rate
return val_loss
def score_test(args, model, dataset, subset, beam, cuda_device):
"""Evaluate the model on the test set and return the BLEU scorer."""
translator = SequenceGenerator([model], dataset.dst_dict, beam_size=beam)
if torch.cuda.is_available():
translator.cuda()
scorer = bleu.Scorer(dataset.dst_dict.pad(), dataset.dst_dict.eos(), dataset.dst_dict.unk())
itr = dataset.dataloader(subset, batch_size=4, max_positions=args.max_positions)
for _, _, ref, hypos in translator.generate_batched_itr(itr, cuda_device=cuda_device):
scorer.add(ref.int().cpu(), hypos[0]['tokens'].int().cpu())
return scorer
if __name__ == '__main__':
main()
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286,
42... | 2.104292 | 2,493 |
#!/usr/bin/env python
"""
This parses a log file series (i.e. log, log.1, log.2, etc..) and
outputs timing and call frequency information for HAL messages.
Hazen 5/18
"""
from datetime import datetime
import os
pattern = '%Y-%m-%d %H:%M:%S,%f'
def getIterable(dict_or_list):
"""
Returns an iterable given a dictionary of a list.
"""
if isinstance(dict_or_list, dict):
iterable = list(dict_or_list.values())
elif isinstance(dict_or_list, list):
iterable = dict_or_list
else:
raise Exception("Unknown type '" + str(type(dict_or_list)) + "'")
return iterable
def groupByMsgType(messages):
"""
Returns a dictionary keyed by message type, with a list of one or
more message objects per message type.
"""
return groupByX(lambda x : x.getType(),
messages)
def groupBySource(messages):
"""
Returns a dictionary keyed by message source, with a list of one or
more message objects per message source.
"""
return groupByX(lambda x : x.getSource(),
messages)
def groupByX(grp_fn, messages):
"""
Returns a dictionary keyed by the requested group.
"""
m_grp = {}
for msg in getIterable(messages):
# Ignore messages that we don't have all the timing for.
if msg.isComplete() or not ignore_incomplete:
m_type = grp_fn(msg)
if m_type in m_grp:
m_grp[m_type].append(msg)
else:
m_grp[m_type] = [msg]
return m_grp
def logTiming(basename, ignore_incomplete = False):
"""
Returns a dictionary of Message objects keyed by their ID number.
"""
zero_time = None
messages = {}
for ext in [".5", ".4", ".3", ".2", ".1", ""]:
fname = basename + ".out" + ext
if not os.path.exists(fname):
print(fname, "not found.")
continue
with open(fname) as fp:
for line in fp:
try:
[time, command] = map(lambda x: x.strip(), line.split(":hal4000:INFO:"))
except ValueError:
continue
if zero_time is None:
zero_time = time
# Message queued.
if (command.startswith("queued,")):
[m_id, source, m_type] = command.split(",")[1:]
messages[m_id] = Message(m_type = m_type,
source = source,
time = time,
zero_time = zero_time)
# Message sent.
elif (command.startswith("sent,")):
m_id = command.split(",")[1]
messages[m_id].sent(time)
# Message processed.
elif (command.startswith("processed,")):
m_id = command.split(",")[1]
messages[m_id].processed(time)
elif (command.startswith("worker done,")):
m_id = command.split(",")[1]
messages[m_id].incNWorkers()
# Ignore messages that we don't have all the timing for.
if not ignore_incomplete:
temp = {}
for m_id in messages:
msg = messages[m_id]
if msg.isComplete():
temp[m_id] = msg
return temp
else:
return messages
def processingTime(messages):
"""
Returns the total processing time for a collection of messages.
"""
accum_time = 0
for msg in getIterable(messages):
if isinstance(msg, list):
for elt in msg:
accum_time += elt.getProcessingTime()
else:
accum_time += msg.getProcessingTime()
return accum_time
def queuedTime(messages):
"""
Returns the total queued time for a a collection of messages.
"""
accum_time = 0
for msg in getIterable(messages):
if isinstance(msg, list):
for elt in msg:
accum_time += elt.getQueuedTime()
else:
accum_time += msg.getQueuedTime()
return accum_time
if (__name__ == "__main__"):
import sys
if (len(sys.argv) != 2):
print("usage: <log file>")
exit()
messages = logTiming(sys.argv[1])
groups = groupByMsgType(messages)
print()
print("All messages:")
for key in sorted(groups):
grp = groups[key]
print(key + ", {0:0d} counts, {1:.3f} seconds".format(len(grp), processingTime(grp)))
print("Total queued time {0:.3f} seconds".format(queuedTime(groups)))
print("Total processing time {0:.3f} seconds".format(processingTime(groups)))
print()
print("Film messages:")
groups = groupByMsgType(groupBySource(messages)["film"])
for key in sorted(groups):
grp = groups[key]
print(key + ", {0:0d} counts, {1:.3f} seconds".format(len(grp), processingTime(grp)))
print("Total processing time {0:.3f} seconds".format(processingTime(groups)))
| [
2,
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14,
8800,
14,
24330,
21015,
198,
37811,
198,
1212,
13544,
274,
257,
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2393,
2168,
357,
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13,
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8,
290,
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290,
869,
8373,
... | 2.05309 | 2,524 |
from django.core.management.base import BaseCommand
from django.utils import termcolors
from jsonschema import Draft4Validator
from jsonschema.exceptions import SchemaError
import json
| [
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42625,
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13650,
19,
47139,
1352,
198,
6738,
44804,
684,
2395,
2611,
13,
106... | 3.72 | 50 |
import cv2, time
import numpy as np
import Tkinter
"""
Wraps up some interfaces to opencv user interface methods (displaying
image frames, event handling, etc).
If desired, an alternative UI could be built and imported into get_pulse.py
instead. Opencv is used to perform much of the data analysis, but there is no
reason it has to be used to handle the UI as well. It just happens to be very
effective for our purposes.
"""
"""
The rest of this file defines some GUI plotting functionality. There are plenty
of other ways to do simple x-y data plots in python, but this application uses
cv2.imshow to do real-time data plotting and handle user interaction.
This is entirely independent of the data calculation functions, so it can be
replaced in the get_pulse.py application easily.
"""
def combine(left, right):
"""Stack images horizontally.
"""
h = max(left.shape[0], right.shape[0])
w = left.shape[1] + right.shape[1]
hoff = left.shape[0]
shape = list(left.shape)
shape[0] = h
shape[1] = w
comb = np.zeros(tuple(shape),left.dtype)
# left will be on left, aligned top, with right on right
comb[:left.shape[0],:left.shape[1]] = left
comb[:right.shape[0],left.shape[1]:] = right
return comb
| [
11748,
269,
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17,
11,
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198,
11748,
299,
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355,
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198,
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13431,
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1785,
9041,
11,
... | 3.033254 | 421 |
# -*- coding: utf-8 -*-
# Natural Language Toolkit: Transformation-based learning
#
# Copyright (C) 2001-2018 NLTK Project
# Author: Marcus Uneson <marcus.uneson@gmail.com>
# based on previous (nltk2) version by
# Christopher Maloof, Edward Loper, Steven Bird
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from __future__ import print_function, division
from collections import defaultdict, Counter
from nltk.tag import TaggerI
from nltk.tbl import Feature, Template
from nltk import jsontags
######################################################################
# Brill Templates
######################################################################
def nltkdemo18():
"""
Return 18 templates, from the original nltk demo, in multi-feature syntax
"""
return [
Template(Pos([-1])),
Template(Pos([1])),
Template(Pos([-2])),
Template(Pos([2])),
Template(Pos([-2, -1])),
Template(Pos([1, 2])),
Template(Pos([-3, -2, -1])),
Template(Pos([1, 2, 3])),
Template(Pos([-1]), Pos([1])),
Template(Word([-1])),
Template(Word([1])),
Template(Word([-2])),
Template(Word([2])),
Template(Word([-2, -1])),
Template(Word([1, 2])),
Template(Word([-3, -2, -1])),
Template(Word([1, 2, 3])),
Template(Word([-1]), Word([1])),
]
def nltkdemo18plus():
"""
Return 18 templates, from the original nltk demo, and additionally a few
multi-feature ones (the motivation is easy comparison with nltkdemo18)
"""
return nltkdemo18() + [
Template(Word([-1]), Pos([1])),
Template(Pos([-1]), Word([1])),
Template(Word([-1]), Word([0]), Pos([1])),
Template(Pos([-1]), Word([0]), Word([1])),
Template(Pos([-1]), Word([0]), Pos([1])),
]
def fntbl37():
"""
Return 37 templates taken from the postagging task of the
fntbl distribution http://www.cs.jhu.edu/~rflorian/fntbl/
(37 is after excluding a handful which do not condition on Pos[0];
fntbl can do that but the current nltk implementation cannot.)
"""
return [
Template(Word([0]), Word([1]), Word([2])),
Template(Word([-1]), Word([0]), Word([1])),
Template(Word([0]), Word([-1])),
Template(Word([0]), Word([1])),
Template(Word([0]), Word([2])),
Template(Word([0]), Word([-2])),
Template(Word([1, 2])),
Template(Word([-2, -1])),
Template(Word([1, 2, 3])),
Template(Word([-3, -2, -1])),
Template(Word([0]), Pos([2])),
Template(Word([0]), Pos([-2])),
Template(Word([0]), Pos([1])),
Template(Word([0]), Pos([-1])),
Template(Word([0])),
Template(Word([-2])),
Template(Word([2])),
Template(Word([1])),
Template(Word([-1])),
Template(Pos([-1]), Pos([1])),
Template(Pos([1]), Pos([2])),
Template(Pos([-1]), Pos([-2])),
Template(Pos([1])),
Template(Pos([-1])),
Template(Pos([-2])),
Template(Pos([2])),
Template(Pos([1, 2, 3])),
Template(Pos([1, 2])),
Template(Pos([-3, -2, -1])),
Template(Pos([-2, -1])),
Template(Pos([1]), Word([0]), Word([1])),
Template(Pos([1]), Word([0]), Word([-1])),
Template(Pos([-1]), Word([-1]), Word([0])),
Template(Pos([-1]), Word([0]), Word([1])),
Template(Pos([-2]), Pos([-1])),
Template(Pos([1]), Pos([2])),
Template(Pos([1]), Pos([2]), Word([1]))
]
def brill24():
"""
Return 24 templates of the seminal TBL paper, Brill (1995)
"""
return [
Template(Pos([-1])),
Template(Pos([1])),
Template(Pos([-2])),
Template(Pos([2])),
Template(Pos([-2, -1])),
Template(Pos([1, 2])),
Template(Pos([-3, -2, -1])),
Template(Pos([1, 2, 3])),
Template(Pos([-1]), Pos([1])),
Template(Pos([-2]), Pos([-1])),
Template(Pos([1]), Pos([2])),
Template(Word([-1])),
Template(Word([1])),
Template(Word([-2])),
Template(Word([2])),
Template(Word([-2, -1])),
Template(Word([1, 2])),
Template(Word([-1, 0])),
Template(Word([0, 1])),
Template(Word([0])),
Template(Word([-1]), Pos([-1])),
Template(Word([1]), Pos([1])),
Template(Word([0]), Word([-1]), Pos([-1])),
Template(Word([0]), Word([1]), Pos([1])),
]
def describe_template_sets():
"""
Print the available template sets in this demo, with a short description"
"""
import inspect
import sys
# a bit of magic to get all functions in this module
templatesets = inspect.getmembers(sys.modules[__name__], inspect.isfunction)
for (name, obj) in templatesets:
if name == "describe_template_sets":
continue
print(name, obj.__doc__, "\n")
######################################################################
# The Brill Tagger
######################################################################
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16984,
15813,
25,
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12,
3106,
4673,
198,
2,
198,
2,
15069,
357,
34,
8,
5878,
12,
7908,
22879,
51,
42,
4935,
198,
2,
6434,
25,
17068,
791,
42038... | 2.301616 | 2,228 |
import json
import logging
import sys
import numpy as np
import torch
from task_config import SuperGLUE_LABEL_MAPPING
from snorkel.mtl.data import MultitaskDataset
sys.path.append("..") # Adds higher directory to python modules path.
logger = logging.getLogger(__name__)
TASK_NAME = "WSC"
| [
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1... | 2.950495 | 101 |
import re
import json
__all__ = ["Simplimental"]
| [
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439,
834,
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] | 2.941176 | 17 |
# This example shows how to read or modify the Axes Optimization settings using the RoboDK API and a JSON string.
# You can select "Axes optimization" in a robot machining menu or the robot parameters to view the axes optimization settings.
# It is possible to update the axes optimization settings attached to a robot or a robot machining project manually or using the API.
#
# More information about the RoboDK API here:
# https://robodk.com/doc/en/RoboDK-API.html
# For more information visit:
# https://robodk.com/doc/en/PythonAPI/robolink.html
from robolink import * # RoboDK API
# JSON tools
import json
# Start the RoboDK API
RDK = Robolink()
# Ask the user to select a robot arm (6 axis robot wich can have external axes)
robot = RDK.ItemUserPick("Select a robot arm",ITEM_TYPE_ROBOT_ARM)
# Default optimization settings test template
AxesOptimSettings = {
# Optimization parameters:
"Active": 1, # Use generic axes optimization: 0=Disabled or 1=Enabled
"Algorithm": 2, # Optimization algorithm to use: 1=Nelder Mead, 2=Samples, 3=Samples+Nelder Mead
"MaxIter": 650, # Max. number of iterations
"Tol": 0.0016, # Tolerance to stop iterations
# Absolute Reference joints (double):
"AbsJnt_1": 104.17,
"AbsJnt_2": 11.22,
"AbsJnt_3": 15.97,
"AbsJnt_4": -87.48,
"AbsJnt_5": -75.36,
"AbsJnt_6": 63.03,
"AbsJnt_7": 174.13,
"AbsJnt_8": 173.60,
"AbsJnt_9": 0,
# Using Absolute reference joints (0: No, 1: Yes):
"AbsOn_1": 1,
"AbsOn_2": 1,
"AbsOn_3": 1,
"AbsOn_4": 1,
"AbsOn_5": 1,
"AbsOn_6": 1,
"AbsOn_7": 1,
"AbsOn_8": 1,
"AbsOn_9": 1,
# Weight for absolute reference joints (double):
"AbsW_1": 100,
"AbsW_2": 100,
"AbsW_3": 100,
"AbsW_4": 89,
"AbsW_5": 90,
"AbsW_6": 92,
"AbsW_7": 92,
"AbsW_8": 96,
"AbsW_9": 50,
# Using for relative joint motion smoothing (0: No, 1: Yes):
"RelOn_1": 1,
"RelOn_2": 1,
"RelOn_3": 1,
"RelOn_4": 1,
"RelOn_5": 1,
"RelOn_6": 1,
"RelOn_7": 1,
"RelOn_8": 1,
"RelOn_9": 1,
# Weight for relative joint motion (double):
"RelW_1": 5,
"RelW_2": 47,
"RelW_3": 44,
"RelW_4": 43,
"RelW_5": 36,
"RelW_6": 47,
"RelW_7": 53,
"RelW_8": 59,
"RelW_9": 0,
}
# Update one value, for example, make it active:
ToUpdate = {}
ToUpdate["Active"] = 1
json_str = json.dumps(json.dumps(ToUpdate))
status = robot.setParam("OptimAxes", json_str)
print(status)
# Example to make a partial or full update
count = 1
while True:
for i in range(7):
# Partial update
ToUpdate = {}
ToUpdate["AbsJnt_" + str(i+1)] = (count+i)*4
ToUpdate["AbsOn_" + str(i+1)] = count % 2
ToUpdate["AbsW_" + str(i+1)] = (count+i)
json_str = json.dumps(json.dumps(ToUpdate))
status = robot.setParam("OptimAxes", json_str)
print(status)
# Full update
#OptimAxes_TEST["RefJoint_" + str(i+1)] = (count+i)*4
#OptimAxes_TEST["RefWeight_" + str(i+1)] = (count+i)
#OptimAxes_TEST["RefOn_" + str(i+1)] = count % 2
# Full update
#print(robot.setParam("OptimAxes", str(AxesOptimSettings)))
count = count + 1
# Read settings
json_data = robot.setParam("OptimAxes")
json_object = json.loads(json_data)
print(json.dumps(json_object, indent=4))
pause(0.2)
# Example to read the current axes optimization settings:
while True:
json_data = robot.setParam("OptimAxes")
json_object = json.loads(json_data)
print(json.dumps(json_object, indent=4))
pause(0.2)
| [
2,
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1672,
2523,
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284,
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274,
23989,
1,
287,
257,
9379,
3235,
3191,
6859,
393,
... | 2.274547 | 1,599 |
from slr_parser.grammar import Grammar
import unittest
if __name__ == '__main__':
unittest.main()
| [
6738,
1017,
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220,
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555,
715,
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12417,
3419,
198
] | 2.625 | 40 |
# Generated by Django 3.1 on 2020-09-08 07:43
from django.db import migrations, models
import django.db.models.deletion
| [
2,
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515,
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295,
628
] | 2.904762 | 42 |
import torch
import numpy as np
from mpi4py import MPI
from parallel_pytorch.ops import tensor_merge
from parallel_pytorch.utils import abort_on_exception
def run_all():
test_1()
test_2()
if __name__ == '__main__':
run_all()
| [
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2679... | 2.595745 | 94 |
"""Day 07"""
if __name__ == '__main__':
process('test.txt')
process('input.txt')
| [
37811,
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198
] | 2.432432 | 37 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
utilities
@author: boyangzhao
"""
import pandas as pd
import re
| [
2,
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279... | 2.32 | 50 |
#!/usr/bin/python3.7
# Copyright 2020 Aragubas
#
# 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.
#
#
# -- Imports -- #
from ENGINE import APPDATA as reg
from ENGINE import UTILS as utils
import ENGINE as tge
from Fogoso.MAIN import ClassesUtils as gameObjs
from Fogoso import MAIN as gameMain
import pygame, sys
import importlib
import time
from random import randint
OptionsScreen_DebugModeEnabled = gameObjs.UpDownButton
OptionsScreen_RandomWindowTitle = gameObjs.UpDownButton
OptionsScreen_NumberFormatting = gameObjs.UpDownButton
ElementsX = 0
ElementsY = 0
| [
2,
48443,
14629,
14,
8800,
14,
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18,
13,
22,
198,
2,
220,
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2,
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362,
13,
15,
357,
1169,
366,
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15341,
198,
2,
220,
22... | 3.328221 | 326 |
from robotpy_ext.control.toggle import Toggle
from robotpy_ext.misc.precise_delay import NotifierDelay
| [
6738,
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13,
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1330,
1892,
7483,
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323,
628,
628
] | 3.419355 | 31 |
'''
This file contains test cases for tflearn
'''
import tensorflow.compat.v1 as tf
import tflearn
import unittest
if __name__ == "__main__":
unittest.main() | [
7061,
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1501,
198,
11748,
555,
715,
395,
198,
... | 2.609375 | 64 |
#!/usr/bin/python3
# *****************************************************************************
#
# 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.
#
# ******************************************************************************
import json
import os
import sys
import subprocess
if __name__ == "__main__":
success = True
try:
subprocess.run('cd /root; fab install-libs', shell=True, check=True)
except:
success = False
reply = dict()
reply['request_id'] = os.environ['request_id']
if success:
reply['status'] = 'ok'
else:
reply['status'] = 'err'
reply['response'] = dict()
try:
with open("/root/result.json") as f:
reply['response']['result'] = json.loads(f.read())
except:
reply['response']['result'] = {"error": "Failed to open result.json"}
reply['response']['log'] = "/var/log/datalab/{0}/{0}_{1}_{2}.log".format(os.environ['conf_resource'],
os.environ['project_name'],
os.environ['request_id'])
with open("/response/{}_{}_{}.json".format(os.environ['conf_resource'], os.environ['project_name'],
os.environ['request_id']), 'w') as response_file:
response_file.write(json.dumps(reply))
try:
subprocess.run('chmod 666 /response/*', shell=True, check=True)
except:
success = False
if not success:
sys.exit(1) | [
2,
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14,
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2,
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517,
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5964,
11704,
13,
220,
4091,
262,
28536,
23... | 2.567148 | 901 |
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
config settings, will be used in finetune.py
"""
from easydict import EasyDict as edict
import mindspore.common.dtype as mstype
from .bert_model import BertConfig
cfg = edict({
'task': 'NER',
'num_labels': 41,
'data_file': '',
'schema_file': None,
'finetune_ckpt': '',
'use_crf': False,
'clue_benchmark': False,
})
bert_net_cfg = BertConfig(
batch_size=8 if not cfg.clue_benchmark else 1,
seq_length=512,
vocab_size=30522,
hidden_size=1024,
num_hidden_layers=24,
num_attention_heads=16,
intermediate_size=4096,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=False,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float16,
)
| [
2,
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43208,
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1539,
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198,
2,
198,
2,
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739,
262,
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11,
10628,
362,
13,
15,
357,
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366,
34156,
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198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
198... | 2.80354 | 565 |
# -*- coding: utf-8 -*-
# utopia-cms 2020. Anbal Pacheco.
from django.core.management import BaseCommand
from django.db.utils import IntegrityError
from apps import core_articleviewedby_mdb
from core.models import ArticleViewedBy
| [
2,
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9,
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69,
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13,
27604,
1330,
7308,
21575,
198,
6738,
42625,
14208,
13,... | 3.106667 | 75 |
import torch
import torch.nn as nn
from torch.optim import SGD
import MinkowskiEngine as ME
from MinkowskiEngine.modules.resnet_block import BasicBlock, Bottleneck
from examples.common import data_loader
from examples.resnet import ResNetBase
if __name__ == '__main__':
# loss and network
criterion = nn.CrossEntropyLoss()
net = MinkUNet14A(in_channels=3, out_channels=5, D=2)
print(net)
# a data loader must return a tuple of coords, features, and labels.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = net.to(device)
optimizer = SGD(net.parameters(), lr=1e-2)
for i in range(10):
optimizer.zero_grad()
# Get new data
coords, feat, label = data_loader(is_classification=False)
input = ME.SparseTensor(feat, coords=coords).to(device)
label = label.to(device)
# Forward
output = net(input)
# Loss
loss = criterion(output.F, label)
print('Iteration: ', i, ', Loss: ', loss.item())
# Gradient
loss.backward()
optimizer.step()
# Saving and loading a network
torch.save(net.state_dict(), 'test.pth')
net.load_state_dict(torch.load('test.pth'))
| [
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1... | 2.458661 | 508 |
"""TODO."""
from setuptools import setup
setup(
name='nginx-access-tailer',
version='0.1',
author='swfrench',
url='https://github.com/swfrench/nginx-tailer',
packages=['nginx_access_tailer',],
license='BSD three-clause license',
entry_points={
'console_scripts': ['nginx-access-tailer = nginx_access_tailer.__main__:main'],
},
install_requires=[
'python-gflags >= 3.1.1',
'google-cloud-monitoring >= 0.25.0',
],
test_suite='nose.collector',
tests_require=['nose', 'mock'],
)
| [
37811,
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46,
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6738,
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263,
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198,
220,
220,
220,
2196,
11639,
15,
13,
16,
3256,
198... | 2.259259 | 243 |
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .. import helpers
from . import integration
| [
2,
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2,
198,
2,
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2,
... | 3.890244 | 164 |
#!/usr/bin/env python
# -*- coding: utf-8 -*
import os
from setuptools import find_packages, setup
# allow setup.py to be run from any path
os.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir)))
with open('requirements.txt') as f:
install_requires = f.read().splitlines()
setup(
name='persistent-celery-beat-scheduler',
version='0.1.1.dev0',
packages=find_packages('src', exclude=('tests',)),
package_dir={'': 'src'},
include_package_data=True,
zip_safe=False,
description=(
'Celery Beat Scheduler that stores the scheduler data in Redis.'
),
author='Richard O\'Dwyer',
author_email='richard@richard.do',
license='Apache 2',
long_description='https://github.com/richardasaurus/persistent-celery-beat-scheduler',
install_requires=install_requires,
classifiers=[
'Intended Audience :: Developers',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Topic :: Internet :: WWW/HTTP',
],
)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
198,
198,
11748,
28686,
198,
198,
6738,
900,
37623,
10141,
1330,
1064,
62,
43789,
11,
9058,
198,
198,
2,
1249,
9058,
13,
9078,
... | 2.584466 | 515 |
import collections
import unittest
import driver
from driver.protocol import *
_server = ('localhost', 11211)
_dead_retry = 30
_socket_timeout = 3
_max_receive_size = 4096
def _raise_exception(message):
raise Exception(message)
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48678... | 3.118421 | 76 |
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
path
# code starts here
bank = pd.read_csv(path)
categorical_var = bank.select_dtypes(include = 'object')
print(categorical_var)
numerical_var = bank.select_dtypes(include = 'number')
print(numerical_var)
# code ends here
# --------------
# code starts here
banks = bank.drop('Loan_ID',axis = 1)
print(banks)
print(banks.isnull().sum())
bank_mode = banks.mode().iloc[0]
banks = banks.fillna(bank_mode)
#code ends here
# --------------
# Code starts here
avg_loan_amount = banks.pivot_table(index=['Gender','Married','Self_Employed'],values = 'LoanAmount')
# code ends here
# --------------
# code starts here
loan_approved_se = ((banks['Self_Employed']=='Yes') & (banks['Loan_Status']=='Y')).value_counts()
#print(loan_approved_se)
loan_approved_nse = ((banks['Self_Employed']=='No') & (banks['Loan_Status']=='Y')).value_counts()
print(loan_approved_nse)
Loan_Status = 614
percentage_se = (56/Loan_Status)*100
percentage_nse = (366/Loan_Status)*100
# code ends here
# --------------
# code starts here
loan_term = banks['Loan_Amount_Term'].apply (lambda x : int(x)/12)
print(loan_term.value_counts())
big_loan = [i for i in loan_term if i >= 25]
big_loan_term = len(big_loan)
print(big_loan_term)
#[loan_term.value_counts()[i] for i in range(len(loan_terms)) if loan_term.value_counts().index[i] >= 25]
# code ends here
# --------------
# code starts here
loan_groupby = banks.groupby('Loan_Status')
loan_groupby = loan_groupby['ApplicantIncome','Credit_History']
mean_values = loan_groupby.mean()
# code ends here
| [
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... | 2.680713 | 617 |
# This file is part of Patsy
# Copyright (C) 2013 Nathaniel Smith <njs@pobox.com>
# See file LICENSE.txt for license information.
# Regression tests for fixed bugs (when not otherwise better covered somewhere
# else)
from patsy import (EvalEnvironment, dmatrix, build_design_matrices,
PatsyError, Origin)
| [
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525... | 3.018519 | 108 |
__all__ = ['imread', 'imsave']
import numpy as np
from PIL import Image
from ...util import img_as_ubyte, img_as_uint
def imread(fname, dtype=None, img_num=None, **kwargs):
"""Load an image from file.
Parameters
----------
fname : str or file
File name or file-like-object.
dtype : numpy dtype object or string specifier
Specifies data type of array elements.
img_num : int, optional
Specifies which image to read in a file with multiple images
(zero-indexed).
kwargs : keyword pairs, optional
Addition keyword arguments to pass through.
Notes
-----
Files are read using the Python Imaging Library.
See PIL docs [1]_ for a list of supported formats.
References
----------
.. [1] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html
"""
if isinstance(fname, str):
with open(fname, 'rb') as f:
im = Image.open(f)
return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
else:
im = Image.open(fname)
return pil_to_ndarray(im, dtype=dtype, img_num=img_num)
def pil_to_ndarray(image, dtype=None, img_num=None):
"""Import a PIL Image object to an ndarray, in memory.
Parameters
----------
Refer to ``imread``.
"""
try:
# this will raise an IOError if the file is not readable
image.getdata()[0]
except IOError as e:
site = "http://pillow.readthedocs.org/en/latest/installation.html#external-libraries"
pillow_error_message = str(e)
error_message = ('Could not load "%s" \n'
'Reason: "%s"\n'
'Please see documentation at: %s'
% (image.filename, pillow_error_message, site))
raise ValueError(error_message)
frames = []
grayscale = None
i = 0
while 1:
try:
image.seek(i)
except EOFError:
break
frame = image
if img_num is not None and img_num != i:
image.getdata()[0]
i += 1
continue
if image.format == 'PNG' and image.mode == 'I' and dtype is None:
dtype = 'uint16'
if image.mode == 'P':
if grayscale is None:
grayscale = _palette_is_grayscale(image)
if grayscale:
frame = image.convert('L')
else:
if image.format == 'PNG' and 'transparency' in image.info:
frame = image.convert('RGBA')
else:
frame = image.convert('RGB')
elif image.mode == '1':
frame = image.convert('L')
elif 'A' in image.mode:
frame = image.convert('RGBA')
elif image.mode == 'CMYK':
frame = image.convert('RGB')
if image.mode.startswith('I;16'):
shape = image.size
dtype = '>u2' if image.mode.endswith('B') else '<u2'
if 'S' in image.mode:
dtype = dtype.replace('u', 'i')
frame = np.fromstring(frame.tobytes(), dtype)
frame.shape = shape[::-1]
else:
frame = np.array(frame, dtype=dtype)
frames.append(frame)
i += 1
if img_num is not None:
break
if hasattr(image, 'fp') and image.fp:
image.fp.close()
if img_num is None and len(frames) > 1:
return np.array(frames)
elif frames:
return frames[0]
elif img_num:
raise IndexError('Could not find image #%s' % img_num)
def _palette_is_grayscale(pil_image):
"""Return True if PIL image in palette mode is grayscale.
Parameters
----------
pil_image : PIL image
PIL Image that is in Palette mode.
Returns
-------
is_grayscale : bool
True if all colors in image palette are gray.
"""
assert pil_image.mode == 'P'
# get palette as an array with R, G, B columns
palette = np.asarray(pil_image.getpalette()).reshape((256, 3))
# Not all palette colors are used; unused colors have junk values.
start, stop = pil_image.getextrema()
valid_palette = palette[start:stop + 1]
# Image is grayscale if channel differences (R - G and G - B)
# are all zero.
return np.allclose(np.diff(valid_palette), 0)
def ndarray_to_pil(arr, format_str=None):
"""Export an ndarray to a PIL object.
Parameters
----------
Refer to ``imsave``.
"""
if arr.ndim == 3:
arr = img_as_ubyte(arr)
mode = {3: 'RGB', 4: 'RGBA'}[arr.shape[2]]
elif format_str in ['png', 'PNG']:
mode = 'I;16'
mode_base = 'I'
if arr.dtype.kind == 'f':
arr = img_as_uint(arr)
elif arr.max() < 256 and arr.min() >= 0:
arr = arr.astype(np.uint8)
mode = mode_base = 'L'
else:
arr = img_as_uint(arr)
else:
arr = img_as_ubyte(arr)
mode = 'L'
mode_base = 'L'
try:
array_buffer = arr.tobytes()
except AttributeError:
array_buffer = arr.tostring() # Numpy < 1.9
if arr.ndim == 2:
im = Image.new(mode_base, arr.T.shape)
try:
im.frombytes(array_buffer, 'raw', mode)
except AttributeError:
im.fromstring(array_buffer, 'raw', mode) # PIL 1.1.7
else:
image_shape = (arr.shape[1], arr.shape[0])
try:
im = Image.frombytes(mode, image_shape, array_buffer)
except AttributeError:
im = Image.fromstring(mode, image_shape, array_buffer) # PIL 1.1.7
return im
def imsave(fname, arr, format_str=None, **kwargs):
"""Save an image to disk.
Parameters
----------
fname : str or file-like object
Name of destination file.
arr : ndarray of uint8 or float
Array (image) to save. Arrays of data-type uint8 should have
values in [0, 255], whereas floating-point arrays must be
in [0, 1].
format_str: str
Format to save as, this is defaulted to PNG if using a file-like
object; this will be derived from the extension if fname is a string
kwargs: dict
Keyword arguments to the Pillow save function (or tifffile save
function, for Tiff files). These are format dependent. For example,
Pillow's JPEG save function supports an integer ``quality`` argument
with values in [1, 95], while TIFFFile supports a ``compress``
integer argument with values in [0, 9].
Notes
-----
Use the Python Imaging Library.
See PIL docs [1]_ for a list of other supported formats.
All images besides single channel PNGs are converted using `img_as_uint8`.
Single Channel PNGs have the following behavior:
- Integer values in [0, 255] and Boolean types -> img_as_uint8
- Floating point and other integers -> img_as_uint16
References
----------
.. [1] http://pillow.readthedocs.org/en/latest/handbook/image-file-formats.html
"""
# default to PNG if file-like object
if not isinstance(fname, str) and format_str is None:
format_str = "PNG"
# Check for png in filename
if (isinstance(fname, str)
and fname.lower().endswith(".png")):
format_str = "PNG"
arr = np.asanyarray(arr)
if arr.dtype.kind == 'b':
arr = arr.astype(np.uint8)
if arr.ndim not in (2, 3):
raise ValueError("Invalid shape for image array: %s" % (arr.shape, ))
if arr.ndim == 3:
if arr.shape[2] not in (3, 4):
raise ValueError("Invalid number of channels in image array.")
img = ndarray_to_pil(arr, format_str=format_str)
img.save(fname, format=format_str, **kwargs)
| [
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62,
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62,
549,
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11,
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62,
292,
... | 2.218286 | 3,500 |
# -*- coding: utf-8 -*-
"""
Linear chain of reactions.
"""
from __future__ import print_function, division
import tellurium as te
model = '''
model feedback()
// Reactions:
J0: $X0 -> S1; (VM1 * (X0 - S1/Keq1))/(1 + X0 + S1 + S4^h);
J1: S1 -> S2; (10 * S1 - 2 * S2) / (1 + S1 + S2);
J2: S2 -> S3; (10 * S2 - 2 * S3) / (1 + S2 + S3);
J3: S3 -> S4; (10 * S3 - 2 * S4) / (1 + S3 + S4);
J4: S4 -> $X1; (V4 * S4) / (KS4 + S4);
// Species initializations:
S1 = 0; S2 = 0; S3 = 0;
S4 = 0; X0 = 10; X1 = 0;
// Variable initialization:
VM1 = 10; Keq1 = 10; h = 10; V4 = 2.5; KS4 = 0.5;
end'''
r = te.loada(model)
result = r.simulate(0, 40, 500)
r.plotWithLegend(result)
| [
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198,
198,
... | 2.002882 | 347 |
from .users import User, UserCreate, UserUpdate
from .transactions import Transaction, TransactionCreate, TransactionUpdate
from .accounts import Account, AccountList, AccountSingle, AccountCreate, AccountUpdate
from .categories import Category, CategoryCreate, CategoryUpdate | [
6738,
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... | 4.928571 | 56 |
def method_accepting_cls(cls, self):
# Using plain `super()` is not valid here, since there's no `__class__` cell found
# (Exact exception would be 'RuntimeError: super(): __class__ cell not found')
# Instead, we expect to *not* see a warning about `super-with-arguments`.
# Explicitly passing `cls`, and `self` to `super()` is what's required.
super(cls, self).__init__()
| [
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628,
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198,
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... | 2.941176 | 136 |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from flask import flash
import numpy as np
| [
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... | 3.662791 | 86 |
# This version of the bitcoin experiment imports data preprocessed in Matlab, and uses the GCN baseline
# The point of this script is to do link prediction
# Imports and aliases
import pickle
import torch as t
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.datasets as datasets
import numpy as np
import matplotlib.pyplot as plt
import cProfile
import pandas as pd
import datetime
from scipy.sparse import csr_matrix
import os.path
import embedding_help_functions as ehf
import scipy.io as sio
unsq = t.unsqueeze
sq = t.squeeze
# Settings
alpha_vec = [.75, .76, .77, .78, .79, .80, .81, .82, .83, .84, .85, .86, .87, .88, .89, .90, .91, .92, .93, .94, .95]
no_layers = 1
dataset = "OTC" # OTC or Alpha
no_epochs = 1000
mat_f_name = "saved_content_bitcoin_otc.mat"
no_trials = 1
beta1 = 19
beta2 = 19
cutoff = 95
eval_type = "MAP-MRR" # "MAP-MRR" or "F1"
data_loc = "data/Bitcoin_" + dataset + "/"
S_train, S_val, S_test = 95, 20, 20
lr = 0.01
momentum = 0.9
# Load and return relevant data
A, A_labels, C_train, C_val, C_test, N = ehf.load_data(data_loc, mat_f_name, S_train, S_val, S_test, transformed=False)
# Create features for the nodes
X_train, X_val, X_test = ehf.create_node_features(A, S_train, S_val, S_test, same_block_size=False)
# Extract edges and labels from A_labels, and augment with nonexisting edges
# edges, beta
edges = A_labels._indices()
edges_aug, labels = ehf.augment_edges(edges, N, beta1, beta2, cutoff)
# Divide adjacency matrices and labels into training, validation and testing sets
edges_train, target_train, e_train, edges_val, target_val, e_val, edges_test, target_test, e_test = ehf.split_data(edges_aug, labels, S_train, S_val, S_test, same_block_size = False)
if no_trials > 1:
ep_acc_loss_vec = []
for tr in range(no_trials):
for alpha in alpha_vec:
class_weights = t.tensor([alpha, 1.0-alpha])
save_res_fname = "results_BASELINE_layers" + str(no_layers) + "_w" + str(round(float(class_weights[0])*100)) + "_" + dataset + "_link_prediction"
# Create gcn for training
if no_layers == 2:
gcn = ehf.EmbeddingKWGCN(C_train[:-1], X_train[:-1], e_train, [6,6,2], nonlin2="selu")
elif no_layers == 1:
gcn = ehf.EmbeddingKWGCN(C_train[:-1], X_train[:-1], e_train, [6,2])
# Train
optimizer = t.optim.SGD(gcn.parameters(), lr=lr, momentum=momentum)
criterion = nn.CrossEntropyLoss(weight=class_weights) # Takes arguments (output, target)
if eval_type == "F1":
ep_acc_loss = np.zeros((no_epochs,12)) # (precision_train, recall_train, f1_train, loss_train, precision_val, recall_val, f1_val, loss_val, precision_test, recall_test, f1_test, loss_test)
elif eval_type == "MAP-MRR":
ep_acc_loss = np.zeros((no_epochs,9)) # (MAP_train, MRR_train, loss_train, MAP_val, MRR_val, loss_val, MAP_test, MRR_test, loss_test)
for ep in range(no_epochs):
# Compute loss and take step
optimizer.zero_grad()
output_train = gcn()
loss_train = criterion(output_train, target_train[edges_train[0]!=0])
loss_train.backward()
optimizer.step()
# Things that don't require gradient
with t.no_grad():
if ep % 100 == 0:
# Compute stats for training data; no point in doing more often than this
guess_train = t.argmax(output_train, dim=1)
if eval_type == "F1":
precision_train, recall_train, f1_train = ehf.compute_f1(guess_train, target_train[edges_train[0]!=0])
elif eval_type == "MAP-MRR":
MAP_train, MRR_train = ehf.compute_MAP_MRR(output_train, target_train[edges_train[0]!=0], edges_train[:, edges_train[0]!=0])
# Compute stats for validation data
output_val = gcn(C_val[:-1], X_val[:-1], e_val)
guess_val = t.argmax(output_val, dim=1)
if eval_type == "F1":
precision_val, recall_val, f1_val = ehf.compute_f1(guess_val, target_val[edges_val[0]!=0])
elif eval_type == "MAP-MRR":
MAP_val, MRR_val = ehf.compute_MAP_MRR(output_val, target_val[edges_val[0]!=0], edges_val[:, edges_val[0]!=0])
loss_val = criterion(output_val, target_val[edges_val[0]!=0])
# Compute stats for test data
output_test = gcn(C_test[:-1], X_test[:-1], e_test)
guess_test = t.argmax(output_test, dim=1)
if eval_type == "F1":
precision_test, recall_test, f1_test = ehf.compute_f1(guess_test, target_test[edges_test[0]!=0])
elif eval_type == "MAP-MRR":
MAP_test, MRR_test = ehf.compute_MAP_MRR(output_test, target_test[edges_test[0]!=0], edges_test[:, edges_test[0]!=0])
loss_test = criterion(output_test, target_test[edges_test[0]!=0])
# Print
if eval_type == "F1":
ehf.print_f1(precision_train, recall_train, f1_train, loss_train, precision_val, recall_val, f1_val, loss_val, precision_test, recall_test, f1_test, loss_test, alpha, tr, ep)
elif eval_type == "MAP-MRR":
print("alpha/Tr/Ep %.2f/%d/%d. Train MAP/MRR %.16f/%.16f. Train loss %.16f." % (alpha, tr, ep, MAP_train, MRR_train, loss_train))
print("alpha/Tr/Ep %.2f/%d/%d. Val MAP/MRR %.16f/%.16f. Val loss %.16f." % (alpha, tr, ep, MAP_val, MRR_val, loss_val))
print("alpha/Tr/Ep %.2f/%d/%d. Test MAP/MRR %.16f/%.16f. Test loss %.16f.\n" % (alpha, tr, ep, MAP_test, MRR_test, loss_test))
# Store values with results
if eval_type == "F1":
ep_acc_loss[ep] = [precision_train, recall_train, f1_train, loss_train, precision_val, recall_val, f1_val, loss_val, precision_test, recall_test, f1_test, loss_test]
elif eval_type == "MAP-MRR":
ep_acc_loss[ep] = [MAP_train, MRR_train, loss_train, MAP_val, MRR_val, loss_val, MAP_test, MRR_test, loss_test]
if eval_type == "F1":
ehf.print_f1(precision_train, recall_train, f1_train, loss_train, precision_val, recall_val, f1_val, loss_val, precision_test, recall_test, f1_test, loss_test, is_final=True)
elif eval_type == "MAP-MRR":
print("FINAL: Train MAP/MRR %.16f/%.16f. Train loss %.16f." % (MAP_train, MRR_train, loss_train))
print("FINAL: Val MAP/MRR %.16f/%.16f. Val loss %.16f." % (MAP_val, MRR_val, loss_val))
print("FINAL: Test MAP/MRR %.16f/%.16f. Test loss %.16f.\n" % (MAP_test, MRR_test, loss_test))
if no_trials == 1:
pickle.dump(ep_acc_loss, open(save_res_fname, "wb"))
print("Results saved for single trial")
else:
ep_acc_loss_vec.append(ep_acc_loss)
if no_trials > 1:
pickle.dump(ep_acc_loss_vec, open(save_res_fname + "_no_trials" + str(no_trials), "wb"))
print("Results saved for all trials") | [
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import cPickle
import numpy as np
from elm import ELMClassifier
from sklearn import linear_model
if __name__ == '__main__':
# Load data sets
train_x, train_y, val_x, val_y, test_x, test_y = get_datasets(load_mnist())
# Build ELM
cls = ELMClassifier(n_hidden=7000,
alpha=0.93,
activation_func='multiquadric',
regressor=linear_model.Ridge(),
random_state=21398023)
cls.fit(train_x, train_y)
# Evaluate model
print 'Validation error:', cls.score(val_x, val_y)
print 'Test error:', cls.score(test_x, test_y)
| [
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834,
6624,
705,
834,
12417,
834,
10354,
198,
... | 2.006309 | 317 |
# -*- coding: utf-8 -*-
#!/usr/bin/env python3
from PKC_Classes import NetworkUser, KDC
from DES import DES
from RSA_Class import RSA
import socket
import os
import sys
import threading
import time
if sys.version_info[0] < 3:
raise Exception("Must be using Python 3")
bob = NetworkUser('Alice', DES(), RSA(9973, 97), 200)
print('bob:', bob.uid)
# socket communication
kdc_host, kdc_port = 'localhost', 9999
bob_host, bob_port = 'localhost', 9200
# talk to kdc for sess key
try:
sock_with_kdc = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock_with_kdc.connect((kdc_host, kdc_port))
print(sock_with_kdc.recv(1024))
# send cipher_key
bob_cipher_key_packet = bob.send_cipher_key()
sock_with_kdc.send(bob_cipher_key_packet.encode())
kdc_bob_cipher_key_packet = sock_with_kdc.recv(1024).decode()
print(kdc_bob_cipher_key_packet)
bob.process_packet(kdc_bob_cipher_key_packet)
except socket.error as msg:
print(msg);
sys.exit(1)
# sock_with_kdc.shutdown(socket.SHUT_WR)
# talk to bob
try:
sock_self = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock_self.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock_self.bind((bob_host, bob_port))
sock_self.listen(10)
except socket.error as msg:
print(msg);
sys.exit(1)
while 1:
conn, addr = sock_self.accept()
thread = threading.Thread(target=reply_conn, args=(conn, addr))
thread.start()
# sock_self.close()
| [
2,
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9,
12,
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23,
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... | 2.368506 | 616 |
from __future__ import print_function
import argparse
import itertools
import os
import pickle
import sys
from datetime import datetime
import matplotlib
import numpy as np
import torch
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import proj.archs as archs
from proj.utils.cluster.general import config_to_str, get_opt, update_lr
from proj.utils.cluster.baselines.triplets import make_triplets_data, \
triplets_eval, triplets_loss
"""
Triplets.
Makes output distribution same as that of attractor, and different to that
of repeller.
Greyscale version (no sobel).
"""
# Options ----------------------------------------------------------------------
parser = argparse.ArgumentParser()
parser.add_argument("--model_ind", type=int, required=True)
parser.add_argument("--arch", type=str, required=True)
parser.add_argument("--opt", type=str, default="Adam")
parser.add_argument("--dataset", type=str, required=True)
parser.add_argument("--dataset_root", type=str, required=True)
parser.add_argument("--gt_k", type=int, required=True)
parser.add_argument("--output_k", type=int, required=True)
parser.add_argument("--lr", type=float, default=0.01)
parser.add_argument("--lr_schedule", type=int, nargs="+", default=[])
parser.add_argument("--lr_mult", type=float, default=0.1)
parser.add_argument("--num_epochs", type=int, default=1000)
parser.add_argument("--batch_sz", type=int, required=True) # num pairs
parser.add_argument("--out_root", type=str,
default="/scratch/shared/slow/xuji/iid_private")
parser.add_argument("--restart", dest="restart", default=False,
action="store_true")
parser.add_argument("--test_code", dest="test_code", default=False,
action="store_true")
parser.add_argument("--save_freq", type=int, default=10)
parser.add_argument("--kmeans_on_features", default=False,
action="store_true")
# transforms
# used for "positive" sample
parser.add_argument("--demean", dest="demean", default=False,
action="store_true")
parser.add_argument("--per_img_demean", dest="per_img_demean", default=False,
action="store_true")
parser.add_argument("--data_mean", type=float, nargs="+",
default=[0.5, 0.5, 0.5])
parser.add_argument("--data_std", type=float, nargs="+",
default=[0.5, 0.5, 0.5])
parser.add_argument("--crop_orig", dest="crop_orig", default=False,
action="store_true")
parser.add_argument("--crop_other", dest="crop_other", default=False,
action="store_true")
parser.add_argument("--tf1_crop", type=str, default="random") # type name
parser.add_argument("--tf2_crop", type=str, default="random")
parser.add_argument("--tf1_crop_sz", type=int, default=84)
parser.add_argument("--tf2_crop_szs", type=int, nargs="+",
default=[84]) # allow diff crop for imgs_tf
parser.add_argument("--tf3_crop_diff", dest="tf3_crop_diff", default=False,
action="store_true")
parser.add_argument("--tf3_crop_sz", type=int, default=0)
parser.add_argument("--input_sz", type=int, default=96)
parser.add_argument("--rot_val", type=float, default=0.)
parser.add_argument("--always_rot", dest="always_rot", default=False,
action="store_true")
parser.add_argument("--no_jitter", dest="no_jitter", default=False,
action="store_true")
parser.add_argument("--no_flip", dest="no_flip", default=False,
action="store_true")
config = parser.parse_args()
# Fixed settings and checks ----------------------------------------------------
config.in_channels = 1
if config.output_k != config.gt_k:
assert (config.output_k > config.gt_k)
assert (config.kmeans_on_features)
config.out_dir = os.path.join(config.out_root, str(config.model_ind))
config.dataloader_batch_sz = config.batch_sz
config.num_dataloaders = 1
if not os.path.exists(config.out_dir):
os.makedirs(config.out_dir)
if config.restart:
given_config = config
reloaded_config_path = os.path.join(given_config.out_dir, "config.pickle")
print("Loading restarting config from: %s" % reloaded_config_path)
with open(reloaded_config_path, "rb") as config_f:
config = pickle.load(config_f)
assert (config.model_ind == given_config.model_ind)
config.restart = True
# copy over new num_epochs and lr schedule
config.num_epochs = given_config.num_epochs
config.lr_schedule = given_config.lr_schedule
if not hasattr(config, "kmeans_on_features"):
config.kmeans_on_features = False
else:
print("Config: %s" % config_to_str(config))
# Data, nets, optimisers -------------------------------------------------------
dataloader_original, dataloader_positive, dataloader_negative, \
dataloader_test = make_triplets_data(config)
train_dataloaders = [dataloader_original, dataloader_positive,
dataloader_negative]
net = archs.__dict__[config.arch](config)
if config.restart:
model_path = os.path.join(config.out_dir, "latest_net.pytorch")
taking_best = not os.path.exists(model_path)
if taking_best:
print("using best instead of latest")
model_path = os.path.join(config.out_dir, "best_net.pytorch")
net.load_state_dict(
torch.load(model_path, map_location=lambda storage, loc: storage))
net.cuda()
net = torch.nn.DataParallel(net)
net.train()
optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr)
if config.restart:
opt_path = os.path.join(config.out_dir, "latest_optimiser.pytorch")
if taking_best:
opt_path = os.path.join(config.out_dir, "best_optimiser.pytorch")
optimiser.load_state_dict(torch.load(opt_path))
# Results storage --------------------------------------------------------------
if config.restart:
if not taking_best:
next_epoch = config.last_epoch + 1 # corresponds to last saved model
else:
next_epoch = np.argmax(np.array(config.epoch_acc)) + 1
print("starting from epoch %d" % next_epoch)
config.epoch_acc = config.epoch_acc[:next_epoch] # in case we overshot
config.epoch_loss = config.epoch_loss[:next_epoch]
config.masses = config.masses[:next_epoch, :]
config.per_class_acc = config.per_class_acc[:next_epoch, :]
else:
config.epoch_acc = []
config.epoch_loss = []
config.masses = None
config.per_class_acc = None
_ = triplets_eval(config, net,
dataloader_test=dataloader_test,
sobel=False)
print("Pre: time %s: \n %s" % (datetime.now(), config.epoch_acc[-1]))
sys.stdout.flush()
next_epoch = 1
fig, axarr = plt.subplots(4, sharex=False, figsize=(20, 20))
# Train ------------------------------------------------------------------------
for e_i in xrange(next_epoch, config.num_epochs):
print("Starting e_i: %d" % (e_i))
if e_i in config.lr_schedule:
optimiser = update_lr(optimiser, lr_mult=config.lr_mult)
avg_loss = 0. # over heads and head_epochs (and sub_heads)
avg_loss_count = 0
sys.stdout.flush()
iterators = (d for d in train_dataloaders)
b_i = 0
for tup in itertools.izip(*iterators):
net.module.zero_grad()
imgs_orig = tup[0][0].cuda()
imgs_pos = tup[1][0].cuda()
imgs_neg = tup[2][0].cuda()
outs_orig = net(imgs_orig)
outs_pos = net(imgs_pos)
outs_neg = net(imgs_neg)
curr_loss = triplets_loss(outs_orig, outs_pos, outs_neg)
if ((b_i % 100) == 0) or (e_i == next_epoch and b_i < 10):
print("Model ind %d epoch %d batch %d "
"loss %f time %s" % \
(config.model_ind, e_i, b_i, curr_loss.item(), datetime.now()))
sys.stdout.flush()
if not np.isfinite(float(curr_loss.item())):
print("Loss is not finite... %s:" % str(curr_loss.item()))
exit(1)
avg_loss += curr_loss.item()
avg_loss_count += 1
curr_loss.backward()
optimiser.step()
b_i += 1
if b_i == 2 and config.test_code:
break
avg_loss = float(avg_loss / avg_loss_count)
config.epoch_loss.append(avg_loss)
# Eval and storage -----------------------------------------------------------
# when epoch over both heads is finished
is_best = triplets_eval(config, net,
dataloader_test=dataloader_test,
sobel=False)
print("Time %s, acc %s" % (datetime.now(), config.epoch_acc[-1]))
sys.stdout.flush()
axarr[0].clear()
axarr[0].plot(config.epoch_acc)
axarr[0].set_title("acc, top: %f" % max(config.epoch_acc))
axarr[1].clear()
axarr[1].plot(config.epoch_loss)
axarr[1].set_title("Loss")
axarr[2].clear()
for c in xrange(config.gt_k):
axarr[2].plot(config.masses[:, c])
axarr[2].set_title("masses")
axarr[3].clear()
for c in xrange(config.gt_k):
axarr[3].plot(config.per_class_acc[:, c])
axarr[3].set_title("per_class_acc")
fig.tight_layout()
fig.canvas.draw_idle()
fig.savefig(os.path.join(config.out_dir, "plots.png"))
if is_best or (e_i % config.save_freq == 0):
net.module.cpu()
if is_best:
torch.save(net.module.state_dict(),
os.path.join(config.out_dir, "best_net.pytorch"))
torch.save(optimiser.state_dict(),
os.path.join(config.out_dir, "best_optimiser.pytorch"))
if e_i % config.save_freq == 0:
torch.save(net.module.state_dict(),
os.path.join(config.out_dir, "latest_net.pytorch"))
torch.save(optimiser.state_dict(),
os.path.join(config.out_dir, "latest_optimiser.pytorch"))
config.last_epoch = e_i # for last saved version
net.module.cuda()
with open(os.path.join(config.out_dir, "config.pickle"),
'wb') as outfile:
pickle.dump(config, outfile)
with open(os.path.join(config.out_dir, "config.txt"),
"w") as text_file:
text_file.write("%s" % config)
if config.test_code:
exit(0)
| [
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8079,
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4818,
8079,
198,
198,
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... | 2.323216 | 4,415 |
"""empty message
Revision ID: 0084_add_job_stats
Revises: 0083_add_perm_types_and_svc_perm
Create Date: 2017-05-12 13:16:14.147368
"""
# revision identifiers, used by Alembic.
revision = "0084_add_job_stats"
down_revision = "0083_add_perm_types_and_svc_perm"
import sqlalchemy as sa
from alembic import op
from sqlalchemy.dialects import postgresql
| [
37811,
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198,
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62,
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66,
62,
16321,
198,
16447,
7536,
25,
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12,
27... | 2.62963 | 135 |
import unittest
from future.moves.urllib.parse import urlparse, urljoin, parse_qs
import pytest
from addons.twofactor.tests.utils import _valid_code
from nose.tools import (assert_equal, assert_false, assert_is_none,
assert_is_not_none, assert_true)
from osf_tests.factories import UserFactory
pytestmark = pytest.mark.django_db
| [
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555,
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395,
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... | 2.664179 | 134 |
import numpy as np
from torchvision import transforms
np.random.seed(1) | [
11748,
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7,
16,
8
] | 3.55 | 20 |
import torch
import os
from torch import nn
import numpy as np
import torch.nn.functional
from termcolor import colored
from .logger import get_logger
| [
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1362,
62... | 3.642857 | 42 |
from django.apps import AppConfig #pragma: no cover
| [
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from __future__ import division
from timeit import default_timer as timer
import csv
import numpy as np
import itertools
from munkres import Munkres, print_matrix, make_cost_matrix
import sys
from classes import *
from functions import *
from math import sqrt
import Tkinter as tk
import tkFileDialog as filedialog
root = tk.Tk()
root.withdraw()
p_file = filedialog.askopenfilename(title='Please select the posting file')
c_file = filedialog.askopenfilename(title='Please select the candidate file')
"""for use with /users/java_jonathan/postings_lge.csv and
/Users/java_jonathan/candidates_lge.csv"""
# p_file = raw_input("Please enter the path for the postings file: ")
# p_file = p_file.strip()
# c_file = raw_input("Please enter the path for the candidate file: ")
# c_file = c_file.strip()
start = timer()
with open(p_file,'r') as f:
#with open('/Users/Jonathan/Google Drive/CPD/Python/postings.csv','r') as f:
reader = csv.reader(f)
postingsAll = list(reader)
with open(c_file,'r') as f:
reader = csv.reader(f)
candidatesAll = list(reader)
"""create empty lists to fill with lists of lists output by iterating function
below"""
names = []
totalMatrix = []
for list in candidatesAll:
candidate = Candidate(*list)
names.append(candidate.name)
n = 0
for list in postingsAll:
posting = Posting(*list)
totalMatrix.append(matchDept(posting,candidate) + matchAnchor(posting,candidate)
+matchLocation(posting,candidate) + matchCompetency(posting,candidate) +
matchSkill(posting,candidate)+matchCohort(posting,candidate))
n += 1
l = len(names)
names.extend([0] * (n-l))
totalMatrix.extend([0] * (n**2 - len(totalMatrix)))
totalMatrix = np.asarray(totalMatrix)
totalMatrix = np.reshape(totalMatrix,(n,-1))
#at this point the matrix is structured as candidates down and jobs across
totalMatrix = np.transpose(totalMatrix)
#now it's switched!
totalMatrix = np.subtract(np.amax(totalMatrix),totalMatrix)
totalMatrix = np.array(totalMatrix)
minSuitability = 18
check = []
result = []
m = Munkres()
indexes = m.compute(totalMatrix)
#print_matrix(totalMatrix, msg='Lowest cost through this matrix:')
total = 0.0
unhappy_candidates = 0
medium_candidates = 0
tenpc_candidates = 0
qs_candidates = 0
vs_candidates = 0
f = open('output.txt', 'w')
for row, column in indexes:
if column < l:
value = totalMatrix[row][column]
if value > minSuitability*0.9:
tenpc_candidates += 1
elif value > minSuitability*0.75:
medium_candidates += 1
elif value > minSuitability/2:
unhappy_candidates += 1
elif value > minSuitability*0.25:
qs_candidates += 1
elif value > minSuitability*0.1:
vs_candidates += 1
total += value
check.append(column+1)
result.append((row,column))
f.write('For candidate %s: \nOptimal position: %d (score %s)\n'
% (names[column], column+1, value))
else:
pass
globalSatisfaction = 100*(1-(total/(l*minSuitability)))
print('Global satisfaction: %.2f%%' % globalSatisfaction)
print('Candidates who are more than 90%% suitable: %d' % vs_candidates)
print('Candidates who are more than 75%% suitable: %d' % qs_candidates)
print('Candidates who are more than 50%% suitable: %d' % (l-unhappy_candidates))
print('Candidates who are more than 75%% unsuitable: %d' % medium_candidates)
print('Candidates who are more than 90%% unsuitable: %d' % tenpc_candidates)
#output from excel:
correct = [1,3,5,9,10,2,4,8,6,7]
#this function tests output above against Excel:
#test(correct,check)
topMatrix = topFive(names,totalMatrix)
#print(topMatrix)
np.savetxt('/Users/java_jonathan/test.csv',topMatrix, fmt='%s', delimiter=',',
newline='\n', header='', footer='', comments='# ')
np.savetxt('/Users/java_jonathan/test2.csv',totalMatrix, fmt='%s', delimiter=',',
newline='\n', header='', footer='', comments='# ')
end = timer()
print(end-start)
"""
#posting = [Posting(*postingsAll)]
#print(posting[0].anchor)
#print(posting)
#print(candidatesAll)
#print(postingsAll)
#print(postingsAll[0].name)
#print(preferences)
#print(postings)
#split up files into relative blocks
postCode = [lists[0] for lists in postings]
postDept = [lists[1] for lists in postings]
postAnchor = [lists[2] for lists in postings]
postSkills = [lists[3:5] for lists in postings]
postLocation = [lists[5] for lists in postings]
postCompetencies = [lists[7:10] for lists in postings]
postSecurity = [lists[10] for lists in postings]
#with open('/Users/Jonathan/Google Drive/CPD/Python/candidates.csv','r') as f:
#gives first column ie candidate a
a=totalMatrix[:,[0]]
#b = totalMatrix[:,[0]]
#print(a)
#converts 1D matrix to list for ease
a = np.array(a).tolist()
#print(a)
#creates list called output containing rank of score
output = [0] * len(a)
for i, x in enumerate(sorted(range(len(a)), key=lambda y: a[y])):
output[x] = i
print(output)
#creates tuples of rank, job and appends to list
jobRank = []
# for rank, b in zip(output, postCode):
# jobScore = (rank,b)
# list(jobScore)
# jobRank.append(jobScore)
# print(jobRank)
output = [0] * len(a)
for i, x in enumerate(sorted(range(len(a)), key=lambda y: a[y])):
output[x] = i
print(output)
# #print(a)
# jobRank = sorted(jobRank, reverse=False)
# print(jobRank)
# print('For candidate a, the best position is %s') % (jobRank[0][1])
# print(candidate[0].skills)
"""
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... | 2.662274 | 2,049 |
"""Setup script for PySyReNN.
Adapted from:
https://hynek.me/articles/sharing-your-labor-of-love-pypi-quick-and-dirty/
"""
import codecs
import os
import re
from setuptools import setup, find_packages
###################################################################
NAME = "pysyrenn"
PACKAGES = [
"syrenn_proto",
"pysyrenn",
"pysyrenn.frontend",
"pysyrenn.helpers",
]
META_PATH = "__metadata__.py"
KEYWORDS = ["class", "attribute", "boilerplate"]
CLASSIFIERS = [
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Natural Language :: English",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python",
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.3",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: Implementation :: CPython",
"Programming Language :: Python :: Implementation :: PyPy",
"Topic :: Software Development :: Libraries :: Python Modules",
]
INSTALL_REQUIRES = ["torch"]
with open("requirements.txt") as requirements:
reading = False
for line in requirements.readlines():
if line.startswith("# PYSYRENN"):
reading = True
elif line.startswith("# END"):
reading = False
elif line.startswith("#"):
pass
elif reading:
INSTALL_REQUIRES.append(line.strip().split("==")[0])
###################################################################
HERE = os.path.abspath(os.path.dirname(__file__))
def read(*parts):
"""
Build an absolute path from *parts* and and return the contents of the
resulting file. Assume UTF-8 encoding.
"""
with codecs.open(os.path.join(HERE, *parts), "rb", "utf-8") as f:
return f.read()
META_FILE = read(META_PATH)
def find_meta(meta):
"""Extract __*meta*__ from META_FILE.
"""
meta_match = re.search(
r"^__{meta}__ = ['\"]([^'\"]*)['\"]".format(meta=meta),
META_FILE, re.M
)
if meta_match:
return meta_match.group(1)
raise RuntimeError("Unable to find __{meta}__ string.".format(meta=meta))
if __name__ == "__main__":
setup(
name=NAME,
description=find_meta("description"),
license=find_meta("license"),
url=find_meta("uri"),
version=find_meta("version"),
author=find_meta("author"),
author_email=find_meta("email"),
maintainer=find_meta("author"),
maintainer_email=find_meta("email"),
keywords=KEYWORDS,
long_description=read("README.md"),
long_description_content_type="text/markdown",
packages=PACKAGES,
package_dir={"": "."},
package_data={"": ["pysyrenn/**/*.py"]},
zip_safe=False,
classifiers=CLASSIFIERS,
install_requires=INSTALL_REQUIRES,
)
| [
37811,
40786,
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422,
25,
198,
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1378,
12114,
710,
74,
13,
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14,
26845,
14,
21987,
12,
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12,
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12,
1659,
12,
23205,
12,
79,
4464,
72,
12,
24209,
12,
3... | 2.533871 | 1,240 |
REST_PATH = u""
WS_PATH = u"/api/notifications/v1"
| [
49,
6465,
62,
34219,
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334,
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198,
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1,
14,
15042,
14,
1662,
6637,
14,
85,
16,
1,
198
] | 2.125 | 24 |
__all__ = ["load"]
import imp
import importlib
def load(name, path):
"""Load and initialize a module implemented as a Python source file and return its module object"""
if hasattr(importlib, "machinery"):
loader = importlib.machinery.SourceFileLoader(name, path)
return loader.load_module()
return imp.load_source(name, path)
| [
834,
439,
834,
796,
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8265,
9177,
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257,
11361,
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290,
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... | 3.033898 | 118 |
import itertools
from pygears.common.sieve import sieve
from pygears.svgen.inst import SVGenInstPlugin
from pygears.svgen.svmod import SVModuleGen
from functools import partial
from pygears.svgen.svgen import SVGenPlugin
from pygears.svgen.util import svgen_visitor
from pygears.core.hier_node import HierVisitorBase
from pygears.svgen.inst import svgen_inst
from pygears.rtl.gear import RTLGearHierVisitor, is_gear_instance
class SVGenSievePlugin(SVGenInstPlugin, SVGenPlugin):
| [
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... | 2.963415 | 164 |
#encoding=utf-8
import qlib
import pandas as pd
import pickle
import xgboost as xgb
import numpy as np
import re
from qlib.constant import REG_US
from qlib.utils import exists_qlib_data, init_instance_by_config
from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
from qlib.utils import flatten_dict
from qlib.data import LocalExpressionProvider
from qlib.data.ops import Operators, OpsList
from qlib.data.base import Feature
from pyecharts import options as opts
from pyecharts.charts import Kline, Line, Grid
from my_data_handler import MyAlphaHandler
# model_file = r'.\mlruns\1\d6536b056ba84a74be6b33971f443cf6\artifacts\trained_model'
model_file = r'.\mlruns\1\148ef1cd7acd48deac3eadc339ad3008\artifacts\trained_model'
with open(model_file, 'rb') as fi:
model = pickle.load(fi)
exprs, columns = MyAlphaHandler.get_custom_config()
raw_data = pd.read_csv('../stock_data/TSLA.csv', parse_dates=['time'])
raw_data['data_time'] = raw_data['time'].dt.strftime("%Y-%m-%d %H:%M:00")
raw_data.set_index('time', inplace=True)
raw_data["vwap"] = np.nan
raw_data.sort_index(inplace=True)
# print(raw_data)
Operators.register(OpsList + [MyFeature])
obj = dict()
for field in exprs:
expression = eval(my_parse_field(field))
series = expression.load('TSLA', "2022-01-02", "2022-02-28", "1min")
series = series.astype(np.float32)
obj[field] = series
data = pd.DataFrame(obj)
data.columns = columns
view_time_start = '2022-02-11'
view_time_end = '2022-02-12'
pre_data = raw_data.loc[view_time_start:view_time_end].copy()
pred=model.model.predict(xgb.DMatrix(data.loc[view_time_start:view_time_end]))
pre_data['pred_score'] = pred
records = pre_data.to_dict("records")
cash = 50000
position = {}
hold_thresh = 5
score_thresh = 0.001
x_axises, y_axises, mark_points, money = [], [], [], []
for record in records:
x_axises.append(record['data_time'])
y_axises.append([
record['open'], record['close'], record['low'], record['high']
])
if 'hold_cnt' in position:
position['hold_cnt'] += 1
if position and (record['open'] >= position['close'] * 1.01 or record['open'] < position['close'] * 0.995 or record['pred_score'] < -score_thresh or position['hold_cnt'] >= hold_thresh):
cash += position['amount'] * record['open']
position = {}
#print("sell")
mark_points.append(opts.MarkPointItem(
coord=[record['data_time'], record['high']],
symbol='triangle', symbol_size=7,
itemstyle_opts=opts.ItemStyleOpts(color="green")
))
elif record['pred_score'] > score_thresh and not position:
position = dict(record)
position['amount'] = int(cash / position['open'])
cash -= position['amount'] * position['open']
# buy
#print("buy")
position['hold_cnt'] = 0
mark_points.append(opts.MarkPointItem(
coord=[record['data_time'], record['high']],
symbol='arrow', symbol_size=7,
itemstyle_opts=opts.ItemStyleOpts(color="yellow")
))
cur_money = cash
if position:
cur_money += position['amount'] * record['close']
money.append(cur_money)
if position:
cash += position['amount'] * records[-1]['close']
print("cash:", cash)
kline_graph = (
Kline()
.add_xaxis(x_axises)
.add_yaxis(
"kline",
y_axises,
markpoint_opts=opts.MarkPointOpts(
data=mark_points
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
title_opts=opts.TitleOpts(title="%s_%s" % (view_time_start, view_time_end)),
datazoom_opts=[opts.DataZoomOpts(type_="inside", xaxis_index=[0, 1],)],
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=x_axises)
.add_yaxis(
series_name="cur_money",
y_axis=money,
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(y=50000)]
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
min_='dataMin'
)
)
)
grid_chart = Grid(init_opts=opts.InitOpts(width='2000px', height='900px'))
grid_chart.add(
kline_graph,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="10%", height="50%"),
)
grid_chart.add(
kline_line,
grid_opts=opts.GridOpts(
pos_left="3%", pos_right="10%", pos_top="60%", height="30%"
),
)
grid_chart.render("kline_markline.html") | [
2,
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299,
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302,
198,
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10... | 2.186313 | 2,265 |
from fastapi import APIRouter
router = APIRouter()
| [
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4663,
39605,
198,
198,
472,
353,
796,
3486,
4663,
39605,
3419,
628
] | 3.117647 | 17 |
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
import networkx as nx
import time
from embed_methods.dgi.models import DGI, LogReg
from embed_methods.dgi.utils import process
| [
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355,
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640,
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11525,
62,
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... | 3.058824 | 68 |
"""Deploys binaries to a GitHub release given the specified tag name."""
import argparse
import os
import time
from github import Github
THIS_FILE_DIRECTORY = os.path.dirname(os.path.realpath(__file__))
GH_REPO_IDENT = "ETCLabs/RDMnet"
GH_USERNAME = "svc-etclabs"
GH_API_TOKEN = os.getenv("SVC_ETCLABS_REPO_TOKEN")
def deploy_binaries(version: str):
"""Deploys staged binaries to a new GitHub Release."""
g = Github(login_or_token=GH_USERNAME, password=GH_API_TOKEN)
repo = g.get_repo(GH_REPO_IDENT)
print(f"Waiting for the correct GitHub tag v{version} to become available...")
keep_trying = True
while keep_trying:
for tag in repo.get_tags():
if tag.name == f"v{version}":
keep_trying = False # Tag now exists
break
if keep_trying:
time.sleep(5)
print(f"Tag v{version} available. Creating release...")
new_release = repo.create_git_release(
tag=f"v{version}",
name=f"RDMnet v{version}",
message=f"Automated release of RDMnet for v{version}",
)
new_release.upload_asset("RDMnetSetup_x86.msi")
new_release.upload_asset("RDMnetSetup_x64.msi")
new_release.upload_asset("RDMnet.pkg")
if __name__ == "__main__":
main()
| [
37811,
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284,
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2650,
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262,
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1822,
29572,
198,
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198,
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198,
198,
6738,
33084,
1330,
38994,
198,
198,
43559,
62,
25664,
62,
17931,
23988,
15513,... | 2.350554 | 542 |
import logging
import numpy
from ..Fragments import Fragments
from ..typing import SpectrumType
logger = logging.getLogger("matchms")
def add_losses(spectrum_in: SpectrumType, loss_mz_from=0.0, loss_mz_to=1000.0) -> SpectrumType:
"""Derive losses based on precursor mass.
Parameters
----------
spectrum_in:
Input spectrum.
loss_mz_from:
Minimum allowed m/z value for losses. Default is 0.0.
loss_mz_to:
Maximum allowed m/z value for losses. Default is 1000.0.
"""
if spectrum_in is None:
return None
spectrum = spectrum_in.clone()
precursor_mz = spectrum.get("precursor_mz", None)
if precursor_mz:
assert isinstance(precursor_mz, (float, int)), ("Expected 'precursor_mz' to be a scalar number.",
"Consider applying 'add_precursor_mz' filter first.")
peaks_mz, peaks_intensities = spectrum.peaks.mz, spectrum.peaks.intensities
losses_mz = (precursor_mz - peaks_mz)[::-1]
losses_intensities = peaks_intensities[::-1]
# Add losses which are within given boundaries
mask = numpy.where((losses_mz >= loss_mz_from)
& (losses_mz <= loss_mz_to))
spectrum.losses = Fragments(mz=losses_mz[mask],
intensities=losses_intensities[mask])
else:
logger.warning("No precursor_mz found. Consider applying 'add_precursor_mz' filter first.")
return spectrum
| [
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198,
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902,
198,
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774,
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1330,
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6030,
628,
198,
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1362,
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13,
1136,
11187,
1362,
7203,
15699,
907,
4943,
628,
198,
4299,
751,
... | 2.258209 | 670 |
import re
from .dict_functions import gen_schema, ParameterSchema, sort_dict
from cornflow_client.constants import JSON_TYPES, DATASCHEMA
| [
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62,
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11,
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19... | 3.088889 | 45 |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import platform
import unittest
import rspub.util.resourcefilter as rf
| [
2,
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198,
198,
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13,
3109... | 2.545455 | 55 |
import os
import testinfra.utils.ansible_runner
testinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner(
os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('all')
def test_package(host):
""" check if packages are installed
"""
assert host.package('grafana').is_installed
def test_service(host):
""" Testing whether the service is running and enabled
"""
assert host.service('grafana-server').is_enabled
assert host.service('grafana-server').is_running
| [
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13,
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7,
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220,
220,
... | 2.861272 | 173 |
"""Class representations of heatsinks."""
import math
from scipy import constants as const
from materials import Aluminium_6063 as aluminium
| [
37811,
9487,
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286,
37876,
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198
] | 4.142857 | 35 |
import inspect
foo() | [
11748,
10104,
220,
198,
220,
198,
21943,
3419
] | 2.875 | 8 |
import base58
from plenum.common.signer_did import DidSigner
from plenum.common.verifier import DidVerifier
from plenum.common.eventually import eventually
from plenum.test.helper import assertEquality
from sovrin.common.identity import Identity
MsgForSigning = {'sender': 'Mario', 'msg': 'Lorem ipsum'}
| [
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935,
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... | 3.181818 | 99 |
import astropy
import datetime
import numpy as np
from edibles.utils.edibles_spectrum import EdiblesSpectrum
if __name__ == "__main__":
filename = "HD170740_w860_redl_20140915_O12.fits"
testEdiblesSpectrum(filename=filename)
| [
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366,
834,
124... | 2.788235 | 85 |
# Copyright (c) 2010-2013 OpenStack, LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
"""Methods & Attributes for shared 'on-disk' data layouts."""
import os
import sys
import errno
from hashlib import md5
from random import shuffle
from ConfigParser import ConfigParser, NoSectionError, NoOptionError
from swift import gettext_ as _
from swift.common.utils import listdir, quote
# Used by hash_path to offer a bit more security when generating hashes for
# paths. It simply appends this value to all paths; guessing the hash a path
# will end up with would also require knowing this suffix.
_hash_conf = ConfigParser()
HASH_PATH_SUFFIX = ''
HASH_PATH_PREFIX = ''
if _hash_conf.read('/etc/swift/swift.conf'):
try:
HASH_PATH_SUFFIX = _hash_conf.get('swift-hash',
'swift_hash_path_suffix')
except (NoSectionError, NoOptionError):
pass
try:
HASH_PATH_PREFIX = _hash_conf.get('swift-hash',
'swift_hash_path_prefix')
except (NoSectionError, NoOptionError):
pass
def hash_path(account, container=None, object=None, raw_digest=False):
"""
Get the canonical hash for an account/container/object
:param account: Account
:param container: Container
:param object: Object
:param raw_digest: If True, return the raw version rather than a hex digest
:returns: hash string
"""
if object and not container:
raise ValueError('container is required if object is provided')
paths = [account]
if container:
paths.append(container)
if object:
paths.append(object)
if raw_digest:
return md5(HASH_PATH_PREFIX + '/' + '/'.join(paths)
+ HASH_PATH_SUFFIX).digest()
else:
return md5(HASH_PATH_PREFIX + '/' + '/'.join(paths)
+ HASH_PATH_SUFFIX).hexdigest()
def normalize_timestamp(timestamp):
"""
Format a timestamp (string or numeric) into a standardized
xxxxxxxxxx.xxxxx (10.5) format.
Note that timestamps using values greater than or equal to November 20th,
2286 at 17:46 UTC will use 11 digits to represent the number of
seconds.
:param timestamp: unix timestamp
:returns: normalized timestamp as a string
"""
return "%016.05f" % (float(timestamp))
def validate_device_partition(device, partition):
"""
Validate that a device and a partition are valid and won't lead to
directory traversal when used.
:param device: device to validate
:param partition: partition to validate
:raises: ValueError if given an invalid device or partition
"""
invalid_device = False
invalid_partition = False
if not device or '/' in device or device in ['.', '..']:
invalid_device = True
if not partition or '/' in partition or partition in ['.', '..']:
invalid_partition = True
if invalid_device:
raise ValueError('Invalid device: %s' % quote(device or ''))
elif invalid_partition:
raise ValueError('Invalid partition: %s' % quote(partition or ''))
def storage_directory(datadir, partition, name_hash):
"""
Get the storage directory
:param datadir: Base data directory
:param partition: Partition
:param name_hash: Account, container or object name hash
:returns: Storage directory
"""
return os.path.join(datadir, str(partition), name_hash[-3:], name_hash)
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... | 2.821937 | 1,404 |
# uncompyle6 version 2.11.3
# Python bytecode 2.7 (62211)
# Decompiled from: Python 2.7.10 (default, May 23 2015, 09:40:32) [MSC v.1500 32 bit (Intel)]
# Embedded file name: scripts/common/dossiers2/custom/cache.py
import nations
from items import vehicles
_g_cache = {} | [
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25,
... | 2.785714 | 98 |
#!/usr/bin/python
from code import TreeNode
from code import ThreeAddressCode
from lexer import tokens
from random import *
from symbol_table import SymbolTable
from symbol_table import SymbolTableNode
import logging
import ply.lex as lex
import ply.yacc as yacc
import sys
from codegen import convert_tac
from code import Code
from codegen import generate_assembly
three_addr_code = ThreeAddressCode()
assembly_code = Code()
parsed = []
symbol_table = SymbolTable()
var_list = []
generated = {'temp': [], 'scope': ['scope_0'], 'label': [], 'str_list': []}
precedence = (
('left','IDENTIFIER'),
('right','ASSIGN_OP'),
('left','COMMA'),
('left','LSQUARE'),
('left','RSQUARE'),
('left','LCURLY'),
('left','RCURLY'),
('left','DDD'),
('left','DOT'),
('left','SEMICOLON'),
('left','COLON'),
('left','SINGLE_QUOTES'),
('left','DOUBLE_QUOTES'),
('left','DECIMAL_LIT'),
('left','OCTAL_LIT'),
('left','HEX_LIT'),
('left','FLOAT_LIT'),
('left','STRING_LIT'),
('left','NEWLINE'),
('left','BREAK'),
('left','CONTINUE'),
('left','RETURN'),
('left','RROUND'),
('left','LROUND'),
('left', 'OR_OR'),
('left', 'AMP_AMP'),
('left', 'EQ_EQ', 'NOT_EQ','LT','LT_EQ','GT','GT_EQ'),
('left', 'PLUS', 'MINUS','OR','CARET'),
('left', 'STAR', 'DIVIDE','MODULO','AMP','AND_OR','LS','RS'),
)
def p_SourceFile(p):
'''SourceFile : PACKAGE IDENTIFIER SEMICOLON ImportDeclList TopLevelDeclList
'''
parsed.append(p.slice)
# TODO: Ignoring package name and Imports for now
p[0] = p[5]
var_list = symbol_table.make_var_list()
three_addr_code = convert_tac(p[0].TAC)
symbol_table.fill_next_use(three_addr_code)
assembly_code = generate_assembly(three_addr_code,var_list,symbol_table)
# p[0].TAC.print_code()
# three_addr_code.print_code()
assembly_code.print_code()
# symbol_table.print_symbol_table()
return
def p_ImportDeclList(p):
'''ImportDeclList : ImportDecl SEMICOLON ImportDeclList
| empty
'''
parsed.append(p.slice)
# TODO: Ignoring Imports for now
return
def p_TopLevelDeclList(p):
'''TopLevelDeclList : TopLevelDecl SEMICOLON TopLevelDeclList
| empty
'''
parsed.append(p.slice)
if len(p) == 4:
if p[3] != None:
p[0] = TreeNode('TopLevelDeclList', 0, 'INT', 0, [p[1]] + p[3].children, p[1].TAC)
p[0].TAC.append_TAC(p[3].TAC)
else:
p[0] = TreeNode('TopLevelDeclList', 0, 'INT', 0, [p[1]], p[1].TAC)
return
def p_TopLevelDecl(p):
'''TopLevelDecl : Declaration
| FunctionDecl
'''
parsed.append(p.slice)
p[0] = p[1]
return
def p_ImportDecl(p):
'''ImportDecl : IMPORT LROUND ImportSpecList RROUND
| IMPORT ImportSpec
'''
parsed.append(p.slice)
# TODO: Ignoring Imports for now
return
def p_ImportSpecList(p):
'''ImportSpecList : ImportSpec SEMICOLON ImportSpecList
| empty
'''
parsed.append(p.slice)
# TODO: Ignoring Imports for now
return
def p_ImportSpec(p):
'''ImportSpec : DOT string_lit
| IDENTIFIER string_lit
| empty string_lit
'''
parsed.append(p.slice)
# TODO: Ignoring Imports for now
return
def p_Block(p):
'''Block : LCURLY ScopeStart StatementList ScopeEnd RCURLY
'''
parsed.append(p.slice)
p[0] = p[3]
p[0].data = p[2].data
p[0].name = 'Block'
return
def p_ScopeStart(p):
'''ScopeStart : empty
'''
parsed.append(p.slice)
symbol_table.add_scope(gen('scope'))
p[0] = TreeNode('ScopeStart', symbol_table.current_scope, 'None')
return
def p_ScopeEnd(p):
'''ScopeEnd : empty
'''
parsed.append(p.slice)
symbol_table.end_scope()
return
def p_StatementList(p):
'''StatementList : Statement SEMICOLON StatementList
| empty
'''
parsed.append(p.slice)
if len(p) == 4:
p[0] = TreeNode('StatementList', 0, 'INT', 0, [p[1].data] + p[3].children, p[1].TAC)
p[0].TAC.append_TAC(p[3].TAC)
else:
p[0] = TreeNode('StatementList', 0, 'INT')
return
def p_Statement(p):
'''Statement : Declaration
| SimpleStmt
| ReturnStmt
| Block
| IfStmt
| SwitchStmt
| ForStmt
| BreakStmt
| ContinueStmt
| GotoStmt
| PrintIntStmt
| PrintStrStmt
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'Statement'
return
def p_PrintIntStmt(p):
'''PrintIntStmt : PRINTLN LROUND IDENTIFIER RROUND
| PRINTLN LROUND int_lit RROUND
'''
if hasattr(p[3], 'name') and p[3].name == 'int_lit':
p[0] = p[3]
# p[0].isLvalue = 0
else:
p[0] = TreeNode('IDENTIFIER', p[3], 'INT', 1, [])
p[0].TAC.add_line(['print_int', check_variable(p[0]), '', ''])
p[0].name = 'PrintIntStmt'
return
def p_PrintStrStmt(p):
'''PrintStrStmt : PRINTLN LROUND string_lit RROUND
'''
p[0] = p[3]
name = symbol_table.current_scope + '_' + gen('str_list')
parametersNode = SymbolTableNode(p[3].data, p[3].input_type)
newNode = SymbolTableNode(name, p[3].input_type, parameters = [parametersNode])
symbol_table.add_var(newNode)
p[0].TAC.add_line(['print_str', name, '', ''])
p[0].name = 'PrintStrStmt'
return
def p_Declaration(p):
'''Declaration : ConstDecl
| TypeDecl
| VarDecl
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'Declaration'
return
def p_ConstDecl(p):
'''ConstDecl : CONST LROUND ConstSpecList RROUND
| CONST ConstSpec
'''
parsed.append(p.slice)
return
def p_ConstSpecList(p):
'''ConstSpecList : empty
| ConstSpecList ConstSpec SEMICOLON
'''
parsed.append(p.slice)
return
def p_ConstSpec(p):
'''ConstSpec : IDENTIFIER
| IdentifierList
| IDENTIFIER EQ Expression
| IdentifierList EQ ExpressionList
| IDENTIFIER Type EQ Expression
| IdentifierList Type EQ ExpressionList
'''
parsed.append(p.slice)
return
def p_IdentifierList(p):
'''IdentifierList : IDENTIFIER COMMA IdentifierBotList
'''
parsed.append(p.slice)
node = TreeNode('IDENTIFIER', p[1], 'INT', 1)
p[0] = TreeNode('IdentifierList', 0, 'None', 0, [node] + p[3].children, p[3].TAC)
return
def p_IdentifierBotList(p):
'''IdentifierBotList : IDENTIFIER COMMA IdentifierBotList
| IDENTIFIER
'''
parsed.append(p.slice)
if len(p) == 2:
node = TreeNode('IDENTIFIER', p[1], 'INT', 1)
p[0] = TreeNode('IdentifierBotList', 0, 'None', 0, [node])
elif len(p) == 4:
node = TreeNode('IDENTIFIER', p[1], 'INT', 1)
p[0] = TreeNode('IdentifierBotList', 0, 'None', 0, [node] + p[3].children, p[3].TAC)
return
def p_ExpressionList(p):
'''ExpressionList : Expression COMMA ExpressionBotList
'''
parsed.append(p.slice)
p[0] = TreeNode('ExpressionList', 0, 'INT', 0, [p[1]] + p[3].children, p[1].TAC)
p[0].TAC.append_TAC(p[3].TAC)
return
def p_ExpressionBotList(p):
'''ExpressionBotList : Expression COMMA ExpressionBotList
| Expression
'''
parsed.append(p.slice)
if len(p) == 2:
p[0] = TreeNode('ExpressionBotList', 0, 'INT', 0, [p[1]], p[1].TAC)
elif len(p) == 4:
p[0] = TreeNode('ExpressionBotList', 0, 'INT', 0, [p[1]] + p[3].children, p[1].TAC)
p[0].TAC.append_TAC(p[3].TAC)
return
def p_TypeDecl(p):
'''TypeDecl : TYPE TypeSpecTopList
'''
parsed.append(p.slice)
return
def p_TypeSpecTopList(p):
'''TypeSpecTopList : TypeSpec
| LROUND TypeSpecList RROUND
'''
parsed.append(p.slice)
return
def p_TypeSpecList(p):
'''TypeSpecList : empty
| TypeSpecList TypeSpec SEMICOLON
'''
parsed.append(p.slice)
return
def p_TypeSpec(p):
'''TypeSpec : AliasDecl
| TypeDef
'''
parsed.append(p.slice)
return
def p_AliasDecl(p):
'''AliasDecl : IDENTIFIER EQ Type
'''
parsed.append(p.slice)
return
def p_TypeDef(p):
'''TypeDef : IDENTIFIER Type
'''
parsed.append(p.slice)
return
def p_Type(p):
'''Type : TypeLit
| StandardTypes
| LROUND Type RROUND
'''
parsed.append(p.slice)
if len(p) == 2:
p[0] = p[1]
else:
p[0] = p[2]
p[0].name = 'Type'
return
def p_StandardTypes(p):
'''StandardTypes : PREDEFINED_TYPES
'''
parsed.append(p.slice)
p[0] = TreeNode('StandardTypes', p[1], 'NONE')
return
def p_TypeLit(p):
'''TypeLit : ArrayType
| StructType
| FunctionType
| PointerType
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'TypeLit'
return
def p_PointerType(p):
'''PointerType : STAR Type
'''
parsed.append(p.slice)
return
def p_ArrayType(p):
'''ArrayType : LSQUARE ArrayLength RSQUARE Type
'''
parsed.append(p.slice)
p[0] = TreeNode('ArrayType', p[2].data, p[4].data)
return
def p_ArrayLength(p):
'''ArrayLength : Expression
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'ArrayLength'
return
def p_StructType(p):
'''StructType : STRUCT LCURLY FieldDeclList RCURLY
'''
parsed.append(p.slice)
return
def p_FieldDeclList(p):
'''FieldDeclList : empty
| FieldDeclList FieldDecl SEMICOLON
'''
parsed.append(p.slice)
return
def p_FieldDecl(p):
'''FieldDecl : IdentifierList Type TagTop
| IDENTIFIER Type TagTop
'''
parsed.append(p.slice)
return
def p_TagTop(p):
'''TagTop : empty
| Tag
'''
parsed.append(p.slice)
return
def p_Tag(p):
'''Tag : string_lit
'''
parsed.append(p.slice)
return
def p_FunctionType(p):
'''FunctionType : FUNC Signature
'''
parsed.append(p.slice)
return
def p_Signature(p):
'''Signature : Parameters
| Parameters Result
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'Signature'
s = 'scope_' + str(len(generated['scope']))
symbol_table.new_scope(s)
for child in p[1].children:
symbol_table.add_identifier(child, s)
newNode = SymbolTableNode(s + '_' + child.data, child.input_type)
symbol_table.add_var(newNode, s)
# symbol_table.print_symbol_table()
if len(p) == 2:
p[0].input_type = TreeNode('Result', 0, 'None')
else:
p[0].input_type = p[2]
return
def p_Result(p):
'''Result : Parameters
| Type
'''
parsed.append(p.slice)
if p[1].name == 'Type':
p[0] = TreeNode('Result', 1, 'None', 0, [p[1]])
else:
p[0] = p[1]
p[0].name = 'Result'
return
def p_Parameters(p):
'''Parameters : LROUND RROUND
| LROUND ParameterList RROUND
'''
parsed.append(p.slice)
if len(p) == 3:
p[0] = TreeNode('Parameters', 0, 'None')
else:
p[0] = p[2]
p[0].name = 'Parameters'
return
def p_ParameterList(p):
'''ParameterList : ParameterDecl
| ParameterList COMMA ParameterDecl
'''
parsed.append(p.slice)
if len(p) == 2:
p[0] = p[1]
p[0].name = 'ParameterList'
elif len(p) == 4:
p[0] = TreeNode('ParameterList', p[1].data + p[3].data, 'None', 0, p[1].children + p[3].children, p[1].TAC)
p[0].TAC.append_TAC(p[3].TAC)
return
def p_ParameterDecl(p):
'''ParameterDecl : IdentifierList Type
| IDENTIFIER Type
| Type
'''
parsed.append(p.slice)
p[0] = TreeNode('ParameterDecl', 0, 'None')
if len(p) == 3:
if hasattr(p[1], 'name') and p[1].name == 'IdentifierList':
for node in p[1].children:
p[0].data += 1
node.input_type = p[2].data
p[0].children += [node]
else:
node = TreeNode('IDENTIFIER', p[1], p[2].data, 1)
p[0].data += 1
p[0].children += [node]
else:
p[0].data += 1
p[0].children += [p[1]]
return
def p_VarDecl(p):
'''VarDecl : VAR VarSpecTopList
'''
parsed.append(p.slice)
p[0] = p[2]
p[0].name = 'VarDecl'
return
def p_VarSpecTopList(p):
'''VarSpecTopList : VarSpec
| LROUND VarSpecList RROUND
'''
parsed.append(p.slice)
if len(p) == 2:
p[0] = p[1]
else:
p[0] = p[2]
p[0].name = 'VarSpecTopList'
return
def p_VarSpecList(p):
'''VarSpecList : empty
| VarSpecList VarSpec SEMICOLON
'''
return
def p_VarSpec(p):
'''VarSpec : IDENTIFIER Type
| IDENTIFIER EQ Expression
| IDENTIFIER Type EQ Expression
| IdentifierList Type
| IdentifierList EQ ExpressionList
| IdentifierList Type EQ ExpressionList
'''
# Insert into symbol table
p[0] = TreeNode('VarSpec', 0, 'NONE')
if hasattr(p[1], 'name') and p[1].name == 'IdentifierList':
zero_val = TreeNode('decimal_lit', 0, 'INT')
# l1 = len(p[1].children)
# if len(p) == 3:
# expr_list = TreeNode('Expr_List', 0, 'NONE', 0, [zero_val] * l1)
# elif len(p) == 4:
# expr_list = p[3]
# elif len(p) == 5:
# expr_list = p[4]
# l2 = len(expr_list.children)
# p[0].TAC.append_TAC(expr_list.TAC)
# p[0].TAC.append_TAC(p[1].TAC)
# if l1 == l2:
# for i in range(l1):
# p[0].TAC.add_line(['=', p[1].children[i], expr_list.children[i].data, ''])
# else:
# print_error("Variable Declaration mismatch: " + str(l1) + " identifier(s) but " + str(l2) + " value(s)")
else:
p[1] = TreeNode('IDENTIFIER',p[1],'INT',1)
if p[2].input_type != 'NONE':
# array case
# p[2].print_node()
if symbol_table.add_identifier(p[1], size = p[2].data) == False:
print_error("Unable to add to SymbolTable")
return
name = symbol_table.search_identifier(p[1].data)
newNode = SymbolTableNode(name, p[1].input_type,size = p[2].data)
symbol_table.add_var(newNode)
p[0] = TreeNode('VarSpec',p[1].data,'INT')
# expr = TreeNode('Expr', 0, 'NONE')
# if len(p) == 4:
# expr = p[3]
# p[0].TAC.append_TAC(p[3].TAC)
# p[0].TAC.add_line(['=', check_variable(p[1]), check_variable(expr), ''])
# elif len(p) == 5:
# expr = p[4]
# p[0].TAC.append_TAC(p[4].TAC)
# p[0].TAC.add_line(['=', check_variable(p[1]), check_variable(expr), ''])
return
def p_FunctionDecl(p):
'''FunctionDecl : FUNC FunctionName Signature
| FUNC FunctionName Signature FunctionBody
'''
parsed.append(p.slice)
# symbol_table.print_symbol_table()
p[0] = TreeNode('FunctionDecl', 0, 'INT')
# print symbol_table.current_scope
# p[4].TAC.print_code()
symbol_table.add_function(p[2].data, p[3].input_type, p[3].children)
if len(p) == 5:
noOfParams = 0
for f in symbol_table.symbol_table[symbol_table.current_scope]['functions']:
if f.name == p[2].data:
noOfParams = len(f.parameters)
p[0].TAC.add_line(['func', check_variable(p[2]), str(noOfParams), ''])
for child in reversed(p[3].children):
p[0].TAC.add_line(['getparam', p[4].data + '_' + child.data, '', ''])
p[0].TAC.add_line(['stack_push', '', '', ''])
p[0].TAC.append_TAC(p[4].TAC)
return
def p_FunctionName(p):
'''FunctionName : IDENTIFIER
'''
parsed.append(p.slice)
p[0] = TreeNode('FunctionName', p[1], 'INT', 1)
return
def p_FunctionBody(p):
'''FunctionBody : Block
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'FunctionBody'
return
def p_SimpleStmt(p):
'''SimpleStmt : Expression
| Assignment
| ShortVarDecl
| IncDecStmt
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'SimpleStmt'
return
def p_IncDecStmt(p):
'''IncDecStmt : Expression PLUS_PLUS
| Expression MINUS_MINUS
'''
parsed.append(p.slice)
one_val = TreeNode('IncDecStmt', '1', 'INT')
p[0] = p[1]
if p[1].isLvalue == 1:
if p[2] == '++':
p[0].TAC.add_line(['+', check_variable(p[1]), check_variable(p[1]), one_val.data])
else:
p[0].TAC.add_line(['-', check_variable(p[1]), check_variable(p[1]), one_val.data])
else:
print_error("Lvalue required")
p[0].name = 'IncDecStmt'
return
def p_ShortVarDecl(p):
'''ShortVarDecl : ExpressionList ASSIGN_OP ExpressionList
| Expression ASSIGN_OP Expression
'''
parsed.append(p.slice)
# TODO: Add in symbol table
p[0] = TreeNode('ShortVarDecl', 0, 'INT')
if p[1].name == 'ExpressionList':
l1 = len(p[1].children)
l2 = len(p[3].children)
p[0].TAC.append_TAC(p[3].TAC)
p[0].TAC.append_TAC(p[1].TAC)
if l1 == l2:
for i in range(l1):
if p[1].children[i].isLvalue == 0:
print_error("Lvalue required")
return
else:
if symbol_table.add_identifier(p[1].children[i]) == False:
print_error("Unable to add to SymbolTable")
return
p[0].TAC.add_line([p[2], check_variable(p[1].children[i]), check_variable(p[3].children[i]), ''])
else:
print_error("Variable Declaration mismatch: " + str(l1) + " identifier(s) but " + str(l2) + " value(s)")
elif p[1].name == 'Expression':
if p[1].isLvalue == 0:
print_error("Lvalue required")
return
else:
if symbol_table.add_identifier(p[1]) == False:
print_error("Unable to add to SymbolTable")
return
p[0].TAC.append_TAC(p[3].TAC)
p[0].TAC.append_TAC(p[1].TAC)
p[0].TAC.add_line([p[2], check_variable(p[1]), check_variable(p[3]), ''])
return
def p_Assignment(p):
'''Assignment : ExpressionList assign_op ExpressionList
| Expression assign_op Expression
'''
parsed.append(p.slice)
p[0] = TreeNode('Assignment', 0, 'INT')
if p[1].name == 'ExpressionList':
l1 = len(p[1].children)
l2 = len(p[3].children)
p[0].TAC.append_TAC(p[3].TAC)
p[0].TAC.append_TAC(p[1].TAC)
if l1 == l2:
for i in range(l1):
if p[1].children[i].isLvalue == 0:
print_error("Lvalue required")
return
else:
if symbol_table.search_identifier(p[1].children[i].data) == False and p[1].children[i].data not in generated['temp']:
print_error("Variable " + p[1].children[i].data + " is undefined")
return
if p[3].children[i].isLvalue == 1 and symbol_table.search_identifier(p[3].children[i].data) == False and p[3].children[i].data not in generated['temp']:
print_error("Variable " + p[3].children[i].data + " is undefined")
return
p[0].TAC.add_line([p[2].data, check_variable(p[1].children[i]), check_variable(p[3].children[i]), ''])
else:
print_error("Variable Declaration mismatch: " + str(l1) + " identifier(s) but " + str(l2) + " value(s)")
elif p[1].name == 'Expression':
if p[1].isLvalue == 0:
print_error("Lvalue required")
return
else:
if symbol_table.search_identifier(p[1].data) == False and p[1].data not in generated['temp']:
print_error("Variable " + p[1].data + " is undefined")
return
if p[3].isLvalue == 1 and symbol_table.search_identifier(p[3].data) == False and p[3].data not in generated['temp']:
print_error("Variable " + p[3].data + " is undefined")
return
# print symbol_table.current_scope
p[0].TAC.append_TAC(p[3].TAC)
p[0].TAC.append_TAC(p[1].TAC)
p[0].TAC.add_line([p[2].data, check_variable(p[1]), check_variable(p[3]), ''])
return
def p_assign_op(p):
'''assign_op : EQ
| PLUS_EQ
| MINUS_EQ
| OR_EQ
| CARET_EQ
| STAR_EQ
| DIVIDE_EQ
| MODULO_EQ
| LS_EQ
| RS_EQ
| AMP_EQ
| AND_OR_EQ
'''
parsed.append(p.slice)
p[0] = TreeNode('assign_op', p[1], 'OPERATOR')
return
def p_IfStmt(p):
'''IfStmt : IF Expression Block
| IF Expression Block ELSE elseTail
'''
parsed.append(p.slice)
if len(p) == 4:
l1 = gen('label')
p[0] = TreeNode('IfStmt', 0, 'INT')
p[0].TAC.append_TAC(p[2].TAC)
p[0].TAC.add_line(['ifgotoeq', check_variable(p[2]), '0', l1])
p[0].TAC.append_TAC(p[3].TAC)
p[0].TAC.add_line(['label', l1, '', ''])
if len(p) == 6:
l1 = gen('label')
l2 = gen('label')
p[0] = TreeNode('IfStmt', 0, 'INT')
p[0].TAC.append_TAC(p[2].TAC)
p[0].TAC.add_line(['ifgotoeq', check_variable(p[2]), '0', l1])
p[0].TAC.append_TAC(p[3].TAC)
p[0].TAC.add_line(['goto', l2, '', ''])
p[0].TAC.add_line(['label', l1, '', ''])
p[0].TAC.append_TAC(p[5].TAC)
p[0].TAC.add_line(['label', l2, '', ''])
return
def p_elseTail(p):
'''elseTail : IfStmt
| Block
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'elseTail'
return
def p_SwitchStmt(p):
'''SwitchStmt : ExprSwitchStmt
'''
parsed.append(p.slice)
p[0] = TreeNode('SwitchStmt', 0, 'INT', 0, [], p[1].TAC)
return
def p_ExprSwitchStmt(p):
'''ExprSwitchStmt : SWITCH SimpleStmt SEMICOLON LCURLY ScopeStart ExprCaseClauseList ScopeEnd RCURLY
| SWITCH SimpleStmt SEMICOLON Expression LCURLY ScopeStart ExprCaseClauseList ScopeEnd RCURLY
| SWITCH LCURLY ScopeStart ExprCaseClauseList ScopeEnd RCURLY
| SWITCH Expression LCURLY ScopeStart ExprCaseClauseList ScopeEnd RCURLY
'''
parsed.append(p.slice)
if len(p) == 8:
l1 = gen('label')
l2 = gen('label')
p[0] = TreeNode('ExprSwitchStmt', 0, 'INT')
p[0].TAC.append_TAC(p[2].TAC)
t1 = TreeNode('IDENTIFIER', gen('temp'), 'INT', 1)
p[0].TAC.add_line(['=', check_variable(t1) , check_variable(p[2]), ''])
p[0].TAC.append_TAC(p[5].data)
for i in range(len(p[5].children)):
p[0].TAC.add_line(['ifgotoeq', check_variable(t1), p[5].children[i][0], p[5].children[i][1]])
p[0].TAC.add_line(['goto', l2, '', ''])
for i in range(p[5].TAC.length()):
if i in p[5].TAC.leaders[1:]:
p[0].TAC.add_line(['goto', l2, '', ''])
p[0].TAC.add_line(p[5].TAC.code[i])
p[0].TAC.add_line(['label', l2, '', ''])
return
def p_ExprCaseClauseList(p):
'''ExprCaseClauseList : empty
| ExprCaseClauseList ExprCaseClause
'''
parsed.append(p.slice)
TAC1 = ThreeAddressCode()
TAC2 = ThreeAddressCode()
if len(p) == 3:
TAC1 = p[1].data
TAC2 = p[2].data
p[0] = TreeNode('ExprCaseClauseList', TAC1, 'INT', 0, p[1].children + p[2].children, p[1].TAC)
p[0].TAC.add_leader(p[0].TAC.length())
p[0].TAC.append_TAC(p[2].TAC)
p[0].data.append_TAC(TAC2)
else:
p[0] = TreeNode('ExprCaseClauseList', TAC1, 'INT')
return
def p_ExprCaseClause(p):
'''ExprCaseClause : ExprSwitchCase COLON StatementList
'''
parsed.append(p.slice)
l1 = gen('label')
p[0] = TreeNode('ExprCaseClause', 0, 'INT')
# p[0].TAC.append_TAC(p[1].TAC)
p[0].TAC.add_line(['label', l1, '', ''])
# p[0].TAC.add_line(['ifgotoneq', p[1].children, p[1].children, l1])
p[0].TAC.append_TAC(p[3].TAC)
p[0].children = [[p[1].data,l1]]
p[0].data = p[1].TAC
return
def p_ExprSwitchCase(p):
'''ExprSwitchCase : CASE ExpressionList
| DEFAULT
| CASE Expression
'''
parsed.append(p.slice)
p[0] = TreeNode('ExprSwitchCase', 0, 'INT')
if len(p) == 3:
p[0].data = p[2].data
p[0].TAC = p[2].TAC
return
def p_ForStmt(p):
'''ForStmt : FOR Expression Block
| FOR Block
'''
parsed.append(p.slice)
p[0] = TreeNode('ForStmt', 0, 'INT')
if len(p) == 4:
l1 = gen('label')
l2 = gen('label')
p[0].TAC.add_line(['label', l1, '', ''])
p[0].TAC.append_TAC(p[2].TAC)
p[0].TAC.add_line(['ifgotoeq',check_variable(p[2]), '0', l2])
p[0].TAC.append_TAC(p[3].TAC)
p[0].TAC.add_line(['goto', l1, '', ''])
p[0].TAC.add_line(['label', l2, '', ''])
if len(p) == 3:
l1 = gen('label')
# l2 = gen('label')
p[0].TAC.add_line(['label', l1, '', ''])
p[0].TAC.append_TAC(p[2].TAC)
p[0].TAC.add_line(['goto', l1, '', ''])
# p[0].TAC.add_line([l2])
return
def p_ReturnStmt(p):
'''ReturnStmt : RETURN
| RETURN Expression
| RETURN ExpressionList
'''
parsed.append(p.slice)
if len(p) == 2:
p[0] = TreeNode('ReturnStmt', 0, 'None')
p[0].TAC.add_line(['return', '', '', ''])
if len(p) == 3:
if p[2].name == 'Expression':
p[0] = p[2]
p[0].name = 'ReturnStmt'
p[0].TAC.add_line(['return', check_variable(p[2]), '', ''])
return
def p_BreakStmt(p):
'''BreakStmt : BREAK IDENTIFIER
'''
parsed.append(p.slice)
return
def p_ContinueStmt(p):
'''ContinueStmt : CONTINUE IDENTIFIER
'''
parsed.append(p.slice)
return
def p_GotoStmt(p):
'''GotoStmt : GOTO IDENTIFIER
'''
parsed.append(p.slice)
return
def p_Expression(p):
'''Expression : UnaryExpr
| Expression OR_OR Expression
| Expression AMP_AMP Expression
| Expression EQ_EQ Expression
| Expression NOT_EQ Expression
| Expression LT Expression
| Expression LT_EQ Expression
| Expression GT Expression
| Expression GT_EQ Expression
| Expression PLUS Expression
| Expression MINUS Expression
| Expression OR Expression
| Expression CARET Expression
| Expression STAR Expression
| Expression DIVIDE Expression
| Expression MODULO Expression
| Expression LS Expression
| Expression RS Expression
| Expression AMP Expression
| Expression AND_OR Expression
'''
parsed.append(p.slice)
if len(p) == 2:
p[0] = p[1]
elif len(p) == 4:
p[0] = TreeNode('IDENTIFIER', gen('temp'), 'INT', 1, [], p[1].TAC)
p[0].TAC.append_TAC(p[3].TAC)
p[0].TAC.add_line([p[2],check_variable(p[0]), check_variable(p[1]), check_variable(p[3])])
p[0].name = 'Expression'
return
def p_UnaryExpr(p):
'''UnaryExpr : PrimaryExpr
| unary_op UnaryExpr
'''
parsed.append(p.slice)
if len(p) == 2:
p[0] = p[1]
elif len(p) == 3:
p[0] = TreeNode('IDENTIFIER', gen('temp'), 'INT', 1)
p[0].TAC.add_line([check_variable(p[1]), check_variable(p[0]), check_variable(p[2]), ''])
p[0].name = 'UnaryExpr'
return
def p_unary_op(p):
'''unary_op : PLUS
| MINUS
| NOT
| CARET
| STAR
| AMP
| LT_MINUS
'''
parsed.append(p.slice)
p[0] = TreeNode('unary_op', p[1], 'OPERATOR')
return
def p_PrimaryExpr(p):
'''PrimaryExpr : Operand
| IDENTIFIER
| PrimaryExpr Selector
| PrimaryExpr Index
| PrimaryExpr Arguments
'''
parsed.append(p.slice)
if len(p) == 2:
if p.slice[1].type == 'IDENTIFIER':
p[0] = TreeNode('IDENTIFIER', p[1], 'INT', 1)
elif p[1].name == 'Operand':
p[0] = p[1]
elif len(p) == 3:
if p[2].name == 'Index':
p[0] = TreeNode('IDENTIFIER', p[1].data, 'INT', 1, p[2].data)
elif p[2].name == 'Arguments':
p[0] = TreeNode('IDENTIFIER', gen('temp'), 'INT', 1)
p[0].TAC.append_TAC(p[1].TAC)
p[0].TAC.append_TAC(p[2].TAC)
# p[1].print_node()
func = check_variable(p[1]).split("_")
scope, funcName = "_".join(func[:2]), "_".join(func[2:])
temp = 0
for f in symbol_table.symbol_table[scope]['functions']:
if f.name == funcName:
temp = len(f.parameters)
# p[2].print_node()
for child in p[2].children:
p[0].TAC.add_line(['putparam', check_variable(child), '', ''])
if temp != p[2].data:
print_error('Function ' + funcName + ' requires ' + str(temp) + ' parameters but ' + str(p[2].data) + ' supplied')
p[0].TAC.add_line(['call', check_variable(p[1]), str(p[2].data), ''])
p[0].TAC.add_line(['return_value', check_variable(p[0]), '', ''])
p[0].name = 'PrimaryExpr'
return
def p_Operand(p):
'''Operand : Literal
| LROUND Expression RROUND
'''
parsed.append(p.slice)
if len(p) == 2:
p[0] = p[1]
else:
p[0] = p[2]
p[0].name = 'Operand'
return
def p_Literal(p):
'''Literal : BasicLit
| FunctionLit
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'Literal'
return
def p_BasicLit(p):
'''BasicLit : int_lit
| float_lit
| string_lit
| rune_lit
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'BasicLit'
return
def p_int_lit(p):
'''int_lit : decimal_lit
| octal_lit
| hex_lit
'''
parsed.append(p.slice)
p[0] = p[1]
p[0].name = 'int_lit'
return
def p_decimal_lit(p):
'''decimal_lit : DECIMAL_LIT
'''
parsed.append(p.slice)
p[0] = TreeNode('decimal_lit', p[1], 'INT')
return
def p_octal_lit(p):
'''octal_lit : OCTAL_LIT
'''
parsed.append(p.slice)
p[0] = TreeNode('octal_lit', p[1], 'OCT')
return
def p_hex_lit(p):
'''hex_lit : HEX_LIT
'''
parsed.append(p.slice)
p[0] = TreeNode('hex_lit', p[1], 'HEX')
return
def p_float_lit(p):
'''float_lit : FLOAT_LIT
'''
parsed.append(p.slice)
p[0] = TreeNode('float_lit', p[1], 'FLOAT')
return
def p_FunctionLit(p):
'''FunctionLit : FUNC Signature FunctionBody
'''
parsed.append(p.slice)
# Anonymous Function
# Not implemented yet
return
def p_Selector(p):
'''Selector : DOT IDENTIFIER
'''
parsed.append(p.slice)
return
def p_Index(p):
'''Index : LSQUARE Expression RSQUARE
'''
parsed.append(p.slice)
p[0] = p[2]
p[0].name = 'Index'
return
def p_Arguments(p):
'''Arguments : LROUND RROUND
| LROUND ExpressionList RROUND
| LROUND Expression RROUND
| LROUND Type RROUND
| LROUND Type COMMA ExpressionList RROUND
| LROUND Type COMMA Expression RROUND
'''
# print p.slice
parsed.append(p.slice)
if len(p) == 3:
p[0] = TreeNode('Arguments', 0, 'None')
if len(p) == 4:
if p[2].name == 'Expression':
p[0] = TreeNode('Arguments', 1, 'None', 0, [p[2]], p[2].TAC)
if p[2].name == 'ExpressionList':
p[0] = p[2]
p[0].name = 'Arguments'
p[0].data = len(p[2].children)
return
def p_string_lit(p):
'''string_lit : STRING_LIT
'''
parsed.append(p.slice)
p[0] = TreeNode('string_lit', p[1], 'STRING')
return
def p_rune_lit(p):
'''rune_lit : RUNE_LIT
'''
parsed.append(p.slice)
p[0] = TreeNode('rune_lit', p[1], 'RUNE')
return
def p_empty(p):
'empty :'
pass
# Standard Logger
logging.basicConfig(
level = logging.DEBUG,
filename = "parselog.txt",
filemode = "w",
format = "%(filename)10s:%(lineno)4d:%(message)s"
)
log = logging.getLogger()
yacc.yacc(debug=True, debuglog=log)
input_file = sys.argv[1]
import os
if os.path.isfile(input_file) is False:
print('Input file ' + input_file + ' does not exist')
sys.exit(1)
input_code = open(input_file, 'r').read()
if input_code[len(input_code)-1] != '\n':
input_code += '\n'
yacc.parse(input_code, debug=log, tracking=True)
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10962,
... | 1.925113 | 17,533 |
import argparse
import imageio
import progressbar
from _routines import ffi, lib
from pylab import *
from random import Random
RESOLUTIONS = {
"2160p": (3840, 2160),
"1440p": (2560, 1440),
"1080p": (1920, 1080),
"720p": (1280, 720),
"480p": (854, 480),
"360p": (640, 360),
"240p": (426, 240),
"160p": (284, 160),
"80p": (142, 80),
"40p": (71, 40),
}
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Render audio samples')
parser.add_argument('outfile', type=str, help='Output file name')
parser.add_argument('--params', type=str, help='Parameter YAML file name')
parser.add_argument('--resolution', choices=RESOLUTIONS.keys(), help='Video and simulation grid resolution')
parser.add_argument('--width', type=int, help='Video and simulation grid width', metavar='W')
parser.add_argument('--height', type=int, help='Video and simulation grid height', metavar='H')
parser.add_argument('--framerate', type=int, help='Video frame rate')
parser.add_argument('--video-quality', type=int, help='Video quality factor')
parser.add_argument('--video-duration', type=float, help='Duration of video to render in seconds')
args = parser.parse_args()
if not args.framerate:
args.framerate = 24
if not args.video_quality:
args.video_quality = 10
writer = imageio.get_writer(args.outfile, fps=args.framerate, quality=args.video_quality, macro_block_size=1)
# Compute derived parameters
if args.resolution:
width, height = RESOLUTIONS[args.resolution]
if not args.width:
args.width = width
if not args.height:
args.height = height
if (not args.width) or (not args.height):
raise ValueError("Invalid or missing resolution")
if not args.video_duration:
raise ValueError("Missing video duration")
args.aspect = args.width / args.height
args.num_frames = int(args.video_duration * args.framerate)
args.dt = 1.0 / args.num_frames
do_render(args, writer)
writer.close()
| [
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3843,
11053,
796,... | 2.682519 | 778 |
# Copyright (c) 2015 Nicolas JOUANIN
#
# See the file license.txt for copying permission.
import anyio
import unittest
from hbmqtt.mqtt.subscribe import SubscribePacket, SubscribePayload
from hbmqtt.mqtt.packet import PacketIdVariableHeader
from hbmqtt.mqtt.constants import QOS_1, QOS_2
from hbmqtt.adapters import BufferAdapter
| [
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80,
... | 2.990991 | 111 |
import logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%Y/%m/%d %H:%M:%S',
level=logging.INFO,
)
logger = logging.getLogger("Main")
import os,random
import numpy as np
import torch
from processing import convert_examples_to_features, read_squad_examples
from processing import ChineseFullTokenizer
from pytorch_pretrained_bert.my_modeling import BertConfig
from optimization import BERTAdam
import config
from utils import read_and_convert, divide_parameters
from modeling import BertForQASimple, BertForQASimpleAdaptorTraining
from textbrewer import DistillationConfig, TrainingConfig, BasicTrainer
from torch.utils.data import TensorDataset, DataLoader, RandomSampler
from functools import partial
from train_eval import predict
if __name__ == "__main__":
main()
| [
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220,... | 3.083942 | 274 |
# EXPERIMENTAL: all may be removed soon
from gym.benchmarks import scoring
from gym.benchmarks.registration import benchmark_spec, register_benchmark, registry, register_benchmark_view # imports used elsewhere
register_benchmark(
id='Atari200M',
scorer=scoring.TotalReward(),
name='Atari200M',
view_group="Atari",
description='7 Atari games, with pixel observations',
tasks=[
{
'env_id': 'BeamRiderNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(2e8),
'reward_floor': 363.9,
'reward_ceiling': 60000.0,
},
{
'env_id': 'BreakoutNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(2e8),
'reward_floor': 1.7,
'reward_ceiling': 800.0,
},
{
'env_id': 'EnduroNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(2e8),
'reward_floor': 0.0,
'reward_ceiling': 5000.0,
},
{
'env_id': 'PongNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(2e8),
'reward_floor': -20.7,
'reward_ceiling': 21.0,
},
{
'env_id': 'QbertNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(2e8),
'reward_floor': 163.9,
'reward_ceiling': 40000.0,
},
{
'env_id': 'SeaquestNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(2e8),
'reward_floor': 68.4,
'reward_ceiling': 100000.0,
},
{
'env_id': 'SpaceInvadersNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(2e8),
'reward_floor': 148.0,
'reward_ceiling': 30000.0,
},
])
register_benchmark(
id='Atari40M',
scorer=scoring.TotalReward(),
name='Atari40M',
view_group="Atari",
description='7 Atari games, with pixel observations',
tasks=[
{
'env_id': 'BeamRiderNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 363.9,
'reward_ceiling': 60000.0,
},
{
'env_id': 'BreakoutNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 1.7,
'reward_ceiling': 800.0,
},
{
'env_id': 'EnduroNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 0.0,
'reward_ceiling': 5000.0,
},
{
'env_id': 'PongNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': -20.7,
'reward_ceiling': 21.0,
},
{
'env_id': 'QbertNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 163.9,
'reward_ceiling': 40000.0,
},
{
'env_id': 'SeaquestNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 68.4,
'reward_ceiling': 100000.0,
},
{
'env_id': 'SpaceInvadersNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 148.0,
'reward_ceiling': 30000.0,
}
])
register_benchmark(
id='AtariExploration40M',
scorer=scoring.TotalReward(),
name='AtariExploration40M',
view_group="Atari",
description='7 Atari games, with pixel observations',
tasks=[
{
'env_id': 'FreewayNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 0.1,
'reward_ceiling': 31.0,
},
{
'env_id': 'GravitarNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 245.5,
'reward_ceiling': 1000.0,
},
{
'env_id': 'MontezumaRevengeNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 25.0,
'reward_ceiling': 10000.0,
},
{
'env_id': 'PitfallNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': -348.8,
'reward_ceiling': 1000.0,
},
{
'env_id': 'PrivateEyeNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 662.8,
'reward_ceiling': 100.0,
},
{
'env_id': 'SolarisNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 2047.2,
'reward_ceiling': 5000.0,
},
{
'env_id': 'VentureNoFrameskip-v4',
'trials': 2,
'max_timesteps': int(4e7),
'reward_floor': 18.0,
'reward_ceiling': 100.0,
}
])
register_benchmark(
id='ClassicControl2-v0',
name='ClassicControl2',
view_group="Control",
description='Simple classic control benchmark',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'CartPole-v0',
'trials': 1,
'max_timesteps': 2000,
},
{'env_id': 'Pendulum-v0',
'trials': 1,
'max_timesteps': 1000,
},
])
register_benchmark(
id='ClassicControl-v0',
name='ClassicControl',
view_group="Control",
description='Simple classic control benchmark',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'CartPole-v1',
'trials': 3,
'max_timesteps': 100000,
'reward_floor': 0.0,
'reward_ceiling': 500.0,
},
{'env_id': 'Acrobot-v1',
'trials': 3,
'max_timesteps': 100000,
'reward_floor': -500.0,
'reward_ceiling': 0.0,
},
{'env_id': 'MountainCar-v0',
'trials': 3,
'max_timesteps': 100000,
'reward_floor': -200.0,
'reward_ceiling': -100.0,
},
{'env_id': 'Pendulum-v0',
'trials': 3,
'max_timesteps': 200000,
'reward_floor': -1400.0,
'reward_ceiling': 0.0,
},
])
### Autogenerated by tinkerbell.benchmark.convert_benchmark.py
register_benchmark(
id='Mujoco10M-v0',
name='Mujoco10M',
view_group="Control",
description='Mujoco benchmark with 10M steps',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'Ant-v1',
'trials': 1,
'max_timesteps': 1000000,
},
{'env_id': 'Hopper-v1',
'trials': 1,
'max_timesteps': 1000000,
},
{'env_id': 'Humanoid-v1',
'trials': 1,
'max_timesteps': 1000000,
},
{'env_id': 'HumanoidStandup-v1',
'trials': 1,
'max_timesteps': 1000000,
},
{'env_id': 'Walker2d-v1',
'trials': 1,
'max_timesteps': 1000000,
}
])
register_benchmark(
id='Mujoco1M-v0',
name='Mujoco1M',
view_group="Control",
description='Mujoco benchmark with 1M steps',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'HalfCheetah-v1',
'trials': 3,
'max_timesteps': 1000000,
'reward_floor': -280.0,
'reward_ceiling': 4000.0,
},
{'env_id': 'Hopper-v1',
'trials': 3,
'max_timesteps': 1000000,
'reward_floor': 16.0,
'reward_ceiling': 4000.0,
},
{'env_id': 'InvertedDoublePendulum-v1',
'trials': 3,
'max_timesteps': 1000000,
'reward_floor': 53.0,
'reward_ceiling': 10000.0,
},
{'env_id': 'InvertedPendulum-v1',
'trials': 3,
'max_timesteps': 1000000,
'reward_floor': 5.6,
'reward_ceiling': 1000.0,
},
{'env_id': 'Reacher-v1',
'trials': 3,
'max_timesteps': 1000000,
'reward_floor': -43.0,
'reward_ceiling': -0.5,
},
{'env_id': 'Swimmer-v1',
'trials': 3,
'max_timesteps': 1000000,
'reward_floor': 0.23,
'reward_ceiling': 500.0,
},
{'env_id': 'Walker2d-v1',
'trials': 3,
'max_timesteps': 1000000,
'reward_floor': 1.6,
'reward_ceiling': 5500.0,
}
])
register_benchmark(
id='MinecraftEasy-v0',
name='MinecraftEasy',
view_group="Minecraft",
description='Minecraft easy benchmark',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'MinecraftBasic-v0',
'trials': 2,
'max_timesteps': 600000,
'reward_floor': -2200.0,
'reward_ceiling': 1000.0,
},
{'env_id': 'MinecraftDefaultFlat1-v0',
'trials': 2,
'max_timesteps': 2000000,
'reward_floor': -500.0,
'reward_ceiling': 0.0,
},
{'env_id': 'MinecraftTrickyArena1-v0',
'trials': 2,
'max_timesteps': 300000,
'reward_floor': -1000.0,
'reward_ceiling': 2800.0,
},
{'env_id': 'MinecraftEating1-v0',
'trials': 2,
'max_timesteps': 300000,
'reward_floor': -300.0,
'reward_ceiling': 300.0,
},
])
register_benchmark(
id='MinecraftMedium-v0',
name='MinecraftMedium',
view_group="Minecraft",
description='Minecraft medium benchmark',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'MinecraftCliffWalking1-v0',
'trials': 2,
'max_timesteps': 400000,
'reward_floor': -100.0,
'reward_ceiling': 100.0,
},
{'env_id': 'MinecraftVertical-v0',
'trials': 2,
'max_timesteps': 900000,
'reward_floor': -1000.0,
'reward_ceiling': 8040.0,
},
{'env_id': 'MinecraftMaze1-v0',
'trials': 2,
'max_timesteps': 600000,
'reward_floor': -1000.0,
'reward_ceiling': 1000.0,
},
{'env_id': 'MinecraftMaze2-v0',
'trials': 2,
'max_timesteps': 2000000,
'reward_floor': -1000.0,
'reward_ceiling': 1000.0,
},
])
register_benchmark(
id='MinecraftHard-v0',
name='MinecraftHard',
view_group="Minecraft",
description='Minecraft hard benchmark',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'MinecraftObstacles-v0',
'trials': 1,
'max_timesteps': 900000,
'reward_floor': -1000.0,
'reward_ceiling': 2080.0,
},
{'env_id': 'MinecraftSimpleRoomMaze-v0',
'trials': 1,
'max_timesteps': 900000,
'reward_floor': -1000.0,
'reward_ceiling': 4160.0,
},
{'env_id': 'MinecraftAttic-v0',
'trials': 1,
'max_timesteps': 600000,
'reward_floor': -1000.0,
'reward_ceiling': 1040.0,
},
{'env_id': 'MinecraftComplexityUsage-v0',
'trials': 1,
'max_timesteps': 600000,
'reward_floor': -1000.0,
'reward_ceiling': 1000.0,
},
])
register_benchmark(
id='MinecraftVeryHard-v0',
name='MinecraftVeryHard',
view_group="Minecraft",
description='Minecraft very hard benchmark',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'MinecraftMedium-v0',
'trials': 2,
'max_timesteps': 1800000,
'reward_floor': -10000.0,
'reward_ceiling': 16280.0,
},
{'env_id': 'MinecraftHard-v0',
'trials': 2,
'max_timesteps': 2400000,
'reward_floor': -10000.0,
'reward_ceiling': 32640.0,
},
])
register_benchmark(
id='MinecraftImpossible-v0',
name='MinecraftImpossible',
view_group="Minecraft",
description='Minecraft impossible benchmark',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[
{'env_id': 'MinecraftDefaultWorld1-v0',
'trials': 2,
'max_timesteps': 6000000,
'reward_floor': -1000.0,
'reward_ceiling': 1000.0,
},
])
bandit_tasks = []
for n_arms in [5, 10, 50]:
for n_episodes in [10, 100, 500]:
bandit_tasks.append({
'env_id': 'BernoulliBandit-{k}.arms-{n}.episodes-v0'.format(k=n_arms, n=n_episodes),
'trials': 1,
'max_timesteps': 10 ** 9,
'reward_floor': 0,
'reward_ceiling': n_episodes,
})
register_benchmark(
id='BernoulliBandit-v0',
name='BernoulliBandit',
description='Multi-armed Bernoulli bandits',
scorer=scoring.ClipTo01ThenAverage(num_episodes=1000),
tasks=bandit_tasks
)
tabular_mdp_tasks = []
for n_states in [10]:
for n_actions in [5]:
for episode_length in [10]:
for n_episodes in [10, 25, 50, 75, 100]:
tabular_mdp_tasks.append({
'env_id': 'RandomTabularMDP-{s}.states-{a}.actions-{t}.timesteps-{n}.episodes-v0'.format(
s=n_states, a=n_actions, t=episode_length, n=n_episodes,
),
'trials': 1,
'max_timesteps': 10 ** 9,
'reward_floor': 0,
'reward_ceiling': episode_length * n_episodes * 2,
})
register_benchmark(
id='RandomTabularMDP-v0',
name='RandomTabularMDP',
description='Random tabular MDPs',
scorer=scoring.ClipTo01ThenAverage(num_episodes=1000),
tasks=tabular_mdp_tasks
)
| [
2,
7788,
18973,
3955,
3525,
1847,
25,
477,
743,
307,
4615,
2582,
198,
198,
6738,
11550,
13,
26968,
14306,
1330,
9689,
198,
6738,
11550,
13,
26968,
14306,
13,
2301,
33397,
1330,
18335,
62,
16684,
11,
7881,
62,
26968,
4102,
11,
20478,
1... | 1.764511 | 7,839 |
#!/usr/bin/env python3
# Copyright 2020 Benjamin Ehret
#
# 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.
#
# title :data/timeseries/preprocess_audioset.py
# author :be
# contact :behret@ethz.ch
# created :31/03/2020
# version :1.0
# python_version :3.7
"""
Script to structure the audioset dataset, which can then be used via
:class:`data.timeseries.audioset_data.AudiosetData`.
The result of this script is available at
https://www.dropbox.com/s/07dfeeuf5aq4w1h/audioset_data_balanced?dl=0
If you want to recreate or modify this dataset, download the audioset data from
https://research.google.com/audioset/download.html
and extract the tar.gz into the following folder:
``datasets/sequential/audioset/audioset_download``.
Subsequently executing this script will create a pickle file containing the 100
class subset of audioset used in this study.
The dataset is stored in tensorflow files. Since we work with pytorch and there
is no utility to read tensorflow files, we extract the data and safe them as
numpy arrays in a pickle file.
Furthermore the data are preprocessed to fit our continual learning experiments.
The original dataset provides three subsets with different compositions of
samples and classes. Since we only work with a subset of classes and samples,
we load all available data and then filter and structure them according to our
criteria.
We use the same criteria as Kemker et al. Classes and samples are restricted in
the following way:
Classes:
- no restriction according to ontology file (parsed from ontology.json)
- no parent / child relationship (parsed from ontology.json)
- confidence level > 70% (data was copied from website into txt file)
- number of samples: we only take classes that have more samples than
a certain threshold
Samples:
- since samples can have multiple labels, we only use samples which
only belong to one of the classes we use
- we exclude samples that don't have the full length of 10 seconds
The chosen classes and samples are then split into train and test data and
saved to a pickle file.
"""
import numpy as np
import pickle
import tensorflow as tf
import os
import json
from warnings import warn
warn('The script was created for one time usage and has to be adapted when ' +
'reusing it. All paths specified here are absolute.')
# Tensorflow eager mode needs to be enabled for dataset mapping to work!
tf.enable_eager_execution()
# Set paths and parameters
data_dir = '../../datasets/sequential/audioset/'
download_dir = os.path.join(data_dir,'audioset_download')
fpath_conf_data = os.path.join(data_dir, 'confidence_data.csv')
fpath_label_inds = os.path.join(data_dir, 'class_labels_indices.csv')
fpath_ontology = os.path.join(data_dir, 'ontology.json')
target_path = os.path.join(data_dir, 'audioset_data_balanced.pickle')
n_classes = 100
n_sample = 1000
test_frac = 0.20
### Load data by serializing files and applying decode function.
def decode(serialized_example):
"""Decode data from TFRecord files.
Args:
serialized_example: serialized_example as created by
tf.data.TFRecordDataset
Returns:
(tuple): Tuple containing:
- **audio** (numpy.ndarray): Array of shape (10,128) representing one
sample with 10 timesteps and 128 features
- **label** (numpy.ndarray): Array of shape (1,) containing the class
of the corresponding sample
"""
sequence_features = {
'audio_embedding': tf.FixedLenSequenceFeature([], tf.string),
}
context_features = {
'start_time_seconds': tf.FixedLenFeature([], tf.float32),
'labels': tf.VarLenFeature(dtype=tf.int64),
}
context_parsed, sequence_parsed = tf.parse_single_sequence_example(
serialized_example,
sequence_features=sequence_features,
context_features=context_features
)
audio = tf.decode_raw(sequence_parsed['audio_embedding'], tf.uint8)
label = tf.cast(context_parsed['labels'], tf.int64)
return audio, label
# Apply decode function to all dataset entries using map function.
# Take files from all three data sets since we repartition anyway.
fpaths = []
for path, subdirs, files in os.walk(download_dir):
for name in files:
if 'tfrecord' in name:
fpaths.append(os.path.join(path, name))
# Create dataset and decode
dataset = tf.data.TFRecordDataset(fpaths)
dataset = dataset.map(decode)
# Extract data to lists
x = []
y = []
for d in dataset:
x.append(d[0].numpy())
y.append(tf.sparse.to_dense(tf.sparse.reorder(d[1])).numpy())
### Filter classes as described above.
# Parse confidence values
conf_data = {}
with open(fpath_conf_data) as f:
for line in f:
tokens = line.split()
# parse confidence
c = 0
for t in tokens:
if t.find('%') is not -1:
c = int(t[:-1])
# parse class name
n = ''
for t in tokens:
if t.find('%') == -1 and t != '-':
if n == '':
n = t
else:
n = n+' '+t
else:
break
conf_data.update({n:c})
# Parse class numbers from label csv file
l = -1
csv_data = {}
with open(fpath_label_inds) as f:
for line in f:
if l == -1:
l += 1
continue
tokens = line.split('"')
n = tokens[1]
csv_data.update({n:l})
l +=1
# Parse ontology info from json file
with open(fpath_ontology, 'r') as f:
json_data = json.load(f)
# Put all data into a single list.
all_data = []
for j in json_data:
if j['name'] in conf_data.keys():
class_info = {
'name' : j['name'],
'restricted' : j['restrictions'] != [],
'has_child' : j['child_ids'] != [],
'conf' : conf_data[j['name']],
'id' : csv_data[j['name']]
}
all_data.append(class_info)
# Filter classes
classes = []
for c in all_data:
if not c['restricted'] and not c['has_child'] and c['conf'] >= 70:
classes.append(c['id'])
### Filter the samples.
# Find samples that belong to only one of the potential classes.
# We also exclude some samples that don't have data for the full 10 seconds.
# First discard labels that are not in the set of potential classes
y_fil = []
for i in range(len(y)):
y_fil.append( np.intersect1d(y[i],classes))
# Find samples with one label
n_labels = np.asarray([len(y) for y in y_fil])
single_label_idx = np.where(n_labels == 1)[0]
# Find samples that are shorter than 10 seconds (to be excluded)
too_short = np.where(np.asarray([x.shape[0] for x in x]) != 10)[0]
# Construct the set of valid samples
valid_idx = np.setdiff1d(single_label_idx,too_short)
# Count number of valid samples for potential classes
y_single = np.asarray([y_fil[i][0] for i in valid_idx])
num_samples = [len(np.where(y_single == i)[0]) for i in classes]
# Take the n classes with the highest number of samples
n_sample_cutoff = np.sort(num_samples)[-n_classes]
class_idx = np.where(np.asarray(num_samples) >= n_sample_cutoff)[0]
our_classes = [classes[i] for i in class_idx]
### Filter the data again according the the chosen classes
y_fil = []
for i in range(len(y)):
y_fil.append( np.intersect1d(y[i],our_classes))
# Find samples that belong to only one of the potential classes
n_labels = np.asarray([len(y) for y in y_fil])
single_label_idx = np.where(n_labels == 1)[0]
# Find samples that dont are shorter than 10 seconds
too_short = np.where(np.asarray([x.shape[0] for x in x]) != 10)[0]
# Construct the set of valid samples
valid_idx = np.setdiff1d(single_label_idx,too_short)
# Restructure data and relabel the classes to be between 0 and n_classes
y_data = [y_fil[i][0] for i in valid_idx]
y_data = [np.where(np.asarray(our_classes) == i)[0][0] for i in y_data]
y_data = np.asarray(y_data)
x_data = [x[i] for i in valid_idx]
x_data = np.stack(x_data)
### Split into test and train and restrict the number of samples per class
np.random.seed(42)
n_train = int(n_sample * (1-test_frac))
n_test = int(n_sample * test_frac)
train_ind = []
test_ind = []
for i in range(n_classes):
sample_idx = np.where(y_data == i)[0]
n_sample_class = len(sample_idx)
rand_idx = np.arange(n_sample_class)
np.random.shuffle(rand_idx)
train_ind.extend(sample_idx[rand_idx[0:n_train]])
test_ind.extend(sample_idx[rand_idx[n_train:n_sample]])
train_ind = np.asarray(train_ind)
test_ind = np.asarray(test_ind)
sub_sample_idx = np.hstack((train_ind,test_ind))
x_data_sub = x_data[sub_sample_idx,:,:]
y_data_sub = y_data[sub_sample_idx]
train_ind = np.arange(0,len(train_ind))
test_ind = np.arange(len(train_ind),len(train_ind)+len(test_ind))
### Save data
with open(target_path, 'wb') as f:
pickle.dump([x_data_sub, y_data_sub, train_ind, test_ind], f)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
15069,
12131,
14533,
31480,
1186,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
198,
2,
345,
743,
407,
779,
428,
2393,
... | 2.600327 | 3,668 |
from django.conf import settings
from django.core import serializers
from django.utils import timezone
import requests
from Posts.commentModel import Comments
#from Posts.commentView import add_Comment
from rest_framework import status
from rest_framework.decorators import api_view, authentication_classes, permission_classes
from rest_framework.response import Response
from django.shortcuts import HttpResponse, render
from requests import get
from .serializers import CommentSerializer, PostSerializer
from Author.serializers import LikeSerializer
from Author.models import Like
from Author.views import updateForeignAuthors, GetForeignAuthors
from .models import Post, Author
from .form import PostForm
from Posts.commentForm import CommentForm
import json
import uuid
import re
import base64
from django.db.models import Q
import django.core
from permissions import CustomAuthentication, AccessPermission
from django.core.paginator import Paginator
import traceback
| [
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
6738,
42625,
14208,
13,
7295,
1330,
11389,
11341,
198,
6738,
42625,
14208,
13,
26791,
1330,
640,
11340,
198,
11748,
7007,
198,
6738,
12043,
13,
23893,
17633,
1330,
19502,
198,
2,
6738,
12043... | 4.061728 | 243 |
import pytest
import sys, os
import xarray as xr
import numpy as np
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import process
from process._common import ProcessArgumentInvalid, ProcessArgumentRequired
###################################
# tests:
###################################
def test_with_xarray_out_bounds(execute_array_element_process, generate_data):
"""
Test array_element process with xarray.DataArrays with out of bounds index
"""
with pytest.raises(ProcessArgumentInvalid) as ex:
result = execute_array_element_process(index=5)
assert ex.value.args[0] == "The argument 'index' in process 'array_element' is invalid: Index out of bounds."
| [
11748,
12972,
9288,
198,
11748,
25064,
11,
28686,
198,
11748,
2124,
18747,
355,
2124,
81,
198,
11748,
299,
32152,
355,
45941,
198,
198,
17597,
13,
6978,
13,
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7,
418,
13,
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13,
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3672,
7,
418,
13,
6978,
13,
15908,
3672,
... | 3.235556 | 225 |
#!/usr/bin/env python
#
# Copyright 2016 Google Inc.
#
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
# Generate Android.bp for Skia from GN configuration.
import json
import os
import pprint
import string
import subprocess
import tempfile
import gn_to_bp_utils
# First we start off with a template for Android.bp,
# with holes for source lists and include directories.
bp = string.Template('''// This file is autogenerated by gn_to_bp.py.
cc_library_static {
name: "libskia",
cflags: [
$cflags
],
cppflags:[
$cflags_cc
],
export_include_dirs: [
$export_includes
],
local_include_dirs: [
$local_includes
],
srcs: [
$srcs
],
arch: {
arm: {
srcs: [
$arm_srcs
],
neon: {
srcs: [
$arm_neon_srcs
],
},
},
arm64: {
srcs: [
$arm64_srcs
],
},
mips: {
srcs: [
$none_srcs
],
},
mips64: {
srcs: [
$none_srcs
],
},
x86: {
srcs: [
$x86_srcs
],
cflags: [
// Clang seems to think new/malloc will only be 4-byte aligned
// on x86 Android. We're pretty sure it's actually 8-byte
// alignment. tests/OverAlignedTest.cpp has more information,
// and should fail if we're wrong.
"-Wno-over-aligned"
],
},
x86_64: {
srcs: [
$x86_srcs
],
},
},
defaults: ["skia_deps",
"skia_pgo",
],
}
// Build libskia with PGO by default.
// Location of PGO profile data is defined in build/soong/cc/pgo.go
// and is separate from skia.
// To turn it off, set ANDROID_PGO_NO_PROFILE_USE environment variable
// or set enable_profile_use property to false.
cc_defaults {
name: "skia_pgo",
pgo: {
instrumentation: true,
profile_file: "hwui/hwui.profdata",
benchmarks: ["hwui", "skia"],
enable_profile_use: true,
},
}
// "defaults" property to disable profile use for Skia tools and benchmarks.
cc_defaults {
name: "skia_pgo_no_profile_use",
defaults: [
"skia_pgo",
],
pgo: {
enable_profile_use: false,
},
}
cc_defaults {
name: "skia_deps",
shared_libs: [
"libEGL",
"libGLESv2",
"libdng_sdk",
"libexpat",
"libft2",
"libheif",
"libicui18n",
"libicuuc",
"libjpeg",
"liblog",
"libpiex",
"libpng",
"libvulkan",
"libz",
"libcutils",
"libnativewindow",
],
static_libs: [
"libarect",
"libsfntly",
"libwebp-decode",
"libwebp-encode",
],
group_static_libs: true,
}
cc_defaults {
name: "skia_tool_deps",
defaults: [
"skia_deps",
"skia_pgo_no_profile_use"
],
static_libs: [
"libjsoncpp",
"libskia",
],
cflags: [
"-Wno-unused-parameter",
"-Wno-unused-variable",
],
}
cc_test {
name: "skia_dm",
defaults: [
"skia_tool_deps"
],
local_include_dirs: [
$dm_includes
],
srcs: [
$dm_srcs
],
shared_libs: [
"libbinder",
"libutils",
],
}
cc_test {
name: "skia_nanobench",
defaults: [
"skia_tool_deps"
],
local_include_dirs: [
$nanobench_includes
],
srcs: [
$nanobench_srcs
],
data: [
"resources/*",
],
}''')
# We'll run GN to get the main source lists and include directories for Skia.
gn_args = {
'is_official_build': 'true',
'skia_enable_tools': 'true',
'skia_enable_skottie': 'false', # requires rapidjson third-party
'skia_use_libheif': 'true',
'skia_use_vulkan': 'true',
'target_cpu': '"none"',
'target_os': '"android"',
'skia_vulkan_header': '"Skia_Vulkan_Android.h"',
}
js = gn_to_bp_utils.GenerateJSONFromGN(gn_args)
srcs = strip_slashes(js['targets']['//:skia']['sources'])
cflags = strip_slashes(js['targets']['//:skia']['cflags'])
cflags_cc = strip_slashes(js['targets']['//:skia']['cflags_cc'])
local_includes = strip_slashes(js['targets']['//:skia']['include_dirs'])
export_includes = strip_slashes(js['targets']['//:public']['include_dirs'])
defines = [str(d) for d in js['targets']['//:skia']['defines']]
dm_srcs = strip_slashes(js['targets']['//:dm']['sources'])
dm_includes = strip_slashes(js['targets']['//:dm']['include_dirs'])
nanobench_target = js['targets']['//:nanobench']
nanobench_srcs = strip_slashes(nanobench_target['sources'])
nanobench_includes = strip_slashes(nanobench_target['include_dirs'])
gn_to_bp_utils.GrabDependentValues(js, '//:skia', 'sources', srcs, None)
gn_to_bp_utils.GrabDependentValues(js, '//:dm', 'sources', dm_srcs, 'skia')
gn_to_bp_utils.GrabDependentValues(js, '//:nanobench', 'sources',
nanobench_srcs, 'skia')
# skcms is a little special, kind of a second-party library.
srcs .add("third_party/skcms/skcms.c")
local_includes.add("third_party/skcms")
dm_includes .add("third_party/skcms")
# No need to list headers.
srcs = {s for s in srcs if not s.endswith('.h')}
dm_srcs = {s for s in dm_srcs if not s.endswith('.h')}
nanobench_srcs = {s for s in nanobench_srcs if not s.endswith('.h')}
cflags = gn_to_bp_utils.CleanupCFlags(cflags)
cflags_cc = gn_to_bp_utils.CleanupCCFlags(cflags_cc)
# We need to add the include path to the vulkan defines and header file set in
# then skia_vulkan_header gn arg that is used for framework builds.
local_includes.add("platform_tools/android/vulkan")
export_includes.add("platform_tools/android/vulkan")
here = os.path.dirname(__file__)
defs = gn_to_bp_utils.GetArchSources(os.path.join(here, 'opts.gni'))
gn_to_bp_utils.WriteUserConfig('include/config/SkUserConfig.h', defines)
# Turn a list of strings into the style bpfmt outputs.
# OK! We have everything to fill in Android.bp...
with open('Android.bp', 'w') as f:
print >>f, bp.substitute({
'export_includes': bpfmt(8, export_includes),
'local_includes': bpfmt(8, local_includes),
'srcs': bpfmt(8, srcs),
'cflags': bpfmt(8, cflags, False),
'cflags_cc': bpfmt(8, cflags_cc),
'arm_srcs': bpfmt(16, defs['armv7']),
'arm_neon_srcs': bpfmt(20, defs['neon']),
'arm64_srcs': bpfmt(16, defs['arm64'] +
defs['crc32']),
'none_srcs': bpfmt(16, defs['none']),
'x86_srcs': bpfmt(16, defs['sse2'] +
defs['ssse3'] +
defs['sse41'] +
defs['sse42'] +
defs['avx' ] +
defs['hsw' ]),
'dm_includes' : bpfmt(8, dm_includes),
'dm_srcs' : bpfmt(8, dm_srcs),
'nanobench_includes' : bpfmt(8, nanobench_includes),
'nanobench_srcs' : bpfmt(8, nanobench_srcs),
})
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
198,
2,
15069,
1584,
3012,
3457,
13,
198,
2,
198,
2,
5765,
286,
428,
2723,
2438,
318,
21825,
416,
257,
347,
10305,
12,
7635,
5964,
326,
460,
307,
198,
2,
1043,
287,
262,
38559,
... | 1.904896 | 3,901 |
"""The Ray autoscaler uses tags/labels to associate metadata with instances."""
# Tag for the name of the node
TAG_RAY_NODE_NAME = "ray-node-name"
# Tag for the kind of node (e.g. Head, Worker). For legacy reasons, the tag
# value says 'type' instead of 'kind'.
TAG_RAY_NODE_KIND = "ray-node-type"
NODE_KIND_HEAD = "head"
NODE_KIND_WORKER = "worker"
NODE_KIND_UNMANAGED = "unmanaged"
# Tag for user defined node types (e.g., m4xl_spot). This is used for multi
# node type clusters.
TAG_RAY_USER_NODE_TYPE = "ray-user-node-type"
# Tag for autofilled node types for legacy cluster yamls without multi
# node type defined in the cluster configs.
NODE_TYPE_LEGACY_HEAD = "ray-legacy-head-node-type"
NODE_TYPE_LEGACY_WORKER = "ray-legacy-worker-node-type"
# Tag that reports the current state of the node (e.g. Updating, Up-to-date)
TAG_RAY_NODE_STATUS = "ray-node-status"
STATUS_UNINITIALIZED = "uninitialized"
STATUS_WAITING_FOR_SSH = "waiting-for-ssh"
STATUS_SYNCING_FILES = "syncing-files"
STATUS_SETTING_UP = "setting-up"
STATUS_UPDATE_FAILED = "update-failed"
STATUS_UP_TO_DATE = "up-to-date"
# Tag uniquely identifying all nodes of a cluster
TAG_RAY_CLUSTER_NAME = "ray-cluster-name"
# Hash of the node launch config, used to identify out-of-date nodes
TAG_RAY_LAUNCH_CONFIG = "ray-launch-config"
# Hash of the node runtime config, used to determine if updates are needed
TAG_RAY_RUNTIME_CONFIG = "ray-runtime-config"
# Hash of the contents of the directories specified by the file_mounts config
# if the node is a worker, this also hashes content of the directories
# specified by the cluster_synced_files config
TAG_RAY_FILE_MOUNTS_CONTENTS = "ray-file-mounts-contents"
| [
37811,
464,
7760,
44619,
9948,
263,
3544,
15940,
14,
23912,
1424,
284,
11602,
20150,
351,
10245,
526,
15931,
198,
198,
2,
17467,
329,
262,
1438,
286,
262,
10139,
198,
42197,
62,
30631,
62,
45,
16820,
62,
20608,
796,
366,
2433,
12,
174... | 2.858844 | 588 |
import unittest
from worldengine.plates import Step, center_land, world_gen
from worldengine.world import World
from tests.draw_test import TestBase
if __name__ == '__main__':
unittest.main()
| [
11748,
555,
715,
395,
198,
6738,
995,
18392,
13,
17041,
1330,
5012,
11,
3641,
62,
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11,
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995,
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13,
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2159,
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198,
6738,
5254,
13,
19334,
62,
9288,
1330,
6208,
14881,
628,
198,
198,
361... | 3.174603 | 63 |
import pytest
import torch
from mmedit.models.builder import build_component
from mmedit.models.components.discriminators.light_cnn import MaxFeature
| [
11748,
12972,
9288,
198,
11748,
28034,
198,
198,
6738,
285,
1150,
270,
13,
27530,
13,
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1330,
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3906,
13,
15410,
3036,
47721,
13,
2971,
62,
66,
20471,
1330,
5436,
388... | 3.477273 | 44 |
import json
import logging
from typing import Iterable
from kafka import KafkaConsumer
log = logging.getLogger(__name__)
log.addHandler(logging.NullHandler())
# I've used this example:
# https://github.com/aiven/aiven-examples/blob/master/kafka/python/consumer_example.py
# as well as Aiven Kafka tutorials
| [
11748,
33918,
198,
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540,
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1362,
7,
834,
3672,
834,
8,
198,
6404,
13,
2860,
25060,
7,
640... | 3.161616 | 99 |
from tensorflow.keras import *
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, Sequential,regularizers
from tensorflow.keras.layers import Dropout
# from tensorflow.keras import *
# 3x3kernel_initializer='he_normal','glorot_normal'
from tensorflow.python.keras.layers import Concatenate
############################### ###############################
############################### ###############################
######################################
def build_resblock(self, filter_num, blocks, stride=1):
res_blocks = Sequential()
# may down sample
res_blocks.add(BasicBlock(filter_num, stride))
for _ in range(1, blocks):
res_blocks.add(BasicBlock(filter_num, stride=1))
return res_blocks
######################################
########################### pp2 ########################################
def network_up(input_layer_up,filters_num,dropout_rate,Block_res):
# input_layer = Input(input_shape)
# conv1 = layers.Conv3D(filters_num[0], kernel_size=(3, 3, 7), padding='same')(input_layer) # filters_num = 8
# conv1 = layers.Conv3D(filters_num[0], kernel_size=(3, 3, 3),padding='same',kernel_initializer='he_normal',kernel_regularizer=regularizers.l2(0.0001))(input_layer_up) # filters_num = 8
conv1 = layers.Conv3D(filters_num[0], kernel_size=(3, 3, 3), padding='same',
kernel_regularizer=regularizers.l2(0.0001))(input_layer_up) #kernel_initializer='he_normal',
# conv_layer1m = tf.keras.layers.MaxPooling3D(pool_size=(1, 1, 1),padding='same')(conv1)
# conv_layer1g = tf.keras.layers.GlobalMaxPooling3D()(conv1)
conv1_bn = layers.BatchNormalization()(conv1)
conv1_relu = layers.Activation('relu')(conv1_bn)
# conv1_relu = Dropout(0.5)(conv1_relu)
# conv1_relu = tf.keras.layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding='same')(conv1_relu)
# conv2 = layers.Conv3D(filters_num[1], kernel_size=(3, 3, 5), padding='same')(conv1_relu) # filters_num = 16
conv2 = layers.Conv3D(filters_num[1], kernel_size=(3, 3, 3),padding='same',kernel_regularizer=regularizers.l2(0.0001))(conv1_relu) # filters_num = 16
conv2_bn = layers.BatchNormalization()(conv2)
conv2_relu = layers.Activation('relu')(conv2_bn)
# conv2_relu = Dropout(0.5)(conv2_relu)
# conv2_relu = tf.keras.layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding='same')(conv2_relu)
conv3 = layers.Conv3D(filters_num[2], kernel_size=(3, 3, 3),padding='same',kernel_regularizer=regularizers.l2(0.0001))(conv2_relu) # filters_num = 32
conv3_bn = layers.BatchNormalization()(conv3)
conv3_relu = layers.Activation('relu')(conv3_bn)
# conv3_relu = Dropout(0.5)(conv3_relu)
# conv3_relu = tf.keras.layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding='same')(conv3_relu)
conv3_relu_reshape = layers.Reshape((conv3_relu.shape[1],conv3_relu.shape[2],conv3_relu.shape[3]*conv3_relu.shape[4]))(conv3_relu)
conv3_relu_reshape = Dropout(0.5)(conv3_relu_reshape)
###########################################
# conv11 = layers.Conv3D(filters_num[0], kernel_size=(5, 5, 3), padding='same',
# kernel_initializer='he_normal', kernel_regularizer=regularizers.l2(0.0001))(input_layer_up)
# conv11_bn = layers.BatchNormalization()(conv11)
# conv11_relu = layers.Activation('relu')(conv11_bn)
#
# # conv2 = layers.Conv3D(filters_num[1], kernel_size=(3, 3, 5), padding='same')(conv1_relu) # filters_num = 16
# conv22 = layers.Conv3D(filters_num[1], kernel_size=(5, 5, 3), padding='same', kernel_initializer='he_normal',
# kernel_regularizer=regularizers.l2(0.0001))(conv11_relu) # filters_num = 16
# conv22_bn = layers.BatchNormalization()(conv22)
# conv22_relu = layers.Activation('relu')(conv22_bn)
#
# conv33 = layers.Conv3D(filters_num[2], kernel_size=(5, 5, 3), padding='same', kernel_initializer='he_normal',
# kernel_regularizer=regularizers.l2(0.0001))(conv22_relu) # filters_num = 32
# conv33_bn = layers.BatchNormalization()(conv33)
# conv33_relu = layers.Activation('relu')(conv33_bn)
#
# conv33_relu_reshape = layers.Reshape(
# (conv3_relu.shape[1], conv3_relu.shape[2], conv3_relu.shape[3] * conv3_relu.shape[4]))(conv33_relu)
####################################################
# conv111 = layers.Conv3D(filters_num[0], kernel_size=(7, 7, 3), padding='same',
# kernel_initializer='he_normal', kernel_regularizer=regularizers.l2(0.0001))(input_layer_up)
# conv111_bn = layers.BatchNormalization()(conv111)
# conv111_relu = layers.Activation('relu')(conv111_bn)
#
# # conv2 = layers.Conv3D(filters_num[1], kernel_size=(3, 3, 5), padding='same')(conv1_relu) # filters_num = 16
# conv222 = layers.Conv3D(filters_num[1], kernel_size=(7, 7, 3), padding='same', kernel_initializer='he_normal',
# kernel_regularizer=regularizers.l2(0.0001))(conv111_relu) # filters_num = 16
# conv222_bn = layers.BatchNormalization()(conv222)
# conv222_relu = layers.Activation('relu')(conv222_bn)
#
# conv333 = layers.Conv3D(filters_num[2], kernel_size=(7, 7, 3), padding='same', kernel_initializer='he_normal',
# kernel_regularizer=regularizers.l2(0.0001))(conv222_relu) # filters_num = 32
# conv333_bn = layers.BatchNormalization()(conv333)
# conv333_relu = layers.Activation('relu')(conv333_bn)
#
# conv333_relu_reshape = layers.Reshape(
# (conv3_relu.shape[1], conv3_relu.shape[2], conv3_relu.shape[3] * conv3_relu.shape[4]))(conv333_relu)
#################concatenate########################
# conv33333_relu_reshape = Concatenate(axis=-1)([conv3_relu_reshape, conv33_relu_reshape])
#########################################
conv4 = layers.Conv2D(filters_num[3], kernel_size=(3, 3), padding='same',kernel_regularizer=regularizers.l2(0.0001))(conv3_relu_reshape) # filters_num = 64
conv4_bn = layers.BatchNormalization()(conv4)
conv4_relu = layers.Activation('relu')(conv4_bn)
# conv4_relu = Dropout(0.5)(conv4_relu)
# conv4_relu = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same')(conv4_relu)
# conv4_relu = tf.keras.layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding='same')(conv4_relu)
conv5 = layers.Conv2D(filters_num[4], kernel_size=(3, 3), padding='same',kernel_regularizer=regularizers.l2(0.0001))(conv4_relu) # filters_num = **
conv5_bn = layers.BatchNormalization()(conv5)
conv5_relu = layers.Activation('relu')(conv5_bn)
# conv5_relu = Dropout(0.5)(conv5_relu)
# conv5_relu = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(1, 1), padding='same')(conv5_relu)
# conv5_relu = tf.keras.layers.MaxPooling3D(pool_size=(2, 2, 2), strides=(1, 1, 1), padding='same')(conv5_relu)
# conv5_dpout = layers.Dropout(dropout_rate)(conv5)
# conv5_reshape = layers.Reshape((conv5_dpout.shape[1],conv5_dpout.shape[2],conv5_dpout.shape[3]))(conv5_dpout)
outputs2,outputs4 = Block_res(conv5_relu)
return conv5,outputs2,outputs4
# layer1 = build_resblock(filters_num[5], layer_dims[0]) # filters_num = 64
# layer2 = build_resblock(filters_num[6], layer_dims[1], stride=2) # filters_num = 128
# layer3 = build_resblock(filters_num[7], layer_dims[2], stride=2) # filters_num = 256
# layer4 = build_resblock(filters_num[8], layer_dims[3], stride=2) # filters_num = 512
| [
201,
198,
6738,
11192,
273,
11125,
13,
6122,
292,
1330,
1635,
201,
198,
11748,
220,
11192,
273,
11125,
355,
48700,
201,
198,
6738,
220,
220,
220,
11192,
273,
11125,
1330,
41927,
292,
201,
198,
6738,
220,
220,
220,
11192,
273,
11125,
1... | 2.277826 | 3,441 |
""" A universal module with functions / classes without dependencies. """
import sys
import contextlib
import functools
import re
import os
from medi._compatibility import reraise
_sep = os.path.sep
if os.path.altsep is not None:
_sep += os.path.altsep
_path_re = re.compile(r'(?:\.[^{0}]+|[{0}]__init__\.py)$'.format(re.escape(_sep)))
del _sep
def unite(iterable):
"""Turns a two dimensional array into a one dimensional."""
return set(typ for types in iterable for typ in types)
def reraise_uncaught(func):
"""
Re-throw uncaught `AttributeError`.
Usage: Put ``@rethrow_uncaught`` in front of the function
which does **not** suppose to raise `AttributeError`.
AttributeError is easily get caught by `hasattr` and another
``except AttributeError`` clause. This becomes problem when you use
a lot of "dynamic" attributes (e.g., using ``@property``) because you
can't distinguish if the property does not exist for real or some code
inside of the "dynamic" attribute through that error. In a well
written code, such error should not exist but getting there is very
difficult. This decorator is to help us getting there by changing
`AttributeError` to `UncaughtAttributeError` to avoid unexpected catch.
This helps us noticing bugs earlier and facilitates debugging.
.. note:: Treating StopIteration here is easy.
Add that feature when needed.
"""
return wrapper
| [
37811,
317,
10112,
8265,
351,
5499,
1220,
6097,
1231,
20086,
13,
37227,
198,
11748,
25064,
198,
11748,
4732,
8019,
198,
11748,
1257,
310,
10141,
198,
11748,
302,
198,
11748,
28686,
198,
198,
6738,
16957,
13557,
5589,
25901,
1330,
302,
402... | 3.202614 | 459 |
"""Bioconductor run git transition code.
This module assembles the classes for the SVN --> Git transition
can be run in a sequential manner.
It runs the following aspects fo the Bioconductor transition.
Note: Update the SVN dump
1. Run Bioconductor Software package transition
2. Run Bioconductor Experiment Data package transition
3. Run Workflow package transition
4. Run Manifest file transition
5. Run Rapid update of master (trunk) and RELEASE_3_5 branches on
software packages
Manual tasks which need to be done:
1. Copy over bare repos to repositories/packages
2. Copy manifest bare git repo to repositories/admin
"""
import src.run_transition as rt
import src.svn_dump_update as sdu
import logging
import time
logging.basicConfig(filename='transition.log',
format='%(levelname)s %(asctime)s %(message)s',
level=logging.DEBUG)
if __name__ == '__main__':
start_time = time.time()
config_file = "./settings.ini"
svn_dump_update(config_file)
run(config_file)
# TODO: Run updates after dump update
svn_dump_update(config_file)
rt.run_updates(config_file)
logging.info("--- %s seconds ---" % (time.time() - start_time))
| [
37811,
23286,
420,
40990,
1057,
17606,
6801,
2438,
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198,
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287,
257,
35582,
5642,
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198,
198,
1026,
4539,
262,
1708,
7612,
11511,
... | 3.015038 | 399 |
from __future__ import with_statement
from .. import Lock, NeedRegenerationException
from ..util import NameRegistry
from . import exception
from ..util import PluginLoader, memoized_property, coerce_string_conf
from .util import function_key_generator, function_multi_key_generator
from .api import NO_VALUE, CachedValue
from .proxy import ProxyBackend
from ..util import compat
import time
import datetime
from numbers import Number
from functools import wraps
import threading
_backend_loader = PluginLoader("dogpile.cache")
register_backend = _backend_loader.register
from . import backends # noqa
value_version = 1
"""An integer placed in the :class:`.CachedValue`
so that new versions of dogpile.cache can detect cached
values from a previous, backwards-incompatible version.
"""
def _unexpired_value_fn(self, expiration_time, ignore_expiration):
if ignore_expiration:
return lambda value: value
else:
if expiration_time is None:
expiration_time = self.expiration_time
current_time = time.time()
return value_fn
def get_multi(self, keys, expiration_time=None, ignore_expiration=False):
"""Return multiple values from the cache, based on the given keys.
Returns values as a list matching the keys given.
E.g.::
values = region.get_multi(["one", "two", "three"])
To convert values to a dictionary, use ``zip()``::
keys = ["one", "two", "three"]
values = region.get_multi(keys)
dictionary = dict(zip(keys, values))
Keys which aren't present in the list are returned as
the ``NO_VALUE`` token. ``NO_VALUE`` evaluates to False,
but is separate from
``None`` to distinguish between a cached value of ``None``.
By default, the configured expiration time of the
:class:`.CacheRegion`, or alternatively the expiration
time supplied by the ``expiration_time`` argument,
is tested against the creation time of the retrieved
value versus the current time (as reported by ``time.time()``).
If stale, the cached value is ignored and the ``NO_VALUE``
token is returned. Passing the flag ``ignore_expiration=True``
bypasses the expiration time check.
.. versionadded:: 0.5.0
"""
if not keys:
return []
if self.key_mangler:
keys = list(map(lambda key: self.key_mangler(key), keys))
backend_values = self.backend.get_multi(keys)
_unexpired_value_fn = self._unexpired_value_fn(
expiration_time, ignore_expiration)
return [
value.payload if value is not NO_VALUE else value
for value in
(
_unexpired_value_fn(value) for value in
backend_values
)
]
def get_or_create(
self, key, creator, expiration_time=None, should_cache_fn=None):
"""Return a cached value based on the given key.
If the value does not exist or is considered to be expired
based on its creation time, the given
creation function may or may not be used to recreate the value
and persist the newly generated value in the cache.
Whether or not the function is used depends on if the
*dogpile lock* can be acquired or not. If it can't, it means
a different thread or process is already running a creation
function for this key against the cache. When the dogpile
lock cannot be acquired, the method will block if no
previous value is available, until the lock is released and
a new value available. If a previous value
is available, that value is returned immediately without blocking.
If the :meth:`.invalidate` method has been called, and
the retrieved value's timestamp is older than the invalidation
timestamp, the value is unconditionally prevented from
being returned. The method will attempt to acquire the dogpile
lock to generate a new value, or will wait
until the lock is released to return the new value.
.. versionchanged:: 0.3.0
The value is unconditionally regenerated if the creation
time is older than the last call to :meth:`.invalidate`.
:param key: Key to be retrieved. While it's typical for a key to be a
string, it is ultimately passed directly down to the cache backend,
before being optionally processed by the key_mangler function, so can
be of any type recognized by the backend or by the key_mangler
function, if present.
:param creator: function which creates a new value.
:param expiration_time: optional expiration time which will overide
the expiration time already configured on this :class:`.CacheRegion`
if not None. To set no expiration, use the value -1.
:param should_cache_fn: optional callable function which will receive
the value returned by the "creator", and will then return True or
False, indicating if the value should actually be cached or not. If
it returns False, the value is still returned, but isn't cached.
E.g.::
def dont_cache_none(value):
return value is not None
value = region.get_or_create("some key",
create_value,
should_cache_fn=dont_cache_none)
Above, the function returns the value of create_value() if
the cache is invalid, however if the return value is None,
it won't be cached.
.. versionadded:: 0.4.3
.. seealso::
:meth:`.CacheRegion.cache_on_arguments` - applies
:meth:`.get_or_create` to any function using a decorator.
:meth:`.CacheRegion.get_or_create_multi` - multiple key/value
version
"""
orig_key = key
if self.key_mangler:
key = self.key_mangler(key)
if expiration_time is None:
expiration_time = self.expiration_time
if (expiration_time is None and
self.region_invalidator.was_soft_invalidated()):
raise exception.DogpileCacheException(
"Non-None expiration time required "
"for soft invalidation")
if expiration_time == -1:
expiration_time = None
if self.async_creation_runner:
else:
async_creator = None
with Lock(
self._mutex(key),
gen_value,
get_value,
expiration_time,
async_creator) as value:
return value
def get_or_create_multi(
self, keys, creator, expiration_time=None, should_cache_fn=None):
"""Return a sequence of cached values based on a sequence of keys.
The behavior for generation of values based on keys corresponds
to that of :meth:`.Region.get_or_create`, with the exception that
the ``creator()`` function may be asked to generate any subset of
the given keys. The list of keys to be generated is passed to
``creator()``, and ``creator()`` should return the generated values
as a sequence corresponding to the order of the keys.
The method uses the same approach as :meth:`.Region.get_multi`
and :meth:`.Region.set_multi` to get and set values from the
backend.
If you are using a :class:`.CacheBackend` or :class:`.ProxyBackend`
that modifies values, take note this function invokes
``.set_multi()`` for newly generated values using the same values it
returns to the calling function. A correct implementation of
``.set_multi()`` will not modify values in-place on the submitted
``mapping`` dict.
:param keys: Sequence of keys to be retrieved.
:param creator: function which accepts a sequence of keys and
returns a sequence of new values.
:param expiration_time: optional expiration time which will overide
the expiration time already configured on this :class:`.CacheRegion`
if not None. To set no expiration, use the value -1.
:param should_cache_fn: optional callable function which will receive
each value returned by the "creator", and will then return True or
False, indicating if the value should actually be cached or not. If
it returns False, the value is still returned, but isn't cached.
.. versionadded:: 0.5.0
.. seealso::
:meth:`.CacheRegion.cache_multi_on_arguments`
:meth:`.CacheRegion.get_or_create`
"""
if expiration_time is None:
expiration_time = self.expiration_time
if (expiration_time is None and
self.region_invalidator.was_soft_invalidated()):
raise exception.DogpileCacheException(
"Non-None expiration time required "
"for soft invalidation")
if expiration_time == -1:
expiration_time = None
mutexes = {}
sorted_unique_keys = sorted(set(keys))
if self.key_mangler:
mangled_keys = [self.key_mangler(k) for k in sorted_unique_keys]
else:
mangled_keys = sorted_unique_keys
orig_to_mangled = dict(zip(sorted_unique_keys, mangled_keys))
values = dict(zip(mangled_keys, self.backend.get_multi(mangled_keys)))
for orig_key, mangled_key in orig_to_mangled.items():
with Lock(
self._mutex(mangled_key),
gen_value,
lambda: get_value(mangled_key),
expiration_time,
async_creator=lambda mutex: async_creator(orig_key, mutex)
):
pass
try:
if mutexes:
# sort the keys, the idea is to prevent deadlocks.
# though haven't been able to simulate one anyway.
keys_to_get = sorted(mutexes)
new_values = creator(*keys_to_get)
values_w_created = dict(
(orig_to_mangled[k], self._value(v))
for k, v in zip(keys_to_get, new_values)
)
if not should_cache_fn:
self.backend.set_multi(values_w_created)
else:
self.backend.set_multi(dict(
(k, v)
for k, v in values_w_created.items()
if should_cache_fn(v[0])
))
values.update(values_w_created)
return [values[orig_to_mangled[k]].payload for k in keys]
finally:
for mutex in mutexes.values():
mutex.release()
def _value(self, value):
"""Return a :class:`.CachedValue` given a value."""
return CachedValue(
value,
{
"ct": time.time(),
"v": value_version
})
def set(self, key, value):
"""Place a new value in the cache under the given key."""
if self.key_mangler:
key = self.key_mangler(key)
self.backend.set(key, self._value(value))
def set_multi(self, mapping):
"""Place new values in the cache under the given keys.
.. versionadded:: 0.5.0
"""
if not mapping:
return
if self.key_mangler:
mapping = dict((
self.key_mangler(k), self._value(v))
for k, v in mapping.items())
else:
mapping = dict((k, self._value(v)) for k, v in mapping.items())
self.backend.set_multi(mapping)
def delete(self, key):
"""Remove a value from the cache.
This operation is idempotent (can be called multiple times, or on a
non-existent key, safely)
"""
if self.key_mangler:
key = self.key_mangler(key)
self.backend.delete(key)
def delete_multi(self, keys):
"""Remove multiple values from the cache.
This operation is idempotent (can be called multiple times, or on a
non-existent key, safely)
.. versionadded:: 0.5.0
"""
if self.key_mangler:
keys = list(map(lambda key: self.key_mangler(key), keys))
self.backend.delete_multi(keys)
def cache_on_arguments(
self, namespace=None,
expiration_time=None,
should_cache_fn=None,
to_str=compat.string_type,
function_key_generator=None):
"""A function decorator that will cache the return
value of the function using a key derived from the
function itself and its arguments.
The decorator internally makes use of the
:meth:`.CacheRegion.get_or_create` method to access the
cache and conditionally call the function. See that
method for additional behavioral details.
E.g.::
@someregion.cache_on_arguments()
def generate_something(x, y):
return somedatabase.query(x, y)
The decorated function can then be called normally, where
data will be pulled from the cache region unless a new
value is needed::
result = generate_something(5, 6)
The function is also given an attribute ``invalidate()``, which
provides for invalidation of the value. Pass to ``invalidate()``
the same arguments you'd pass to the function itself to represent
a particular value::
generate_something.invalidate(5, 6)
Another attribute ``set()`` is added to provide extra caching
possibilities relative to the function. This is a convenience
method for :meth:`.CacheRegion.set` which will store a given
value directly without calling the decorated function.
The value to be cached is passed as the first argument, and the
arguments which would normally be passed to the function
should follow::
generate_something.set(3, 5, 6)
The above example is equivalent to calling
``generate_something(5, 6)``, if the function were to produce
the value ``3`` as the value to be cached.
.. versionadded:: 0.4.1 Added ``set()`` method to decorated function.
Similar to ``set()`` is ``refresh()``. This attribute will
invoke the decorated function and populate a new value into
the cache with the new value, as well as returning that value::
newvalue = generate_something.refresh(5, 6)
.. versionadded:: 0.5.0 Added ``refresh()`` method to decorated
function.
Lastly, the ``get()`` method returns either the value cached
for the given key, or the token ``NO_VALUE`` if no such key
exists::
value = generate_something.get(5, 6)
.. versionadded:: 0.5.3 Added ``get()`` method to decorated
function.
The default key generation will use the name
of the function, the module name for the function,
the arguments passed, as well as an optional "namespace"
parameter in order to generate a cache key.
Given a function ``one`` inside the module
``myapp.tools``::
@region.cache_on_arguments(namespace="foo")
def one(a, b):
return a + b
Above, calling ``one(3, 4)`` will produce a
cache key as follows::
myapp.tools:one|foo|3 4
The key generator will ignore an initial argument
of ``self`` or ``cls``, making the decorator suitable
(with caveats) for use with instance or class methods.
Given the example::
class MyClass(object):
@region.cache_on_arguments(namespace="foo")
def one(self, a, b):
return a + b
The cache key above for ``MyClass().one(3, 4)`` will
again produce the same cache key of ``myapp.tools:one|foo|3 4`` -
the name ``self`` is skipped.
The ``namespace`` parameter is optional, and is used
normally to disambiguate two functions of the same
name within the same module, as can occur when decorating
instance or class methods as below::
class MyClass(object):
@region.cache_on_arguments(namespace='MC')
def somemethod(self, x, y):
""
class MyOtherClass(object):
@region.cache_on_arguments(namespace='MOC')
def somemethod(self, x, y):
""
Above, the ``namespace`` parameter disambiguates
between ``somemethod`` on ``MyClass`` and ``MyOtherClass``.
Python class declaration mechanics otherwise prevent
the decorator from having awareness of the ``MyClass``
and ``MyOtherClass`` names, as the function is received
by the decorator before it becomes an instance method.
The function key generation can be entirely replaced
on a per-region basis using the ``function_key_generator``
argument present on :func:`.make_region` and
:class:`.CacheRegion`. If defaults to
:func:`.function_key_generator`.
:param namespace: optional string argument which will be
established as part of the cache key. This may be needed
to disambiguate functions of the same name within the same
source file, such as those
associated with classes - note that the decorator itself
can't see the parent class on a function as the class is
being declared.
:param expiration_time: if not None, will override the normal
expiration time.
May be specified as a callable, taking no arguments, that
returns a value to be used as the ``expiration_time``. This callable
will be called whenever the decorated function itself is called, in
caching or retrieving. Thus, this can be used to
determine a *dynamic* expiration time for the cached function
result. Example use cases include "cache the result until the
end of the day, week or time period" and "cache until a certain date
or time passes".
.. versionchanged:: 0.5.0
``expiration_time`` may be passed as a callable to
:meth:`.CacheRegion.cache_on_arguments`.
:param should_cache_fn: passed to :meth:`.CacheRegion.get_or_create`.
.. versionadded:: 0.4.3
:param to_str: callable, will be called on each function argument
in order to convert to a string. Defaults to ``str()``. If the
function accepts non-ascii unicode arguments on Python 2.x, the
``unicode()`` builtin can be substituted, but note this will
produce unicode cache keys which may require key mangling before
reaching the cache.
.. versionadded:: 0.5.0
:param function_key_generator: a function that will produce a
"cache key". This function will supersede the one configured on the
:class:`.CacheRegion` itself.
.. versionadded:: 0.5.5
.. seealso::
:meth:`.CacheRegion.cache_multi_on_arguments`
:meth:`.CacheRegion.get_or_create`
"""
expiration_time_is_callable = compat.callable(expiration_time)
if function_key_generator is None:
function_key_generator = self.function_key_generator
return decorator
def cache_multi_on_arguments(
self, namespace=None, expiration_time=None,
should_cache_fn=None,
asdict=False, to_str=compat.string_type,
function_multi_key_generator=None):
"""A function decorator that will cache multiple return
values from the function using a sequence of keys derived from the
function itself and the arguments passed to it.
This method is the "multiple key" analogue to the
:meth:`.CacheRegion.cache_on_arguments` method.
Example::
@someregion.cache_multi_on_arguments()
def generate_something(*keys):
return [
somedatabase.query(key)
for key in keys
]
The decorated function can be called normally. The decorator
will produce a list of cache keys using a mechanism similar to
that of :meth:`.CacheRegion.cache_on_arguments`, combining the
name of the function with the optional namespace and with the
string form of each key. It will then consult the cache using
the same mechanism as that of :meth:`.CacheRegion.get_multi`
to retrieve all current values; the originally passed keys
corresponding to those values which aren't generated or need
regeneration will be assembled into a new argument list, and
the decorated function is then called with that subset of
arguments.
The returned result is a list::
result = generate_something("key1", "key2", "key3")
The decorator internally makes use of the
:meth:`.CacheRegion.get_or_create_multi` method to access the
cache and conditionally call the function. See that
method for additional behavioral details.
Unlike the :meth:`.CacheRegion.cache_on_arguments` method,
:meth:`.CacheRegion.cache_multi_on_arguments` works only with
a single function signature, one which takes a simple list of
keys as arguments.
Like :meth:`.CacheRegion.cache_on_arguments`, the decorated function
is also provided with a ``set()`` method, which here accepts a
mapping of keys and values to set in the cache::
generate_something.set({"k1": "value1",
"k2": "value2", "k3": "value3"})
...an ``invalidate()`` method, which has the effect of deleting
the given sequence of keys using the same mechanism as that of
:meth:`.CacheRegion.delete_multi`::
generate_something.invalidate("k1", "k2", "k3")
...a ``refresh()`` method, which will call the creation
function, cache the new values, and return them::
values = generate_something.refresh("k1", "k2", "k3")
...and a ``get()`` method, which will return values
based on the given arguments::
values = generate_something.get("k1", "k2", "k3")
.. versionadded:: 0.5.3 Added ``get()`` method to decorated
function.
Parameters passed to :meth:`.CacheRegion.cache_multi_on_arguments`
have the same meaning as those passed to
:meth:`.CacheRegion.cache_on_arguments`.
:param namespace: optional string argument which will be
established as part of each cache key.
:param expiration_time: if not None, will override the normal
expiration time. May be passed as an integer or a
callable.
:param should_cache_fn: passed to
:meth:`.CacheRegion.get_or_create_multi`. This function is given a
value as returned by the creator, and only if it returns True will
that value be placed in the cache.
:param asdict: if ``True``, the decorated function should return
its result as a dictionary of keys->values, and the final result
of calling the decorated function will also be a dictionary.
If left at its default value of ``False``, the decorated function
should return its result as a list of values, and the final
result of calling the decorated function will also be a list.
When ``asdict==True`` if the dictionary returned by the decorated
function is missing keys, those keys will not be cached.
:param to_str: callable, will be called on each function argument
in order to convert to a string. Defaults to ``str()``. If the
function accepts non-ascii unicode arguments on Python 2.x, the
``unicode()`` builtin can be substituted, but note this will
produce unicode cache keys which may require key mangling before
reaching the cache.
.. versionadded:: 0.5.0
:param function_multi_key_generator: a function that will produce a
list of keys. This function will supersede the one configured on the
:class:`.CacheRegion` itself.
.. versionadded:: 0.5.5
.. seealso::
:meth:`.CacheRegion.cache_on_arguments`
:meth:`.CacheRegion.get_or_create_multi`
"""
expiration_time_is_callable = compat.callable(expiration_time)
if function_multi_key_generator is None:
function_multi_key_generator = self.function_multi_key_generator
return decorator
def make_region(*arg, **kw):
"""Instantiate a new :class:`.CacheRegion`.
Currently, :func:`.make_region` is a passthrough
to :class:`.CacheRegion`. See that class for
constructor arguments.
"""
return CacheRegion(*arg, **kw)
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... | 2.491484 | 10,216 |
# -*- coding: utf-8 -*-
"""
Module projectparallelprogrammeren.codesimulatie
=================================================================
Deze module simuleert alles.
"""
import projectparallelprogrammeren
def simulatie():
"""
Deze functie voert alle versies uit zodat deze vergeleken kunnen worden qua timing.
"""
from importlib import import_module
for i in range(4):
#alle versies van de simulatie importeren en achtereenvolgens uitvoeren.
version = f"montecarlo_v{i}"
montecarlo = import_module(version)
montecarlo.simulatie(100,50) #Deze waarden dienen enkel als test
if __name__ == "__main__":
simulatie()
#eof
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... | 2.780172 | 232 |
#!/usr/bin/env python
from __future__ import unicode_literals
# Allow direct execution
import os
import sys
import unittest
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from haruhi_dl.aes import aes_decrypt, aes_encrypt, aes_cbc_decrypt, aes_cbc_encrypt, aes_decrypt_text
from haruhi_dl.utils import bytes_to_intlist, intlist_to_bytes
import base64
# the encrypted data can be generate with 'devscripts/generate_aes_testdata.py'
if __name__ == '__main__':
unittest.main()
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... | 2.629442 | 197 |
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
def generate_dropdown_selection():
"return a Div containing the dropdown selection box"
return dcc.Dropdown(
id='dropdown_select_process',
style={"display": "none"},
options=[
{'label': 'Homogeneous Poisson process', 'value': 1},
{'label': 'Inhomogeneous Poisson process', 'value': 2},
{'label': 'Cluster process', 'value': 3},
# {'label': 'Strauss process', 'value': 4}
],
# set the initial value=0 to hide the user input interface
value=0)
def generate_user_input():
"return a Div containing users' input interface"
input_n_toolkits = html.Div(html.Div([html.A('Number of transects:', className='col-sm-4'),
dcc.Input(
type='number',
placeholder=2,
value = 2,
id='input_n_toolkits',
className='col-sm-4'
)
], className='row'), id='input_n_toolkits_container', style={'display': 'none'})
# slider
# input_n_toolkits = html.Div(html.Div([
# html.A("Number of transects",className='col-sm-4'),
# dcc.Slider(min=1,
# max=5,
# step=1,
# value=2,
# marks={i: '{}'.format(i) for i in range(1, 6)},
# id='input_n_toolkits',
# className='col-sm-4')
# ], className='row'), id='input_n_toolkits_container',
# className='row',
# style={'display': 'none'})
input_disease_prevalence = html.Div(html.Div([html.A('disease prevalence: ', id='input_disease_prevalence_tooltip', className='col-sm-4'),
dcc.Input(
type='number',
placeholder=0.1,
value = 0.1,
step=0.1,
min=0,
max=1,
id='input_disease_prevalence',
className='col-sm-4'
)
], className='row'), id='input_disease_prevalence_container', style={'display': 'none'})
input_disease_prevalence_tooltip = dbc.Tooltip('the proportion of corals which get infected by a disease', target='input_disease_prevalence_tooltip')
# text or number input
input_fun_lambda = html.Div(html.Div([html.A('proportion cover function:', className='col-sm-4'), dcc.Input(
id="input_fun_lambda",
type='text',
placeholder="1000 * np.exp(-(((x - 50) / 50) ** 2 + ((y - 50) / 50) ** 2) / 0.5 ** 2)",
value="1000 * np.exp(-(((x - 50) / 50) ** 2 + ((y - 50) / 50) ** 2) / 0.5 ** 2)",
className='col-sm-4'
)],className='row'),id='show_input_fun_lambda',style={'display':'none'})
input_parent_prop = html.Div(html.Div([html.A('parent corals / total corals:', className='col-sm-4'), dcc.Input(
id="input_parent_prop",
type='number',
placeholder=0.01,
value=0.01,
step=0.01,
className='col-sm-4'
)],className='row'),id='show_input_parent_prop',style={'display':'none'})
input_parent_range = html.Div(html.Div([html.A('parent range:', className='col-sm-4'), dcc.Input(
id="input_parent_range",
type='number',
placeholder=5,
value=5,
className='col-sm-4'
)],className='row'),id='show_input_parent_range',style={'display':'none'})
input_strauss_beta = dcc.Input(
id="input_strauss_beta",
type='number',
placeholder="strauss_beta",
style={'display': 'none'}
)
input_strauss_gamma = dcc.Input(
id="input_strauss_gamma",
type='number',
placeholder="strauss_gamma",
style={'display': 'none'}
)
input_strauss_R = dcc.Input(
id="input_strauss_R",
type='number',
placeholder="strauss_R",
style={'display': 'none'}
)
input_transect_length = html.Div(html.Div([html.A('transect width (m): ', className='col-sm-4'),
dcc.Input(
type='number',
placeholder=25,
value=25,
id='dcc_input_transect_length',
className='col-sm-4'
)
], className='row'), id='input_transect_length', style={'display': 'none'})
input_transect_width = html.Div(html.Div([html.A('transect length (m): ', className='col-sm-4'),
dcc.Input(
type='number',
placeholder=6,
value = 6,
id='dcc_input_transect_width',
className='col-sm-4'
)
], className='row'), id='input_transect_width', style={'display': 'none'})
line_intercept_ratio = html.Div(html.Div([html.A('transect width / plot width', className='col-sm-4'),
dcc.Input(
type='number',
placeholder=1/5,
value = 1/5,
step=0.1,
id='dcc_line_intercept_ratio',
className='col-sm-4')
],className='row'), id='line_intercept_ratio', style={'display': 'none'})
coral_size = html.Div(html.Div([html.A('coral size (m^2): ', id='coral_size_tooltip',className='col-sm-4'),
dcc.Input(
type='number',
placeholder=0.0068,
value = 0.0068,
step=0.0001,
id='coral_size',
className='col-sm-4'
)
],className='row' ),
id='coral_size_input',
style={'display': 'none'})
coral_size_tooltip = dbc.Tooltip('the average size of an individual coral, measured in m^3', target='coral_size_tooltip')
coral_size_std = html.Div(html.Div([html.A('coral size standard error: ', id='coral_size_std_tooltip', className='col-sm-4'),
dcc.Input(
type='number',
placeholder=0.001,
value = 0.001,
step=0.001,
id='coral_size_std',
className='col-sm-4'
)], className='row')
, id='coral_size_std_input', style={'display': 'none'})
coral_size_std_tooltip = dbc.Tooltip('the standard deviation of the average size of an individual coral', target='coral_size_std_tooltip')
prop_cover = html.Div(html.Div([html.A('proportion cover: ', className='col-sm-4', id='prop_cover_tooltip'),
dcc.Input(
type='number',
placeholder=0,
value = 0,
step=0.1,
min=0,
max=1,
id='prop_cover',
className='col-sm-4'
)
],className='row'), id='prop_cover_input', style={'display': 'none'})
prop_cover_tooltip = dbc.Tooltip('Proportion cover of coral. If it equals 0, its estimation based on the historical data will be used in the simulation', target='prop_cover_tooltip')
num_of_replications = html.Div(html.Div([html.A('number of replications', className='col-sm-4'),
dcc.Input(
type='number',
placeholder=10,
value = 10,
step=1,
min=1,
id='num_of_replications',
className='col-sm-4'
)
],className='row'), id='number_of_replications_input', style={'display': 'none'})
return html.Div([
input_n_toolkits,
prop_cover,
prop_cover_tooltip,
input_fun_lambda,
coral_size,
coral_size_tooltip,
coral_size_std,
coral_size_std_tooltip,
input_disease_prevalence,
input_disease_prevalence_tooltip,
input_parent_prop,
input_parent_range,
input_strauss_beta,
input_strauss_gamma,
input_strauss_R,
input_transect_length,
input_transect_width,
line_intercept_ratio,
num_of_replications
], id='input_process_parameters') | [
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33... | 1.63074 | 6,272 |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# This test is based on the test suite implemented for Recommenders project
# https://github.com/Microsoft/Recommenders/tree/master/tests
import papermill as pm
import pytest
import scrapbook as sb
from utils_cv.common.data import unzip_url
from utils_cv.detection.data import Urls
# Unless manually modified, python3 should be
# the name of the current jupyter kernel
# that runs on the activated conda environment
KERNEL_NAME = "python3"
OUTPUT_NOTEBOOK = "output.ipynb"
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1... | 3.512195 | 164 |
_base_ = [
'../retinanet_r50_fpn_1x_coco.py',
'../../_base_/datasets/hdr_detection_minmax_glob_gamma.py',
]
# optimizer
# lr is set for a batch size of 8
optimizer = dict(type='SGD', lr=0.0005, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None) # dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[10])
runner = dict(
type='EpochBasedRunner', max_epochs=20)
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... | 2.21097 | 237 |