content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
import os
import sys
if sys.version_info[0] == 2:
_ENCODE = sys.getfilesystemencoding()
logfile_open = open
else:
path_join = os.path.join
str_join = str.join
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198,... | 2.590909 | 66 |
#!/usr/bin/env python
""" VAPI test """
import unittest
import os
import signal
from framework import VppTestCase, running_extended_tests, \
VppTestRunner, Worker
if __name__ == '__main__':
unittest.main(testRunner=VppTestRunner)
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... | 2.847059 | 85 |
import json
import re
import scrapy
from locations.hourstudy import inputoutput
DAYS = {
'mo': 'Mo',
'tu': 'Tu',
'we': 'We',
'fr': 'Fr',
'th': 'Th',
'sa': 'Sa',
'su': 'Su',
}
| [
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... | 2.112245 | 98 |
from importlib import import_module
import re
from copy import deepcopy
from collections import OrderedDict
from astropy.utils.data_info import MixinInfo
from .column import Column
from .table import Table, QTable, has_info_class
from astropy.units.quantity import QuantityInfo
__construct_mixin_classes = ('astropy.time.core.Time',
'astropy.time.core.TimeDelta',
'astropy.units.quantity.Quantity',
'astropy.coordinates.angles.Latitude',
'astropy.coordinates.angles.Longitude',
'astropy.coordinates.angles.Angle',
'astropy.coordinates.distances.Distance',
'astropy.coordinates.earth.EarthLocation',
'astropy.coordinates.sky_coordinate.SkyCoord',
'astropy.table.table.NdarrayMixin',
'astropy.table.column.MaskedColumn')
def _represent_mixin_as_column(col, name, new_cols, mixin_cols,
exclude_classes=()):
"""Carry out processing needed to serialize ``col`` in an output table
consisting purely of plain ``Column`` or ``MaskedColumn`` columns. This
relies on the object determine if any transformation is required and may
depend on the ``serialize_method`` and ``serialize_context`` context
variables. For instance a ``MaskedColumn`` may be stored directly to
FITS, but can also be serialized as separate data and mask columns.
This function builds up a list of plain columns in the ``new_cols`` arg (which
is passed as a persistent list). This includes both plain columns from the
original table and plain columns that represent data from serialized columns
(e.g. ``jd1`` and ``jd2`` arrays from a ``Time`` column).
For serialized columns the ``mixin_cols`` dict is updated with required
attributes and information to subsequently reconstruct the table.
Table mixin columns are always serialized and get represented by one
or more data columns. In earlier versions of the code *only* mixin
columns were serialized, hence the use within this code of "mixin"
to imply serialization. Starting with version 3.1, the non-mixin
``MaskedColumn`` can also be serialized.
"""
obj_attrs = col.info._represent_as_dict()
ordered_keys = col.info._represent_as_dict_attrs
# If serialization is not required (see function docstring above)
# or explicitly specified as excluded, then treat as a normal column.
if not obj_attrs or col.__class__ in exclude_classes:
new_cols.append(col)
return
# Subtlety here is handling mixin info attributes. The basic list of such
# attributes is: 'name', 'unit', 'dtype', 'format', 'description', 'meta'.
# - name: handled directly [DON'T store]
# - unit: DON'T store if this is a parent attribute
# - dtype: captured in plain Column if relevant [DON'T store]
# - format: possibly irrelevant but settable post-object creation [DO store]
# - description: DO store
# - meta: DO store
info = {}
for attr, nontrivial, xform in (('unit', lambda x: x is not None and x != '', str),
('format', lambda x: x is not None, None),
('description', lambda x: x is not None, None),
('meta', lambda x: x, None)):
col_attr = getattr(col.info, attr)
if nontrivial(col_attr):
info[attr] = xform(col_attr) if xform else col_attr
data_attrs = [key for key in ordered_keys if key in obj_attrs and
getattr(obj_attrs[key], 'shape', ())[:1] == col.shape[:1]]
for data_attr in data_attrs:
data = obj_attrs[data_attr]
# New column name combines the old name and attribute
# (e.g. skycoord.ra, skycoord.dec).unless it is the primary data
# attribute for the column (e.g. value for Quantity or data
# for MaskedColumn)
if data_attr == col.info._represent_as_dict_primary_data:
new_name = name
else:
new_name = name + '.' + data_attr
if not has_info_class(data, MixinInfo):
new_cols.append(Column(data, name=new_name, **info))
obj_attrs[data_attr] = SerializedColumn({'name': new_name})
else:
# recurse. This will define obj_attrs[new_name].
_represent_mixin_as_column(data, new_name, new_cols, obj_attrs)
obj_attrs[data_attr] = SerializedColumn(obj_attrs.pop(new_name))
# Strip out from info any attributes defined by the parent
for attr in col.info.attrs_from_parent:
if attr in info:
del info[attr]
if info:
obj_attrs['__info__'] = info
# Store the fully qualified class name
obj_attrs['__class__'] = col.__module__ + '.' + col.__class__.__name__
mixin_cols[name] = obj_attrs
def represent_mixins_as_columns(tbl, exclude_classes=()):
"""Represent input Table ``tbl`` using only `~astropy.table.Column`
or `~astropy.table.MaskedColumn` objects.
This function represents any mixin columns like `~astropy.time.Time` in
``tbl`` to one or more plain ``~astropy.table.Column`` objects and returns
a new Table. A single mixin column may be split into multiple column
components as needed for fully representing the column. This includes the
possibility of recursive splitting, as shown in the example below. The
new column names are formed as ``<column_name>.<component>``, e.g.
``sc.ra`` for a `~astropy.coordinates.SkyCoord` column named ``sc``.
In addition to splitting columns, this function updates the table ``meta``
dictionary to include a dict named ``__serialized_columns__`` which provides
additional information needed to construct the original mixin columns from
the split columns.
This function is used by astropy I/O when writing tables to ECSV, FITS,
HDF5 formats.
Note that if the table does not include any mixin columns then the original
table is returned with no update to ``meta``.
Parameters
----------
tbl : `~astropy.table.Table` or subclass
Table to represent mixins as Columns
exclude_classes : tuple of classes
Exclude any mixin columns which are instannces of any classes in the tuple
Returns
-------
tbl : `~astropy.table.Table`
New Table with updated columns, or else the original input ``tbl``
Examples
--------
>>> from astropy.table import Table, represent_mixins_as_columns
>>> from astropy.time import Time
>>> from astropy.coordinates import SkyCoord
>>> x = [100.0, 200.0]
>>> obstime = Time([1999.0, 2000.0], format='jyear')
>>> sc = SkyCoord([1, 2], [3, 4], unit='deg', obstime=obstime)
>>> tbl = Table([sc, x], names=['sc', 'x'])
>>> represent_mixins_as_columns(tbl)
<Table length=2>
sc.ra sc.dec sc.obstime.jd1 sc.obstime.jd2 x
deg deg
float64 float64 float64 float64 float64
------- ------- -------------- -------------- -------
1.0 3.0 2451180.0 -0.25 100.0
2.0 4.0 2451545.0 0.0 200.0
"""
# Dict of metadata for serializing each column, keyed by column name.
# Gets filled in place by _represent_mixin_as_column().
mixin_cols = {}
# List of columns for the output table. For plain Column objects
# this will just be the original column object.
new_cols = []
# Go through table columns and represent each column as one or more
# plain Column objects (in new_cols) + metadata (in mixin_cols).
for col in tbl.itercols():
_represent_mixin_as_column(col, col.info.name, new_cols, mixin_cols,
exclude_classes=exclude_classes)
# If no metadata was created then just return the original table.
if not mixin_cols:
return tbl
meta = deepcopy(tbl.meta)
meta['__serialized_columns__'] = mixin_cols
out = Table(new_cols, meta=meta, copy=False)
return out
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'''
Created on Jun 18, 2013
@author: Yubin Bai
All rights reserved.
'''
import time
from multiprocessing.pool import Pool
parallelSolve = False
infinity = 1 << 30
if __name__ == '__main__':
solver = Solver()
if parallelSolve:
solver.parallel()
else:
solver.sequential()
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... | 2.491803 | 122 |
from typing import Dict, Optional, List, Any
import torch
import torch.nn.functional as F
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import FeedForward, TextFieldEmbedder, Seq2SeqEncoder
from allennlp.nn import InitializerApplicator, RegularizerApplicator
from allennlp.nn import util
from allennlp.training.metrics import CategoricalAccuracy, F1Measure
from overrides import overrides
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... | 3.609756 | 123 |
#
# -*- coding: utf-8 -*-
"""Development related tasks to be run with 'invoke'"""
import os
import pathlib
import shutil
import invoke
TASK_ROOT = pathlib.Path(__file__).resolve().parent
TASK_ROOT_STR = str(TASK_ROOT)
# shared function
def rmrf(items, verbose=True):
"""Silently remove a list of directories or files"""
if isinstance(items, str):
items = [items]
for item in items:
if verbose:
print("Removing {}".format(item))
shutil.rmtree(item, ignore_errors=True)
# rmtree doesn't remove bare files
try:
os.remove(item)
except FileNotFoundError:
pass
# create namespaces
namespace = invoke.Collection()
namespace_clean = invoke.Collection('clean')
namespace.add_collection(namespace_clean, 'clean')
#####
#
# pytest, pylint, and codecov
#
#####
namespace.add_task(pytest)
namespace_clean.add_task(pytest_clean, 'pytest')
namespace.add_task(pylint)
namespace.add_task(pylint_tests)
#####
#
# build and distribute
#
#####
BUILDDIR = 'build'
DISTDIR = 'dist'
namespace_clean.add_task(build_clean, 'build')
namespace_clean.add_task(dist_clean, 'dist')
namespace_clean.add_task(eggs_clean, 'eggs')
namespace_clean.add_task(bytecode_clean, 'bytecode')
#
# make a dummy clean task which runs all the tasks in the clean namespace
clean_tasks = list(namespace_clean.tasks.values())
namespace_clean.add_task(clean_all, 'all')
namespace.add_task(sdist)
namespace.add_task(wheel)
#
# these two tasks are commented out so you don't
# accidentally run them and upload this template to pypi
#
# @invoke.task(pre=[sdist, wheel])
# def pypi(context):
# """Build and upload a distribution to pypi"""
# context.run('twine upload dist/*')
# namespace.add_task(pypi)
# @invoke.task(pre=[sdist, wheel])
# def pypi_test(context):
# """Build and upload a distribution to https://test.pypi.org"""
# context.run('twine upload --repository-url https://test.pypi.org/legacy/ dist/*')
# namespace.add_task(pypi_test)
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2634... | 2.561798 | 801 |
import stl_path
# dynamic load of python module
def register():
return MyNDRPlugin() | [
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"""Support for Epson Workforce Printer."""
from datetime import timedelta
import logging
import voluptuous as vol
from homeassistant.components.sensor import PLATFORM_SCHEMA
from homeassistant.const import CONF_HOST, CONF_MONITORED_CONDITIONS
from homeassistant.exceptions import PlatformNotReady
import homeassistant.helpers.config_validation as cv
from homeassistant.helpers.entity import Entity
REQUIREMENTS = ['epsonprinter==0.0.8']
_LOGGER = logging.getLogger(__name__)
MONITORED_CONDITIONS = {
'black': ['Inklevel Black', '%', 'mdi:water'],
'magenta': ['Inklevel Magenta', '%', 'mdi:water'],
'cyan': ['Inklevel Cyan', '%', 'mdi:water'],
'yellow': ['Inklevel Yellow', '%', 'mdi:water'],
'clean': ['Inklevel Cleaning', '%', 'mdi:water'],
}
PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({
vol.Required(CONF_HOST): cv.string,
vol.Required(CONF_MONITORED_CONDITIONS):
vol.All(cv.ensure_list, [vol.In(MONITORED_CONDITIONS)]),
})
SCAN_INTERVAL = timedelta(minutes=60)
def setup_platform(hass, config, add_devices, discovery_info=None):
"""Set up the cartridge sensor."""
host = config.get(CONF_HOST)
from epsonprinter_pkg.epsonprinterapi import EpsonPrinterAPI
api = EpsonPrinterAPI(host)
if not api.available:
raise PlatformNotReady()
sensors = [EpsonPrinterCartridge(api, condition)
for condition in config[CONF_MONITORED_CONDITIONS]]
add_devices(sensors, True)
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228... | 2.598214 | 560 |
import asyncio
import logging
import random
import typing as t
from datetime import datetime, timezone
from operator import attrgetter
import discord
import discord.abc
from discord.ext import commands
from bot import constants
from bot.bot import Bot
from bot.exts.help_channels import _caches, _channel, _cooldown, _message, _name, _stats
from bot.utils import channel as channel_utils, lock, scheduling
log = logging.getLogger(__name__)
NAMESPACE = "help"
HELP_CHANNEL_TOPIC = """
This is a Python help channel. You can claim your own help channel in the Python Help: Available category.
"""
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393... | 3.529412 | 170 |
import os
import math
import random
import time
from code.menu.menu import Menu
from code.tools.eventqueue import EventQueue
from code.tools.xml import XMLParser
from code.utils.common import coalesce, intersect, offset_rect, log, log2, xml_encode, xml_decode, translate_rgb_to_string
from code.constants.common import SCREEN_WIDTH, SCREEN_HEIGHT, PAUSE_MENU_X, PAUSE_MENU_Y, PAUSE_MENU_WIDTH, PAUSE_MENU_HEIGHT, MODE_GAME, TILE_WIDTH, TILE_HEIGHT, DIR_UP, DIR_RIGHT, DIR_DOWN, DIR_LEFT, SPLASH_MODE_GREYSCALE_ANIMATED
from code.constants.states import STATUS_ACTIVE, STATUS_INACTIVE, GAME_STATE_ACTIVE, GAME_STATE_NOT_READY
from code.constants.newsfeeder import *
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# -*- coding: utf-8 ; test-case-name: bridgedb.test.test_runner -*-
#
# This file is part of BridgeDB, a Tor bridge distribution system.
#
# :authors: Isis Lovecruft 0xA3ADB67A2CDB8B35 <isis@torproject.org>
# please also see AUTHORS file
# :copyright: (c) 2007-2015, The Tor Project, Inc.
# (c) 2007-2015, all entities within the AUTHORS file
# (c) 2012-2015, Isis Lovecruft
# :license: 3-clause BSD, see included LICENSE for information
"""Classes for running components and servers, as well as daemonisation.
** Module Overview: **
"""
from __future__ import print_function
import logging
import sys
import os
from twisted.python import procutils
def find(filename):
"""Find the executable ``filename``.
:param string filename: The executable to search for. Must be in the
effective user ID's $PATH.
:rtype: string
:returns: The location of the executable, if found. Otherwise, returns
None.
"""
executable = None
logging.debug("Searching for installed '%s'..." % filename)
which = procutils.which(filename, os.X_OK)
if len(which) > 0:
for that in which:
if os.stat(that).st_uid == os.geteuid():
executable = that
break
if not executable:
return None
logging.debug("Found installed script at '%s'" % executable)
return executable
def generateDescriptors(count=None, rundir=None):
"""Run a script which creates fake bridge descriptors for testing purposes.
This will run Leekspin_ to create bridge server descriptors, bridge
extra-info descriptors, and networkstatus document.
.. warning: This function can take a very long time to run, especially in
headless environments where entropy sources are minimal, because it
creates the keys for each mocked OR, which are embedded in the server
descriptors, used to calculate the OR fingerprints, and sign the
descriptors, among other things.
.. _Leekspin: https://gitweb.torproject.org/user/isis/leekspin.git
:param integer count: Number of mocked bridges to generate descriptor
for. (default: 3)
:type rundir: string or None
:param rundir: If given, use this directory as the current working
directory for the bridge descriptor generator script to run in. The
directory MUST already exist, and the descriptor files will be created
in it. If None, use the whatever directory we are currently in.
"""
import subprocess
import os.path
proc = None
statuscode = 0
script = 'leekspin'
rundir = rundir if os.path.isdir(rundir) else None
count = count if count else 3
try:
proc = subprocess.Popen([script, '-n', str(count)],
close_fds=True, cwd=rundir)
finally:
if proc is not None:
proc.wait()
if proc.returncode:
print("There was an error generating bridge descriptors.",
"(Returncode: %d)" % proc.returncode)
statuscode = proc.returncode
else:
print("Sucessfully generated %s descriptors." % str(count))
del subprocess
return statuscode
def doDumpBridges(config):
"""Dump bridges by assignment to a file.
This function handles the commandline '--dump-bridges' option.
:type config: :class:`bridgedb.Main.Conf`
:param config: The current configuration.
"""
import bridgedb.Bucket as bucket
bucketManager = bucket.BucketManager(config)
bucketManager.assignBridgesToBuckets()
bucketManager.dumpBridges()
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#!/usr/bin/env python3
#
# Create disk image
#
import re, sys, traceback
from .tasks import task_fetch_partitions, task_refresh_partitions, task_mount, task_remove_persistent_rules, task_remove_logs, task_fsck, task_shrink_partition, task_expand_partition, task_unmount
from .partclone_tasks import task_create_disk_image
from .ops_ui import console_ui
from ..components.disk import create_storage_instance
from .runner import Runner
from ..lib.disk_images import make_disk_image_name
from .json_ui import json_ui
from ..lib.util import init_triage_logger, is_block_device
# "Waiting", "Prepare", "Preflight", "Running", "Success", "Failed"]
my_messages = { "Waiting": "Saving disk is waiting.",
"Prepare": "Savign disk is preparing.",
"Preflight": "Saving disk is preparing.",
"Running": "{step} of {steps}: Running {task}",
"Success": "Saving disk completed successfully.",
"Failed": "Saving disk failed." }
#
if __name__ == "__main__":
tlog = init_triage_logger()
if len(sys.argv) == 1:
print( 'Unloader: devicename part destdir')
sys.exit(0)
# NOTREACHED
pass
devname = sys.argv[1]
if not is_block_device(devname):
print( '%s is not a block device.' % devname)
sys.exit(1)
# NOTREACHED
pass
part = sys.argv[2] # This is a partition id
destdir = sys.argv[3] # Destination directory
disk = create_storage_instance(devname)
# Preflight is for me to see the tasks. http server runs this with json_ui.
do_it = True
if destdir == "preflight":
ui = console_ui()
do_it = False
pass
elif destdir == "testflight":
ui = console_ui()
do_it = True
pass
else:
ui = json_ui(wock_event="saveimage", message_catalog=my_messages)
pass
if re.match(part, '\d+'):
part = int(part)
pass
runner_id = disk.device_name
runner = ImageDiskRunner(ui, runner_id, disk, destdir, partition_id=part)
try:
runner.prepare()
runner.preflight()
runner.explain()
runner.run()
sys.exit(0)
# NOTREACHED
except Exception as exc:
sys.stderr.write(traceback.format_exc(exc) + "\n")
sys.exit(1)
# NOTREACHED
pass
pass
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
198,
2,
13610,
11898,
2939,
198,
2,
198,
198,
11748,
302,
11,
25064,
11,
12854,
1891,
198,
198,
6738,
764,
83,
6791,
1330,
4876,
62,
69,
7569,
62,
3911,
1756,
11,
4876,
62,
... | 2.488839 | 896 |
import argparse
import logging
import apache_beam as beam
from apache_beam.io import WriteToBigQuery
from apache_beam.io import ReadFromText, WriteToText
from apache_beam.options.pipeline_options import PipelineOptions
def run(argv=None):
"""Main entry point. It defines and runs the pipeline."""
parser = argparse.ArgumentParser()
parser.add_argument('--input',
dest='input',
default='gs://meetup-batch-processing/input/googleplaystore.csv',
help='Input file to process.')
parser.add_argument('--output',
dest='output',
default='gs://meetup-batch-processing/output/googleplaystore.csv',
help='Output file to process.')
parser.add_argument('--table-output',
dest='table_output',
default='meetup-hands-on-gcp-2019:googleplaystore_batch_dataflow.play_store',
help='Bigquery table name for output.')
known_args, pipeline_args = parser.parse_known_args(argv)
pipeline_options = PipelineOptions(pipeline_args)
with beam.Pipeline(options=pipeline_options) as pipeline:
raw_lines = pipeline | 'ReadFromCsv' >> ReadFromText(known_args.input, skip_header_lines=1)
lines = raw_lines | 'processCsv' >> beam.ParDo(ProcessCSV())
output = lines | 'parseRecord' >> beam.ParDo(ParseRecord())
output | 'writeBigQuery' >> WriteToBigQuery(known_args.table_output,
write_disposition=beam.io.BigQueryDisposition.WRITE_TRUNCATE,
create_disposition=beam.io.BigQueryDisposition.CREATE_NEVER)
logging.info('Finished.')
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
run()
| [
11748,
1822,
29572,
198,
11748,
18931,
198,
198,
11748,
2471,
4891,
62,
40045,
355,
15584,
198,
6738,
2471,
4891,
62,
40045,
13,
952,
1330,
19430,
2514,
12804,
20746,
198,
6738,
2471,
4891,
62,
40045,
13,
952,
1330,
4149,
4863,
8206,
11... | 2.318471 | 785 |
from flask import request
from flask_jwt import jwt_required
from flask_restful import Resource
from main.server import app, cache, db
from main.server.models import Message, MessageSchema
messages_schema = MessageSchema(many=True)
message_schema = MessageSchema()
| [
6738,
42903,
1330,
2581,
198,
6738,
42903,
62,
73,
46569,
1330,
474,
46569,
62,
35827,
198,
198,
6738,
42903,
62,
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913,
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20857,
198,
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6738,
1388,
13,
15388,
1330,
598,
11,
12940,
11,
20613,
198,
6738,
1388,
13,
15388,
13... | 3.5 | 78 |
#!/usr/bin/env python3
import rospy
from wecook.msg import ActionMsg, TaskMsg, SceneMsg, ObjectMsg, ContainingMsg, AgentMsg
if __name__ == '__main__':
try:
talker()
except rospy.ROSInterruptException:
pass
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
198,
11748,
686,
2777,
88,
198,
6738,
356,
27916,
13,
19662,
1330,
7561,
50108,
11,
15941,
50108,
11,
28315,
50108,
11,
9515,
50108,
11,
2345,
1397,
50108,
11,
15906,
50108,
628,
1... | 2.463158 | 95 |
# -*- coding: utf-8 -*- {{{
# vim: set fenc=utf-8 ft=python sw=4 ts=4 sts=4 et:
#
# Copyright 2017, Battelle Memorial Institute.
#
# 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.
#
# This material was prepared as an account of work sponsored by an agency of
# the United States Government. Neither the United States Government nor the
# United States Department of Energy, nor Battelle, nor any of their
# employees, nor any jurisdiction or organization that has cooperated in the
# development of these materials, makes any warranty, express or
# implied, or assumes any legal liability or responsibility for the accuracy,
# completeness, or usefulness or any information, apparatus, product,
# software, or process disclosed, or represents that its use would not infringe
# privately owned rights. Reference herein to any specific commercial product,
# process, or service by trade name, trademark, manufacturer, or otherwise
# does not necessarily constitute or imply its endorsement, recommendation, or
# favoring by the United States Government or any agency thereof, or
# Battelle Memorial Institute. The views and opinions of authors expressed
# herein do not necessarily state or reflect those of the
# United States Government or any agency thereof.
#
# PACIFIC NORTHWEST NATIONAL LABORATORY operated by
# BATTELLE for the UNITED STATES DEPARTMENT OF ENERGY
# under Contract DE-AC05-76RL01830
# }}}
import os
import weakref
from datetime import datetime
from .base import SubsystemBase
from volttron.platform.messaging.headers import TIMESTAMP
from volttron.platform.agent.utils import (get_aware_utc_now,
format_timestamp)
from volttron.platform.scheduling import periodic
from ..errors import Unreachable, VIPError
"""The heartbeat subsystem adds an optional periodic publish to all agents.
Heartbeats can be started with agents and toggled on and off at runtime.
"""
__docformat__ = 'reStructuredText'
__version__ = '1.0'
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
22935,
90,
198,
2,
43907,
25,
900,
277,
12685,
28,
40477,
12,
23,
10117,
28,
29412,
1509,
28,
19,
40379,
28,
19,
39747,
28,
19,
2123,
25,
198,
2,
198,
2,
15069,
2177,
1... | 3.778626 | 655 |
"""Ni-Superalloy dataset.
Scientific Machine Learning Benchmark
A benchmark of regression models in chem- and materials informatics.
2019, Brendan Folie, Citrine Informatics.
See class NiSuperalloyDataset for details.
"""
import os
import json
import zipfile
from typing import List, Optional, Tuple, Union
import numpy as np
from smlb.exceptions import InvalidParameterError
from smlb.parameters import params
from smlb.tabular_data import TabularData
def _parse_json_labels(self, entry: dict, labels_to_load: Optional[List[str]] = None):
"""
Helper function to parse labels in a single row from the raw json.
Parameters:
entry (dict): A json entry corresponding to a row in the dataset.
labels_to_load (List[str]): Optional list of labels to load.
Returns: array
Array of labels in this row that we are interested in.
"""
if labels_to_load is None:
labels_to_load = [
"Yield Strength",
"Ultimate Tensile Strength",
"Stress Rupture Time",
"Stress Rupture Stress",
"Elongation",
]
properties = entry.get("properties")
if properties is None or not isinstance(properties, list):
raise InvalidParameterError(
expected="A list of dictionaries, one for each property", got=properties
)
labels_array = []
for label in labels_to_load:
labels_array.append(self._get_scalar_property(properties, label, default_value=None))
return labels_array
def _get_scalar_property(
self,
properties: List[dict],
property_name: str,
units: Optional[str] = None,
default_value: Optional[float] = None,
) -> float:
"""
A helper function to get a single scalar property.
This calls _get_single_property and then checks that the result can be
turned into a float.
Parameters:
properties: A list of dicts, each of which is a single property.
property_name: The name of the property to get the value of.
units: Optional expected units string.
default_value: Value to return if `property_name` is not present.
Raises:
InvalidParameterError: if the value cannot be expressed as a float
Returns: float
The value of the desired property.
"""
try:
val = self._get_single_property(properties, property_name, units, default_value)
if val is None:
return None
return float(val)
except ValueError:
raise InvalidParameterError(
expected=f"Property {property_name} should have a value "
f"that can be expressed as a float",
got=properties,
)
def _get_categorical_property(
self, properties: List[dict], property_name: str, categories_dict: dict
) -> int:
"""
Helper function to get a single categorical property as an int.
Parameters:
properties: A list of dicts, each of which is a single property.
property_name: The name of the property to get the value of.
categories_dict: Dict from the categorical property (string) to a unique integer value.
Raises:
InvalidParameterError: if the value is not in the expected list of possible categories
as given by the keys in `categories_dict`
Returns: int
An integer that corresponds to the value of the desired property.
"""
category = self._get_single_property(properties, property_name)
try:
return categories_dict[category]
except KeyError:
raise InvalidParameterError(
f"A value in the array: {categories_dict.keys()}", category
)
| [
37811,
34153,
12,
12442,
439,
726,
27039,
13,
198,
198,
23010,
811,
10850,
18252,
25187,
4102,
198,
32,
18335,
286,
20683,
4981,
287,
4607,
12,
290,
5696,
4175,
23372,
13,
198,
23344,
11,
26134,
30938,
494,
11,
15792,
7640,
554,
18982,
... | 2.45533 | 1,623 |
import metricbeat
import os
import pytest
import sys
import unittest
| [
11748,
18663,
12945,
198,
11748,
28686,
198,
11748,
12972,
9288,
198,
11748,
25064,
198,
11748,
555,
715,
395,
628
] | 3.684211 | 19 |
# Copyright The PyTorch Lightning team.
#
# 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 torch
from pytorch_lightning.accelerators.base_backend import Accelerator
from pytorch_lightning.utilities import AMPType, rank_zero_warn
from pytorch_lightning.utilities.exceptions import MisconfigurationException
| [
2,
15069,
383,
9485,
15884,
354,
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1074,
13,
198,
2,
198,
2,
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739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
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366,
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198,
2,
345,
743,
407,
779,
428,
2393,
2845,
287,
11846,
351,
262,
13789,
13,
1... | 3.75 | 216 |
from django.contrib import admin
from books.models import Genre, Author, Book, TBR
# Register your models here.
admin.site.register(Genre)
admin.site.register(Author)
admin.site.register(Book)
admin.site.register(TBR) | [
6738,
42625,
14208,
13,
3642,
822,
1330,
13169,
198,
6738,
3835,
13,
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1330,
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260,
11,
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11,
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198,
198,
2,
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534,
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994,
13,
198,
198,
28482,
13,
15654,
13,
30238,
7,
13746,
260,
8,
198,
... | 3.128571 | 70 |
# (C) Datadog, Inc. 2018-present
# All rights reserved
# Licensed under a 3-clause BSD style license (see LICENSE)
import os
from packaging import version
from datadog_checks.base.utils.common import get_docker_hostname
HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.dirname(os.path.dirname(HERE))
RABBITMQ_VERSION_RAW = os.environ['RABBITMQ_VERSION']
RABBITMQ_VERSION = version.parse(RABBITMQ_VERSION_RAW)
CHECK_NAME = 'rabbitmq'
HOST = get_docker_hostname()
PORT = 15672
URL = 'http://{}:{}/api/'.format(HOST, PORT)
CONFIG = {
'rabbitmq_api_url': URL,
'rabbitmq_user': 'guest',
'rabbitmq_pass': 'guest',
'queues': ['test1'],
'tags': ["tag1:1", "tag2"],
'exchanges': ['test1'],
}
CONFIG_NO_NODES = {
'rabbitmq_api_url': URL,
'rabbitmq_user': 'guest',
'rabbitmq_pass': 'guest',
'queues': ['test1'],
'tags': ["tag1:1", "tag2"],
'exchanges': ['test1'],
'collect_node_metrics': False,
}
CONFIG_REGEX = {
'rabbitmq_api_url': URL,
'rabbitmq_user': 'guest',
'rabbitmq_pass': 'guest',
'queues_regexes': [r'test\d+'],
'exchanges_regexes': [r'test\d+'],
}
CONFIG_VHOSTS = {
'rabbitmq_api_url': URL,
'rabbitmq_user': 'guest',
'rabbitmq_pass': 'guest',
'vhosts': ['/', 'myvhost'],
}
CONFIG_WITH_FAMILY = {
'rabbitmq_api_url': URL,
'rabbitmq_user': 'guest',
'rabbitmq_pass': 'guest',
'tag_families': True,
'queues_regexes': [r'(test)\d+'],
'exchanges_regexes': [r'(test)\d+'],
}
CONFIG_DEFAULT_VHOSTS = {
'rabbitmq_api_url': URL,
'rabbitmq_user': 'guest',
'rabbitmq_pass': 'guest',
'vhosts': ['/', 'test'],
}
CONFIG_TEST_VHOSTS = {
'rabbitmq_api_url': URL,
'rabbitmq_user': 'guest',
'rabbitmq_pass': 'guest',
'vhosts': ['test', 'test2'],
}
EXCHANGE_MESSAGE_STATS = {
'ack': 1.0,
'ack_details': {'rate': 1.0},
'confirm': 1.0,
'confirm_details': {'rate': 1.0},
'deliver_get': 1.0,
'deliver_get_details': {'rate': 1.0},
'publish': 1.0,
'publish_details': {'rate': 1.0},
'publish_in': 1.0,
'publish_in_details': {'rate': 1.0},
'publish_out': 1.0,
'publish_out_details': {'rate': 1.0},
'return_unroutable': 1.0,
'return_unroutable_details': {'rate': 1.0},
'redeliver': 1.0,
'redeliver_details': {'rate': 1.0},
}
| [
2,
357,
34,
8,
16092,
324,
519,
11,
3457,
13,
2864,
12,
25579,
198,
2,
1439,
2489,
10395,
198,
2,
49962,
739,
257,
513,
12,
565,
682,
347,
10305,
3918,
5964,
357,
3826,
38559,
24290,
8,
198,
198,
11748,
28686,
198,
198,
6738,
1684... | 2.007732 | 1,164 |
# Copyright 2020 The StackStorm Authors.
# Copyright 2019 Extreme Networks, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import os
import re
import collections
import six
from st2common.util import schema as util_schema
from st2common.constants.pack import MANIFEST_FILE_NAME
from st2common.constants.pack import PACK_REF_WHITELIST_REGEX
from st2common.content.loader import MetaLoader
from st2common.persistence.pack import Pack
from st2common.exceptions.apivalidation import ValueValidationException
from st2common.util import jinja as jinja_utils
__all__ = [
'get_pack_ref_from_metadata',
'get_pack_metadata',
'get_pack_warnings',
'get_pack_common_libs_path_for_pack_ref',
'get_pack_common_libs_path_for_pack_db',
'validate_config_against_schema',
'normalize_pack_version'
]
# Common format for python 2.7 warning
if six.PY2:
PACK_PYTHON2_WARNING = "DEPRECATION WARNING: Pack %s only supports Python 2.x. " \
"Python 2 support will be dropped in future releases. " \
"Please consider updating your packs to work with Python 3.x"
else:
PACK_PYTHON2_WARNING = "DEPRECATION WARNING: Pack %s only supports Python 2.x. " \
"Python 2 support has been removed since st2 v3.4.0. " \
"Please update your packs to work with Python 3.x"
def get_pack_ref_from_metadata(metadata, pack_directory_name=None):
"""
Utility function which retrieves pack "ref" attribute from the pack metadata file.
If this attribute is not provided, an attempt is made to infer "ref" from the "name" attribute.
:rtype: ``str``
"""
pack_ref = None
# The rules for the pack ref are as follows:
# 1. If ref attribute is available, we used that
# 2. If pack_directory_name is available we use that (this only applies to packs
# which are in sub-directories)
# 2. If attribute is not available, but pack name is and pack name meets the valid name
# criteria, we use that
if metadata.get('ref', None):
pack_ref = metadata['ref']
elif pack_directory_name and re.match(PACK_REF_WHITELIST_REGEX, pack_directory_name):
pack_ref = pack_directory_name
else:
if re.match(PACK_REF_WHITELIST_REGEX, metadata['name']):
pack_ref = metadata['name']
else:
msg = ('Pack name "%s" contains invalid characters and "ref" attribute is not '
'available. You either need to add "ref" attribute which contains only word '
'characters to the pack metadata file or update name attribute to contain only'
'word characters.')
raise ValueError(msg % (metadata['name']))
return pack_ref
def get_pack_metadata(pack_dir):
"""
Return parsed metadata for a particular pack directory.
:rtype: ``dict``
"""
manifest_path = os.path.join(pack_dir, MANIFEST_FILE_NAME)
if not os.path.isfile(manifest_path):
raise ValueError('Pack "%s" is missing %s file' % (pack_dir, MANIFEST_FILE_NAME))
meta_loader = MetaLoader()
content = meta_loader.load(manifest_path)
if not content:
raise ValueError('Pack "%s" metadata file is empty' % (pack_dir))
return content
def get_pack_warnings(pack_metadata):
"""
Return warning string if pack metadata indicates only python 2 is supported
:rtype: ``str``
"""
warning = None
versions = pack_metadata.get('python_versions', None)
pack_name = pack_metadata.get('name', None)
if versions and set(versions) == set(['2']):
warning = PACK_PYTHON2_WARNING % pack_name
return warning
def validate_config_against_schema(config_schema, config_object, config_path,
pack_name=None):
"""
Validate provided config dictionary against the provided config schema
dictionary.
"""
# NOTE: Lazy improt to avoid performance overhead of importing this module when it's not used
import jsonschema
pack_name = pack_name or 'unknown'
schema = util_schema.get_schema_for_resource_parameters(parameters_schema=config_schema,
allow_additional_properties=True)
instance = config_object
try:
cleaned = util_schema.validate(instance=instance, schema=schema,
cls=util_schema.CustomValidator, use_default=True,
allow_default_none=True)
for key in cleaned:
if (jinja_utils.is_jinja_expression(value=cleaned.get(key)) and
"decrypt_kv" in cleaned.get(key) and config_schema.get(key).get('secret')):
raise ValueValidationException('Values specified as "secret: True" in config '
'schema are automatically decrypted by default. Use '
'of "decrypt_kv" jinja filter is not allowed for '
'such values. Please check the specified values in '
'the config or the default values in the schema.')
except jsonschema.ValidationError as e:
attribute = getattr(e, 'path', [])
if isinstance(attribute, (tuple, list, collections.Iterable)):
attribute = [str(item) for item in attribute]
attribute = '.'.join(attribute)
else:
attribute = str(attribute)
msg = ('Failed validating attribute "%s" in config for pack "%s" (%s): %s' %
(attribute, pack_name, config_path, six.text_type(e)))
raise jsonschema.ValidationError(msg)
return cleaned
def get_pack_common_libs_path_for_pack_db(pack_db):
"""
Return the pack's common lib path. This is the path where common code for sensors
and actions are placed.
For example, if the pack is at /opt/stackstorm/packs/my_pack, you can place
common library code for actions and sensors in /opt/stackstorm/packs/my_pack/lib/.
This common library code is only available for python sensors and actions. The lib
structure also needs to follow a python convention with a __init__.py file.
:param pack_db: Pack DB model
:type pack_db: :class:`PackDB`
:rtype: ``str``
"""
pack_dir = getattr(pack_db, 'path', None)
if not pack_dir:
return None
libs_path = os.path.join(pack_dir, 'lib')
return libs_path
def normalize_pack_version(version):
"""
Normalize old, pre StackStorm v2.1 non valid semver version string (e.g. 0.2) to a valid
semver version string (0.2.0).
:rtype: ``str``
"""
version = str(version)
version_seperator_count = version.count('.')
if version_seperator_count == 1:
version = version + '.0'
return version
| [
2,
15069,
12131,
383,
23881,
32173,
46665,
13,
198,
2,
15069,
13130,
18111,
27862,
11,
3457,
13,
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,
... | 2.553767 | 2,920 |
# Look at the charactercreator_character table
# GET_CHARACTERS = """
# SELECT *
# FROM charactercreator_character;
# """
# How many total Characters are there? (302)
TOTAL_CHARACTERS = """
SELECT COUNT(*) as number_of_characters
FROM charactercreator_character;
"""
# How many of each specific subclass?
# TOTAL_SUBCLASS = """
# SELECT
# (SELECT COUNT(*) FROM charactercreator_necromancer) AS necros,
# (SELECT COUNT(*) FROM charactercreator_mage) AS mages,
# (SELECT COUNT(*) FROM charactercreator_thief) AS thiefs,
# (SELECT COUNT(*) FROM charactercreator_cleric) AS clerics,
# (SELECT COUNT(*) FROM charactercreator_fighter) AS fighters;
# """
CLASS = "SELECT COUNT(*) FROM charactercreator_"
# How many total Items? (174)
TOTAL_ITEMS = """
SELECT COUNT(item_id) as items
FROM armory_item;
"""
# How many of the Items are weapons? (37)
WEAPONS = """
SELECT COUNT(item_ptr_id)
FROM armory_weapon;
"""
# How many of the items are not weapons? (137)
NON_WEAPONS = """
SELECT COUNT(items.name)
FROM armory_item as items
WHERE items.item_id NOT IN(
SELECT armory_weapon.item_ptr_id
FROM armory_weapon);
"""
# How many Items does each character have? (Return first 20 rows)
CHARACTER_ITEMS = """
SELECT character.name as "character_name", COUNT(inventory.id) as "#_of_items"
FROM charactercreator_character AS character, charactercreator_character_inventory AS inventory
WHERE character.character_id = inventory.character_id
GROUP BY character.name
ORDER BY character.name
LIMIT 20;
"""
# How many Weapons does each character have? (Return first 20 rows)
CHARACTER_WEAPONS = """
SELECT character.name as "character_name", COUNT(weapon.item_ptr_id) as "#_of_weapons"
FROM charactercreator_character AS character, charactercreator_character_inventory AS inventory, armory_weapon as weapon
WHERE character.character_id = inventory.character_id AND inventory.item_id = weapon.item_ptr_id
GROUP BY character.name
ORDER BY character.name
LIMIT 20;
"""
# On average, how many Items does each Character have? (3.02)
AVG_CHARACTER_ITEMS = """
SELECT
AVG("#_of_items") as "avg_#_of_items"
FROM
(
SELECT
COUNT(inventory.id) AS "#_of_items"
FROM
charactercreator_character AS character,
charactercreator_character_inventory AS inventory
WHERE
character.character_id = inventory.character_id
GROUP BY character.name
);
"""
# On average, how many Weapons does each character have? (0.67)
AVG_CHARACTER_WEAPONS = """
SELECT
AVG(weapon_count) as avg_weapons_per_char
FROM (
SELECT
character.character_id,
COUNT(DISTINCT weapon.item_ptr_id) as weapon_count
FROM
charactercreator_character AS character
LEFT JOIN charactercreator_character_inventory inventory -- characters may have zero items
ON character.character_id = inventory.character_id
LEFT JOIN armory_weapon weapon -- many items are not weapons, so only retain weapons
ON inventory.item_id = weapon.item_ptr_id
GROUP BY character.character_id
) subq;
"""
| [
198,
2,
6803,
379,
262,
2095,
45382,
62,
22769,
3084,
198,
2,
17151,
62,
38019,
10659,
4877,
796,
37227,
198,
2,
33493,
1635,
198,
2,
16034,
2095,
45382,
62,
22769,
26,
198,
2,
37227,
198,
198,
2,
1374,
867,
2472,
26813,
389,
612,
... | 3.104101 | 951 |
#! /usr/bin/env python
#
import warnings
import numpy as np
UNIT_SQUARE = np.asarray([[0,0],[0,1],[1,1],[1,0]])-0.5
from propobject import BaseObject
from shapely import geometry
import pandas
import geopandas
# ======================= #
# #
# Functions #
# #
# ======================= #
def get_simple_grid(xbounds, ybounds, shift_origin=None):
""" """
xbounds = np.atleast_1d(xbounds)
if len(xbounds)==1:
xmin,xmax = 0,xbounds[0]
else:
xmin,xmax = xbounds
ybounds = np.atleast_1d(ybounds)
if len(ybounds)==1:
ymin,ymax = 0,ybounds[0]
else:
ymin,ymax = ybounds
pixels = np.mgrid[xmin:xmax,ymin:ymax]
pixels2_flat = np.concatenate(pixels.T, axis=0)
if shift_origin is not None:
# not += because conflict between int and float array
pixels2_flat = pixels2_flat+ shift_origin
return Grid(pixels2_flat, UNIT_SQUARE)
# ======================= #
# #
# Classes #
# #
# ======================= #
def set_pixels(self, pixels, shape=None, update=True):
""" provide the pixels.
Pixels define the position up on which the geometries are defined.
NB: vertices = pixels+shape
"""
# Setting the pixels
if np.shape(pixels)[-1] != 2:
raise ValueError("pixels must be [N,2] arrays")
self._properties["pixels"] = np.asarray(pixels)
if shape is not None:
self.set_pixelshapes(shape, update=False)
if update:
self._update_geodataframe_()
def set_pixelshapes(self, shape, update=True):
""" """
# Setting the pixel shape.s
if len(np.shape(shape))==2:
self._properties["shape"] = np.asarray(shape)
elif len(np.shape(shape))==3:
if self.pixels is not None and np.shape(shape)[0] != self.npixels:
raise AssertionError("`shape` must be unique or have the same lenth as pixels")
self._properties["shape"] = np.asarray(shape)
else:
raise ValueError("Cannot parse the given shape, must be [M,2] or [N,M,2] when N is the number of pixel and M the number of vertices")
if update:
self._update_geodataframe_()
def set_vertices(self, vertices, overwrite=False, **kwargs):
""" """
if not overwrite and (self.pixels is not None and self.shape is not None):
raise ValueError("Pixels and shape already defined. set the overwrite option to true, to update vertices")
try:
pixels = np.mean(vertices, axis=1)
except:
# Means vertices have different size.
self._derived_properties["vertices"] = vertices
pixels = np.asarray([np.mean(v_, axis=0) for v_ in vertices])
self.set_pixels(pixels, None, **kwargs)
return
self._derived_properties["vertices"] = np.asarray(vertices)
shape = self.vertices - pixels[:,None]
shape_unique = np.unique(shape, axis=0)
if len(shape_unique)==1:
shape = shape_unique[0]
self.set_pixels(pixels, shape, **kwargs)
def set_geodataframe(self, geodataframe, overwrite=False):
""" """
if not overwrite and (self.pixels is not None and self.shape is not None):
raise ValueError("Pixels and shape already defined. set the overwrite option to true, to update geodataframe")
if "geometry" not in geodataframe.columns:
raise TypeError("The given geodataframe does not have 'geometry' column. It is required")
self._derived_properties["geodataframe"] = geodataframe
if "id" not in geodataframe.columns:
self.geodataframe["id"] = self.indexes if self.pixels is not None else np.arange( len(geodataframe) )
# - get the vertices:
vertices = geodataframe["geometry"].apply(get_verts).values
self.set_vertices(vertices, update=False) # don't update the geodataframe
# --------- #
# UPDATE #
# --------- #
def _update_geodataframe_(self):
""" """
dataseries = self.get_geoseries()
x,y = self.pixels.T
self._derived_properties["geodataframe"] = \
geopandas.GeoDataFrame({'geometry': dataseries,
'id':self.indexes,
'x':x,'y':y})
def add_data(self, data, name, indexes=None, inplace=True):
""" """
if indexes is None:
indexes = self.indexes
s_ = pandas.Series(data, name=name, index=indexes)
if not inplace:
return self.geodataframe.join(s_)
self._derived_properties["geodataframe"] = self.geodataframe.join(s_)
# --------- #
# GETTER #
# --------- #
def get_geoseries(self):
""" build a new geodataframe and returns it. """
import geopandas
return geopandas.GeoSeries([geometry.Polygon(v) for v in self.vertices])
def get_triangulation_grid(self):
""" Returns a grid of triangulation. """
return Grid.set_from( np.concatenate(self.triangulation, axis=0) )
def get_pixels_in(self, polygon, invert=False):
""" checks if the centroid of the pixel is in or out the given shapely polygon.
Parameters
----------
polygon: [shapely.geometry]
reference polygon
invert: [bool] -optional-
Get the pixel inside the polygon [invert=False] or outsite [invert=True]
Returns
-------
list of pixels and boolean mask
"""
from shapely import vectorized
flagin = vectorized.contains(polygon, *self.pixels.T)
if invert:
flagin = ~flagin
return self.pixels[flagin], flagin
# --------- #
# Project #
# --------- #
def project_to(self, othergrid, column="*", asgrid=True, use="sum"):
""" project data in the given grid
Parameters
----------
othergrid: [Grid]
New grid where data should be projected to
column: [str/None/list of] -optional-
Which data should be projected ?
If None or '*' all the non-structural columns will be
(structural columns are 'geometry', 'id', 'x', 'y')
asgrid: [bool] -optional-
Should this return a new Grid (actually same object as othergrid)
or a dict [asgrid=False]?
Returns
-------
Grid or dict (see asgrid)
"""
gproj = GridProjector(self, othergrid)
if column is None or column in ["*","all"]:
column = [k for k in self.geodataframe if k not in ['geometry', 'id', 'x', 'y']]
datas = {k:gproj.project_data(k, use=use) for k in column}
if not asgrid:
return datas
# building and setting the new grid
gout = othergrid.__class__.set_from(othergrid.geodataframe)
for k in column:
gout.add_data(datas[k],k)
return gout
def project_to_wcs(self, wcs_, asgrid=True, **kwargs):
""" provide an astropy.wcs.WCS and this will project
the current grid into it (assuming grid's vertices coordinates are in pixels)
Parameters
----------
wcs_: [astropy.wcs.WCS]
The world coordinate solution
asgrid: [bool] -optional-
Should this return a load Grid object or an array of vertices (in degree)
**kwargs goes to wcs_.all_pix2world
Returns
-------
Grid or array (see asgrid)
"""
verts = self.vertices
verts_shape = np.shape(verts)
flatten_verts = np.concatenate(verts, axis=0)
#
flatten_verts_wcs = np.asarray(wcs_.all_pix2world(flatten_verts[:,0],
flatten_verts[:,1], 0,
**kwargs)).T
#
verts_wcs = flatten_verts_wcs.reshape(verts_shape)
if not asgrid:
return verts_wcs
g_wcs = Grid.set_from(verts_wcs)
g_wcs.geodataframe["x_pix"],g_wcs.geodataframe["y_pix"] = self.pixels.T
return g_wcs
def derive_triangulation(self, fast_unique=True):
""" """
def triangulate(geom):
""" Return triangulate format that quadpy likes """
from shapely import ops
triangles = ops.triangulate(geom)
return np.stack([np.asarray(t.exterior.coords.xy).T[:-1] for t in triangles])
if not self.is_shape_unique or not fast_unique:
self._derived_properties["triangulation"] = self.geodataframe["geometry"].apply(triangulate)
else:
self._derived_properties["triangulation"] = self.pixels[:,None,None] + triangulate(geometry.Polygon(self.shape))
# --------- #
# PLOTTER #
# --------- #
def show(self, column=None, ax=None, edgecolor="0.7", facecolor="None", **kwargs):
""" """
if column is not None:
facecolor=None
return self.geodataframe.plot(column, ax=ax,facecolor=facecolor,
edgecolor=edgecolor, **kwargs)
# =================== #
# Properties #
# =================== #
# -- Side
# -- Derived
| [
2,
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1220,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
198,
198,
11748,
14601,
198,
11748,
299,
32152,
355,
45941,
198,
4944,
2043,
62,
50,
10917,
12203,
796,
45941,
13,
292,
18747,
26933,
58,
15,
11,
15,
38430,
15,
11,
16,
38430,
... | 2.109514 | 4,593 |
"""
Binary serialization
NPY format
==========
A simple format for saving numpy arrays to disk with the full
information about them.
The ``.npy`` format is the standard binary file format in NumPy for
persisting a *single* arbitrary NumPy array on disk. The format stores all
of the shape and dtype information necessary to reconstruct the array
correctly even on another machine with a different architecture.
The format is designed to be as simple as possible while achieving
its limited goals.
The ``.npz`` format is the standard format for persisting *multiple* NumPy
arrays on disk. A ``.npz`` file is a zip file containing multiple ``.npy``
files, one for each array.
Capabilities
------------
- Can represent all NumPy arrays including nested record arrays and
object arrays.
- Represents the data in its native binary form.
- Supports Fortran-contiguous arrays directly.
- Stores all of the necessary information to reconstruct the array
including shape and dtype on a machine of a different
architecture. Both little-endian and big-endian arrays are
supported, and a file with little-endian numbers will yield
a little-endian array on any machine reading the file. The
types are described in terms of their actual sizes. For example,
if a machine with a 64-bit C "long int" writes out an array with
"long ints", a reading machine with 32-bit C "long ints" will yield
an array with 64-bit integers.
- Is straightforward to reverse engineer. Datasets often live longer than
the programs that created them. A competent developer should be
able to create a solution in their preferred programming language to
read most ``.npy`` files that they have been given without much
documentation.
- Allows memory-mapping of the data. See `open_memmap`.
- Can be read from a filelike stream object instead of an actual file.
- Stores object arrays, i.e. arrays containing elements that are arbitrary
Python objects. Files with object arrays are not to be mmapable, but
can be read and written to disk.
Limitations
-----------
- Arbitrary subclasses of numpy.ndarray are not completely preserved.
Subclasses will be accepted for writing, but only the array data will
be written out. A regular numpy.ndarray object will be created
upon reading the file.
.. warning::
Due to limitations in the interpretation of structured dtypes, dtypes
with fields with empty names will have the names replaced by 'f0', 'f1',
etc. Such arrays will not round-trip through the format entirely
accurately. The data is intact; only the field names will differ. We are
working on a fix for this. This fix will not require a change in the
file format. The arrays with such structures can still be saved and
restored, and the correct dtype may be restored by using the
``loadedarray.view(correct_dtype)`` method.
File extensions
---------------
We recommend using the ``.npy`` and ``.npz`` extensions for files saved
in this format. This is by no means a requirement; applications may wish
to use these file formats but use an extension specific to the
application. In the absence of an obvious alternative, however,
we suggest using ``.npy`` and ``.npz``.
Version numbering
-----------------
The version numbering of these formats is independent of NumPy version
numbering. If the format is upgraded, the code in `numpy.io` will still
be able to read and write Version 1.0 files.
Format Version 1.0
------------------
The first 6 bytes are a magic string: exactly ``\\x93NUMPY``.
The next 1 byte is an unsigned byte: the major version number of the file
format, e.g. ``\\x01``.
The next 1 byte is an unsigned byte: the minor version number of the file
format, e.g. ``\\x00``. Note: the version of the file format is not tied
to the version of the numpy package.
The next 2 bytes form a little-endian unsigned short int: the length of
the header data HEADER_LEN.
The next HEADER_LEN bytes form the header data describing the array's
format. It is an ASCII string which contains a Python literal expression
of a dictionary. It is terminated by a newline (``\\n``) and padded with
spaces (``\\x20``) to make the total of
``len(magic string) + 2 + len(length) + HEADER_LEN`` be evenly divisible
by 64 for alignment purposes.
The dictionary contains three keys:
"descr" : dtype.descr
An object that can be passed as an argument to the `numpy.dtype`
constructor to create the array's dtype.
"fortran_order" : bool
Whether the array data is Fortran-contiguous or not. Since
Fortran-contiguous arrays are a common form of non-C-contiguity,
we allow them to be written directly to disk for efficiency.
"shape" : tuple of int
The shape of the array.
For repeatability and readability, the dictionary keys are sorted in
alphabetic order. This is for convenience only. A writer SHOULD implement
this if possible. A reader MUST NOT depend on this.
Following the header comes the array data. If the dtype contains Python
objects (i.e. ``dtype.hasobject is True``), then the data is a Python
pickle of the array. Otherwise the data is the contiguous (either C-
or Fortran-, depending on ``fortran_order``) bytes of the array.
Consumers can figure out the number of bytes by multiplying the number
of elements given by the shape (noting that ``shape=()`` means there is
1 element) by ``dtype.itemsize``.
Format Version 2.0
------------------
The version 1.0 format only allowed the array header to have a total size of
65535 bytes. This can be exceeded by structured arrays with a large number of
columns. The version 2.0 format extends the header size to 4 GiB.
`numpy.save` will automatically save in 2.0 format if the data requires it,
else it will always use the more compatible 1.0 format.
The description of the fourth element of the header therefore has become:
"The next 4 bytes form a little-endian unsigned int: the length of the header
data HEADER_LEN."
Format Version 3.0
------------------
This version replaces the ASCII string (which in practice was latin1) with
a utf8-encoded string, so supports structured types with any unicode field
names.
Notes
-----
The ``.npy`` format, including motivation for creating it and a comparison of
alternatives, is described in the
:doc:`"npy-format" NEP <neps:nep-0001-npy-format>`, however details have
evolved with time and this document is more current.
"""
import numpy
import io
import warnings
from numpy.lib.utils import safe_eval
from numpy.compat import (
isfileobj, os_fspath, pickle
)
__all__ = []
EXPECTED_KEYS = {'descr', 'fortran_order', 'shape'}
MAGIC_PREFIX = b'\x93NUMPY'
MAGIC_LEN = len(MAGIC_PREFIX) + 2
ARRAY_ALIGN = 64 # plausible values are powers of 2 between 16 and 4096
BUFFER_SIZE = 2**18 # size of buffer for reading npz files in bytes
# difference between version 1.0 and 2.0 is a 4 byte (I) header length
# instead of 2 bytes (H) allowing storage of large structured arrays
_header_size_info = {
(1, 0): ('<H', 'latin1'),
(2, 0): ('<I', 'latin1'),
(3, 0): ('<I', 'utf8'),
}
def magic(major, minor):
""" Return the magic string for the given file format version.
Parameters
----------
major : int in [0, 255]
minor : int in [0, 255]
Returns
-------
magic : str
Raises
------
ValueError if the version cannot be formatted.
"""
if major < 0 or major > 255:
raise ValueError("major version must be 0 <= major < 256")
if minor < 0 or minor > 255:
raise ValueError("minor version must be 0 <= minor < 256")
return MAGIC_PREFIX + bytes([major, minor])
def read_magic(fp):
""" Read the magic string to get the version of the file format.
Parameters
----------
fp : filelike object
Returns
-------
major : int
minor : int
"""
magic_str = _read_bytes(fp, MAGIC_LEN, "magic string")
if magic_str[:-2] != MAGIC_PREFIX:
msg = "the magic string is not correct; expected %r, got %r"
raise ValueError(msg % (MAGIC_PREFIX, magic_str[:-2]))
major, minor = magic_str[-2:]
return major, minor
def dtype_to_descr(dtype):
"""
Get a serializable descriptor from the dtype.
The .descr attribute of a dtype object cannot be round-tripped through
the dtype() constructor. Simple types, like dtype('float32'), have
a descr which looks like a record array with one field with '' as
a name. The dtype() constructor interprets this as a request to give
a default name. Instead, we construct descriptor that can be passed to
dtype().
Parameters
----------
dtype : dtype
The dtype of the array that will be written to disk.
Returns
-------
descr : object
An object that can be passed to `numpy.dtype()` in order to
replicate the input dtype.
"""
if _has_metadata(dtype):
warnings.warn("metadata on a dtype may be saved or ignored, but will "
"raise if saved when read. Use another form of storage.",
UserWarning, stacklevel=2)
if dtype.names is not None:
# This is a record array. The .descr is fine. XXX: parts of the
# record array with an empty name, like padding bytes, still get
# fiddled with. This needs to be fixed in the C implementation of
# dtype().
return dtype.descr
else:
return dtype.str
def descr_to_dtype(descr):
"""
Returns a dtype based off the given description.
This is essentially the reverse of `dtype_to_descr()`. It will remove
the valueless padding fields created by, i.e. simple fields like
dtype('float32'), and then convert the description to its corresponding
dtype.
Parameters
----------
descr : object
The object retreived by dtype.descr. Can be passed to
`numpy.dtype()` in order to replicate the input dtype.
Returns
-------
dtype : dtype
The dtype constructed by the description.
"""
if isinstance(descr, str):
# No padding removal needed
return numpy.dtype(descr)
elif isinstance(descr, tuple):
# subtype, will always have a shape descr[1]
dt = descr_to_dtype(descr[0])
return numpy.dtype((dt, descr[1]))
titles = []
names = []
formats = []
offsets = []
offset = 0
for field in descr:
if len(field) == 2:
name, descr_str = field
dt = descr_to_dtype(descr_str)
else:
name, descr_str, shape = field
dt = numpy.dtype((descr_to_dtype(descr_str), shape))
# Ignore padding bytes, which will be void bytes with '' as name
# Once support for blank names is removed, only "if name == ''" needed)
is_pad = (name == '' and dt.type is numpy.void and dt.names is None)
if not is_pad:
title, name = name if isinstance(name, tuple) else (None, name)
titles.append(title)
names.append(name)
formats.append(dt)
offsets.append(offset)
offset += dt.itemsize
return numpy.dtype({'names': names, 'formats': formats, 'titles': titles,
'offsets': offsets, 'itemsize': offset})
def header_data_from_array_1_0(array):
""" Get the dictionary of header metadata from a numpy.ndarray.
Parameters
----------
array : numpy.ndarray
Returns
-------
d : dict
This has the appropriate entries for writing its string representation
to the header of the file.
"""
d = {'shape': array.shape}
if array.flags.c_contiguous:
d['fortran_order'] = False
elif array.flags.f_contiguous:
d['fortran_order'] = True
else:
# Totally non-contiguous data. We will have to make it C-contiguous
# before writing. Note that we need to test for C_CONTIGUOUS first
# because a 1-D array is both C_CONTIGUOUS and F_CONTIGUOUS.
d['fortran_order'] = False
d['descr'] = dtype_to_descr(array.dtype)
return d
def _wrap_header(header, version):
"""
Takes a stringified header, and attaches the prefix and padding to it
"""
import struct
assert version is not None
fmt, encoding = _header_size_info[version]
if not isinstance(header, bytes): # always true on python 3
header = header.encode(encoding)
hlen = len(header) + 1
padlen = ARRAY_ALIGN - ((MAGIC_LEN + struct.calcsize(fmt) + hlen) % ARRAY_ALIGN)
try:
header_prefix = magic(*version) + struct.pack(fmt, hlen + padlen)
except struct.error:
msg = "Header length {} too big for version={}".format(hlen, version)
raise ValueError(msg) from None
# Pad the header with spaces and a final newline such that the magic
# string, the header-length short and the header are aligned on a
# ARRAY_ALIGN byte boundary. This supports memory mapping of dtypes
# aligned up to ARRAY_ALIGN on systems like Linux where mmap()
# offset must be page-aligned (i.e. the beginning of the file).
return header_prefix + header + b' '*padlen + b'\n'
def _wrap_header_guess_version(header):
"""
Like `_wrap_header`, but chooses an appropriate version given the contents
"""
try:
return _wrap_header(header, (1, 0))
except ValueError:
pass
try:
ret = _wrap_header(header, (2, 0))
except UnicodeEncodeError:
pass
else:
warnings.warn("Stored array in format 2.0. It can only be"
"read by NumPy >= 1.9", UserWarning, stacklevel=2)
return ret
header = _wrap_header(header, (3, 0))
warnings.warn("Stored array in format 3.0. It can only be "
"read by NumPy >= 1.17", UserWarning, stacklevel=2)
return header
def _write_array_header(fp, d, version=None):
""" Write the header for an array and returns the version used
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string representation
to the header of the file.
version: tuple or None
None means use oldest that works
explicit version will raise a ValueError if the format does not
allow saving this data. Default: None
"""
header = ["{"]
for key, value in sorted(d.items()):
# Need to use repr here, since we eval these when reading
header.append("'%s': %s, " % (key, repr(value)))
header.append("}")
header = "".join(header)
if version is None:
header = _wrap_header_guess_version(header)
else:
header = _wrap_header(header, version)
fp.write(header)
def write_array_header_1_0(fp, d):
""" Write the header for an array using the 1.0 format.
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string
representation to the header of the file.
"""
_write_array_header(fp, d, (1, 0))
def write_array_header_2_0(fp, d):
""" Write the header for an array using the 2.0 format.
The 2.0 format allows storing very large structured arrays.
.. versionadded:: 1.9.0
Parameters
----------
fp : filelike object
d : dict
This has the appropriate entries for writing its string
representation to the header of the file.
"""
_write_array_header(fp, d, (2, 0))
def read_array_header_1_0(fp):
"""
Read an array header from a filelike object using the 1.0 file format
version.
This will leave the file object located just after the header.
Parameters
----------
fp : filelike object
A file object or something with a `.read()` method like a file.
Returns
-------
shape : tuple of int
The shape of the array.
fortran_order : bool
The array data will be written out directly if it is either
C-contiguous or Fortran-contiguous. Otherwise, it will be made
contiguous before writing it out.
dtype : dtype
The dtype of the file's data.
Raises
------
ValueError
If the data is invalid.
"""
return _read_array_header(fp, version=(1, 0))
def read_array_header_2_0(fp):
"""
Read an array header from a filelike object using the 2.0 file format
version.
This will leave the file object located just after the header.
.. versionadded:: 1.9.0
Parameters
----------
fp : filelike object
A file object or something with a `.read()` method like a file.
Returns
-------
shape : tuple of int
The shape of the array.
fortran_order : bool
The array data will be written out directly if it is either
C-contiguous or Fortran-contiguous. Otherwise, it will be made
contiguous before writing it out.
dtype : dtype
The dtype of the file's data.
Raises
------
ValueError
If the data is invalid.
"""
return _read_array_header(fp, version=(2, 0))
def _filter_header(s):
"""Clean up 'L' in npz header ints.
Cleans up the 'L' in strings representing integers. Needed to allow npz
headers produced in Python2 to be read in Python3.
Parameters
----------
s : string
Npy file header.
Returns
-------
header : str
Cleaned up header.
"""
import tokenize
from io import StringIO
tokens = []
last_token_was_number = False
for token in tokenize.generate_tokens(StringIO(s).readline):
token_type = token[0]
token_string = token[1]
if (last_token_was_number and
token_type == tokenize.NAME and
token_string == "L"):
continue
else:
tokens.append(token)
last_token_was_number = (token_type == tokenize.NUMBER)
return tokenize.untokenize(tokens)
def _read_array_header(fp, version):
"""
see read_array_header_1_0
"""
# Read an unsigned, little-endian short int which has the length of the
# header.
import struct
hinfo = _header_size_info.get(version)
if hinfo is None:
raise ValueError("Invalid version {!r}".format(version))
hlength_type, encoding = hinfo
hlength_str = _read_bytes(fp, struct.calcsize(hlength_type), "array header length")
header_length = struct.unpack(hlength_type, hlength_str)[0]
header = _read_bytes(fp, header_length, "array header")
header = header.decode(encoding)
# The header is a pretty-printed string representation of a literal
# Python dictionary with trailing newlines padded to a ARRAY_ALIGN byte
# boundary. The keys are strings.
# "shape" : tuple of int
# "fortran_order" : bool
# "descr" : dtype.descr
# Versions (2, 0) and (1, 0) could have been created by a Python 2
# implementation before header filtering was implemented.
if version <= (2, 0):
header = _filter_header(header)
try:
d = safe_eval(header)
except SyntaxError as e:
msg = "Cannot parse header: {!r}"
raise ValueError(msg.format(header)) from e
if not isinstance(d, dict):
msg = "Header is not a dictionary: {!r}"
raise ValueError(msg.format(d))
if EXPECTED_KEYS != d.keys():
keys = sorted(d.keys())
msg = "Header does not contain the correct keys: {!r}"
raise ValueError(msg.format(keys))
# Sanity-check the values.
if (not isinstance(d['shape'], tuple) or
not all(isinstance(x, int) for x in d['shape'])):
msg = "shape is not valid: {!r}"
raise ValueError(msg.format(d['shape']))
if not isinstance(d['fortran_order'], bool):
msg = "fortran_order is not a valid bool: {!r}"
raise ValueError(msg.format(d['fortran_order']))
try:
dtype = descr_to_dtype(d['descr'])
except TypeError as e:
msg = "descr is not a valid dtype descriptor: {!r}"
raise ValueError(msg.format(d['descr'])) from e
return d['shape'], d['fortran_order'], dtype
def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):
"""
Write an array to an NPY file, including a header.
If the array is neither C-contiguous nor Fortran-contiguous AND the
file_like object is not a real file object, this function will have to
copy data in memory.
Parameters
----------
fp : file_like object
An open, writable file object, or similar object with a
``.write()`` method.
array : ndarray
The array to write to disk.
version : (int, int) or None, optional
The version number of the format. None means use the oldest
supported version that is able to store the data. Default: None
allow_pickle : bool, optional
Whether to allow writing pickled data. Default: True
pickle_kwargs : dict, optional
Additional keyword arguments to pass to pickle.dump, excluding
'protocol'. These are only useful when pickling objects in object
arrays on Python 3 to Python 2 compatible format.
Raises
------
ValueError
If the array cannot be persisted. This includes the case of
allow_pickle=False and array being an object array.
Various other errors
If the array contains Python objects as part of its dtype, the
process of pickling them may raise various errors if the objects
are not picklable.
"""
_check_version(version)
_write_array_header(fp, header_data_from_array_1_0(array), version)
if array.itemsize == 0:
buffersize = 0
else:
# Set buffer size to 16 MiB to hide the Python loop overhead.
buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)
if array.dtype.hasobject:
# We contain Python objects so we cannot write out the data
# directly. Instead, we will pickle it out
if not allow_pickle:
raise ValueError("Object arrays cannot be saved when "
"allow_pickle=False")
if pickle_kwargs is None:
pickle_kwargs = {}
pickle.dump(array, fp, protocol=3, **pickle_kwargs)
elif array.flags.f_contiguous and not array.flags.c_contiguous:
if isfileobj(fp):
array.T.tofile(fp)
else:
for chunk in numpy.nditer(
array, flags=['external_loop', 'buffered', 'zerosize_ok'],
buffersize=buffersize, order='F'):
fp.write(chunk.tobytes('C'))
else:
if isfileobj(fp):
array.tofile(fp)
else:
for chunk in numpy.nditer(
array, flags=['external_loop', 'buffered', 'zerosize_ok'],
buffersize=buffersize, order='C'):
fp.write(chunk.tobytes('C'))
def read_array(fp, allow_pickle=False, pickle_kwargs=None):
"""
Read an array from an NPY file.
Parameters
----------
fp : file_like object
If this is not a real file object, then this may take extra memory
and time.
allow_pickle : bool, optional
Whether to allow writing pickled data. Default: False
.. versionchanged:: 1.16.3
Made default False in response to CVE-2019-6446.
pickle_kwargs : dict
Additional keyword arguments to pass to pickle.load. These are only
useful when loading object arrays saved on Python 2 when using
Python 3.
Returns
-------
array : ndarray
The array from the data on disk.
Raises
------
ValueError
If the data is invalid, or allow_pickle=False and the file contains
an object array.
"""
version = read_magic(fp)
_check_version(version)
shape, fortran_order, dtype = _read_array_header(fp, version)
if len(shape) == 0:
count = 1
else:
count = numpy.multiply.reduce(shape, dtype=numpy.int64)
# Now read the actual data.
if dtype.hasobject:
# The array contained Python objects. We need to unpickle the data.
if not allow_pickle:
raise ValueError("Object arrays cannot be loaded when "
"allow_pickle=False")
if pickle_kwargs is None:
pickle_kwargs = {}
try:
array = pickle.load(fp, **pickle_kwargs)
except UnicodeError as err:
# Friendlier error message
raise UnicodeError("Unpickling a python object failed: %r\n"
"You may need to pass the encoding= option "
"to numpy.load" % (err,)) from err
else:
if isfileobj(fp):
# We can use the fast fromfile() function.
array = numpy.fromfile(fp, dtype=dtype, count=count)
else:
# This is not a real file. We have to read it the
# memory-intensive way.
# crc32 module fails on reads greater than 2 ** 32 bytes,
# breaking large reads from gzip streams. Chunk reads to
# BUFFER_SIZE bytes to avoid issue and reduce memory overhead
# of the read. In non-chunked case count < max_read_count, so
# only one read is performed.
# Use np.ndarray instead of np.empty since the latter does
# not correctly instantiate zero-width string dtypes; see
# https://github.com/numpy/numpy/pull/6430
array = numpy.ndarray(count, dtype=dtype)
if dtype.itemsize > 0:
# If dtype.itemsize == 0 then there's nothing more to read
max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize)
for i in range(0, count, max_read_count):
read_count = min(max_read_count, count - i)
read_size = int(read_count * dtype.itemsize)
data = _read_bytes(fp, read_size, "array data")
array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,
count=read_count)
if fortran_order:
array.shape = shape[::-1]
array = array.transpose()
else:
array.shape = shape
return array
def open_memmap(filename, mode='r+', dtype=None, shape=None,
fortran_order=False, version=None):
"""
Open a .npy file as a memory-mapped array.
This may be used to read an existing file or create a new one.
Parameters
----------
filename : str or path-like
The name of the file on disk. This may *not* be a file-like
object.
mode : str, optional
The mode in which to open the file; the default is 'r+'. In
addition to the standard file modes, 'c' is also accepted to mean
"copy on write." See `memmap` for the available mode strings.
dtype : data-type, optional
The data type of the array if we are creating a new file in "write"
mode, if not, `dtype` is ignored. The default value is None, which
results in a data-type of `float64`.
shape : tuple of int
The shape of the array if we are creating a new file in "write"
mode, in which case this parameter is required. Otherwise, this
parameter is ignored and is thus optional.
fortran_order : bool, optional
Whether the array should be Fortran-contiguous (True) or
C-contiguous (False, the default) if we are creating a new file in
"write" mode.
version : tuple of int (major, minor) or None
If the mode is a "write" mode, then this is the version of the file
format used to create the file. None means use the oldest
supported version that is able to store the data. Default: None
Returns
-------
marray : memmap
The memory-mapped array.
Raises
------
ValueError
If the data or the mode is invalid.
IOError
If the file is not found or cannot be opened correctly.
See Also
--------
numpy.memmap
"""
if isfileobj(filename):
raise ValueError("Filename must be a string or a path-like object."
" Memmap cannot use existing file handles.")
if 'w' in mode:
# We are creating the file, not reading it.
# Check if we ought to create the file.
_check_version(version)
# Ensure that the given dtype is an authentic dtype object rather
# than just something that can be interpreted as a dtype object.
dtype = numpy.dtype(dtype)
if dtype.hasobject:
msg = "Array can't be memory-mapped: Python objects in dtype."
raise ValueError(msg)
d = dict(
descr=dtype_to_descr(dtype),
fortran_order=fortran_order,
shape=shape,
)
# If we got here, then it should be safe to create the file.
with open(os_fspath(filename), mode+'b') as fp:
_write_array_header(fp, d, version)
offset = fp.tell()
else:
# Read the header of the file first.
with open(os_fspath(filename), 'rb') as fp:
version = read_magic(fp)
_check_version(version)
shape, fortran_order, dtype = _read_array_header(fp, version)
if dtype.hasobject:
msg = "Array can't be memory-mapped: Python objects in dtype."
raise ValueError(msg)
offset = fp.tell()
if fortran_order:
order = 'F'
else:
order = 'C'
# We need to change a write-only mode to a read-write mode since we've
# already written data to the file.
if mode == 'w+':
mode = 'r+'
marray = numpy.memmap(filename, dtype=dtype, shape=shape, order=order,
mode=mode, offset=offset)
return marray
def _read_bytes(fp, size, error_template="ran out of data"):
"""
Read from file-like object until size bytes are read.
Raises ValueError if not EOF is encountered before size bytes are read.
Non-blocking objects only supported if they derive from io objects.
Required as e.g. ZipExtFile in python 2.6 can return less data than
requested.
"""
data = bytes()
while True:
# io files (default in python3) return None or raise on
# would-block, python2 file will truncate, probably nothing can be
# done about that. note that regular files can't be non-blocking
try:
r = fp.read(size - len(data))
data += r
if len(r) == 0 or len(data) == size:
break
except io.BlockingIOError:
pass
if len(data) != size:
msg = "EOF: reading %s, expected %d bytes got %d"
raise ValueError(msg % (error_template, size, len(data)))
else:
return data
| [
37811,
198,
33,
3219,
11389,
1634,
198,
198,
22182,
56,
5794,
198,
2559,
855,
198,
198,
32,
2829,
5794,
329,
8914,
299,
32152,
26515,
284,
11898,
351,
262,
1336,
198,
17018,
546,
606,
13,
198,
198,
464,
7559,
13,
77,
9078,
15506,
57... | 2.654268 | 11,668 |
"""
Enumerator Test
"""
from typing import Any | [
37811,
198,
4834,
6975,
1352,
6208,
198,
37811,
198,
6738,
19720,
1330,
4377
] | 3.538462 | 13 |
#!/usr/bin/env python
from zoneinfo import ZoneInfo
import flask
from dateutil.parser import parse
from flask_assets import Bundle, Environment
from logzero import logger, setup_logger
from webassets.filter import get_filter
from config import cfg
from apis import calendar as gcal
setup_logger(name=__name__)
app = flask.Flask(__name__)
libsass = get_filter(
"libsass",
as_output=True,
style="compressed",
)
assets = Environment(app) # create an Environment instance
bundles = { # define nested Bundle
"style": Bundle(
"scss/*.scss",
filters=(libsass),
output="style.css",
)
}
assets.register(bundles)
def get_base_url():
if prefix := cfg.gcs_bucket_prefix:
return f"https://{cfg.hostname}/{prefix}"
return f"https://{cfg.hostname}"
def create_app():
cfg.load()
# TODO: do this default settings thing better?
default_app_config = dict(
display_timezone=cfg.display_timezone,
FREEZER_BASE_URL=get_base_url(),
FREEZER_STATIC_IGNORE=["*.scss", ".webassets-cache/*", ".DS_Store"],
FREEZER_RELATIVE_URLS=False,
FREEZER_REMOVE_EXTRA_FILES=True,
)
logger.info(f"create_app() => {default_app_config=}")
app.config.update(default_app_config)
return app
if __name__ == "__main__":
app = create_app()
app.run(
host="0.0.0.0",
debug=True,
)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
6738,
6516,
10951,
1330,
13035,
12360,
198,
198,
11748,
42903,
198,
6738,
3128,
22602,
13,
48610,
1330,
21136,
198,
6738,
42903,
62,
19668,
1330,
25282,
11,
9344,
198,
6738,
2604,
22570,
... | 2.42069 | 580 |
#!/usr/bin/env python
import astropy.io.fits as fits
import numpy as np
import os
import matplotlib.pyplot as plt
import argparse
if __name__ == "__main__":
descr = 'GFA focus sequence plots/analysis'
parser = argparse.ArgumentParser(description=descr)
parser.add_argument('first_expid', type=int, nargs=1)
parser.add_argument('night', type=str, nargs=1)
parser.add_argument('--basedir', default='/n/home/datasystems/users/ameisner/reduced/focus',
type=str, help='base directory for GFA reductions')
parser.add_argument('--outdir', default='/n/home/desiobserver/focus_scan_pngs',
type=str, help='output directory for plot PNGs')
parser.add_argument('--no_popups', default=False, action='store_true',
help='write PNGs without popping up plot windows')
args = parser.parse_args()
expids = args.first_expid + np.arange(16, dtype=int)
print(expids)
print(args.night[0])
print(args.basedir)
outdir = args.outdir if os.path.exists(args.outdir) else '.'
focus_plots(args.night[0], expids, basedir=args.basedir, outdir=outdir, no_popups=args.no_popups)
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
11748,
6468,
28338,
13,
952,
13,
21013,
355,
11414,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
28686,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
11748,
... | 2.429735 | 491 |
from typing import List
from ddq.taxonomy.reference import Reference
from ddq.topics.topic import Topic
| [
6738,
19720,
1330,
7343,
198,
6738,
49427,
80,
13,
19290,
30565,
13,
35790,
1330,
20984,
198,
6738,
49427,
80,
13,
4852,
873,
13,
26652,
1330,
47373,
628,
220,
220,
220,
220,
220,
220,
220,
220
] | 3.228571 | 35 |
# Copyright (c) Facebook, Inc. and its affiliates.
import base64
import logging
import os
import sys
from tensorboardX import SummaryWriter
from pythia.utils.distributed_utils import is_main_process
from pythia.utils.general import (ckpt_name_from_core_args,
foldername_from_config_override)
from pythia.utils.timer import Timer
| [
2,
15069,
357,
66,
8,
3203,
11,
3457,
13,
290,
663,
29116,
13,
198,
11748,
2779,
2414,
198,
11748,
18931,
198,
11748,
28686,
198,
11748,
25064,
198,
198,
6738,
11192,
273,
3526,
55,
1330,
21293,
34379,
198,
198,
6738,
279,
5272,
544,
... | 2.731343 | 134 |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
from ._inputs import *
__all__ = ['UserDataMappingArgs', 'UserDataMapping']
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
consent_store_id: Optional[pulumi.Input[str]] = None,
data_id: Optional[pulumi.Input[str]] = None,
dataset_id: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
project: Optional[pulumi.Input[str]] = None,
resource_attributes: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['AttributeArgs']]]]] = None,
user_id: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = UserDataMappingArgs.__new__(UserDataMappingArgs)
if consent_store_id is None and not opts.urn:
raise TypeError("Missing required property 'consent_store_id'")
__props__.__dict__["consent_store_id"] = consent_store_id
if data_id is None and not opts.urn:
raise TypeError("Missing required property 'data_id'")
__props__.__dict__["data_id"] = data_id
if dataset_id is None and not opts.urn:
raise TypeError("Missing required property 'dataset_id'")
__props__.__dict__["dataset_id"] = dataset_id
__props__.__dict__["location"] = location
__props__.__dict__["name"] = name
__props__.__dict__["project"] = project
__props__.__dict__["resource_attributes"] = resource_attributes
if user_id is None and not opts.urn:
raise TypeError("Missing required property 'user_id'")
__props__.__dict__["user_id"] = user_id
__props__.__dict__["archive_time"] = None
__props__.__dict__["archived"] = None
super(UserDataMapping, __self__).__init__(
'google-native:healthcare/v1beta1:UserDataMapping',
resource_name,
__props__,
opts)
| [
2,
19617,
28,
40477,
12,
23,
198,
2,
17202,
39410,
25,
428,
2393,
373,
7560,
416,
262,
21624,
12994,
26144,
35986,
13,
17202,
198,
2,
17202,
2141,
407,
4370,
416,
1021,
4556,
345,
821,
1728,
345,
760,
644,
345,
389,
1804,
0,
17202,
... | 2.228358 | 1,340 |
import sys
import os
import sphinx_rtd_theme
source_path = os.path.normpath(
os.path.join(
os.path.abspath(
os.path.split(__file__)[0])))
try:
from conf_base import *
except ImportError:
sys.path.append(source_path)
from conf_base import *
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
templates_path = [os.path.join(source_path, 'phdoc_static')]
html_static_path = [os.path.join(source_path, 'phdoc_static')]
if not os.path.exists(templates_path[0]):
raise FileNotFoundError(templates_path[0])
blog_root = "http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx3/"
| [
11748,
25064,
198,
11748,
28686,
198,
11748,
599,
20079,
87,
62,
81,
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62,
43810,
628,
198,
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62,
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796,
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13,
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13,
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6978,
7,
198,
220,
220,
220,
28686,
13,
6978,
13,
22179,
7,
198,
220,
220,
220,
220,
... | 2.335689 | 283 |
"""
This module contains routines for reading and working with adv
data. It contains:
+-----------------------------------+-----------------------------------------+
| Name | Description |
+===================================+=========================================+
| :func:`~dolfyn.adv.base.load` | A function for loading ADV data in |
| | DOLfYN format. |
+-----------------------------------+-----------------------------------------+
| :func:`~dolfyn.adv.base.mmload` | A function for loading ADV data in |
| | DOLfYN format (as memory mapped arrays).|
+-----------------------------------+-----------------------------------------+
| :func:`~dolfyn.io.nortek.\ | A function for reading Nortek Vector |
| read_nortek` | files. |
+-----------------------------------+-----------------------------------------+
| :mod:`rotate <dolfyn.adv.rotate>` | A module containing classes and |
| | functions for rotating adv data between |
| | different coordinate systems |
+-----------------------------------+-----------------------------------------+
| :mod:`motion <dolfyn.adv.rotate>` | A module containing classes and |
| | functions for performing motion |
| | correction. |
+-----------------------------------+-----------------------------------------+
| :class:`~dolfyn.\ | A class for breaking ADV data into |
| adv.turbulence.TurbBinner` | 'bins', averaging it and estimating |
| | various turbulence statistics. |
+-----------------------------------+-----------------------------------------+
Examples
--------
.. literalinclude:: ../examples/adv_example01.py
"""
from .base import load, mmload
from .turbulence import TurbBinner
from . import clean
from ..io.nortek import read_nortek
from . import rotate
from . import motion
| [
37811,
198,
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8265,
4909,
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13,
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198,
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10,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
22... | 2.439739 | 921 |
# Generated by Django 3.1.2 on 2020-10-11 10:53
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
513,
13,
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6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
#
# 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 time
import socket
from collections import Iterable
from typing import Union, Tuple
from mongoengine import connect
from notification_service.event_storage import BaseEventStorage
from notification_service.base_notification import BaseEvent
from notification_service.mongo_notification import MongoEvent
| [
2,
198,
2,
49962,
284,
262,
24843,
10442,
5693,
357,
1921,
37,
8,
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2,
393,
517,
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11704,
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2,
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428,
670,
329,
3224,
1321,
198,
2,
5115,
6634,
9238,
13,
220,
... | 4.174242 | 264 |
__author__ = "Dave Wapstra <dwapstra@cisco.com>"
from unicon.plugins.confd import ConfdServiceList, ConfdConnection, ConfdConnectionProvider
from .statemachine import CspStateMachine
from .settings import CspSettings
from . import service_implementation as csp_svc
| [
834,
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796,
366,
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67,
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13,
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67,
1330,
7326,
67,
16177,
8053,
11,
7326,
67,
32048,
11,
7326,
67,
32048,
... | 3.405063 | 79 |
# -*- coding: utf-8 -*-
from setuptools import setup
import search_google as package
setup(
name=package.__name__,
version=package.__version__,
description=package.__description__,
long_description=readme(),
author=package.__author__,
author_email=package.__email__,
license=package.__license__,
url=package.__url__,
download_url=package.__download_url__,
keywords =package. __keywords__,
entry_points=package.__entry_points__,
packages=package.__packages__,
package_data=package.__package_data__,
install_requires=package.__install_requires__
)
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
6738,
900,
37623,
10141,
1330,
9058,
198,
198,
11748,
2989,
62,
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355,
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198,
220,
220,
198,
40406,
7,
198,
220,
1438,
28,
26495,
13,
834,
3672,
834,
11,... | 2.969231 | 195 |
import setuptools
import os
try:
import pypandoc
description = pypandoc.convert('README.md', 'rst') if os.path.exists('README.md') else ''
except ImportError:
description = ''
setuptools.setup(
name = 'macpack',
packages = setuptools.find_packages(),
version = '1.0.3',
description = 'Makes a macOS binary redistributable by searching the dependency tree and copying/patching non-system libraries.',
long_description = description,
author = 'Caleb Hearon',
author_email = 'caleb@chearon.net',
url = 'https://github.com/chearon/macpack',
download_url = 'https://github.com/chearon/macpack/tarball/v1.0.3',
keywords = ['macos', 'bundle', 'package', 'redistribute', 'redistributable', 'install_name_tool', 'otool', 'mach'],
classifiers = [],
entry_points = {
'console_scripts': ['macpack=macpack.patcher:main'],
}
)
| [
11748,
900,
37623,
10141,
198,
11748,
28686,
198,
198,
28311,
25,
198,
220,
1330,
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420,
198,
220,
6764,
796,
279,
4464,
392,
420,
13,
1102,
1851,
10786,
15675,
11682,
13,
9132,
3256,
705,
81,
301,
11537,
611,
28686,
13,... | 2.884746 | 295 |
import vtk
# Read the file (to test that it was written correctly)
reader = vtk.vtkXMLImageDataReader()
reader.SetFileName("../data/wind_image.vti")
reader.Update()
print(reader.GetOutput())
# Convert the image to a polydata
imageDataGeometryFilter = vtk.vtkImageDataGeometryFilter()
imageDataGeometryFilter.SetInputConnection(reader.GetOutputPort())
imageDataGeometryFilter.Update()
scalarRange = reader.GetOutput().GetPointData().GetScalars().GetRange(-1)
contoursFilter = vtk.vtkContourFilter()
contoursFilter.SetInputConnection(imageDataGeometryFilter.GetOutputPort())
contoursFilter.GenerateValues(60, scalarRange)
contoursMapper = vtk.vtkPolyDataMapper()
contoursMapper.SetInputConnection(contoursFilter.GetOutputPort())
contoursMapper.SetColorModeToMapScalars()
contoursMapper.ScalarVisibilityOn()
contoursMapper.SelectColorArray("JPEGImage")
contoursMapper.SetScalarRange(scalarRange)
contoursActor = vtk.vtkActor()
contoursActor.SetMapper(contoursMapper)
actor = vtk.vtkActor()
actor.SetMapper(contoursMapper)
# Setup rendering
renderer = vtk.vtkRenderer()
renderer.AddActor(actor)
renderer.SetBackground(1,1,1)
renderer.ResetCamera()
renderWindow = vtk.vtkRenderWindow()
renderWindow.AddRenderer(renderer)
renderWindowInteractor = vtk.vtkRenderWindowInteractor()
renderWindowInteractor.SetRenderWindow(renderWindow)
renderWindowInteractor.Start()
| [
11748,
410,
30488,
198,
198,
2,
4149,
262,
2393,
357,
1462,
1332,
326,
340,
373,
3194,
9380,
8,
198,
46862,
796,
410,
30488,
13,
85,
30488,
55,
5805,
5159,
6601,
33634,
3419,
198,
46862,
13,
7248,
8979,
5376,
7203,
40720,
7890,
14,
... | 3.046667 | 450 |
"""
# -----------------------------------------------------------------------------
# @brief:
# Tingwu: reset the reward function so that it's more similar to the one
# defined in GYM
# -----------------------------------------------------------------------------
"""
import numpy as np
from mbbl.config import init_path
from mbbl.env import base_env_wrapper as bew
from mbbl.env import env_register
from mbbl.env import env_util
from mbbl.util.common import logger
if __name__ == '__main__':
# test_env_name = ['gym_doublePendulum']
test_env_name = ['gym_invertedPendulum']
for env_name in test_env_name:
test_env = env(env_name, 1234, {})
api_env = env(env_name, 1234, {})
api_env.reset()
ob, reward, _, _ = test_env.reset()
for _ in range(100):
action = np.random.uniform(-1, 1, test_env._env.action_space.shape)
new_ob, reward, _, _ = test_env.step(action)
# test the reward api
reward_from_api = \
api_env.reward({'start_state': ob, 'action': action})
reward_error = np.sum(np.abs(reward_from_api - reward))
# test the dynamics api
newob_from_api = \
api_env.fdynamics({'start_state': ob, 'action': action})
ob_error = np.sum(np.abs(newob_from_api - new_ob))
ob = new_ob
print('reward error: {}, dynamics error: {}'.format(
reward_error, ob_error)
)
| [
37811,
198,
2,
16529,
32501,
198,
2,
220,
220,
2488,
65,
3796,
25,
198,
2,
220,
220,
220,
220,
220,
220,
309,
278,
43812,
25,
13259,
262,
6721,
2163,
523,
326,
340,
338,
517,
2092,
284,
262,
530,
198,
2,
220,
220,
220,
220,
220,... | 2.325116 | 649 |
from .bar import *
from .create_session import *
from .image import *
from .line import *
from .mesh import *
from .pie import *
from .text import *
from .VisdomFigure import *
from .VisdomScene import *
| [
6738,
764,
5657,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1330,
1635,
198,
6738,
764,
17953,
62,
29891,
1330,
1635,
198,
6738,
764,
9060,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1330,
1635,
198,
6738,
764,
1370,
... | 2.125984 | 127 |
# Ping Pong Pi web UI running on flask.
# Uses zmq to speak to daemon controlling screen.
from flask import Flask, render_template, appcontext_tearing_down, request
from multiprocessing import Process, Queue
from multiprocessing.connection import Client
import atexit
import time
import zmq
app = Flask(__name__)
MODE="mode"
message_queue = Queue()
message_process = None
atexit.register(stop_message_loop)
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0')
| [
2,
34263,
350,
506,
13993,
3992,
12454,
2491,
319,
42903,
13,
198,
2,
36965,
1976,
76,
80,
284,
2740,
284,
33386,
12755,
3159,
13,
198,
198,
6738,
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1330,
46947,
11,
8543,
62,
28243,
11,
598,
22866,
62,
83,
6648,
62,
2902,
11,
... | 3.123377 | 154 |
# -*- encoding: utf-8 -*-
# Copyright (c) 2019 European Organization for Nuclear Research (CERN)
#
# 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 unittest import mock
from watcher.common import exception
from watcher.decision_engine.datasources import manager
from watcher.decision_engine.model import model_root
from watcher.decision_engine.strategy import strategies
from watcher.tests import base
from watcher.tests.decision_engine.model import faker_cluster_state
| [
2,
532,
9,
12,
21004,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
15069,
357,
66,
8,
13130,
3427,
12275,
329,
19229,
4992,
357,
34,
28778,
8,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,... | 3.727969 | 261 |
from sudoku import Sudoku
if __name__ == '__main__':
main()
| [
6738,
424,
67,
11601,
1330,
14818,
11601,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
197,
12417,
3419,
198
] | 2.583333 | 24 |
import numpy as np
import pandas as pd
from autogbt.sampler import MajorityUnderSampler
| [
11748,
299,
32152,
355,
45941,
198,
11748,
19798,
292,
355,
279,
67,
198,
6738,
1960,
519,
18347,
13,
37687,
20053,
1330,
22171,
9203,
16305,
20053,
628,
628,
198
] | 3.285714 | 28 |
import numpy as np
import sys
from timeit import default_timer as timer
sys.path.append("../../")
from core import wnn
from encoding import thermometer
from encoding import util
#Load Diabetes data
base_path = "../../dataset/diabetes/"
#2/3 Test
bits_encoding = 20
train_data, train_label, test_data, test_label, data_min, data_max = util.load_3data(base_path)
ths = []
for i in range(len(data_max)):
ths.append(thermometer.Thermometer(data_min[i], data_max[i], bits_encoding))
train_bin = []
test_bin = []
i = 0
for data in train_data:
train_bin.append(np.array([], dtype=bool))
t = 0
for v in data:
binarr = ths[t].binarize(v)
train_bin[i] = np.append(train_bin[i], binarr)
t += 1
i += 1
i = 0
for data in test_data:
test_bin.append(np.array([], dtype=bool))
t = 0
for v in data:
binarr = ths[t].binarize(v)
test_bin[i] = np.append(test_bin[i], binarr)
t += 1
i += 1
#print test_label
#Wisard
num_classes = 2
tuple_list = [2, 4, 8, 14, 16, 18, 20, 22, 24, 26, 28, 30]
acc_list = []
test_length = len(test_label)
entry_size = len(train_bin[0])
#print entry_size
for t in tuple_list:
wisard = wnn.Wisard(entry_size, t, num_classes)
wisard.train(train_bin, train_label)
rank_result = wisard.rank(test_bin)
num_hits = 0
for i in range(test_length):
if rank_result[i] == test_label[i]:
num_hits += 1
acc_list.append(float(num_hits)/float(test_length))
#Bloom Wisard
btuple_list = [2, 4, 8, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 40, 56]
bacc_list = []
#capacity = len(train_bin)
capacity = 10
print capacity
for t in btuple_list:
bwisard = wnn.BloomWisard(entry_size, t, num_classes, capacity)
bwisard.train(train_bin, train_label)
rank_result = bwisard.rank(test_bin)
num_hits = 0
for i in range(test_length):
if rank_result[i] == test_label[i]:
num_hits += 1
bacc_list.append(float(num_hits)/float(test_length))
print "Tuples=", tuple_list
print "Wisard Accuracy=", acc_list
print "Tuples=", btuple_list
print "BloomWisard Accuracy=",bacc_list
| [
11748,
299,
32152,
355,
45941,
198,
11748,
25064,
198,
6738,
640,
270,
1330,
4277,
62,
45016,
355,
19781,
198,
17597,
13,
6978,
13,
33295,
7203,
40720,
40720,
4943,
220,
198,
6738,
4755,
1330,
266,
20471,
198,
6738,
21004,
1330,
21969,
... | 2.242204 | 962 |
from pygamehelper import *
from pygame import *
from pygame.locals import *
from vec2d import *
from random import uniform
import numpy as np
s = Starter()
s.mainLoop(40)
| [
6738,
12972,
6057,
2978,
525,
1330,
1635,
198,
6738,
12972,
6057,
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1635,
198,
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13,
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874,
1330,
1635,
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6738,
43030,
17,
67,
1330,
1635,
198,
6738,
4738,
1330,
8187,
198,
198,
11748,
299,
32152,
355,
45941... | 2.828125 | 64 |
import glob
import os
import sys
import utils
from recorder import StreamRec
OUTDIR = ""
if __name__ == '__main__':
main()
| [
11748,
15095,
198,
11748,
28686,
198,
11748,
25064,
198,
198,
11748,
3384,
4487,
198,
6738,
38156,
1330,
13860,
6690,
198,
198,
12425,
34720,
796,
13538,
628,
628,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
... | 2.934783 | 46 |
# Copyright (c) 2017 Huawei Technologies Co., Ltd.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from oslo_policy import policy
from karbor.policies import base
GET_POLICY = 'protectable:get'
GET_ALL_POLICY = 'protectable:get_all'
INSTANCES_GET_POLICY = 'protectable:instance_get'
INSTANCES_GET_ALL_POLICY = 'protectable:instance_get_all'
protectables_policies = [
policy.DocumentedRuleDefault(
name=GET_POLICY,
check_str=base.RULE_ADMIN_OR_OWNER,
description='Show a protectable type.',
operations=[
{
'method': 'GET',
'path': '/protectables/{protectable_type}'
}
]),
policy.DocumentedRuleDefault(
name=GET_ALL_POLICY,
check_str=base.RULE_ADMIN_OR_OWNER,
description='List protectable types.',
operations=[
{
'method': 'GET',
'path': '/protectables'
}
]),
policy.DocumentedRuleDefault(
name=INSTANCES_GET_POLICY,
check_str=base.RULE_ADMIN_OR_OWNER,
description='Show a protectable instance.',
operations=[
{
'method': 'GET',
'path': '/protectables/{protectable_type}/'
'instances/{resource_id}'
}
]),
policy.DocumentedRuleDefault(
name=INSTANCES_GET_ALL_POLICY,
check_str=base.RULE_ADMIN_OR_OWNER,
description='List protectable instances.',
operations=[
{
'method': 'GET',
'path': '/protectables/{protectable_type}/instances'
}
]),
]
| [
2,
15069,
357,
66,
8,
2177,
43208,
21852,
1766,
1539,
12052,
13,
198,
2,
1439,
6923,
33876,
13,
198,
2,
198,
2,
220,
220,
220,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
15341,
345,
743,
198,
2,
... | 2.207171 | 1,004 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# routers are dictionaries of URL routing parameters.
#
# For each request, the effective router is:
# the built-in default base router (shown below),
# updated by the BASE router in routes.py routers,
# updated by the app-specific router in routes.py routers (if any),
# updated by the app-specific router from applcations/app/routes.py routers (if any)
#
#
# Router members:
#
# default_application: default application name
# applications: list of all recognized applications, or 'ALL' to use all currently installed applications
# Names in applications are always treated as an application names when they appear first in an incoming URL.
# Set applications to None to disable the removal of application names from outgoing URLs.
# domains: optional dict mapping domain names to application names
# The domain name can include a port number: domain.com:8080
# The application name can include a controller: appx/ctlrx
# path_prefix: a path fragment that is prefixed to all outgoing URLs and stripped from all incoming URLs
#
# Note: default_application, applications, domains & path_prefix are permitted only in the BASE router,
# and domain makes sense only in an application-specific router.
# The remaining members can appear in the BASE router (as defaults for all applications)
# or in application-specific routers.
#
# default_controller: name of default controller
# default_function: name of default function (all controllers)
# controllers: list of valid controllers in selected app
# or "DEFAULT" to use all controllers in the selected app plus 'static'
# or None to disable controller-name removal.
# Names in controllers are always treated as controller names when they appear in an incoming URL after
# the (optional) application and language names.
# languages: list of all supported languages
# Names in controllers are always treated as language names when they appear in an incoming URL after
# the (optional) application name.
# default_language
# The language code (for example: en, it-it) optionally appears in the URL following
# the application (which may be omitted). For incoming URLs, the code is copied to
# request.language; for outgoing URLs it is taken from request.language.
# If languages=None, language support is disabled.
# The default_language, if any, is omitted from the URL.
# root_static: list of static files accessed from root
# (mapped to the current application's static/ directory)
# Each application has its own root-static files.
# domain: the domain that maps to this application (alternative to using domains in the BASE router)
# map_hyphen: If True (default), hyphens in incoming /a/c/f fields are converted to underscores,
# and back to hyphens in outgoing URLs. Language, args and the query string are not affected.
# map_static: By default, the default application is not stripped from static URLs. Set map_static=True
# to override this policy.
# acfe_match: regex for valid application, controller, function, extension /a/c/f.e
# file_match: regex for valid file (used for static file names)
# args_match: regex for valid args
# This validation provides a measure of security.
# If it is changed, the application perform its own validation.
#
#
# The built-in default router supplies default values (undefined members are None):
#
# default_router = dict(
# default_application = 'init',
# applications = 'ALL',
# default_controller = 'default',
# controllers = 'DEFAULT',
# default_function = 'index',
# default_language = None,
# languages = None,
# root_static = ['favicon.ico', 'robots.txt'],
# domains = None,
# map_hyphen = True,
# acfe_match = r'\w+$', # legal app/ctlr/fcn/ext
# file_match = r'(\w+[-=./]?)+$', # legal file (path) name
# args_match = r'([\w@ -]+[=.]?)+$', # legal arg in args
# )
#
# See rewrite.map_url_in() and rewrite.map_url_out() for implementation details.
# This simple router set overrides only the default application name,
# but provides full rewrite functionality.
routers = dict(
# base router
BASE = dict(
default_application = 'welcome',
),
# 'admin' application router
admin = dict(
controllers = [], # don't remove controller names from admin URLs
map_hyphen = False, # don't map hyphens to underscores
),
)
# Error-handling redirects all HTTP errors (status codes >= 400) to a specified
# path. If you wish to use error-handling redirects, uncomment the tuple
# below. You can customize responses by adding a tuple entry with the first
# value in 'appName/HTTPstatusCode' format. ( Only HTTP codes >= 400 are
# routed. ) and the value as a path to redirect the user to. You may also use
# '*' as a wildcard.
#
# The error handling page is also passed the error code and ticket as
# variables. Traceback information will be stored in the ticket.
#
# routes_onerror = [
# (r'init/400', r'/init/default/login')
# ,(r'init/*', r'/init/static/fail.html')
# ,(r'*/404', r'/init/static/cantfind.html')
# ,(r'*/*', r'/init/error/index')
# ]
# specify action in charge of error handling
#
# error_handler = dict(application='error',
# controller='default',
# function='index')
# In the event that the error-handling page itself returns an error, web2py will
# fall back to its old static responses. You can customize them here.
# ErrorMessageTicket takes a string format dictionary containing (only) the
# "ticket" key.
# error_message = '<html><body><h1>Invalid request</h1></body></html>'
# error_message_ticket = '<html><body><h1>Internal error</h1>Ticket issued: <a href="/admin/default/ticket/%(ticket)s" target="_blank">%(ticket)s</a></body></html>'
def __routes_doctest():
'''
Dummy function for doctesting routes.py.
Use filter_url() to test incoming or outgoing routes;
filter_err() for error redirection.
filter_url() accepts overrides for method and remote host:
filter_url(url, method='get', remote='0.0.0.0', out=False)
filter_err() accepts overrides for application and ticket:
filter_err(status, application='app', ticket='tkt')
>>> import os
>>> import gluon.main
>>> from gluon.rewrite import load, filter_url, filter_err, get_effective_router
>>> load(routes=os.path.basename(__file__))
>>> filter_url('http://domain.com/abc', app=True)
'welcome'
>>> filter_url('http://domain.com/welcome', app=True)
'welcome'
>>> os.path.relpath(filter_url('http://domain.com/favicon.ico'))
'applications/welcome/static/favicon.ico'
>>> filter_url('http://domain.com/abc')
'/welcome/default/abc'
>>> filter_url('http://domain.com/index/abc')
"/welcome/default/index ['abc']"
>>> filter_url('http://domain.com/default/abc.css')
'/welcome/default/abc.css'
>>> filter_url('http://domain.com/default/index/abc')
"/welcome/default/index ['abc']"
>>> filter_url('http://domain.com/default/index/a bc')
"/welcome/default/index ['a bc']"
>>> filter_url('http://domain.com/admin/bad!ctl')
Traceback (most recent call last):
...
HTTP: 400 BAD REQUEST [invalid controller]
>>> filter_url('http://domain.com/admin/ctl/bad!fcn')
Traceback (most recent call last):
...
HTTP: 400 BAD REQUEST [invalid function]
>>> filter_url('http://domain.com/admin/ctl/fcn.bad!ext')
Traceback (most recent call last):
...
HTTP: 400 BAD REQUEST [invalid extension]
>>> filter_url('http://domain.com/admin/ctl/fcn/bad!arg')
Traceback (most recent call last):
...
HTTP: 400 BAD REQUEST [invalid arg <bad!arg>]
>>> filter_url('https://domain.com/app/ctr/fcn', out=True)
'/app/ctr/fcn'
>>> filter_url('https://domain.com/welcome/ctr/fcn', out=True)
'/ctr/fcn'
>>> filter_url('https://domain.com/welcome/default/fcn', out=True)
'/fcn'
>>> filter_url('https://domain.com/welcome/default/index', out=True)
'/'
>>> filter_url('https://domain.com/welcome/appadmin/index', out=True)
'/appadmin'
>>> filter_url('http://domain.com/welcome/default/fcn?query', out=True)
'/fcn?query'
>>> filter_url('http://domain.com/welcome/default/fcn#anchor', out=True)
'/fcn#anchor'
>>> filter_url('http://domain.com/welcome/default/fcn?query#anchor', out=True)
'/fcn?query#anchor'
>>> filter_url('http://domain.com/appadmin/fcn-1')
'/welcome/appadmin/fcn_1'
>>> filter_url('http://domain.com/welcome/appadmin/fcn_1', out=True)
'/appadmin/fcn-1'
>>> filter_url('http://domain.com/examples/appadmin/fcn-1')
'/examples/appadmin/fcn_1'
>>> filter_url('http://domain.com/examples/appadmin/fcn_1', out=True)
'/examples/appadmin/fcn-1'
>>> filter_url('http://domain.com/app/static/filename-with_underscore', out=True)
'/app/static/filename-with_underscore'
>>> os.path.relpath(filter_url('http://domain.com/admin/static/filename-with_underscore'))
'applications/admin/static/filename-with_underscore'
>>> filter_err(200)
200
>>> filter_err(399)
399
>>> filter_err(400)
400
'''
pass
if __name__ == '__main__':
import doctest
doctest.testmod()
| [
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... | 2.860969 | 3,323 |
#%%
from uarray.core import *
#%%
s = Scalar(Int(10))
#%%
#%%
register(Call(Always(w("a")), w("idx")), lambda a, idx: a)
#%%
a_ten = Always(s)
#%%
s = Sequence(Int(10), a_ten)
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266,... | 2.067416 | 89 |
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
| [
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33234,... | 3.47619 | 63 |
# -*- coding: utf-8 -*-
# @Author: Manuel Rodriguez <valle>
# @Date: 27-Aug-2017
# @Email: valle.mrv@gmail.com
# @Filename: admin_extras.py
# @Last modified by: valle
# @Last modified time: 02-Feb-2018
# @License: Apache license vesion 2.0
from django import template
from django.db.models import Q
try:
from django.core.urlresolvers import reverse
except ImportError:
from django.urls import reverse
from adminshop.models import Testeo, Compras, Presupuesto
import json
import sys
register = template.Library()
| [
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13... | 2.810526 | 190 |
#!/usr/bin/env python
# Aesara tutorial
# Solution to Exercise in section 'Using the GPU'
# 1. Raw results
import numpy as np
import aesara
import aesara.tensor as tt
aesara.config.floatX = "float32"
rng = np.random
N = 400
feats = 784
D = (
rng.randn(N, feats).astype(aesara.config.floatX),
rng.randint(size=N, low=0, high=2).astype(aesara.config.floatX),
)
training_steps = 10000
# Declare Aesara symbolic variables
x = aesara.shared(D[0], name="x")
y = aesara.shared(D[1], name="y")
w = aesara.shared(rng.randn(feats).astype(aesara.config.floatX), name="w")
b = aesara.shared(np.asarray(0.0, dtype=aesara.config.floatX), name="b")
x.tag.test_value = D[0]
y.tag.test_value = D[1]
# print "Initial model:"
# print w.get_value(), b.get_value()
# Construct Aesara expression graph
p_1 = 1 / (1 + tt.exp(-tt.dot(x, w) - b)) # Probability of having a one
prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
xent = -y * tt.log(p_1) - (1 - y) * tt.log(1 - p_1) # Cross-entropy
cost = tt.cast(xent.mean(), "float32") + 0.01 * (w ** 2).sum() # The cost to optimize
gw, gb = tt.grad(cost, [w, b])
# Compile expressions to functions
train = aesara.function(
inputs=[],
outputs=[prediction, xent],
updates=[(w, w - 0.01 * gw), (b, b - 0.01 * gb)],
name="train",
)
predict = aesara.function(inputs=[], outputs=prediction, name="predict")
if any(
[
n.op.__class__.__name__ in ["Gemv", "CGemv", "Gemm", "CGemm"]
for n in train.maker.fgraph.toposort()
]
):
print("Used the cpu")
elif any(
[
n.op.__class__.__name__ in ["GpuGemm", "GpuGemv"]
for n in train.maker.fgraph.toposort()
]
):
print("Used the gpu")
else:
print("ERROR, not able to tell if aesara used the cpu or the gpu")
print(train.maker.fgraph.toposort())
for i in range(training_steps):
pred, err = train()
# print "Final model:"
# print w.get_value(), b.get_value()
print("target values for D")
print(D[1])
print("prediction on D")
print(predict())
"""
# 2. Profiling
# 2.1 Profiling for CPU computations
# In your terminal, type:
$ THEANO_FLAGS=profile=True,device=cpu python using_gpu_solution_1.py
# You'll see first the output of the script:
Used the cpu
target values for D
prediction on D
# Followed by the output of profiling.. You'll see profiling results for each function
# in the script, followed by a summary for all functions.
# We'll show here only the summary:
Results were produced using an Intel(R) Core(TM) i7-5930K CPU @ 3.50GHz
Function profiling
==================
Message: Sum of all(2) printed profiles at exit excluding Scan op profile.
Time in 10001 calls to Function.__call__: 1.300452e+00s
Time in Function.fn.__call__: 1.215823e+00s (93.492%)
Time in thunks: 1.157602e+00s (89.015%)
Total compile time: 8.922548e-01s
Number of Apply nodes: 17
Aesara Optimizer time: 6.270301e-01s
Aesara validate time: 5.993605e-03s
Aesara Linker time (includes C, CUDA code generation/compiling): 2.949309e-02s
Import time 3.543139e-03s
Time in all call to aesara.grad() 1.848292e-02s
Time since aesara import 2.864s
Class
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>
64.5% 64.5% 0.747s 3.73e-05s C 20001 3 aesara.tensor.blas_c.CGemv
33.1% 97.7% 0.384s 4.79e-06s C 80001 9 aesara.tensor.elemwise.Elemwise
1.0% 98.6% 0.011s 1.14e-06s C 10000 1 aesara.tensor.elemwise.Sum
0.7% 99.4% 0.009s 2.85e-07s C 30001 4 aesara.tensor.elemwise.DimShuffle
0.3% 99.7% 0.004s 3.64e-07s C 10001 2 aesara.tensor.basic.AllocEmpty
0.3% 100.0% 0.004s 1.78e-07s C 20001 3 aesara.compile.ops.Shape_i
... (remaining 0 Classes account for 0.00%(0.00s) of the runtime)
Ops
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>
64.5% 64.5% 0.747s 3.73e-05s C 20001 3 CGemv{inplace}
18.7% 83.2% 0.217s 2.17e-05s C 10000 1 Elemwise{Composite{((i0 * scalar_softplus(i1)) - (i2 * i3 * scalar_softplus(i4)))}}[(0, 4)]
8.9% 92.1% 0.103s 1.03e-05s C 10000 1 Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((scalar_sigmoid((-i0)) * i1 * i4) / i3))}}[(0, 0)]
4.3% 96.4% 0.050s 4.98e-06s C 10000 1 Elemwise{Composite{GT(scalar_sigmoid(i0), i1)}}
1.0% 97.4% 0.011s 1.14e-06s C 10000 1 Sum{acc_dtype=float64}
0.5% 97.9% 0.006s 2.83e-07s C 20001 3 InplaceDimShuffle{x}
0.4% 98.3% 0.004s 4.22e-07s C 10000 1 Elemwise{sub,no_inplace}
0.3% 98.6% 0.004s 3.70e-07s C 10000 1 Elemwise{neg,no_inplace}
0.3% 98.9% 0.004s 3.64e-07s C 10001 2 AllocEmpty{dtype='float32'}
0.3% 99.2% 0.004s 1.78e-07s C 20001 3 Shape_i{0}
0.2% 99.5% 0.003s 2.88e-07s C 10000 1 InplaceDimShuffle{1,0}
0.2% 99.7% 0.003s 2.65e-07s C 10000 1 Elemwise{Composite{((-i0) - i1)}}[(0, 0)]
0.2% 99.9% 0.002s 1.98e-07s C 10000 1 Elemwise{Cast{float32}}
0.1% 100.0% 0.002s 1.54e-07s C 10000 1 Elemwise{Composite{(i0 - (i1 * i2))}}[(0, 0)]
0.0% 100.0% 0.000s 4.77e-06s C 1 1 Elemwise{Composite{GT(scalar_sigmoid((-((-i0) - i1))), i2)}}
... (remaining 0 Ops account for 0.00%(0.00s) of the runtime)
Apply
------
<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>
34.0% 34.0% 0.394s 3.94e-05s 10000 7 CGemv{inplace}(AllocEmpty{dtype='float32'}.0, TensorConstant{1.0}, x, w, TensorConstant{0.0})
30.5% 64.5% 0.353s 3.53e-05s 10000 15 CGemv{inplace}(w, TensorConstant{-0.00999999977648}, x.T, Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((scalar_sigmoid((-i0)) * i1 * i4) / i3))}}[(0, 0)].0, TensorConstant{0.999800026417})
18.7% 83.2% 0.217s 2.17e-05s 10000 12 Elemwise{Composite{((i0 * scalar_softplus(i1)) - (i2 * i3 * scalar_softplus(i4)))}}[(0, 4)](y, Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0, TensorConstant{(1,) of -1.0}, Elemwise{sub,no_inplace}.0, Elemwise{neg,no_inplace}.0)
8.9% 92.1% 0.103s 1.03e-05s 10000 13 Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((scalar_sigmoid((-i0)) * i1 * i4) / i3))}}[(0, 0)](Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0, TensorConstant{(1,) of -1.0}, y, Elemwise{Cast{float32}}.0, Elemwise{sub,no_inplace}.0)
4.3% 96.4% 0.050s 4.98e-06s 10000 11 Elemwise{Composite{GT(scalar_sigmoid(i0), i1)}}(Elemwise{neg,no_inplace}.0, TensorConstant{(1,) of 0.5})
1.0% 97.4% 0.011s 1.14e-06s 10000 14 Sum{acc_dtype=float64}(Elemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((scalar_sigmoid((-i0)) * i1 * i4) / i3))}}[(0, 0)].0)
0.4% 97.8% 0.004s 4.22e-07s 10000 4 Elemwise{sub,no_inplace}(TensorConstant{(1,) of 1.0}, y)
0.3% 98.1% 0.004s 3.76e-07s 10000 0 InplaceDimShuffle{x}(b)
0.3% 98.4% 0.004s 3.70e-07s 10000 10 Elemwise{neg,no_inplace}(Elemwise{Composite{((-i0) - i1)}}[(0, 0)].0)
0.3% 98.7% 0.004s 3.64e-07s 10000 5 AllocEmpty{dtype='float32'}(Shape_i{0}.0)
0.2% 99.0% 0.003s 2.88e-07s 10000 2 InplaceDimShuffle{1,0}(x)
0.2% 99.2% 0.003s 2.65e-07s 10000 9 Elemwise{Composite{((-i0) - i1)}}[(0, 0)](CGemv{inplace}.0, InplaceDimShuffle{x}.0)
0.2% 99.4% 0.002s 2.21e-07s 10000 1 Shape_i{0}(x)
0.2% 99.6% 0.002s 1.98e-07s 10000 8 Elemwise{Cast{float32}}(InplaceDimShuffle{x}.0)
0.2% 99.7% 0.002s 1.90e-07s 10000 6 InplaceDimShuffle{x}(Shape_i{0}.0)
0.1% 99.9% 0.002s 1.54e-07s 10000 16 Elemwise{Composite{(i0 - (i1 * i2))}}[(0, 0)](b, TensorConstant{0.00999999977648}, Sum{acc_dtype=float64}.0)
0.1% 100.0% 0.001s 1.34e-07s 10000 3 Shape_i{0}(y)
0.0% 100.0% 0.000s 3.89e-05s 1 3 CGemv{inplace}(AllocEmpty{dtype='float32'}.0, TensorConstant{1.0}, x, w, TensorConstant{0.0})
0.0% 100.0% 0.000s 4.77e-06s 1 4 Elemwise{Composite{GT(scalar_sigmoid((-((-i0) - i1))), i2)}}(CGemv{inplace}.0, InplaceDimShuffle{x}.0, TensorConstant{(1,) of 0.5})
0.0% 100.0% 0.000s 1.19e-06s 1 0 InplaceDimShuffle{x}(b)
... (remaining 2 Apply instances account for 0.00%(0.00s) of the runtime)
# 2.2 Profiling for GPU computations
# In your terminal, type:
$ CUDA_LAUNCH_BLOCKING=1 THEANO_FLAGS=profile=True,device=cuda python using_gpu_solution_1.py
# You'll see first the output of the script:
Used the gpu
target values for D
prediction on D
Results were produced using a GeForce GTX TITAN X
# Profiling summary for all functions:
Function profiling
==================
Message: Sum of all(2) printed profiles at exit excluding Scan op profile.
Time in 10001 calls to Function.__call__: 4.181247e+00s
Time in Function.fn.__call__: 4.081113e+00s (97.605%)
Time in thunks: 3.915566e+00s (93.646%)
Total compile time: 9.256095e+00s
Number of Apply nodes: 21
Aesara Optimizer time: 9.996419e-01s
Aesara validate time: 6.523132e-03s
Aesara Linker time (includes C, CUDA code generation/compiling): 8.239602e+00s
Import time 4.228115e-03s
Time in all call to aesara.grad() 3.286195e-02s
Time since aesara import 15.415s
Class
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>
59.5% 59.5% 2.329s 1.16e-04s C 20001 3 aesara.sandbox.gpuarray.blas.GpuGemv
29.8% 89.3% 1.166s 1.30e-05s C 90001 10 aesara.sandbox.gpuarray.elemwise.GpuElemwise
4.1% 93.4% 0.162s 8.10e-06s C 20001 3 aesara.sandbox.gpuarray.basic_ops.HostFromGpu
3.3% 96.7% 0.131s 1.31e-05s C 10000 1 aesara.sandbox.gpuarray.elemwise.GpuCAReduceCuda
1.6% 98.3% 0.061s 6.10e-06s C 10000 1 aesara.sandbox.gpuarray.basic_ops.GpuFromHost
0.8% 99.1% 0.033s 1.09e-06s C 30001 4 aesara.sandbox.gpuarray.elemwise.GpuDimShuffle
0.7% 99.8% 0.026s 2.59e-06s C 10001 2 aesara.sandbox.gpuarray.basic_ops.GpuAllocEmpty
0.2% 100.0% 0.008s 3.95e-07s C 20001 3 aesara.compile.ops.Shape_i
... (remaining 0 Classes account for 0.00%(0.00s) of the runtime)
Ops
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>
59.5% 59.5% 2.329s 1.16e-04s C 20001 3 GpuGemv{inplace=True}
4.1% 63.6% 0.162s 8.10e-06s C 20001 3 HostFromGpu(gpuarray)
4.0% 67.6% 0.157s 1.57e-05s C 10000 1 GpuElemwise{Composite{((i0 * scalar_softplus(i1)) - (i2 * i3 * scalar_softplus(i4)))}}[]<gpuarray>
3.8% 71.4% 0.149s 1.49e-05s C 10000 1 GpuElemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)]<gpuarray>
3.7% 75.1% 0.144s 1.44e-05s C 10000 1 GpuElemwise{sub,no_inplace}
3.6% 78.7% 0.141s 1.41e-05s C 10000 1 GpuElemwise{gt,no_inplace}
3.4% 82.1% 0.133s 1.33e-05s C 10000 1 GpuElemwise{Cast{float32}}[]<gpuarray>
3.4% 85.5% 0.133s 1.33e-05s C 10000 1 GpuElemwise{Composite{((-i0) - i1)}}[(0, 0)]<gpuarray>
3.3% 88.8% 0.131s 1.31e-05s C 10000 1 GpuCAReduceCuda{add}
2.9% 91.7% 0.112s 1.12e-05s C 10000 1 GpuElemwise{neg,no_inplace}
2.6% 94.3% 0.102s 1.02e-05s C 10000 1 GpuElemwise{Composite{(i0 - (i1 * i2))}}[(0, 0)]<gpuarray>
2.5% 96.7% 0.096s 9.63e-06s C 10000 1 GpuElemwise{ScalarSigmoid}[(0, 0)]<gpuarray>
1.6% 98.3% 0.061s 6.10e-06s C 10000 1 GpuFromHost<None>
0.7% 99.0% 0.026s 2.59e-06s C 10001 2 GpuAllocEmpty{dtype='float32', context_name=None}
0.5% 99.5% 0.021s 1.06e-06s C 20001 3 InplaceGpuDimShuffle{x}
0.3% 99.8% 0.011s 1.14e-06s C 10000 1 InplaceGpuDimShuffle{1,0}
0.2% 100.0% 0.008s 3.95e-07s C 20001 3 Shape_i{0}
0.0% 100.0% 0.000s 2.00e-05s C 1 1 GpuElemwise{Composite{GT(scalar_sigmoid((-((-i0) - i1))), i2)}}[]<gpuarray>
... (remaining 0 Ops account for 0.00%(0.00s) of the runtime)
Apply
------
<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>
55.0% 55.0% 2.154s 2.15e-04s 10000 7 GpuGemv{inplace=True}(GpuAllocEmpty{dtype='float32', context_name=None}.0, TensorConstant{1.0}, x, w, TensorConstant{0.0})
4.5% 59.5% 0.176s 1.76e-05s 10000 18 GpuGemv{inplace=True}(w, TensorConstant{-0.00999999977648}, InplaceGpuDimShuffle{1,0}.0, GpuElemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)]<gpuarray>.0, TensorConstant{0.999800026417})
4.0% 63.5% 0.157s 1.57e-05s 10000 12 GpuElemwise{Composite{((i0 * scalar_softplus(i1)) - (i2 * i3 * scalar_softplus(i4)))}}[]<gpuarray>(y, GpuElemwise{Composite{((-i0) - i1)}}[(0, 0)]<gpuarray>.0, GpuArrayConstant{[-1.]}, GpuElemwise{sub,no_inplace}.0, GpuElemwise{neg,no_inplace}.0)
3.8% 67.3% 0.149s 1.49e-05s 10000 15 GpuElemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)]<gpuarray>(GpuElemwise{Composite{((-i0) - i1)}}[(0, 0)]<gpuarray>.0, GpuArrayConstant{[-1.]}, y, GpuElemwise{Cast{float32}}[]<gpuarray>.0, GpuElemwise{ScalarSigmoid}[(0, 0)]<gpuarray>.0, GpuElemwise{sub,no_inplace}.0)
3.7% 71.0% 0.144s 1.44e-05s 10000 4 GpuElemwise{sub,no_inplace}(GpuArrayConstant{[ 1.]}, y)
3.6% 74.6% 0.141s 1.41e-05s 10000 16 GpuElemwise{gt,no_inplace}(GpuElemwise{ScalarSigmoid}[(0, 0)]<gpuarray>.0, GpuArrayConstant{[ 0.5]})
3.4% 78.0% 0.133s 1.33e-05s 10000 10 GpuElemwise{Cast{float32}}[]<gpuarray>(InplaceGpuDimShuffle{x}.0)
3.4% 81.4% 0.133s 1.33e-05s 10000 9 GpuElemwise{Composite{((-i0) - i1)}}[(0, 0)]<gpuarray>(GpuGemv{inplace=True}.0, InplaceGpuDimShuffle{x}.0)
3.3% 84.7% 0.131s 1.31e-05s 10000 17 GpuCAReduceCuda{add}(GpuElemwise{Composite{(((scalar_sigmoid(i0) * i1 * i2) / i3) - ((i4 * i1 * i5) / i3))}}[(0, 0)]<gpuarray>.0)
2.9% 87.5% 0.112s 1.12e-05s 10000 11 GpuElemwise{neg,no_inplace}(GpuElemwise{Composite{((-i0) - i1)}}[(0, 0)]<gpuarray>.0)
2.6% 90.1% 0.102s 1.02e-05s 10000 20 GpuElemwise{Composite{(i0 - (i1 * i2))}}[(0, 0)]<gpuarray>(b, GpuArrayConstant{0.00999999977648}, GpuCAReduceCuda{add}.0)
2.5% 92.6% 0.096s 9.63e-06s 10000 13 GpuElemwise{ScalarSigmoid}[(0, 0)]<gpuarray>(GpuElemwise{neg,no_inplace}.0)
2.3% 94.9% 0.090s 9.04e-06s 10000 19 HostFromGpu(gpuarray)(GpuElemwise{gt,no_inplace}.0)
1.8% 96.7% 0.072s 7.16e-06s 10000 14 HostFromGpu(gpuarray)(GpuElemwise{Composite{((i0 * scalar_softplus(i1)) - (i2 * i3 * scalar_softplus(i4)))}}[]<gpuarray>.0)
1.6% 98.3% 0.061s 6.10e-06s 10000 6 GpuFromHost<None>(Shape_i{0}.0)
0.7% 99.0% 0.026s 2.59e-06s 10000 5 GpuAllocEmpty{dtype='float32', context_name=None}(Shape_i{0}.0)
0.3% 99.3% 0.013s 1.33e-06s 10000 0 InplaceGpuDimShuffle{x}(b)
0.3% 99.6% 0.011s 1.14e-06s 10000 2 InplaceGpuDimShuffle{1,0}(x)
0.2% 99.8% 0.008s 7.94e-07s 10000 8 InplaceGpuDimShuffle{x}(GpuFromHost<None>.0)
0.1% 99.9% 0.005s 5.27e-07s 10000 1 Shape_i{0}(x)
... (remaining 7 Apply instances account for 0.07%(0.00s) of the runtime)
# 3. Conclusions
Examine and compare 'Ops' summaries for CPU and GPU. Usually GPU ops 'GpuFromHost' and 'HostFromGpu' by themselves
consume a large amount of extra time, but by making as few as possible data transfers between GPU and CPU, you can minimize their overhead.
Notice that each of the GPU ops consumes more time than its CPU counterpart. This is because the ops operate on small inputs;
if you increase the input data size (e.g. set N = 4000), you will see a gain from using the GPU.
"""
| [
2,
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2482,
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11748,
299,
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355,
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198,
11748... | 1.812566 | 9,470 |
def translate_bbox(bbox, y_offset=0, x_offset=0):
"""Translate bounding boxes.
This method is mainly used together with image transforms, such as padding
and cropping, which translates the left top point of the image from
coordinate :math:`(0, 0)` to coordinate
:math:`(y, x) = (y_{offset}, x_{offset})`.
The bounding boxes are expected to be packed into a two dimensional
tensor of shape :math:`(R, 4)`, where :math:`R` is the number of
bounding boxes in the image. The second axis represents attributes of
the bounding box. They are :math:`(y_{min}, x_{min}, y_{max}, x_{max})`,
where the four attributes are coordinates of the top left and the
bottom right vertices.
Args:
bbox (~numpy.ndarray): Bounding boxes to be transformed. The shape is
:math:`(R, 4)`. :math:`R` is the number of bounding boxes.
y_offset (int or float): The offset along y axis.
x_offset (int or float): The offset along x axis.
Returns:
~numpy.ndarray:
Bounding boxes translated according to the given offsets.
"""
out_bbox = bbox.copy()
out_bbox[:, :2] += (y_offset, x_offset)
out_bbox[:, 2:] += (y_offset, x_offset)
return out_bbox
| [
4299,
15772,
62,
65,
3524,
7,
65,
3524,
11,
331,
62,
28968,
28,
15,
11,
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62,
28968,
28,
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2599,
198,
220,
220,
220,
37227,
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278,
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13,
628,
220,
220,
220,
770,
2446,
318,
8384,
973,
1978,
351,
2939,
... | 2.723684 | 456 |
import os
from configparser import ConfigParser
from helper.helper_web import get_browser
| [
11748,
28686,
198,
6738,
4566,
48610,
1330,
17056,
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198,
6738,
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13,
2978,
525,
62,
12384,
1330,
651,
62,
40259,
628,
220,
220,
220,
220
] | 3.653846 | 26 |
from pathlib import Path
from tkinter import Frame, Label
from recipe_organizer.events.event import Event, EventType
from recipe_organizer.events.event_observer import EventObserver
from recipe_organizer.events.event_publisher import EventPublisher
from recipe_organizer.gui.interfaces.widget_container import WidgetContainer
from recipe_organizer.gui.recipe_summary.recipe_summary import RecipeSummary
from recipe_organizer.recipe.recipe import Recipe
| [
6738,
3108,
8019,
1330,
10644,
198,
6738,
256,
74,
3849,
1330,
25184,
11,
36052,
198,
198,
6738,
8364,
62,
9971,
7509,
13,
31534,
13,
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1330,
8558,
11,
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6030,
198,
6738,
8364,
62,
9971,
7509,
13,
31534,
13,
15596,
62,
672,
... | 3.855932 | 118 |
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
| [
2,
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12,
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329,
3307,
13,
198,
2,
198,
2,
30628,
55,
12,
34156,
12,
33234,... | 3.47619 | 63 |
k=0
while k!=1:
print(k)
k+=1
| [
74,
28,
15,
201,
198,
4514,
479,
0,
28,
16,
25,
201,
198,
220,
220,
220,
3601,
7,
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8,
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220,
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47932,
16,
201,
198
] | 1.4 | 30 |
# ________
# /
# \ /
# \ /
# \/
import random
import textwrap
import emd_mean
import AdvEMDpy
import emd_basis
import emd_utils
import numpy as np
import pandas as pd
import cvxpy as cvx
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.integrate import odeint
from scipy.ndimage import gaussian_filter
from emd_utils import time_extension, Utility
from scipy.interpolate import CubicSpline
from emd_hilbert import Hilbert, hilbert_spectrum
from emd_preprocess import Preprocess
from emd_mean import Fluctuation
from AdvEMDpy import EMD
# alternate packages
from PyEMD import EMD as pyemd0215
import emd as emd040
sns.set(style='darkgrid')
pseudo_alg_time = np.linspace(0, 2 * np.pi, 1001)
pseudo_alg_time_series = np.sin(pseudo_alg_time) + np.sin(5 * pseudo_alg_time)
pseudo_utils = Utility(time=pseudo_alg_time, time_series=pseudo_alg_time_series)
# plot 0 - addition
fig = plt.figure(figsize=(9, 4))
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('First Iteration of Sifting Algorithm')
plt.plot(pseudo_alg_time, pseudo_alg_time_series, label=r'$h_{(1,0)}(t)$', zorder=1)
plt.scatter(pseudo_alg_time[pseudo_utils.max_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.max_bool_func_1st_order_fd()],
c='r', label=r'$M(t_i)$', zorder=2)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) + 1, '--', c='r', label=r'$\tilde{h}_{(1,0)}^M(t)$', zorder=4)
plt.scatter(pseudo_alg_time[pseudo_utils.min_bool_func_1st_order_fd()],
pseudo_alg_time_series[pseudo_utils.min_bool_func_1st_order_fd()],
c='c', label=r'$m(t_j)$', zorder=3)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time) - 1, '--', c='c', label=r'$\tilde{h}_{(1,0)}^m(t)$', zorder=5)
plt.plot(pseudo_alg_time, np.sin(pseudo_alg_time), '--', c='purple', label=r'$\tilde{h}_{(1,0)}^{\mu}(t)$', zorder=5)
plt.yticks(ticks=[-2, -1, 0, 1, 2])
plt.xticks(ticks=[0, np.pi, 2 * np.pi],
labels=[r'0', r'$\pi$', r'$2\pi$'])
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.95, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/pseudo_algorithm.png')
plt.show()
knots = np.arange(12)
time = np.linspace(0, 11, 1101)
basis = emd_basis.Basis(time=time, time_series=time)
b_spline_basis = basis.cubic_b_spline(knots)
chsi_basis = basis.chsi_basis(knots)
# plot 1
plt.title('Non-Natural Cubic B-Spline Bases at Boundary')
plt.plot(time[500:], b_spline_basis[2, 500:].T, '--', label=r'$ B_{-3,4}(t) $')
plt.plot(time[500:], b_spline_basis[3, 500:].T, '--', label=r'$ B_{-2,4}(t) $')
plt.plot(time[500:], b_spline_basis[4, 500:].T, '--', label=r'$ B_{-1,4}(t) $')
plt.plot(time[500:], b_spline_basis[5, 500:].T, '--', label=r'$ B_{0,4}(t) $')
plt.plot(time[500:], b_spline_basis[6, 500:].T, '--', label=r'$ B_{1,4}(t) $')
plt.xticks([5, 6], [r'$ \tau_0 $', r'$ \tau_1 $'])
plt.xlim(4.4, 6.6)
plt.plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
plt.legend(loc='upper left')
plt.savefig('jss_figures/boundary_bases.png')
plt.show()
# plot 1a - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
knots_uniform = np.linspace(0, 2 * np.pi, 51)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs = emd.empirical_mode_decomposition(knots=knots_uniform, edge_effect='anti-symmetric', verbose=False)[0]
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Uniform Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Uniform Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Uniform Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots_uniform[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots_uniform)):
axs[i].plot(knots_uniform[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_uniform.png')
plt.show()
# plot 1b - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=1, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Statically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Statically Optimised Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Statically Optimised Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots)):
axs[i].plot(knots[j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_1.png')
plt.show()
# plot 1c - addition
knot_demonstrate_time = np.linspace(0, 2 * np.pi, 1001)
knot_demonstrate_time_series = np.sin(knot_demonstrate_time) + np.sin(5 * knot_demonstrate_time)
emd = EMD(time=knot_demonstrate_time, time_series=knot_demonstrate_time_series)
imfs, _, _, _, knots, _, _ = emd.empirical_mode_decomposition(edge_effect='anti-symmetric',
optimise_knots=2, verbose=False)
fig, axs = plt.subplots(3, 1)
fig.subplots_adjust(hspace=0.6)
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Time Series and Dynamically Optimised Knots')
axs[0].plot(knot_demonstrate_time, knot_demonstrate_time_series, Linewidth=2, zorder=100)
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].set_title('IMF 1 and Dynamically Knots')
axs[1].plot(knot_demonstrate_time, imfs[1, :], Linewidth=2, zorder=100)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[1].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[2].set_title('IMF 2 and Dynamically Knots')
axs[2].plot(knot_demonstrate_time, imfs[2, :], Linewidth=2, zorder=100)
axs[2].set_yticks(ticks=[-2, 0, 2])
axs[2].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[2].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[0].plot(knots[0][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[0].legend(loc='lower left')
axs[1].plot(knots[1][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
axs[2].plot(knots[2][0] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey', label='Knots')
for i in range(3):
for j in range(1, len(knots[i])):
axs[i].plot(knots[i][j] * np.ones(101), np.linspace(-2, 2, 101), '--', c='grey')
plt.savefig('jss_figures/knot_2.png')
plt.show()
# plot 1d - addition
window = 81
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Filtering Demonstration')
axs[1].set_title('Zoomed Region')
preprocess_time = pseudo_alg_time.copy()
np.random.seed(1)
random.seed(1)
preprocess_time_series = pseudo_alg_time_series + np.random.normal(0, 0.1, len(preprocess_time))
for i in random.sample(range(1000), 500):
preprocess_time_series[i] += np.random.normal(0, 1)
preprocess = Preprocess(time=preprocess_time, time_series=preprocess_time_series)
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[0].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[0].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[0].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[0].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple', label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.mean_filter(window_width=window)[1], label=textwrap.fill('Mean filter', 12))
axs[1].plot(preprocess_time, preprocess.median_filter(window_width=window)[1], label=textwrap.fill('Median filter', 13))
axs[1].plot(preprocess_time, preprocess.winsorize(window_width=window, a=0.8)[1], label=textwrap.fill('Windsorize filter', 12))
axs[1].plot(preprocess_time, preprocess.winsorize_interpolate(window_width=window, a=0.8)[1],
label=textwrap.fill('Windsorize interpolation filter', 14))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.90)[1], c='grey',
label=textwrap.fill('Quantile window', 12))
axs[1].plot(preprocess_time, preprocess.quantile_filter(window_width=window, q=0.10)[1], c='grey')
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_filter.png')
plt.show()
# plot 1e - addition
fig, axs = plt.subplots(2, 1)
fig.subplots_adjust(hspace=0.4)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
axs[0].set_title('Preprocess Smoothing Demonstration')
axs[1].set_title('Zoomed Region')
axs[0].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[0].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[0].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[0].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
downsampled_and_decimated = preprocess.downsample()
axs[0].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 11))
downsampled = preprocess.downsample(decimate=False)
axs[0].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), -3 * np.ones(101), '--', c='black',
label=textwrap.fill('Zoomed region', 10))
axs[0].plot(np.linspace(0.85 * np.pi, 1.15 * np.pi, 101), 3 * np.ones(101), '--', c='black')
axs[0].plot(0.85 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].plot(1.15 * np.pi * np.ones(101), np.linspace(-3, 3, 101), '--', c='black')
axs[0].set_yticks(ticks=[-2, 0, 2])
axs[0].set_xticks(ticks=[0, np.pi, 2 * np.pi])
axs[0].set_xticklabels(labels=['0', r'$\pi$', r'$2\pi$'])
axs[1].plot(preprocess_time, preprocess_time_series, label='x(t)')
axs[1].plot(pseudo_alg_time, pseudo_alg_time_series, '--', c='purple',
label=textwrap.fill('Noiseless time series', 12))
axs[1].plot(preprocess_time, preprocess.hp()[1],
label=textwrap.fill('Hodrick-Prescott smoothing', 12))
axs[1].plot(preprocess_time, preprocess.hw(order=51)[1],
label=textwrap.fill('Henderson-Whittaker smoothing', 13))
axs[1].plot(downsampled_and_decimated[0], downsampled_and_decimated[1],
label=textwrap.fill('Downsampled & decimated', 13))
axs[1].plot(downsampled[0], downsampled[1],
label=textwrap.fill('Downsampled', 13))
axs[1].set_xlim(0.85 * np.pi, 1.15 * np.pi)
axs[1].set_ylim(-3, 3)
axs[1].set_yticks(ticks=[-2, 0, 2])
axs[1].set_xticks(ticks=[np.pi])
axs[1].set_xticklabels(labels=[r'$\pi$'])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.06, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, -0.15))
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.06, box_1.y0, box_1.width * 0.85, box_1.height])
plt.savefig('jss_figures/preprocess_smooth.png')
plt.show()
# plot 2
fig, axs = plt.subplots(1, 2, sharey=True)
axs[0].set_title('Cubic B-Spline Bases')
axs[0].plot(time, b_spline_basis[2, :].T, '--', label='Basis 1')
axs[0].plot(time, b_spline_basis[3, :].T, '--', label='Basis 2')
axs[0].plot(time, b_spline_basis[4, :].T, '--', label='Basis 3')
axs[0].plot(time, b_spline_basis[5, :].T, '--', label='Basis 4')
axs[0].legend(loc='upper left')
axs[0].plot(5 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].plot(6 * np.ones(100), np.linspace(-0.2, 0.8, 100), 'k-')
axs[0].set_xticks([5, 6])
axs[0].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[0].set_xlim(4.5, 6.5)
axs[1].set_title('Cubic Hermite Spline Bases')
axs[1].plot(time, chsi_basis[10, :].T, '--')
axs[1].plot(time, chsi_basis[11, :].T, '--')
axs[1].plot(time, chsi_basis[12, :].T, '--')
axs[1].plot(time, chsi_basis[13, :].T, '--')
axs[1].plot(5 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].plot(6 * np.ones(100), np.linspace(-0.2, 1.2, 100), 'k-')
axs[1].set_xticks([5, 6])
axs[1].set_xticklabels([r'$ \tau_k $', r'$ \tau_{k+1} $'])
axs[1].set_xlim(4.5, 6.5)
plt.savefig('jss_figures/comparing_bases.png')
plt.show()
# plot 3
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_dash = maxima_y[-1] * np.ones_like(max_dash_time)
min_dash_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_dash = minima_y[-1] * np.ones_like(min_dash_time)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
max_discard = maxima_y[-1]
max_discard_time = minima_x[-1] - maxima_x[-1] + minima_x[-1]
max_discard_dash_time = np.linspace(max_discard_time - width, max_discard_time + width, 101)
max_discard_dash = max_discard * np.ones_like(max_discard_dash_time)
dash_2_time = np.linspace(minima_x[-1], max_discard_time, 101)
dash_2 = np.linspace(minima_y[-1], max_discard, 101)
end_point_time = time[-1]
end_point = time_series[-1]
time_reflect = np.linspace((5 - a) * np.pi, (5 + a) * np.pi, 101)
time_series_reflect = np.flip(np.cos(np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)) + np.cos(5 * np.linspace((5 - 2.6 * a) * np.pi,
(5 - a) * np.pi, 101)))
time_series_anti_reflect = time_series_reflect[0] - time_series_reflect
utils = emd_utils.Utility(time=time, time_series=time_series_anti_reflect)
anti_max_bool = utils.max_bool_func_1st_order_fd()
anti_max_point_time = time_reflect[anti_max_bool]
anti_max_point = time_series_anti_reflect[anti_max_bool]
utils = emd_utils.Utility(time=time, time_series=time_series_reflect)
no_anchor_max_time = time_reflect[utils.max_bool_func_1st_order_fd()]
no_anchor_max = time_series_reflect[utils.max_bool_func_1st_order_fd()]
point_1 = 5.4
length_distance = np.linspace(maxima_y[-1], minima_y[-1], 101)
length_distance_time = point_1 * np.pi * np.ones_like(length_distance)
length_time = np.linspace(point_1 * np.pi - width, point_1 * np.pi + width, 101)
length_top = maxima_y[-1] * np.ones_like(length_time)
length_bottom = minima_y[-1] * np.ones_like(length_time)
point_2 = 5.2
length_distance_2 = np.linspace(time_series[-1], minima_y[-1], 101)
length_distance_time_2 = point_2 * np.pi * np.ones_like(length_distance_2)
length_time_2 = np.linspace(point_2 * np.pi - width, point_2 * np.pi + width, 101)
length_top_2 = time_series[-1] * np.ones_like(length_time_2)
length_bottom_2 = minima_y[-1] * np.ones_like(length_time_2)
symmetry_axis_1_time = minima_x[-1] * np.ones(101)
symmetry_axis_2_time = time[-1] * np.ones(101)
symmetry_axis = np.linspace(-2, 2, 101)
end_time = np.linspace(time[-1] - width, time[-1] + width, 101)
end_signal = time_series[-1] * np.ones_like(end_time)
anti_symmetric_time = np.linspace(time[-1] - 0.5, time[-1] + 0.5, 101)
anti_symmetric_signal = time_series[-1] * np.ones_like(anti_symmetric_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Symmetry Edge Effects Example')
plt.plot(time_reflect, time_series_reflect, 'g--', LineWidth=2, label=textwrap.fill('Symmetric signal', 10))
plt.plot(time_reflect[:51], time_series_anti_reflect[:51], '--', c='purple', LineWidth=2,
label=textwrap.fill('Anti-symmetric signal', 10))
plt.plot(max_dash_time, max_dash, 'k-')
plt.plot(min_dash_time, min_dash, 'k-')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(length_distance_time, length_distance, 'k--')
plt.plot(length_distance_time_2, length_distance_2, 'k--')
plt.plot(length_time, length_top, 'k-')
plt.plot(length_time, length_bottom, 'k-')
plt.plot(length_time_2, length_top_2, 'k-')
plt.plot(length_time_2, length_bottom_2, 'k-')
plt.plot(end_time, end_signal, 'k-')
plt.plot(symmetry_axis_1_time, symmetry_axis, 'r--', zorder=1)
plt.plot(anti_symmetric_time, anti_symmetric_signal, 'r--', zorder=1)
plt.plot(symmetry_axis_2_time, symmetry_axis, 'r--', label=textwrap.fill('Axes of symmetry', 10), zorder=1)
plt.text(5.1 * np.pi, -0.7, r'$\beta$L')
plt.text(5.34 * np.pi, -0.05, 'L')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(max_discard_time, max_discard, c='purple', zorder=4, label=textwrap.fill('Symmetric Discard maxima', 10))
plt.scatter(end_point_time, end_point, c='orange', zorder=4, label=textwrap.fill('Symmetric Anchor maxima', 10))
plt.scatter(anti_max_point_time, anti_max_point, c='green', zorder=4, label=textwrap.fill('Anti-Symmetric maxima', 10))
plt.scatter(no_anchor_max_time, no_anchor_max, c='gray', zorder=4, label=textwrap.fill('Symmetric maxima', 10))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_symmetry_anti.png')
plt.show()
# plot 4
a = 0.21
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
max_dash_1 = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_dash_2 = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_dash_time_1 = maxima_x[-1] * np.ones_like(max_dash_1)
max_dash_time_2 = maxima_x[-2] * np.ones_like(max_dash_1)
min_dash_1 = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_dash_2 = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_dash_time_1 = minima_x[-1] * np.ones_like(min_dash_1)
min_dash_time_2 = minima_x[-2] * np.ones_like(min_dash_1)
dash_1_time = np.linspace(maxima_x[-1], minima_x[-1], 101)
dash_1 = np.linspace(maxima_y[-1], minima_y[-1], 101)
dash_2_time = np.linspace(maxima_x[-1], minima_x[-2], 101)
dash_2 = np.linspace(maxima_y[-1], minima_y[-2], 101)
s1 = (minima_y[-2] - maxima_y[-1]) / (minima_x[-2] - maxima_x[-1])
slope_based_maximum_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
slope_based_maximum = minima_y[-1] + (slope_based_maximum_time - minima_x[-1]) * s1
max_dash_time_3 = slope_based_maximum_time * np.ones_like(max_dash_1)
max_dash_3 = np.linspace(slope_based_maximum - width, slope_based_maximum + width, 101)
dash_3_time = np.linspace(minima_x[-1], slope_based_maximum_time, 101)
dash_3 = np.linspace(minima_y[-1], slope_based_maximum, 101)
s2 = (minima_y[-1] - maxima_y[-1]) / (minima_x[-1] - maxima_x[-1])
slope_based_minimum_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
slope_based_minimum = slope_based_maximum - (slope_based_maximum_time - slope_based_minimum_time) * s2
min_dash_time_3 = slope_based_minimum_time * np.ones_like(min_dash_1)
min_dash_3 = np.linspace(slope_based_minimum - width, slope_based_minimum + width, 101)
dash_4_time = np.linspace(slope_based_maximum_time, slope_based_minimum_time)
dash_4 = np.linspace(slope_based_maximum, slope_based_minimum)
maxima_dash = np.linspace(2.5 - width, 2.5 + width, 101)
maxima_dash_time_1 = maxima_x[-2] * np.ones_like(maxima_dash)
maxima_dash_time_2 = maxima_x[-1] * np.ones_like(maxima_dash)
maxima_dash_time_3 = slope_based_maximum_time * np.ones_like(maxima_dash)
maxima_line_dash_time = np.linspace(maxima_x[-2], slope_based_maximum_time, 101)
maxima_line_dash = 2.5 * np.ones_like(maxima_line_dash_time)
minima_dash = np.linspace(-3.4 - width, -3.4 + width, 101)
minima_dash_time_1 = minima_x[-2] * np.ones_like(minima_dash)
minima_dash_time_2 = minima_x[-1] * np.ones_like(minima_dash)
minima_dash_time_3 = slope_based_minimum_time * np.ones_like(minima_dash)
minima_line_dash_time = np.linspace(minima_x[-2], slope_based_minimum_time, 101)
minima_line_dash = -3.4 * np.ones_like(minima_line_dash_time)
# slightly edit signal to make difference between slope-based method and improved slope-based method more clear
time_series[time >= minima_x[-1]] = 1.5 * (time_series[time >= minima_x[-1]] - time_series[time == minima_x[-1]]) + \
time_series[time == minima_x[-1]]
improved_slope_based_maximum_time = time[-1]
improved_slope_based_maximum = time_series[-1]
improved_slope_based_minimum_time = slope_based_minimum_time
improved_slope_based_minimum = improved_slope_based_maximum + s2 * (improved_slope_based_minimum_time -
improved_slope_based_maximum_time)
min_dash_4 = np.linspace(improved_slope_based_minimum - width, improved_slope_based_minimum + width, 101)
min_dash_time_4 = improved_slope_based_minimum_time * np.ones_like(min_dash_4)
dash_final_time = np.linspace(improved_slope_based_maximum_time, improved_slope_based_minimum_time, 101)
dash_final = np.linspace(improved_slope_based_maximum, improved_slope_based_minimum, 101)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.gcf().subplots_adjust(bottom=0.10)
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.title('Slope-Based Edge Effects Example')
plt.plot(max_dash_time_1, max_dash_1, 'k-')
plt.plot(max_dash_time_2, max_dash_2, 'k-')
plt.plot(max_dash_time_3, max_dash_3, 'k-')
plt.plot(min_dash_time_1, min_dash_1, 'k-')
plt.plot(min_dash_time_2, min_dash_2, 'k-')
plt.plot(min_dash_time_3, min_dash_3, 'k-')
plt.plot(min_dash_time_4, min_dash_4, 'k-')
plt.plot(maxima_dash_time_1, maxima_dash, 'k-')
plt.plot(maxima_dash_time_2, maxima_dash, 'k-')
plt.plot(maxima_dash_time_3, maxima_dash, 'k-')
plt.plot(minima_dash_time_1, minima_dash, 'k-')
plt.plot(minima_dash_time_2, minima_dash, 'k-')
plt.plot(minima_dash_time_3, minima_dash, 'k-')
plt.text(4.34 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.74 * np.pi, -3.2, r'$\Delta{t^{min}_{m}}$')
plt.text(4.12 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.50 * np.pi, 2, r'$\Delta{t^{max}_{M}}$')
plt.text(4.30 * np.pi, 0.35, r'$s_1$')
plt.text(4.43 * np.pi, -0.20, r'$s_2$')
plt.text(4.30 * np.pi + (minima_x[-1] - minima_x[-2]), 0.35 + (minima_y[-1] - minima_y[-2]), r'$s_1$')
plt.text(4.43 * np.pi + (slope_based_minimum_time - minima_x[-1]),
-0.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.text(4.50 * np.pi + (slope_based_minimum_time - minima_x[-1]),
1.20 + (slope_based_minimum - minima_y[-1]), r'$s_2$')
plt.plot(minima_line_dash_time, minima_line_dash, 'k--')
plt.plot(maxima_line_dash_time, maxima_line_dash, 'k--')
plt.plot(dash_1_time, dash_1, 'k--')
plt.plot(dash_2_time, dash_2, 'k--')
plt.plot(dash_3_time, dash_3, 'k--')
plt.plot(dash_4_time, dash_4, 'k--')
plt.plot(dash_final_time, dash_final, 'k--')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(slope_based_maximum_time, slope_based_maximum, c='orange', zorder=4,
label=textwrap.fill('Slope-based maximum', 11))
plt.scatter(slope_based_minimum_time, slope_based_minimum, c='purple', zorder=4,
label=textwrap.fill('Slope-based minimum', 11))
plt.scatter(improved_slope_based_maximum_time, improved_slope_based_maximum, c='deeppink', zorder=4,
label=textwrap.fill('Improved slope-based maximum', 11))
plt.scatter(improved_slope_based_minimum_time, improved_slope_based_minimum, c='dodgerblue', zorder=4,
label=textwrap.fill('Improved slope-based minimum', 11))
plt.xlim(3.9 * np.pi, 5.5 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-3, -2, -1, 0, 1, 2), ('-3', '-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_slope_based.png')
plt.show()
# plot 5
a = 0.25
width = 0.2
time = np.linspace(0, (5 - a) * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
A2 = np.abs(maxima_y[-2] - minima_y[-2]) / 2
A1 = np.abs(maxima_y[-1] - minima_y[-1]) / 2
P2 = 2 * np.abs(maxima_x[-2] - minima_x[-2])
P1 = 2 * np.abs(maxima_x[-1] - minima_x[-1])
Huang_time = (P1 / P2) * (time[time >= maxima_x[-2]] - time[time == maxima_x[-2]]) + maxima_x[-1]
Huang_wave = (A1 / A2) * (time_series[time >= maxima_x[-2]] - time_series[time == maxima_x[-2]]) + maxima_y[-1]
Coughlin_time = Huang_time
Coughlin_wave = A1 * np.cos(2 * np.pi * (1 / P1) * (Coughlin_time - Coughlin_time[0]))
Average_max_time = maxima_x[-1] + (maxima_x[-1] - maxima_x[-2])
Average_max = (maxima_y[-2] + maxima_y[-1]) / 2
Average_min_time = minima_x[-1] + (minima_x[-1] - minima_x[-2])
Average_min = (minima_y[-2] + minima_y[-1]) / 2
utils_Huang = emd_utils.Utility(time=time, time_series=Huang_wave)
Huang_max_bool = utils_Huang.max_bool_func_1st_order_fd()
Huang_min_bool = utils_Huang.min_bool_func_1st_order_fd()
utils_Coughlin = emd_utils.Utility(time=time, time_series=Coughlin_wave)
Coughlin_max_bool = utils_Coughlin.max_bool_func_1st_order_fd()
Coughlin_min_bool = utils_Coughlin.min_bool_func_1st_order_fd()
Huang_max_time = Huang_time[Huang_max_bool]
Huang_max = Huang_wave[Huang_max_bool]
Huang_min_time = Huang_time[Huang_min_bool]
Huang_min = Huang_wave[Huang_min_bool]
Coughlin_max_time = Coughlin_time[Coughlin_max_bool]
Coughlin_max = Coughlin_wave[Coughlin_max_bool]
Coughlin_min_time = Coughlin_time[Coughlin_min_bool]
Coughlin_min = Coughlin_wave[Coughlin_min_bool]
max_2_x_time = np.linspace(maxima_x[-2] - width, maxima_x[-2] + width, 101)
max_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
max_2_x = maxima_y[-2] * np.ones_like(max_2_x_time)
min_2_x_time = np.linspace(minima_x[-2] - width, minima_x[-2] + width, 101)
min_2_x_time_side = np.linspace(5.3 * np.pi - width, 5.3 * np.pi + width, 101)
min_2_x = minima_y[-2] * np.ones_like(min_2_x_time)
dash_max_min_2_x = np.linspace(minima_y[-2], maxima_y[-2], 101)
dash_max_min_2_x_time = 5.3 * np.pi * np.ones_like(dash_max_min_2_x)
max_2_y = np.linspace(maxima_y[-2] - width, maxima_y[-2] + width, 101)
max_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
max_2_y_time = maxima_x[-2] * np.ones_like(max_2_y)
min_2_y = np.linspace(minima_y[-2] - width, minima_y[-2] + width, 101)
min_2_y_side = np.linspace(-1.8 - width, -1.8 + width, 101)
min_2_y_time = minima_x[-2] * np.ones_like(min_2_y)
dash_max_min_2_y_time = np.linspace(minima_x[-2], maxima_x[-2], 101)
dash_max_min_2_y = -1.8 * np.ones_like(dash_max_min_2_y_time)
max_1_x_time = np.linspace(maxima_x[-1] - width, maxima_x[-1] + width, 101)
max_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
max_1_x = maxima_y[-1] * np.ones_like(max_1_x_time)
min_1_x_time = np.linspace(minima_x[-1] - width, minima_x[-1] + width, 101)
min_1_x_time_side = np.linspace(5.4 * np.pi - width, 5.4 * np.pi + width, 101)
min_1_x = minima_y[-1] * np.ones_like(min_1_x_time)
dash_max_min_1_x = np.linspace(minima_y[-1], maxima_y[-1], 101)
dash_max_min_1_x_time = 5.4 * np.pi * np.ones_like(dash_max_min_1_x)
max_1_y = np.linspace(maxima_y[-1] - width, maxima_y[-1] + width, 101)
max_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
max_1_y_time = maxima_x[-1] * np.ones_like(max_1_y)
min_1_y = np.linspace(minima_y[-1] - width, minima_y[-1] + width, 101)
min_1_y_side = np.linspace(-2.1 - width, -2.1 + width, 101)
min_1_y_time = minima_x[-1] * np.ones_like(min_1_y)
dash_max_min_1_y_time = np.linspace(minima_x[-1], maxima_x[-1], 101)
dash_max_min_1_y = -2.1 * np.ones_like(dash_max_min_1_y_time)
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Characteristic Wave Effects Example')
plt.plot(time, time_series, LineWidth=2, label='Signal')
plt.scatter(Huang_max_time, Huang_max, c='magenta', zorder=4, label=textwrap.fill('Huang maximum', 10))
plt.scatter(Huang_min_time, Huang_min, c='lime', zorder=4, label=textwrap.fill('Huang minimum', 10))
plt.scatter(Coughlin_max_time, Coughlin_max, c='darkorange', zorder=4,
label=textwrap.fill('Coughlin maximum', 14))
plt.scatter(Coughlin_min_time, Coughlin_min, c='dodgerblue', zorder=4,
label=textwrap.fill('Coughlin minimum', 14))
plt.scatter(Average_max_time, Average_max, c='orangered', zorder=4,
label=textwrap.fill('Average maximum', 14))
plt.scatter(Average_min_time, Average_min, c='cyan', zorder=4,
label=textwrap.fill('Average minimum', 14))
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.plot(Huang_time, Huang_wave, '--', c='darkviolet', label=textwrap.fill('Huang Characteristic Wave', 14))
plt.plot(Coughlin_time, Coughlin_wave, '--', c='darkgreen', label=textwrap.fill('Coughlin Characteristic Wave', 14))
plt.plot(max_2_x_time, max_2_x, 'k-')
plt.plot(max_2_x_time_side, max_2_x, 'k-')
plt.plot(min_2_x_time, min_2_x, 'k-')
plt.plot(min_2_x_time_side, min_2_x, 'k-')
plt.plot(dash_max_min_2_x_time, dash_max_min_2_x, 'k--')
plt.text(5.16 * np.pi, 0.85, r'$2a_2$')
plt.plot(max_2_y_time, max_2_y, 'k-')
plt.plot(max_2_y_time, max_2_y_side, 'k-')
plt.plot(min_2_y_time, min_2_y, 'k-')
plt.plot(min_2_y_time, min_2_y_side, 'k-')
plt.plot(dash_max_min_2_y_time, dash_max_min_2_y, 'k--')
plt.text(4.08 * np.pi, -2.2, r'$\frac{p_2}{2}$')
plt.plot(max_1_x_time, max_1_x, 'k-')
plt.plot(max_1_x_time_side, max_1_x, 'k-')
plt.plot(min_1_x_time, min_1_x, 'k-')
plt.plot(min_1_x_time_side, min_1_x, 'k-')
plt.plot(dash_max_min_1_x_time, dash_max_min_1_x, 'k--')
plt.text(5.42 * np.pi, -0.1, r'$2a_1$')
plt.plot(max_1_y_time, max_1_y, 'k-')
plt.plot(max_1_y_time, max_1_y_side, 'k-')
plt.plot(min_1_y_time, min_1_y, 'k-')
plt.plot(min_1_y_time, min_1_y_side, 'k-')
plt.plot(dash_max_min_1_y_time, dash_max_min_1_y, 'k--')
plt.text(4.48 * np.pi, -2.5, r'$\frac{p_1}{2}$')
plt.xlim(3.9 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/edge_effects_characteristic_wave.png')
plt.show()
# plot 6
t = np.linspace(5, 95, 100)
signal_orig = np.cos(2 * np.pi * t / 50) + 0.6 * np.cos(2 * np.pi * t / 25) + 0.5 * np.sin(2 * np.pi * t / 200)
util_nn = emd_utils.Utility(time=t, time_series=signal_orig)
maxima = signal_orig[util_nn.max_bool_func_1st_order_fd()]
minima = signal_orig[util_nn.min_bool_func_1st_order_fd()]
cs_max = CubicSpline(t[util_nn.max_bool_func_1st_order_fd()], maxima)
cs_min = CubicSpline(t[util_nn.min_bool_func_1st_order_fd()], minima)
time = np.linspace(0, 5 * np.pi, 1001)
lsq_signal = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 101)
time_extended = time_extension(time)
time_series_extended = np.zeros_like(time_extended) / 0
time_series_extended[int(len(lsq_signal) - 1):int(2 * (len(lsq_signal) - 1) + 1)] = lsq_signal
neural_network_m = 200
neural_network_k = 100
# forward ->
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[(-(neural_network_m + neural_network_k - col)):(-(neural_network_m - col))]
P[-1, col] = 1 # for additive constant
t = lsq_signal[-neural_network_m:]
# test - top
seed_weights = np.ones(neural_network_k) / neural_network_k
weights = 0 * seed_weights.copy()
train_input = P[:-1, :]
lr = 0.01
for iterations in range(1000):
output = np.matmul(weights, train_input)
error = (t - output)
gradients = error * (- train_input)
# guess average gradients
average_gradients = np.mean(gradients, axis=1)
# steepest descent
max_gradient_vector = average_gradients * (np.abs(average_gradients) == max(np.abs(average_gradients)))
adjustment = - lr * average_gradients
# adjustment = - lr * max_gradient_vector
weights += adjustment
# test - bottom
weights_right = np.hstack((weights, 0))
max_count_right = 0
min_count_right = 0
i_right = 0
while ((max_count_right < 1) or (min_count_right < 1)) and (i_right < len(lsq_signal) - 1):
time_series_extended[int(2 * (len(lsq_signal) - 1) + 1 + i_right)] = \
sum(weights_right * np.hstack((time_series_extended[
int(2 * (len(lsq_signal) - 1) + 1 - neural_network_k + i_right):
int(2 * (len(lsq_signal) - 1) + 1 + i_right)], 1)))
i_right += 1
if i_right > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_right += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)],
time_series=time_series_extended[int(2 * (len(lsq_signal) - 1) + 1):
int(2 * (len(lsq_signal) - 1) + 1 + i_right + 1)])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_right += 1
# backward <-
P = np.zeros((int(neural_network_k + 1), neural_network_m))
for col in range(neural_network_m):
P[:-1, col] = lsq_signal[int(col + 1):int(col + neural_network_k + 1)]
P[-1, col] = 1 # for additive constant
t = lsq_signal[:neural_network_m]
vx = cvx.Variable(int(neural_network_k + 1))
objective = cvx.Minimize(cvx.norm((2 * (vx * P) + 1 - t), 2)) # linear activation function is arbitrary
prob = cvx.Problem(objective)
result = prob.solve(verbose=True, solver=cvx.ECOS)
weights_left = np.array(vx.value)
max_count_left = 0
min_count_left = 0
i_left = 0
while ((max_count_left < 1) or (min_count_left < 1)) and (i_left < len(lsq_signal) - 1):
time_series_extended[int(len(lsq_signal) - 2 - i_left)] = \
2 * sum(weights_left * np.hstack((time_series_extended[int(len(lsq_signal) - 1 - i_left):
int(len(lsq_signal) - 1 - i_left + neural_network_k)],
1))) + 1
i_left += 1
if i_left > 1:
emd_utils_max = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_max.max_bool_func_1st_order_fd()) > 0:
max_count_left += 1
emd_utils_min = \
emd_utils.Utility(time=time_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))],
time_series=time_series_extended[int(len(lsq_signal) - 1 - i_left):int(len(lsq_signal))])
if sum(emd_utils_min.min_bool_func_1st_order_fd()) > 0:
min_count_left += 1
lsq_utils = emd_utils.Utility(time=time, time_series=lsq_signal)
utils_extended = emd_utils.Utility(time=time_extended, time_series=time_series_extended)
maxima = lsq_signal[lsq_utils.max_bool_func_1st_order_fd()]
maxima_time = time[lsq_utils.max_bool_func_1st_order_fd()]
maxima_extrapolate = time_series_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
maxima_extrapolate_time = time_extended[utils_extended.max_bool_func_1st_order_fd()][-1]
minima = lsq_signal[lsq_utils.min_bool_func_1st_order_fd()]
minima_time = time[lsq_utils.min_bool_func_1st_order_fd()]
minima_extrapolate = time_series_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
minima_extrapolate_time = time_extended[utils_extended.min_bool_func_1st_order_fd()][-2:]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Single Neuron Neural Network Example')
plt.plot(time, lsq_signal, zorder=2, label='Signal')
plt.plot(time_extended, time_series_extended, c='g', zorder=1, label=textwrap.fill('Extrapolated signal', 12))
plt.scatter(maxima_time, maxima, c='r', zorder=3, label='Maxima')
plt.scatter(minima_time, minima, c='b', zorder=3, label='Minima')
plt.scatter(maxima_extrapolate_time, maxima_extrapolate, c='magenta', zorder=3,
label=textwrap.fill('Extrapolated maxima', 12))
plt.scatter(minima_extrapolate_time, minima_extrapolate, c='cyan', zorder=4,
label=textwrap.fill('Extrapolated minima', 12))
plt.plot(((time[-302] + time[-301]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k',
label=textwrap.fill('Neural network inputs', 13))
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time[-302] + time[-301]) / 2), ((time[-302] + time[-301]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='k')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1002]) / 2),
((time_extended[-1001] + time_extended[-1002]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='k')
plt.plot(((time_extended[-1001] + time_extended[-1002]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='k')
plt.plot(((time[-202] + time[-201]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray', linestyle='dashed',
label=textwrap.fill('Neural network targets', 13))
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
-2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time[-202] + time[-201]) / 2), ((time[-202] + time[-201]) / 2) + 0.1, 100),
2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), -2.75 * np.ones(100), c='gray')
plt.plot(np.linspace(((time_extended[-1001] + time_extended[-1000]) / 2),
((time_extended[-1001] + time_extended[-1000]) / 2) - 0.1, 100), 2.75 * np.ones(100), c='gray')
plt.plot(((time_extended[-1001] + time_extended[-1000]) / 2) * np.ones(100), np.linspace(-2.75, 2.75, 100), c='gray',
linestyle='dashed')
plt.xlim(3.4 * np.pi, 5.6 * np.pi)
plt.xticks((4 * np.pi, 5 * np.pi), (r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/neural_network.png')
plt.show()
# plot 6a
np.random.seed(0)
time = np.linspace(0, 5 * np.pi, 1001)
knots_51 = np.linspace(0, 5 * np.pi, 51)
time_series = np.cos(2 * time) + np.cos(4 * time) + np.cos(8 * time)
noise = np.random.normal(0, 1, len(time_series))
time_series += noise
advemdpy = EMD(time=time, time_series=time_series)
imfs_51, hts_51, ifs_51 = advemdpy.empirical_mode_decomposition(knots=knots_51, max_imfs=3,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_31 = np.linspace(0, 5 * np.pi, 31)
imfs_31, hts_31, ifs_31 = advemdpy.empirical_mode_decomposition(knots=knots_31, max_imfs=2,
edge_effect='symmetric_anchor', verbose=False)[:3]
knots_11 = np.linspace(0, 5 * np.pi, 11)
imfs_11, hts_11, ifs_11 = advemdpy.empirical_mode_decomposition(knots=knots_11, max_imfs=1,
edge_effect='symmetric_anchor', verbose=False)[:3]
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
print(f'DFA fluctuation with 51 knots: {np.round(np.var(time_series - (imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :])), 3)}')
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[0].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[0].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[0].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
print(f'DFA fluctuation with 31 knots: {np.round(np.var(time_series - (imfs_31[1, :] + imfs_31[2, :])), 3)}')
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[1].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[1].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[1].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
print(f'DFA fluctuation with 11 knots: {np.round(np.var(time_series - imfs_51[3, :]), 3)}')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[2].set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$', r'$5\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), 5.5 * np.ones(101), 'k--')
axs[2].plot(np.linspace(0.95 * np.pi, 1.55 * np.pi, 101), -5.5 * np.ones(101), 'k--')
axs[2].plot(0.95 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--')
axs[2].plot(1.55 * np.pi * np.ones(101), np.linspace(-5.5, 5.5, 101), 'k--', label='Zoomed region')
plt.savefig('jss_figures/DFA_different_trends.png')
plt.show()
# plot 6b
fig, axs = plt.subplots(3, 1)
plt.suptitle(textwrap.fill('Comparison of Trends Extracted with Different Knot Sequences Zoomed Region', 40))
plt.subplots_adjust(hspace=0.1)
axs[0].plot(time, time_series, label='Time series')
axs[0].plot(time, imfs_51[1, :] + imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 1, IMF 2, & IMF 3 with 51 knots', 21))
for knot in knots_51:
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[0].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[0].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[0].set_xticklabels(['', '', '', '', '', ''])
box_0 = axs[0].get_position()
axs[0].set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.85, box_0.height])
axs[0].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[0].set_ylim(-5.5, 5.5)
axs[0].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[1].plot(time, time_series, label='Time series')
axs[1].plot(time, imfs_31[1, :] + imfs_31[2, :], label=textwrap.fill('Sum of IMF 1 and IMF 2 with 31 knots', 19))
axs[1].plot(time, imfs_51[2, :] + imfs_51[3, :], label=textwrap.fill('Sum of IMF 2 and IMF 3 with 51 knots', 19))
for knot in knots_31:
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[1].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[1].set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi])
axs[1].set_xticklabels(['', '', '', '', '', ''])
box_1 = axs[1].get_position()
axs[1].set_position([box_1.x0 - 0.05, box_1.y0, box_1.width * 0.85, box_1.height])
axs[1].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[1].set_ylim(-5.5, 5.5)
axs[1].set_xlim(0.95 * np.pi, 1.55 * np.pi)
axs[2].plot(time, time_series, label='Time series')
axs[2].plot(time, imfs_11[1, :], label='IMF 1 with 11 knots')
axs[2].plot(time, imfs_31[2, :], label='IMF 2 with 31 knots')
axs[2].plot(time, imfs_51[3, :], label='IMF 3 with 51 knots')
for knot in knots_11:
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1)
axs[2].plot(knot * np.ones(101), np.linspace(-5, 5, 101), '--', c='grey', zorder=1, label='Knots')
axs[2].set_xticks([np.pi, (3 / 2) * np.pi])
axs[2].set_xticklabels([r'$\pi$', r'$\frac{3}{2}\pi$'])
box_2 = axs[2].get_position()
axs[2].set_position([box_2.x0 - 0.05, box_2.y0, box_2.width * 0.85, box_2.height])
axs[2].legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
axs[2].set_ylim(-5.5, 5.5)
axs[2].set_xlim(0.95 * np.pi, 1.55 * np.pi)
plt.savefig('jss_figures/DFA_different_trends_zoomed.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs_51, hts_51, ifs_51, max_frequency=12, plot=False)
# plot 6c
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 0.9
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Simple Sinusoidal Time Seres with Added Noise', 50))
x_hs, y, z = hs_ouputs
z_min, z_max = 0, np.abs(z).max()
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.plot(x_hs[0, :], 8 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 8$', Linewidth=3)
ax.plot(x_hs[0, :], 4 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 4$', Linewidth=3)
ax.plot(x_hs[0, :], 2 * np.ones_like(x_hs[0, :]), '--', label=r'$\omega = 2$', Linewidth=3)
ax.set_xticks([0, np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi])
ax.set_xticklabels(['$0$', r'$\pi$', r'$2\pi$', r'$3\pi$', r'$4\pi$'])
plt.ylabel(r'Frequency (rad.s$^{-1}$)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.85, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/DFA_hilbert_spectrum.png')
plt.show()
# plot 6c
time = np.linspace(0, 5 * np.pi, 1001)
time_series = np.cos(time) + np.cos(5 * time)
knots = np.linspace(0, 5 * np.pi, 51)
fluc = Fluctuation(time=time, time_series=time_series)
max_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=False)
max_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='maxima', smooth=True)
min_unsmoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=False)
min_smoothed = fluc.envelope_basis_function_approximation(knots_for_envelope=knots, extrema_type='minima', smooth=True)
util = Utility(time=time, time_series=time_series)
maxima = util.max_bool_func_1st_order_fd()
minima = util.min_bool_func_1st_order_fd()
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title(textwrap.fill('Plot Demonstrating Unsmoothed Extrema Envelopes if SchoenbergWhitney Conditions are Not Satisfied', 50))
plt.plot(time, time_series, label='Time series', zorder=2, LineWidth=2)
plt.scatter(time[maxima], time_series[maxima], c='r', label='Maxima', zorder=10)
plt.scatter(time[minima], time_series[minima], c='b', label='Minima', zorder=10)
plt.plot(time, max_unsmoothed[0], label=textwrap.fill('Unsmoothed maxima envelope', 10), c='darkorange')
plt.plot(time, max_smoothed[0], label=textwrap.fill('Smoothed maxima envelope', 10), c='red')
plt.plot(time, min_unsmoothed[0], label=textwrap.fill('Unsmoothed minima envelope', 10), c='cyan')
plt.plot(time, min_smoothed[0], label=textwrap.fill('Smoothed minima envelope', 10), c='blue')
for knot in knots[:-1]:
plt.plot(knot * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', zorder=1)
plt.plot(knots[-1] * np.ones(101), np.linspace(-3.0, -2.0, 101), '--', c='grey', label='Knots', zorder=1)
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi),
(r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$', r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Schoenberg_Whitney_Conditions.png')
plt.show()
# plot 7
a = 0.25
width = 0.2
time = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 1001)
knots = np.linspace((0 + a) * np.pi, (5 - a) * np.pi, 11)
time_series = np.cos(time) + np.cos(5 * time)
utils = emd_utils.Utility(time=time, time_series=time_series)
max_bool = utils.max_bool_func_1st_order_fd()
maxima_x = time[max_bool]
maxima_y = time_series[max_bool]
min_bool = utils.min_bool_func_1st_order_fd()
minima_x = time[min_bool]
minima_y = time_series[min_bool]
inflection_bool = utils.inflection_point()
inflection_x = time[inflection_bool]
inflection_y = time_series[inflection_bool]
fluctuation = emd_mean.Fluctuation(time=time, time_series=time_series)
maxima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
maxima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'maxima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=False,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
minima_envelope_smooth = fluctuation.envelope_basis_function_approximation(knots, 'minima', smooth=True,
smoothing_penalty=0.2, edge_effect='none',
spline_method='b_spline')[0]
inflection_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='inflection_points')[0]
binomial_points_envelope = fluctuation.direct_detrended_fluctuation_estimation(knots,
smooth=True,
smoothing_penalty=0.2,
technique='binomial_average', order=21,
increment=20)[0]
derivative_of_lsq = utils.derivative_forward_diff()
derivative_time = time[:-1]
derivative_knots = np.linspace(knots[0], knots[-1], 31)
# change (1) detrended_fluctuation_technique and (2) max_internal_iter and (3) debug (confusing with external debugging)
emd = AdvEMDpy.EMD(time=derivative_time, time_series=derivative_of_lsq)
imf_1_of_derivative = emd.empirical_mode_decomposition(knots=derivative_knots,
knot_time=derivative_time, text=False, verbose=False)[0][1, :]
utils = emd_utils.Utility(time=time[:-1], time_series=imf_1_of_derivative)
optimal_maxima = np.r_[False, utils.derivative_forward_diff() < 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
optimal_minima = np.r_[False, utils.derivative_forward_diff() > 0, False] & \
np.r_[utils.zero_crossing() == 1, False]
EEMD_maxima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'maxima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
EEMD_minima_envelope = fluctuation.envelope_basis_function_approximation_fixed_points(knots, 'minima',
optimal_maxima,
optimal_minima,
smooth=False,
smoothing_penalty=0.2,
edge_effect='none')[0]
ax = plt.subplot(111)
plt.gcf().subplots_adjust(bottom=0.10)
plt.title('Detrended Fluctuation Analysis Examples')
plt.plot(time, time_series, LineWidth=2, label='Time series')
plt.scatter(maxima_x, maxima_y, c='r', zorder=4, label='Maxima')
plt.scatter(minima_x, minima_y, c='b', zorder=4, label='Minima')
plt.scatter(time[optimal_maxima], time_series[optimal_maxima], c='darkred', zorder=4,
label=textwrap.fill('Optimal maxima', 10))
plt.scatter(time[optimal_minima], time_series[optimal_minima], c='darkblue', zorder=4,
label=textwrap.fill('Optimal minima', 10))
plt.scatter(inflection_x, inflection_y, c='magenta', zorder=4, label=textwrap.fill('Inflection points', 10))
plt.plot(time, maxima_envelope, c='darkblue', label=textwrap.fill('EMD envelope', 10))
plt.plot(time, minima_envelope, c='darkblue')
plt.plot(time, (maxima_envelope + minima_envelope) / 2, c='darkblue')
plt.plot(time, maxima_envelope_smooth, c='darkred', label=textwrap.fill('SEMD envelope', 10))
plt.plot(time, minima_envelope_smooth, c='darkred')
plt.plot(time, (maxima_envelope_smooth + minima_envelope_smooth) / 2, c='darkred')
plt.plot(time, EEMD_maxima_envelope, c='darkgreen', label=textwrap.fill('EEMD envelope', 10))
plt.plot(time, EEMD_minima_envelope, c='darkgreen')
plt.plot(time, (EEMD_maxima_envelope + EEMD_minima_envelope) / 2, c='darkgreen')
plt.plot(time, inflection_points_envelope, c='darkorange', label=textwrap.fill('Inflection point envelope', 10))
plt.plot(time, binomial_points_envelope, c='deeppink', label=textwrap.fill('Binomial average envelope', 10))
plt.plot(time, np.cos(time), c='black', label='True mean')
plt.xticks((0, 1 * np.pi, 2 * np.pi, 3 * np.pi, 4 * np.pi, 5 * np.pi), (r'$0$', r'$\pi$', r'2$\pi$', r'3$\pi$',
r'4$\pi$', r'5$\pi$'))
plt.yticks((-2, -1, 0, 1, 2), ('-2', '-1', '0', '1', '2'))
plt.xlim(-0.25 * np.pi, 5.25 * np.pi)
box_0 = ax.get_position()
ax.set_position([box_0.x0 - 0.05, box_0.y0, box_0.width * 0.84, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/detrended_fluctuation_analysis.png')
plt.show()
# Duffing Equation Example
t = np.linspace(0, 150, 1501)
XY0 = [1, 1]
solution = odeint(duffing_equation, XY0, t)
x = solution[:, 0]
dxdt = solution[:, 1]
x_points = [0, 50, 100, 150]
x_names = {0, 50, 100, 150}
y_points_1 = [-2, 0, 2]
y_points_2 = [-1, 0, 1]
fig, axs = plt.subplots(2, 1)
plt.subplots_adjust(hspace=0.2)
axs[0].plot(t, x)
axs[0].set_title('Duffing Equation Displacement')
axs[0].set_ylim([-2, 2])
axs[0].set_xlim([0, 150])
axs[1].plot(t, dxdt)
axs[1].set_title('Duffing Equation Velocity')
axs[1].set_ylim([-1.5, 1.5])
axs[1].set_xlim([0, 150])
axis = 0
for ax in axs.flat:
ax.label_outer()
if axis == 0:
ax.set_ylabel('x(t)')
ax.set_yticks(y_points_1)
if axis == 1:
ax.set_ylabel(r'$ \dfrac{dx(t)}{dt} $')
ax.set(xlabel='t')
ax.set_yticks(y_points_2)
ax.set_xticks(x_points)
ax.set_xticklabels(x_names)
axis += 1
plt.savefig('jss_figures/Duffing_equation.png')
plt.show()
# compare other packages Duffing - top
pyemd = pyemd0215()
py_emd = pyemd(x)
IP, IF, IA = emd040.spectra.frequency_transform(py_emd.T, 10, 'hilbert')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using PyEMD 0.2.10', 40))
plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht))))
plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15))
plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15))
plt.xticks([0, 50, 100, 150])
plt.yticks([0, 0.1, 0.2])
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Duffing_equation_ht_pyemd.png')
plt.show()
plt.show()
emd_sift = emd040.sift.sift(x)
IP, IF, IA = emd040.spectra.frequency_transform(emd_sift, 10, 'hilbert')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 0.2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
ax = plt.subplot(111)
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using emd 0.3.3', 40))
plt.pcolormesh(t, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht))))
plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15))
plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15))
plt.xticks([0, 50, 100, 150])
plt.yticks([0, 0.1, 0.2])
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Duffing_equation_ht_emd.png')
plt.show()
# compare other packages Duffing - bottom
emd_duffing = AdvEMDpy.EMD(time=t, time_series=x)
emd_duff, emd_ht_duff, emd_if_duff, _, _, _, _ = emd_duffing.empirical_mode_decomposition(verbose=False)
fig, axs = plt.subplots(2, 1)
plt.subplots_adjust(hspace=0.3)
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
axs[0].plot(t, emd_duff[1, :], label='AdvEMDpy')
axs[0].plot(t, py_emd[0, :], '--', label='PyEMD 0.2.10')
axs[0].plot(t, emd_sift[:, 0], '--', label='emd 0.3.3')
axs[0].set_title('IMF 1')
axs[0].set_ylim([-2, 2])
axs[0].set_xlim([0, 150])
axs[1].plot(t, emd_duff[2, :], label='AdvEMDpy')
print(f'AdvEMDpy driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - emd_duff[2, :])), 3)}')
axs[1].plot(t, py_emd[1, :], '--', label='PyEMD 0.2.10')
print(f'PyEMD driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - py_emd[1, :])), 3)}')
axs[1].plot(t, emd_sift[:, 1], '--', label='emd 0.3.3')
print(f'emd driving function error: {np.round(sum(abs(0.1 * np.cos(0.04 * 2 * np.pi * t) - emd_sift[:, 1])), 3)}')
axs[1].plot(t, 0.1 * np.cos(0.04 * 2 * np.pi * t), '--', label=r'$0.1$cos$(0.08{\pi}t)$')
axs[1].set_title('IMF 2')
axs[1].set_ylim([-0.2, 0.4])
axs[1].set_xlim([0, 150])
axis = 0
for ax in axs.flat:
ax.label_outer()
if axis == 0:
ax.set_ylabel(r'$\gamma_1(t)$')
ax.set_yticks([-2, 0, 2])
if axis == 1:
ax.set_ylabel(r'$\gamma_2(t)$')
ax.set_yticks([-0.2, 0, 0.2])
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0, box_0.width * 0.85, box_0.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=8)
ax.set_xticks(x_points)
ax.set_xticklabels(x_names)
axis += 1
plt.savefig('jss_figures/Duffing_equation_imfs.png')
plt.show()
hs_ouputs = hilbert_spectrum(t, emd_duff, emd_ht_duff, emd_if_duff, max_frequency=1.3, plot=False)
ax = plt.subplot(111)
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of Duffing Equation using AdvEMDpy', 40))
x, y, z = hs_ouputs
y = y / (2 * np.pi)
z_min, z_max = 0, np.abs(z).max()
figure_size = plt.gcf().get_size_inches()
factor = 1.0
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
ax.pcolormesh(x, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
plt.plot(t[:-1], 0.124 * np.ones_like(t[:-1]), '--', label=textwrap.fill('Hamiltonian frequency approximation', 15))
plt.plot(t[:-1], 0.04 * np.ones_like(t[:-1]), 'g--', label=textwrap.fill('Driving function frequency', 15))
plt.xticks([0, 50, 100, 150])
plt.yticks([0, 0.1, 0.2])
plt.ylabel('Frequency (Hz)')
plt.xlabel('Time (s)')
box_0 = ax.get_position()
ax.set_position([box_0.x0, box_0.y0 + 0.05, box_0.width * 0.75, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/Duffing_equation_ht.png')
plt.show()
# Carbon Dioxide Concentration Example
CO2_data = pd.read_csv('Data/co2_mm_mlo.csv', header=51)
plt.plot(CO2_data['month'], CO2_data['decimal date'])
plt.title(textwrap.fill('Mean Monthly Concentration of Carbon Dioxide in the Atmosphere', 35))
plt.ylabel('Parts per million')
plt.xlabel('Time (years)')
plt.savefig('jss_figures/CO2_concentration.png')
plt.show()
signal = CO2_data['decimal date']
signal = np.asarray(signal)
time = CO2_data['month']
time = np.asarray(time)
# compare other packages Carbon Dioxide - top
pyemd = pyemd0215()
py_emd = pyemd(signal)
IP, IF, IA = emd040.spectra.frequency_transform(py_emd[:2, :].T, 12, 'hilbert')
print(f'PyEMD annual frequency error: {np.round(sum(np.abs(IF[:, 0] - np.ones_like(IF[:, 0]))), 3)}')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
fig, ax = plt.subplots()
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of CO$_{2}$ Concentration using PyEMD 0.2.10', 45))
plt.ylabel('Frequency (year$^{-1}$)')
plt.xlabel('Time (years)')
plt.pcolormesh(time, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht))))
plt.plot(time, np.ones_like(time), 'k--', label=textwrap.fill('Annual cycle', 10))
box_0 = ax.get_position()
ax.set_position([box_0.x0 + 0.0125, box_0.y0 + 0.075, box_0.width * 0.8, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/CO2_Hilbert_pyemd.png')
plt.show()
emd_sift = emd040.sift.sift(signal)
IP, IF, IA = emd040.spectra.frequency_transform(emd_sift[:, :1], 12, 'hilbert')
print(f'emd annual frequency error: {np.round(sum(np.abs(IF - np.ones_like(IF)))[0], 3)}')
freq_edges, freq_bins = emd040.spectra.define_hist_bins(0, 2, 100)
hht = emd040.spectra.hilberthuang(IF, IA, freq_edges)
hht = gaussian_filter(hht, sigma=1)
fig, ax = plt.subplots()
figure_size = plt.gcf().get_size_inches()
factor = 0.8
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
plt.title(textwrap.fill('Gaussian Filtered Hilbert Spectrum of CO$_{2}$ Concentration using emd 0.3.3', 45))
plt.ylabel('Frequency (year$^{-1}$)')
plt.xlabel('Time (years)')
plt.pcolormesh(time, freq_bins, hht, cmap='gist_rainbow', vmin=0, vmax=np.max(np.max(np.abs(hht))))
plt.plot(time, np.ones_like(time), 'k--', label=textwrap.fill('Annual cycle', 10))
box_0 = ax.get_position()
ax.set_position([box_0.x0 + 0.0125, box_0.y0 + 0.075, box_0.width * 0.8, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/CO2_Hilbert_emd.png')
plt.show()
# compare other packages Carbon Dioxide - bottom
knots = np.linspace(time[0], time[-1], 200)
emd_example = AdvEMDpy.EMD(time=time, time_series=signal)
imfs, hts, ifs, _, _, _, _ = \
emd_example.empirical_mode_decomposition(knots=knots, knot_time=time, verbose=False)
print(f'AdvEMDpy annual frequency error: {np.round(sum(np.abs(ifs[1, :] / (2 * np.pi) - np.ones_like(ifs[1, :]))), 3)}')
fig, axs = plt.subplots(2, 2)
plt.subplots_adjust(hspace=0.5)
axs[0, 0].plot(time, signal)
axs[0, 1].plot(time, signal)
axs[0, 1].plot(time, imfs[0, :], label='Smoothed')
axs[0, 1].legend(loc='lower right')
axs[1, 0].plot(time, imfs[1, :])
axs[1, 1].plot(time, imfs[2, :])
axis = 0
for ax in axs.flat:
if axis == 0:
ax.set(ylabel=R'C0$_2$ concentration')
if axis == 1:
pass
if axis == 2:
ax.set(ylabel=R'C0$_2$ concentration')
ax.set(xlabel='Time (years)')
if axis == 3:
ax.set(xlabel='Time (years)')
axis += 1
plt.gcf().subplots_adjust(bottom=0.15)
axs[0, 0].set_title(r'Original CO$_2$ Concentration')
axs[0, 1].set_title('Smoothed CO$_2$ Concentration')
axs[1, 0].set_title('IMF 1')
axs[1, 1].set_title('Residual')
plt.gcf().subplots_adjust(bottom=0.15)
plt.savefig('jss_figures/CO2_EMD.png')
plt.show()
hs_ouputs = hilbert_spectrum(time, imfs, hts, ifs, max_frequency=10, which_imfs=[1], plot=False)
x_hs, y, z = hs_ouputs
y = y / (2 * np.pi)
z_min, z_max = 0, np.abs(z).max()
fig, ax = plt.subplots()
figure_size = plt.gcf().get_size_inches()
factor = 0.7
plt.gcf().set_size_inches((figure_size[0], factor * figure_size[1]))
ax.pcolormesh(x_hs, y, np.abs(z), cmap='gist_rainbow', vmin=z_min, vmax=z_max)
ax.set_title(textwrap.fill(r'Gaussian Filtered Hilbert Spectrum of CO$_{2}$ Concentration using AdvEMDpy', 40))
plt.ylabel('Frequency (year$^{-1}$)')
plt.xlabel('Time (years)')
plt.plot(x_hs[0, :], np.ones_like(x_hs[0, :]), 'k--', label=textwrap.fill('Annual cycle', 10))
ax.axis([x_hs.min(), x_hs.max(), y.min(), y.max()])
box_0 = ax.get_position()
ax.set_position([box_0.x0 + 0.0125, box_0.y0 + 0.075, box_0.width * 0.8, box_0.height * 0.9])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.savefig('jss_figures/CO2_Hilbert.png')
plt.show()
| [
198,
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220,
220,
220,
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2,
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3467,
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198... | 2.036219 | 36,335 |
import argparse
import logging
import torch
import torch.nn.functional as F
import numpy as np
from torch import nn
from torch.autograd import Variable
from transformers import GPT2Config
from transformers import GPT2LMHeadModel, GPT2Tokenizer, BertTokenizer
from DataLoader import *
from Model import BERTGen
from utils import sample_sequence
import torch.optim as optim
import math
import sys
import pandas
import os
import numpy
import nltk
from torch.utils.tensorboard import SummaryWriter
import warnings
from tqdm import tqdm, trange
from torch.utils.data import RandomSampler, SequentialSampler
from torch.utils.data import DataLoader as DL
import torch
from torch.utils.data.distributed import DistributedSampler
warnings.filterwarnings("ignore", category=UserWarning)
device = torch.device('cuda')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--model", default='gpt2', type=str)
parser.add_argument("--top_k", type=int, default=0)
parser.add_argument("--top_p", type=float, default=0.9)
parser.add_argument('--seed', type=int, default=42, help="random seed for initialization")
parser.add_argument('--do_train', default=False, action="store_true", help="whether to train or test the model")
parser.add_argument('--do_rl', default=False, action="store_true", help="whether to train or test the model")
parser.add_argument('--do_val', default=False, action="store_true", help="whether to train or test the model")
parser.add_argument('--do_test', default=False, action="store_true", help="whether to compute the BLEU scores on test split")
parser.add_argument('--do_test_challenge', default=False, action="store_true", help="whether to compute the BLEU scores on challenge split")
parser.add_argument('--do_ppl', default=False, action="store_true", help="whether to compute perplexity of the model")
parser.add_argument('--do_verify', default=False, action="store_true", help="whether compute the adv-acc score on test split")
parser.add_argument('--do_verify_challenge', default=False, action="store_true", help="whether compute the adv-acc score on challenge split")
parser.add_argument('--epoch', default=10, type=int, help="whether to train or test the model")
parser.add_argument('--batch_size', default=6, type=int, help="whether to train or test the model")
parser.add_argument('--local_rank', default=-1, type=int, help="whether to train or test the model")
parser.add_argument('--learning_rate', default=2e-6, type=float, help="whether to train or test the model")
parser.add_argument('--dataset', default='table', type=str, help="whether to train or test the model")
parser.add_argument('--every', default=50, type=int, help="whether to train or test the model")
parser.add_argument('--load_from', default='', type=str, help="whether to train or test the model")
parser.add_argument('--id', default='models', type=str, help="specify the id of the experiment")
parser.add_argument('--max_len', default=800, type=int, help="whether to train or test the model")
parser.add_argument('--dim', default=768, type=int, help="whether to train or test the model")
parser.add_argument('--layers', default=3, type=int, help="whether to train or test the model")
parser.add_argument('--head', default=4, type=int, help="whether to train or test the model")
parser.add_argument("--modelpath", type=str, default="bert-base-uncased",
help="For distributed training: local_rank")
parser.add_argument('--gradient_accumulation_steps', type=int, default=5, help="accumulation steps for gradient")
parser.add_argument('--decode_first_K', type=int, default=10000, help="For debugging purpose")
args = parser.parse_args()
if args.local_rank == -1:
device = torch.device("cuda")
args.n_gpu = 1
else:
torch.cuda.set_device(args.local_rank)
device = torch.device('cuda', args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
if args.local_rank not in [-1, 0]:
torch.distributed.barrier()
tokenizer = GPT2Tokenizer.from_pretrained(args.model)
model = GPT2LMHeadModel.from_pretrained(args.model)
#model = nn.DataParallel(model)
model.to(args.device)
if args.local_rank == 0:
torch.distributed.barrier()
criterion = nn.CrossEntropyLoss(reduction='none', ignore_index=-1)
if args.do_train:
if args.local_rank in [-1, 0]:
if not os.path.exists(args.id):
os.mkdir(args.id)
tb_writer = SummaryWriter(log_dir='tensorboard/GPT2-{}'.format(args.model))
dataset = GPTTableDataset2('data/train_lm_preprocessed.json', tokenizer, args.max_len)
if args.local_rank == -1:
sampler = RandomSampler(dataset)
else:
sampler = DistributedSampler(dataset)
train_dataloader = DL(dataset, sampler=sampler, batch_size=args.batch_size, num_workers=0)
model.train()
optimizer = optim.Adam(model.parameters(), args.learning_rate)
avg_loss = 0
global_step = 0
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank, find_unused_parameters=True)
else:
model = torch.nn.DataParallel(model)
for epoch_idx in trange(0, args.epoch, desc='Epoch', disable=args.local_rank not in [-1, 0]):
#for idx in range(0, dataset.train_len()):
for idx, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
batch = tuple(Variable(t).to(device) for t in batch)
trg_inp, trg_out, mask, caption = batch
inputs = torch.cat([caption, trg_inp], 1)
model.zero_grad()
optimizer.zero_grad()
logits = model(inputs)[0]
logits = logits[:, -trg_out.shape[1]:, :].contiguous()
loss = criterion(logits.view(-1, logits.shape[-1]), trg_out.view(-1))
loss = loss * mask.view(-1)
loss = loss.sum() / mask.sum()
avg_loss += loss.item()
loss.backward()
optimizer.step()
global_step += 1
if args.local_rank in [-1, 0] and idx % args.every == 0 and idx > 0:
tb_writer.add_scalar("perplexity", math.exp(avg_loss / args.every), global_step)
fake_inputs = caption
gt_inputs = trg_out.cpu().data.numpy()
#samples = model.sample(fake_inputs, tabfeat, caption, highlight_idx, bert)
samples = sample_sequence(model, 30, fake_inputs, [])
samples = samples[:, caption.shape[1]:]
samples = samples.cpu().data.numpy()
for s, gt in zip(samples, gt_inputs):
text = tokenizer.decode(s, clean_up_tokenization_spaces=True)
text = text[: text.find(tokenizer.eos_token)]
print("PREDICTION |||||| ", text)
text = tokenizer.decode(gt, clean_up_tokenization_spaces=True)
text = text[: text.find(tokenizer.eos_token)]
print("GROUNDTRUH |||||| ",text)
break
avg_loss = 0
if args.local_rank in [-1, 0]:
if args.model == 'gpt2':
torch.save(model.state_dict(), '{}/GPT_ep{}.pt'.format(args.id, epoch_idx))
else:
torch.save(model.state_dict(), '{}/GPT_medium_ep{}.pt'.format(args.id, epoch_idx))
if args.local_rank in [-1, 0]:
tb_writer.close() | [
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6... | 2.368312 | 3,383 |
# Copyright 2019 Atalaya Tech, Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import os
import sys
import tarfile
import logging
import tempfile
import shutil
from functools import wraps
from contextlib import contextmanager
from urllib.parse import urlparse
from typing import TYPE_CHECKING
from pathlib import PureWindowsPath, PurePosixPath
from bentoml.utils.s3 import is_s3_url
from bentoml.utils.gcs import is_gcs_url
from bentoml.exceptions import BentoMLException
from bentoml.saved_bundle.config import SavedBundleConfig
from bentoml.saved_bundle.pip_pkg import ZIPIMPORT_DIR
if TYPE_CHECKING:
from bentoml.yatai.proto.repository_pb2 import BentoServiceMetadata
logger = logging.getLogger(__name__)
def resolve_remote_bundle(func):
"""Decorate a function to handle remote bundles."""
return wrapper
| [
2,
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1629,
282,
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11,
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198,
2,
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... | 3.391414 | 396 |
"""
Revision ID: 0158_remove_rate_limit_default
Revises: 0157_add_rate_limit_to_service
Create Date: 2018-01-09 14:33:08.313893
"""
import sqlalchemy as sa
from alembic import op
revision = "0158_remove_rate_limit_default"
down_revision = "0157_add_rate_limit_to_service"
| [
37811,
198,
198,
18009,
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62,
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198,
16447,
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25,
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12,
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12,
2931,
1478... | 2.613208 | 106 |
import os
import errno
from datetime import datetime
print("Generating A New Post\n")
post_name = input('Input Post Name: ')
date_time = datetime.now()
date_time_dir = date_time.strftime("%Y-%m-%d")
date_time_post = date_time.strftime("%Y-%m-%d %H:%M:%S")
p_name = post_name.replace(" ","-")
p_name = p_name.replace("[","")
p_name = p_name.replace("]","")
p_name = p_name.lower()
f_name = date_time_dir+"---"+p_name
dir = "./src/pages/articles/"+f_name+"/"
f_dir = dir+f_name+".md"
try:
if not(os.path.isdir(dir)):
os.makedirs(os.path.join(dir))
except OSError as e:
if e.errno != errno.EEXIST:
print("Failed to create directory!!!!!")
raise
print("Generating post : ",f_dir)
with open(f_dir, 'w') as f:
f.write('---')
f.write('\n')
f.write('draft: true')
f.write('\n')
f.write('title: \"'+post_name+'\"')
f.write('\n')
f.write('date: \"'+date_time_post+'\"')
f.write('\n')
f.write('layout: post')
f.write('\n')
f.write('path: \"/posts/'+p_name+'/\"')
f.write('\n')
f.write('category: \"\"')
f.write('\n')
f.write('tags: ')
f.write('\n')
f.write('description: ""')
f.write('\n')
f.write('---')
f.write('\n')
print("Done :)") | [
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... | 2.04685 | 619 |
#!/usr/bin/env python
# encoding: utf-8 -*-
"""
This module contains unit tests of the rmgpy.reaction module.
"""
import numpy
import unittest
from external.wip import work_in_progress
from rmgpy.species import Species, TransitionState
from rmgpy.reaction import Reaction
from rmgpy.statmech.translation import Translation, IdealGasTranslation
from rmgpy.statmech.rotation import Rotation, LinearRotor, NonlinearRotor, KRotor, SphericalTopRotor
from rmgpy.statmech.vibration import Vibration, HarmonicOscillator
from rmgpy.statmech.torsion import Torsion, HinderedRotor
from rmgpy.statmech.conformer import Conformer
from rmgpy.kinetics import Arrhenius
from rmgpy.thermo import Wilhoit
import rmgpy.constants as constants
################################################################################
################################################################################
if __name__ == '__main__':
unittest.main(testRunner=unittest.TextTestRunner(verbosity=2))
| [
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11748,
29... | 3.53405 | 279 |
from django.conf.urls import url, include
from core.routers import OptionalTrailingSlashRouter
from blog import views as blogViews
from snippets import views as snippetsViews
from projects import views as projectsViews
from tags import views as tagsViews
from contents import views as contentsViews
from contact import views as contactViews
router = OptionalTrailingSlashRouter()
router.register(r"blog", blogViews.PostViewSet)
router.register(r"snippets", snippetsViews.SnippetViewSet)
router.register(r"languages", snippetsViews.ProgrammingLanguageViewSet)
router.register(r"projects", projectsViews.ProjectViewSet)
router.register(r"tags", tagsViews.TagViewSet)
router.register(r"contents", contentsViews.ContentViewSet)
router.register(r"contact", contactViews.MessageViewSet)
# List or url patterns for the api subdomain
urlpatterns = [
url(r"^v2/", include(router.urls)),
]
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1330,
5009,
... | 3.288889 | 270 |
"""
closed-loop MILP solved to determine optimal ordering defined by ADG
"""
import sys
import yaml
import time
import matplotlib.colors as mcolors
import matplotlib
import matplotlib.pyplot as plt
import random
import logging
import time
import networkx as nx
import csv
import statistics as stat
import os
import sys
from mip import Model, ProgressLog, xsum, maximize, minimize, BINARY, CONTINUOUS, Constr, ConstrList
sys.path.insert(1, "functions/")
from planners import *
from visualizers import *
from milp_formulation import *
from robot import *
from adg import *
from adg_node import *
from process_results import *
logger = logging.getLogger(__name__)
logging.basicConfig(format='%(name)s - %(levelname)s :: %(message)s', level=logging.INFO)
def main():
""" --------------------------- INPUTS --------------------------------- """
show_visual = False
show_ADG = True #not show_visual
run_MILP = True #False #True
save_file = False
sim_timeout = 500
# define prediction and control horizons: H_prediction >= H_control
H_prediction = np.NaN # integer value for forward node lookup
H_control = 5
random_seed = 0
mu = 0.5
robust_param = 0.0
delay_amount = 5
delayed_robot_cnt = 2
w = 1.4 # sub-optimality bound: w = 1.0 -> CBS, else ECBS!
fldr = "nuernberg_small" # auto_gen_01_nuernberg | auto_gen_00_large | auto_gen_02_simple | manual_03_maxplus
random.seed(random_seed)
np.random.seed(random_seed)
""" -------------------------------------------------------------------- """
# start initial
pwd = os.path.dirname(os.path.abspath(__file__))
logger.info(pwd)
map_file = pwd + "/data/" + fldr + "/csv_map_yaml.yaml"
robot_file = pwd + "/data/" + fldr + "/csv_robots_yaml.yaml"
robot_file_tmp = pwd + "/data/tmp/robots.yaml"
start_time = time.time()
plans = run_CBS(map_file, robot_file, w=w) # if w > 1.0, run_CBS uses ECBS!
logger.info(" with sub-optimality w={}".format(w))
logger.info(" plan statistics: {} \n".format(plans["statistics"]))
logger.debug(plans["schedule"])
# show factory map
# show_factory_map(map_file, robot_file, True)
# plt.show()
map_gen_robot_count = 10
map_gen_seedval = "NaN"
try:
map_gen_robot_count = int(sys.argv[1])
map_gen_seedval = int(sys.argv[2])
H_control = int(sys.argv[3])
robust_param = int(sys.argv[4])
random.seed(map_gen_seedval) # map_gen_seedval
np.random.seed(map_gen_seedval) # map_gen_seedval
except:
print(" no valid inputs given, ignoring ...")
# determine ADG, reverse ADG and dependency groups
ADG, robot_plan, goal_positions = determine_ADG(plans, show_graph=False)
nodes_all, edges_type_1, dependency_groups = analyze_ADG(ADG, plans, show_graph=False)
ADG_reverse = ADG.reverse(copy=False)
# initialize simulation
robots = []
solve_time = []
robots_done = []
time_to_goal = {}
colors = plt.cm.rainbow( np.arange(len(robot_plan))/len(robot_plan) )
for robot_id in robot_plan:
plan = robot_plan[robot_id]
logger.debug("Robot {} - plan: {} \t \t positions: {}".format(robot_id, plan["nodes"], plan["positions"]))
new_robot = Robot(robot_id, plan, colors[robot_id], goal_positions[robot_id])
robots.append(new_robot)
robots_done.append(False)
time_to_goal[robot_id] = 0
if show_visual:
visualizer = Visualizer(map_file, robots)
# initialize optimization MIP object m_opt
m_opt = Model('MILP_sequence', solver='CBC')
# print(m_opt.max_nodes)
pl_opt = ProgressLog()
# pl_opt.settings = "objective_value"
# print("pl_opt.settings: {}".format(pl_opt.settings))
# print("pl_opt.log: {}".format(pl_opt.log))
# pl_opt.instance = m_opt.name
# print("pl_opt.instance: {}".format(pl_opt.instance))
ADG_fig = plt.figure(figsize=(12,8))
plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
metadata = dict(title='Movie Test', artist='Matplotlib',
comment='Movie support!')
writer = FFMpegWriter(fps=2, metadata=metadata)
with writer.saving(ADG_fig, "ADG_video.mp4", 500):
# run a simulation in time
k = 0
robot_IDs_to_delay = []
while (not all(robots_done)) and (k < sim_timeout):
print("pl_opt.log: {}".format(pl_opt.log))
m_opt.clear()
# show current robot status
logger.info("-------------------- @ time step k = {} --------------------".format(k))
for robot in robots:
node_info = ADG.node[robot.current_node]["data"]
logger.debug(" - Robot {} # {} @ {} => status: {}".format(robot.robot_ID, node_info.ID, node_info.s_loc, robot.status))
# solve MILP for the advanced ADG to potentially adjust ordering
res, solve_t = solve_MILP(robots, dependency_groups, ADG, ADG_reverse, H_control, H_prediction, m_opt, pl_opt, run=run_MILP, uncertainty_bound=robust_param)
solve_time.append(solve_t)
if not (res is None or res == "OptimizationStatus.OPTIMAL"):
ValueError("Optimization NOT optimal")
# ADG after MILP
if show_ADG:
#
draw_ADG(ADG, robots, "ADG after MILP ADG | k = {}".format(k), writer=writer)
# plt.show()
# check for cycles
try:
nx.find_cycle(ADG, orientation="original")
logger.warning("Cycle detected!!")
raise Exception("ADG has a cycle => deadlock! something is wrong with optimization")
except nx.NetworkXNoCycle:
logger.debug("no cycle detected in ADG => no deadlock. good!")
pass
if (k % delay_amount) == 0:
robot_IDs = np.arange(map_gen_robot_count)
robot_IDs_to_delay = np.random.choice(map_gen_robot_count, size=delayed_robot_cnt, replace=False)
logger.info("delaying robots (ID): {}".format(robot_IDs_to_delay))
# Advance robots if possible (dependencies have been met)
for robot in robots:
# check if all dependencies have been met, to advance to next node
node_info = ADG.node[robot.current_node]["data"]
node_dependencies_list = list(ADG_reverse.neighbors(robot.current_node))
all_dependencies_completed = True
for dependency in node_dependencies_list:
if (ADG.node[dependency]["data"].status != Status.FINISHED):
all_dependencies_completed = False
# if all dependencies are completed, the robot can advance!
# delay_amount = np.random.poisson(mu) # same sample every time
if all_dependencies_completed and k > 0: # (robot.robot_ID == 2 or k > 5)
if (not (robot.robot_ID in robot_IDs_to_delay)): # or (k < 10 or k > 20)): # or (robot.robot_ID == 3 or k > 8):
ADG.node[robot.current_node]["data"].status = Status.FINISHED
robot.advance()
if not robot.is_done():
time_to_goal[robot.robot_ID] += 1
else:
robots_done[robot.robot_ID] = True
if show_visual:
visualizer.redraw(robots, pause_length=0.1)
# return 0
k += 1
# end of while loop
total_time = 0
for idx, t in time_to_goal.items():
total_time += t
logger.info("Total time to complete missions: {}".format(total_time))
logger.info("horizon = {}".format(H_control))
logger.info("")
logger.info("Computation time:")
logger.info(" - max: {}".format(max(solve_time)))
logger.info(" - avg: {}".format(stat.mean(solve_time)))
# create data to save to YAML file
simulation_results = {}
simulation_results["parameters"] = {}
simulation_results["parameters"]["H_control"] = H_control
simulation_results["parameters"]["random seed"] = random_seed
simulation_results["parameters"]["ECBS w"] = w
simulation_results["parameters"]["mu"] = mu
simulation_results["parameters"]["robust param"] = robust_param
simulation_results["parameters"]["delay amount"] = delay_amount
simulation_results["map details"] = {}
simulation_results["map details"]["robot_count"] = map_gen_robot_count
simulation_results["map details"]["seed val"] = map_gen_seedval
simulation_results["results"] = {}
simulation_results["results"]["comp time"] = {}
simulation_results["results"]["comp time"]["solve_time"] = [solve_time]
simulation_results["results"]["comp time"]["max"] = max(solve_time)
simulation_results["results"]["comp time"]["avg"] = stat.mean(solve_time)
simulation_results["results"]["total time"] = total_time
logger.info(simulation_results)
file_name = pwd + "/results/robust_" +str(delayed_robot_cnt) + "x" + str(delay_amount) + "/res_robots_" + str(map_gen_robot_count) + "_horizon_" + str(H_control) + "_mapseed_" + str(map_gen_seedval) + "_robustparam_" + str(robust_param) + ".yaml"
if save_file:
save_to_yaml(simulation_results, file_name)
if __name__ == "__main__":
main()
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669,
355,
... | 2.332424 | 4,031 |
import pytest
from unittest.mock import patch
import rfidsecuritysvc.model.guest as model
from rfidsecuritysvc.model.color import Color
from rfidsecuritysvc.model.guest import Guest
from rfidsecuritysvc.model.sound import Sound
from rfidsecuritysvc.exception import SoundNotFoundError
def test__model_no_color(creatable_guest):
row = creatable_guest.test_to_row()
row['color'] = None
g = model.__model(row)
assert g.color is None
def test__model_no_sound(creatable_guest):
row = creatable_guest.test_to_row()
row['sound'] = None
g = model.__model(row)
assert g.sound is None
def _default(index=1):
return _model(index, f'first {index}', f'last {index}', Sound(index, f'sound_name {index}', '2021-09-25 23:13:25'), Color(0xABCDEF))
def _model(id, first_name, last_name, sound, color):
return Guest(id, first_name, last_name, sound, color)
| [
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69,
312,
12961,
21370,
66,
13,
19849,
13,
8043,
1330,
5315,... | 2.651917 | 339 |
# encoding: utf-8
import os
import roms
def console_roms_directory(configuration, console):
"""
If the user has specified a custom ROMs directory in consoles.txt then
return that.
Otherwise, append the shortname of the console to the default ROMs
directory given by config.txt.
"""
if console.custom_roms_directory:
return console.custom_roms_directory
return os.path.join(roms.roms_directory(configuration), console.shortname)
def path_is_rom(console, path):
"""
This function determines if a given path is actually a valid ROM file.
If a list of extensions is supplied for this console, we check if the path has a valid extension
If no extensions are defined for this console, we just accept any file
"""
if console.extensions == "":
return True
# Normalize the extension based on the things we validly ignore.
# Aka capitalization, whitespace, and leading dots
normalize = lambda ext: ext.lower().strip().lstrip('.')
(name, ext) = os.path.splitext(path)
valid_extensions = console.extensions.split(',')
return normalize(ext) in map(normalize, valid_extensions)
| [
2,
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25,
3384,
69,
12,
23,
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198,
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1002,
262,
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468,
7368,
257,
2183,
21... | 3.432515 | 326 |
#! -*- coding:utf-8 -*-
# CLUE
# c3
#
import json
import numpy as np
from snippets import *
from bert4keras.backend import keras
from bert4keras.snippets import sequence_padding, DataGenerator
from bert4keras.snippets import open
from bert4keras.snippets import truncate_sequences
from tqdm import tqdm
#
num_classes = 4
maxlen = 512
batch_size = 4
epochs = 10
def load_data(filename):
"""
[(, , , id)]
"""
D = []
with open(filename) as f:
data = json.load(f)
for d in data:
p = u'||'.join(d[0])
for qa in d[1]:
q = qa['question']
while len(qa['choice']) < num_classes:
qa['choice'].append(u'')
c = qa['choice'][:num_classes]
if 'answer' in qa:
a = qa['choice'].index(qa['answer'])
else:
a = 0
D.append((p, q, c, a))
return D
#
train_data = load_data(data_path + 'c3/m-train.json')
train_data += load_data(data_path + 'c3/d-train.json')
valid_data = load_data(data_path + 'c3/m-dev.json')
valid_data += load_data(data_path + 'c3/d-dev.json')
#
train_generator = data_generator(train_data, batch_size)
valid_generator = data_generator(valid_data, batch_size)
def multichoice_crossentropy(y_true, y_pred):
"""
"""
y_true = K.cast(y_true, 'int32')[::num_classes]
y_pred = K.reshape(y_pred, (-1, num_classes))
return K.mean(
K.sparse_categorical_crossentropy(y_true, y_pred, from_logits=True)
)
def multichoice_accuracy(y_true, y_pred):
"""
"""
y_true = K.cast(y_true, 'int32')[::num_classes, 0]
y_pred = K.reshape(y_pred, (-1, num_classes))
y_pred = K.cast(K.argmax(y_pred, axis=1), 'int32')
return K.mean(K.cast(K.equal(y_true, y_pred), K.floatx()))
#
output = base.model.output
output = keras.layers.Lambda(lambda x: x[:, 0])(output)
output = keras.layers.Dense(units=1,
kernel_initializer=base.initializer)(output)
model = keras.models.Model(base.model.input, output)
model.summary()
model.compile(
loss=multichoice_crossentropy,
optimizer=optimizer4,
metrics=[multichoice_accuracy]
)
def test_predict(in_file, out_file):
"""
https://www.cluebenchmarks.com
"""
test_data = load_data(in_file)
test_generator = data_generator(test_data, batch_size)
results = []
for x_true, _ in tqdm(test_generator, ncols=0):
y_pred = model.predict(x_true).reshape((-1, num_classes))
y_pred = y_pred.argmax(axis=1)
results.extend(y_pred)
fw = open(out_file, 'w')
with open(in_file) as fr:
data = json.load(fr)
i = 0
for d in data:
for qa in d[1]:
l = json.dumps({'id': str(qa['id']), 'label': str(results[i])})
fw.write(l + '\n')
i += 1
fw.close()
if __name__ == '__main__':
evaluator = Evaluator()
model.fit_generator(
train_generator.forfit(),
steps_per_epoch=len(train_generator),
epochs=epochs,
callbacks=[evaluator]
)
model.load_weights('weights/c3.weights')
test_predict(
in_file=data_path + 'c3/test1.0.json',
out_file='results/c310_predict.json'
)
test_predict(
in_file=data_path + 'c3/test1.1.json',
out_file='results/c311_predict.json'
)
else:
model.load_weights('weights/c3.weights')
| [
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2,
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198,
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1330,
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198,
6738,
275,
861,
19,
6122,
... | 2.020337 | 1,721 |
# Copyright 2018 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from benchmarks import smoothness,thread_times
import page_sets
from telemetry import benchmark
# pylint: disable=protected-access
def CustomizeBrowserOptionsForOopRasterization(options):
"""Enables flags needed for out of process rasterization."""
options.AppendExtraBrowserArgs('--force-gpu-rasterization')
options.AppendExtraBrowserArgs('--enable-oop-rasterization')
| [
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383,
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13,
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13,
198,
2,
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286,
428,
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2438,
318,
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416,
257,
347,
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12,
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5964,
326,
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307,
198,
2,
1043,
287,
262,
38559,
24290,
2393,
13,
198,
6738,
3174... | 3.737931 | 145 |
import itertools as it
from toolz.compatibility import range, map, iteritems
def transitive_get(key, d):
""" Transitive dict.get
>>> from logpy.util import transitive_get
>>> d = {1: 2, 2: 3, 3: 4}
>>> d.get(1)
2
>>> transitive_get(1, d)
4
"""
while hashable(key) and key in d:
key = d[key]
return key
def deep_transitive_get(key, d):
""" Transitive get that propagates within tuples
>>> from logpy.util import transitive_get, deep_transitive_get
>>> d = {1: (2, 3), 2: 12, 3: 13}
>>> transitive_get(1, d)
(2, 3)
>>> deep_transitive_get(1, d)
(12, 13)
"""
key = transitive_get(key, d)
if isinstance(key, tuple):
return tuple(map(lambda k: deep_transitive_get(k, d), key))
else:
return key
def take(n, seq):
if n is None:
return seq
if n == 0:
return tuple(seq)
return tuple(it.islice(seq, 0, n))
def evalt(t):
""" Evaluate tuple if unevaluated
>>> from logpy.util import evalt
>>> add = lambda x, y: x + y
>>> evalt((add, 2, 3))
5
>>> evalt(add(2, 3))
5
"""
if isinstance(t, tuple) and len(t) >= 1 and callable(t[0]):
return t[0](*t[1:])
else:
return t
def intersection(*seqs):
return (item for item in seqs[0]
if all(item in seq for seq in seqs[1:]))
def pprint(g):
""" Pretty print a tree of goals """
if callable(g) and hasattr(g, '__name__'):
return g.__name__
if isinstance(g, type):
return g.__name__
if isinstance(g, tuple):
return "(" + ', '.join(map(pprint, g)) + ")"
return str(g)
def index(tup, ind):
""" Fancy indexing with tuples """
return tuple(tup[i] for i in ind)
| [
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198,
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13,
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1330,
2837,
11,
3975,
11,
11629,
23814,
198,
198,
4299,
1007,
1800,
62,
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220,
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628... | 2.209799 | 796 |
import rinobot_plugin as bot
import numpy as np
if __name__ == "__main__":
main()
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834,
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198,
220,
220,
220,
1388,
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198
] | 2.558824 | 34 |
from gluon.contrib.memcache.memcache import Client
import time
"""
examle of usage:
cache.memcache=MemcacheClient(request,[127.0.0.1:11211],debug=true)
"""
import cPickle as pickle
import thread
locker = thread.allocate_lock()
| [
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261,
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198,
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293,
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28,
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7,
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16... | 2.608696 | 92 |
__book_url__ = "dummy"
__book_version__ = "dummy"
associate_ref_with(Reference)
| [
628,
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1,
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67,
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1,
198,
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62,
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62,
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7,
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8,
198
] | 2.4 | 35 |
#!/usr/bin/env python
# encoding: utf-8
import numbers
import os
import re
import sys
from itertools import chain
import numpy as np
import scipy.sparse as sp
import six
import pickle
from .model import get_convo_nn2
from .stop_words import THAI_STOP_WORDS
from .utils import CHAR_TYPES_MAP, CHARS_MAP, create_feature_array
MODULE_PATH = os.path.dirname(__file__)
WEIGHT_PATH = os.path.join(MODULE_PATH, 'weight', 'cnn_without_ne_ab.h5')
TOKENIZER = None
def tokenize(text, custom_dict=None):
"""
Tokenize given Thai text string
Input
=====
text: str, Thai text string
custom_dict: str (or list), path to customized dictionary file
It allows the function not to tokenize given dictionary wrongly.
The file should contain custom words separated by line.
Alternatively, you can provide list of custom words too.
Output
======
tokens: list, list of tokenized words
Example
=======
>> deepcut.tokenize('')
>> ['','','','']
"""
global TOKENIZER
if not TOKENIZER:
TOKENIZER = DeepcutTokenizer()
return TOKENIZER.tokenize(text, custom_dict=custom_dict)
def _document_frequency(X):
"""
Count the number of non-zero values for each feature in sparse X.
"""
if sp.isspmatrix_csr(X):
return np.bincount(X.indices, minlength=X.shape[1])
return np.diff(sp.csc_matrix(X, copy=False).indptr)
def _check_stop_list(stop):
"""
Check stop words list
ref: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/feature_extraction/text.py#L87-L95
"""
if stop == "thai":
return THAI_STOP_WORDS
elif isinstance(stop, six.string_types):
raise ValueError("not a built-in stop list: %s" % stop)
elif stop is None:
return None
# assume it's a collection
return frozenset(stop)
def load_model(file_path):
"""
Load saved pickle file of DeepcutTokenizer
Parameters
==========
file_path: str, path to saved model from ``save_model`` method in DeepcutTokenizer
"""
tokenizer = pickle.load(open(file_path, 'rb'))
tokenizer.model = get_convo_nn2()
tokenizer.model = tokenizer.model.load_weights(WEIGHT_PATH)
return tokenizer
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
21004,
25,
3384,
69,
12,
23,
198,
11748,
3146,
198,
11748,
28686,
198,
11748,
302,
198,
11748,
25064,
198,
6738,
340,
861,
10141,
1330,
6333,
198,
198,
11748,
299,
32152,
355,
45941... | 2.609756 | 861 |
# Copyright 2021 Yan Yan
#
# 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
from cumm import tensorview as tv
from contextlib import AbstractContextManager
from spconv.cppconstants import CPU_ONLY_BUILD
from spconv.core_cc.csrc.utils.boxops import BoxOps
from spconv.core_cc.csrc.sparse.all.ops_cpu1d import Point2VoxelCPU as Point2VoxelCPU1d
from spconv.core_cc.csrc.sparse.all.ops_cpu2d import Point2VoxelCPU as Point2VoxelCPU2d
from spconv.core_cc.csrc.sparse.all.ops_cpu3d import Point2VoxelCPU as Point2VoxelCPU3d
from spconv.core_cc.csrc.sparse.all.ops_cpu4d import Point2VoxelCPU as Point2VoxelCPU4d
if not CPU_ONLY_BUILD:
from spconv.core_cc.csrc.sparse.all.ops1d import Point2Voxel as Point2VoxelGPU1d
from spconv.core_cc.csrc.sparse.all.ops2d import Point2Voxel as Point2VoxelGPU2d
from spconv.core_cc.csrc.sparse.all.ops3d import Point2Voxel as Point2VoxelGPU3d
from spconv.core_cc.csrc.sparse.all.ops4d import Point2Voxel as Point2VoxelGPU4d
| [
2,
15069,
33448,
10642,
10642,
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,
2845,
287,
11846,
351,
262,
13789,
13,
198,
2,
921,
743,
73... | 2.827652 | 528 |
#!/usr/bin/env python
#
# Copyright (c) 2013 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Runs Android's lint tool."""
import optparse
import os
import sys
from xml.dom import minidom
from util import build_utils
_SRC_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__),
'..', '..', '..'))
if __name__ == '__main__':
sys.exit(main())
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
198,
2,
15069,
357,
66,
8,
2211,
383,
18255,
1505,
46665,
13,
1439,
2489,
10395,
13,
198,
2,
5765,
286,
428,
2723,
2438,
318,
21825,
416,
257,
347,
10305,
12,
7635,
5964,
326,
4... | 2.487562 | 201 |
# Copyright 2020 Google 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.
"""Generates pybind11 bindings code."""
from typing import Dict, Generator, List, Text, Set
from clif.protos import ast_pb2
from clif.pybind11 import classes
from clif.pybind11 import enums
from clif.pybind11 import function
from clif.pybind11 import function_lib
from clif.pybind11 import type_casters
from clif.pybind11 import utils
I = utils.I
def write_to(channel, lines):
"""Writes the generated code to files."""
for s in lines:
channel.write(s)
channel.write('\n')
| [
2,
15069,
12131,
3012,
11419,
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,
2845,
287,
11846,
351,
262,
13789,
13,
198,
2,
921,
743,
733... | 3.384127 | 315 |
from torch import nn
# def get_fe_discriminator(num_classes, ndf=64): # 256-128-64-32-16
# return nn.Sequential(
# nn.Conv2d(num_classes, ndf * 4, kernel_size=4, stride=2, padding=1),
# nn.LeakyReLU(negative_slope=0.2, inplace=True),
# nn.Conv2d(ndf * 4, ndf * 2, kernel_size=4, stride=2, padding=1),
# nn.LeakyReLU(negative_slope=0.2, inplace=True),
# nn.Conv2d(ndf * 2, ndf, kernel_size=2, stride=2, padding=0),
# nn.LeakyReLU(negative_slope=0.2, inplace=True),
# # nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1),
# # nn.LeakyReLU(negative_slope=0.2, inplace=True),
# nn.Conv2d(ndf, 1, kernel_size=2, stride=2, padding=0),
# )
# def get_fe_discriminator(num_classes, ndf=64):
# return nn.Sequential(
# nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2, padding=1),
# nn.LeakyReLU(negative_slope=0.2, inplace=True),
# nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1),
# nn.LeakyReLU(negative_slope=0.2, inplace=True),
# nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2, padding=1),
# nn.LeakyReLU(negative_slope=0.2, inplace=True),
# # nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1),
# # nn.LeakyReLU(negative_slope=0.2, inplace=True),
# nn.Conv2d(ndf * 4, 1, kernel_size=1, stride=1, padding=0),
# ) | [
6738,
28034,
1330,
299,
77,
628,
198,
198,
2,
825,
651,
62,
5036,
62,
15410,
3036,
20900,
7,
22510,
62,
37724,
11,
299,
7568,
28,
2414,
2599,
1303,
17759,
12,
12762,
12,
2414,
12,
2624,
12,
1433,
198,
2,
220,
220,
220,
220,
1441,
... | 1.856955 | 762 |
# -*- coding: utf-8 -*-
#
# Copyright Spyder Project Contributors
# Licensed under the terms of the MIT License
# (see spyder/__init__.py for details)
"""Module checking Spyder runtime dependencies"""
import os
# Local imports
from spyder.utils import programs
DEPENDENCIES = []
def add(modname, features, required_version, installed_version=None,
optional=False):
"""Add Spyder dependency"""
global DEPENDENCIES
for dependency in DEPENDENCIES:
if dependency.modname == modname:
raise ValueError("Dependency has already been registered: %s"\
% modname)
DEPENDENCIES += [Dependency(modname, features, required_version,
installed_version, optional)]
def check(modname):
"""Check if required dependency is installed"""
for dependency in DEPENDENCIES:
if dependency.modname == modname:
return dependency.check()
else:
raise RuntimeError("Unkwown dependency %s" % modname)
def status(deps=DEPENDENCIES, linesep=os.linesep):
"""Return a status of dependencies"""
maxwidth = 0
col1 = []
col2 = []
for dependency in deps:
title1 = dependency.modname
title1 += ' ' + dependency.required_version
col1.append(title1)
maxwidth = max([maxwidth, len(title1)])
col2.append(dependency.get_installed_version())
text = ""
for index in range(len(deps)):
text += col1[index].ljust(maxwidth) + ': ' + col2[index] + linesep
return text[:-1]
def missing_dependencies():
"""Return the status of missing dependencies (if any)"""
missing_deps = []
for dependency in DEPENDENCIES:
if not dependency.check() and not dependency.optional:
missing_deps.append(dependency)
if missing_deps:
return status(deps=missing_deps, linesep='<br>')
else:
return ""
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
201,
198,
2,
201,
198,
2,
15069,
220,
23688,
1082,
4935,
25767,
669,
201,
198,
2,
49962,
739,
262,
2846,
286,
262,
17168,
13789,
201,
198,
2,
357,
3826,
13997,
1082,
14,
... | 2.400966 | 828 |
import os
from setuptools import find_packages, setup
# Load version number
__version__ = None
src_dir = os.path.abspath(os.path.dirname(__file__))
version_file = os.path.join(src_dir, 'chemprop', '_version.py')
with open(version_file, encoding='utf-8') as fd:
exec(fd.read())
# Load README
with open('README.md', encoding='utf-8') as f:
long_description = f.read()
setup(
name='chemprop',
version=__version__,
author='Kyle Swanson, Kevin Yang, Wengong Jin, Lior Hirschfeld, Allison Tam',
author_email='chemprop@mit.edu',
description='Molecular Property Prediction with Message Passing Neural Networks',
long_description=long_description,
long_description_content_type='text/markdown',
url='https://github.com/chemprop/chemprop',
download_url=f'https://github.com/chemprop/chemprop/v_{__version__}.tar.gz',
project_urls={
'Documentation': 'https://chemprop.readthedocs.io/en/latest/',
'Source': 'https://github.com/chemprop/chemprop',
'PyPi': 'https://pypi.org/project/chemprop/',
'Demo': 'http://chemprop.csail.mit.edu/',
},
license='MIT',
packages=find_packages(),
package_data={'chemprop': ['py.typed']},
entry_points={
'console_scripts': [
'chemprop_train=chemprop.train:chemprop_train',
'chemprop_predict=chemprop.train:chemprop_predict',
'chemprop_fingerprint=chemprop.train:chemprop_fingerprint',
'chemprop_hyperopt=chemprop.hyperparameter_optimization:chemprop_hyperopt',
'chemprop_interpret=chemprop.interpret:chemprop_interpret',
'chemprop_web=chemprop.web.run:chemprop_web',
'sklearn_train=chemprop.sklearn_train:sklearn_train',
'sklearn_predict=chemprop.sklearn_predict:sklearn_predict',
]
},
install_requires=[
'flask>=1.1.2',
'hyperopt>=0.2.3',
'matplotlib>=3.1.3',
'numpy>=1.18.1',
'pandas>=1.0.3',
'pandas-flavor>=0.2.0',
'scikit-learn>=0.22.2.post1',
'scipy>=1.4.1',
'sphinx>=3.1.2',
'tensorboardX>=2.0',
'torch>=1.5.1',
'tqdm>=4.45.0',
'typed-argument-parser>=1.6.1'
],
extras_require={
'test': [
'pytest>=6.2.2',
'parameterized>=0.8.1'
]
},
python_requires='>=3.6',
classifiers=[
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent'
],
keywords=[
'chemistry',
'machine learning',
'property prediction',
'message passing neural network',
'graph neural network'
]
)
| [
11748,
28686,
201,
198,
6738,
900,
37623,
10141,
1330,
1064,
62,
43789,
11,
9058,
201,
198,
201,
198,
2,
8778,
2196,
1271,
201,
198,
834,
9641,
834,
796,
6045,
201,
198,
201,
198,
10677,
62,
15908,
796,
28686,
13,
6978,
13,
397,
277... | 2.106505 | 1,399 |
# Copyright 1999-2020 Alibaba Group Holding 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.
from ... import opcodes as OperandDef
from ...serialize import KeyField, ListField
from ..operands import TensorHasInput, TensorOperandMixin
from ..array_utils import get_array_module
from ..core import TensorOrder
| [
2,
15069,
7358,
12,
42334,
41992,
4912,
31703,
12052,
13,
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,
2845,
287,
11846,
351,
262,
13789,... | 3.821596 | 213 |
"""
Main module.
Implement the central Checker class.
Also, it models the Bindings and Scopes.
"""
import __future__
import doctest
import os
import sys
PY2 = sys.version_info < (3, 0)
PY32 = sys.version_info < (3, 3) # Python 2.5 to 3.2
PY33 = sys.version_info < (3, 4) # Python 2.5 to 3.3
PY34 = sys.version_info < (3, 5) # Python 2.5 to 3.4
try:
sys.pypy_version_info
PYPY = True
except AttributeError:
PYPY = False
builtin_vars = dir(__import__('__builtin__' if PY2 else 'builtins'))
try:
import ast
except ImportError: # Python 2.5
import _ast as ast
if 'decorator_list' not in ast.ClassDef._fields:
# Patch the missing attribute 'decorator_list'
ast.ClassDef.decorator_list = ()
ast.FunctionDef.decorator_list = property(lambda s: s.decorators)
from pyflakes import messages
if PY2:
else:
# Python >= 3.3 uses ast.Try instead of (ast.TryExcept + ast.TryFinally)
if PY32:
else:
if PY34:
LOOP_TYPES = (ast.While, ast.For)
else:
LOOP_TYPES = (ast.While, ast.For, ast.AsyncFor)
def counter(items):
"""
Simplest required implementation of collections.Counter. Required as 2.6
does not have Counter in collections.
"""
results = {}
for item in items:
results[item] = results.get(item, 0) + 1
return results
# Globally defined names which are not attributes of the builtins module, or
# are only present on some platforms.
_MAGIC_GLOBALS = ['__file__', '__builtins__', 'WindowsError']
def getNodeName(node):
# Returns node.id, or node.name, or None
if hasattr(node, 'id'): # One of the many nodes with an id
return node.id
if hasattr(node, 'name'): # an ExceptHandler node
return node.name
def checkDeadScopes(self):
"""
Look at scopes which have been fully examined and report names in them
which were imported but unused.
"""
for scope in self.deadScopes:
# imports in classes are public members
if isinstance(scope, ClassScope):
continue
all_binding = scope.get('__all__')
if all_binding and not isinstance(all_binding, ExportBinding):
all_binding = None
if all_binding:
all_names = set(all_binding.names)
undefined = all_names.difference(scope)
else:
all_names = undefined = []
if undefined:
if not scope.importStarred and \
os.path.basename(self.filename) != '__init__.py':
# Look for possible mistakes in the export list
for name in undefined:
self.report(messages.UndefinedExport,
scope['__all__'].source, name)
# mark all import '*' as used by the undefined in __all__
if scope.importStarred:
for binding in scope.values():
if isinstance(binding, StarImportation):
binding.used = all_binding
# Look for imported names that aren't used.
for value in scope.values():
if isinstance(value, Importation):
used = value.used or value.name in all_names
if not used:
messg = messages.UnusedImport
self.report(messg, value.source, str(value))
for node in value.redefined:
if isinstance(self.getParent(node), ast.For):
messg = messages.ImportShadowedByLoopVar
elif used:
continue
else:
messg = messages.RedefinedWhileUnused
self.report(messg, node, value.name, value.source)
def differentForks(self, lnode, rnode):
"""True, if lnode and rnode are located on different forks of IF/TRY"""
ancestor = self.getCommonAncestor(lnode, rnode, self.root)
parts = getAlternatives(ancestor)
if parts:
for items in parts:
if self.descendantOf(lnode, items, ancestor) ^ \
self.descendantOf(rnode, items, ancestor):
return True
return False
def addBinding(self, node, value):
"""
Called when a binding is altered.
- `node` is the statement responsible for the change
- `value` is the new value, a Binding instance
"""
# assert value.source in (node, node.parent):
for scope in self.scopeStack[::-1]:
if value.name in scope:
break
existing = scope.get(value.name)
if existing and not self.differentForks(node, existing.source):
parent_stmt = self.getParent(value.source)
if isinstance(existing, Importation) and isinstance(parent_stmt, ast.For):
self.report(messages.ImportShadowedByLoopVar,
node, value.name, existing.source)
elif scope is self.scope:
if (isinstance(parent_stmt, ast.comprehension) and
not isinstance(self.getParent(existing.source),
(ast.For, ast.comprehension))):
self.report(messages.RedefinedInListComp,
node, value.name, existing.source)
elif not existing.used and value.redefines(existing):
self.report(messages.RedefinedWhileUnused,
node, value.name, existing.source)
elif isinstance(existing, Importation) and value.redefines(existing):
existing.redefined.append(node)
if value.name in self.scope:
# then assume the rebound name is used as a global or within a loop
value.used = self.scope[value.name].used
self.scope[value.name] = value
def isDocstring(self, node):
"""
Determine if the given node is a docstring, as long as it is at the
correct place in the node tree.
"""
return isinstance(node, ast.Str) or (isinstance(node, ast.Expr) and
isinstance(node.value, ast.Str))
_getDoctestExamples = doctest.DocTestParser().get_examples
# "stmt" type nodes
DELETE = PRINT = FOR = ASYNCFOR = WHILE = IF = WITH = WITHITEM = \
ASYNCWITH = ASYNCWITHITEM = RAISE = TRYFINALLY = EXEC = \
EXPR = ASSIGN = handleChildren
PASS = ignore
# "expr" type nodes
BOOLOP = BINOP = UNARYOP = IFEXP = SET = \
COMPARE = CALL = REPR = ATTRIBUTE = SUBSCRIPT = \
STARRED = NAMECONSTANT = handleChildren
NUM = STR = BYTES = ELLIPSIS = ignore
# "slice" type nodes
SLICE = EXTSLICE = INDEX = handleChildren
# expression contexts are node instances too, though being constants
LOAD = STORE = DEL = AUGLOAD = AUGSTORE = PARAM = ignore
# same for operators
AND = OR = ADD = SUB = MULT = DIV = MOD = POW = LSHIFT = RSHIFT = \
BITOR = BITXOR = BITAND = FLOORDIV = INVERT = NOT = UADD = USUB = \
EQ = NOTEQ = LT = LTE = GT = GTE = IS = ISNOT = IN = NOTIN = \
MATMULT = ignore
# additional node types
COMPREHENSION = KEYWORD = FORMATTEDVALUE = JOINEDSTR = handleChildren
def GLOBAL(self, node):
"""
Keep track of globals declarations.
"""
global_scope_index = 1 if self._in_doctest() else 0
global_scope = self.scopeStack[global_scope_index]
# Ignore 'global' statement in global scope.
if self.scope is not global_scope:
# One 'global' statement can bind multiple (comma-delimited) names.
for node_name in node.names:
node_value = Assignment(node_name, node)
# Remove UndefinedName messages already reported for this name.
# TODO: if the global is not used in this scope, it does not
# become a globally defined name. See test_unused_global.
self.messages = [
m for m in self.messages if not
isinstance(m, messages.UndefinedName) or
m.message_args[0] != node_name]
# Bind name to global scope if it doesn't exist already.
global_scope.setdefault(node_name, node_value)
# Bind name to non-global scopes, but as already "used".
node_value.used = (global_scope, node)
for scope in self.scopeStack[global_scope_index + 1:]:
scope[node_name] = node_value
NONLOCAL = GLOBAL
LISTCOMP = handleChildren if PY2 else GENERATOREXP
DICTCOMP = SETCOMP = GENERATOREXP
def NAME(self, node):
"""
Handle occurrence of Name (which can be a load/store/delete access.)
"""
# Locate the name in locals / function / globals scopes.
if isinstance(node.ctx, (ast.Load, ast.AugLoad)):
self.handleNodeLoad(node)
if (node.id == 'locals' and isinstance(self.scope, FunctionScope)
and isinstance(node.parent, ast.Call)):
# we are doing locals() call in current scope
self.scope.usesLocals = True
elif isinstance(node.ctx, (ast.Store, ast.AugStore)):
self.handleNodeStore(node)
elif isinstance(node.ctx, ast.Del):
self.handleNodeDelete(node)
else:
# must be a Param context -- this only happens for names in function
# arguments, but these aren't dispatched through here
raise RuntimeError("Got impossible expression context: %r" % (node.ctx,))
BREAK = CONTINUE
AWAIT = YIELDFROM = YIELD
ASYNCFUNCTIONDEF = FUNCTIONDEF
def CLASSDEF(self, node):
"""
Check names used in a class definition, including its decorators, base
classes, and the body of its definition. Additionally, add its name to
the current scope.
"""
for deco in node.decorator_list:
self.handleNode(deco, node)
for baseNode in node.bases:
self.handleNode(baseNode, node)
if not PY2:
for keywordNode in node.keywords:
self.handleNode(keywordNode, node)
self.pushScope(ClassScope)
# doctest does not process doctest within a doctest
# classes within classes are processed.
if (self.withDoctest and
not self._in_doctest() and
not isinstance(self.scope, FunctionScope)):
self.deferFunction(lambda: self.handleDoctests(node))
for stmt in node.body:
self.handleNode(stmt, node)
self.popScope()
self.addBinding(node, ClassDefinition(node.name, node))
LIST = TUPLE
TRYEXCEPT = TRY
| [
37811,
201,
198,
13383,
8265,
13,
201,
198,
201,
198,
3546,
26908,
262,
4318,
6822,
263,
1398,
13,
201,
198,
7583,
11,
340,
4981,
262,
41211,
654,
290,
1446,
13920,
13,
201,
198,
37811,
201,
198,
11748,
11593,
37443,
834,
201,
198,
... | 2.090156 | 5,435 |
# Project Name: Auto Screenshot
# Description: Take screenshot of screen when any change take place.
# Author: Mani (Infinyte7)
# Date: 26-10-2020
# License: MIT
from pyscreenshot import grab
from PIL import ImageChops
import os
import time
import subprocess, sys
from datetime import datetime
import tkinter as tk
from tkinter import *
from tkinter import font
if __name__ == "__main__":
root = Tk()
gui = AutoScreenshot(root)
root.mainloop()
| [
2,
4935,
6530,
25,
11160,
1446,
26892,
198,
2,
12489,
25,
7214,
22032,
286,
3159,
618,
597,
1487,
1011,
1295,
13,
198,
2,
6434,
25,
1869,
72,
357,
18943,
3541,
660,
22,
8,
198,
2,
7536,
25,
2608,
12,
940,
12,
42334,
198,
2,
1378... | 3.086667 | 150 |
# -*- coding: utf-8 -*-
# 2019
#
#
#
# *
# Apache License 2.0Apache 2.0 Apache 2.0 http://www.apache.org/licenses/LICENSE-2.0
#
#
# *
#
# Apache 2.0 Apache 2.0
# public@ricequant.com
from datetime import datetime
import logbook
from logbook import Logger, StderrHandler
from rqalpha.utils.py2 import to_utf8
logbook.set_datetime_format("local")
# patch warn
logbook.base._level_names[logbook.base.WARNING] = 'WARN'
__all__ = [
"user_log",
"system_log",
"user_system_log",
]
DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S.%f"
user_std_handler = StderrHandler(bubble=True)
user_std_handler.formatter = user_std_handler_log_formatter
# loggers
# logger
user_log = Logger("user_log")
#
user_system_log = Logger("user_system_log")
#
user_detail_log = Logger("user_detail_log")
# user_detail_log.handlers.append(StderrHandler(bubble=True))
#
system_log = Logger("system_log")
basic_system_log = Logger("basic_system_log")
#
std_log = Logger("std_log")
init_logger()
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
2,
220,
13130,
220,
198,
2,
198,
2,
220,
198,
2,
198,
2,
220,
220,
220,
220,
1635,
220,
198,
2,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24843,
13789,
362,
1... | 2.21308 | 474 |
# -*- coding: utf-8 -*-
'''
Oracle DataBase connection module
:mainteiner: Vladimir Bormotov <bormotov@gmail.com>
:maturity: new
:depends: cx_Oracle
:platform: all
:configuration: module provide connections for multiple Oracle DB instances.
**OS Environment**
.. code-block:: text
ORACLE_HOME: path to oracle product
PATH: path to Oracle Client libs need to be in PATH
**pillar**
.. code-block:: text
oracle.dbs: list of known based
oracle.dbs.<db>.uri: connection credentials in format:
user/password@host[:port]/sid[ as {sysdba|sysoper}]
'''
import os
import logging
from salt.utils.decorators import depends
log = logging.getLogger(__name__)
try:
import cx_Oracle
MODE = {
'sysdba': cx_Oracle.SYSDBA,
'sysoper': cx_Oracle.SYSOPER
}
HAS_CX_ORACLE = True
except ImportError:
MODE = {'sysdba': 2, 'sysoper': 4}
HAS_CX_ORACLE = False
__virtualname__ = 'oracle'
def __virtual__():
'''
Load module only if cx_Oracle installed
'''
return __virtualname__ if HAS_CX_ORACLE else False
def _cx_oracle_req():
'''
Fallback function stub
'''
return 'Need "cx_Oracle" and Oracle Client installed for this functin exist'
def _unicode_output(cursor, name, default_type, size, precision, scale):
'''
Return strings values as python unicode string
http://www.oracle.com/technetwork/articles/dsl/tuininga-cx-oracle-084866.html
'''
if default_type in (cx_Oracle.STRING, cx_Oracle.LONG_STRING,
cx_Oracle.FIXED_CHAR, cx_Oracle.CLOB):
return cursor.var(unicode, size, cursor.arraysize)
def _connect(uri):
'''
uri = user/password@host[:port]/sid[ as {sysdba|sysoper}]
Return cx_Oracle.Connection instance
'''
# cx_Oracle.Connection() not support 'as sysdba' syntax
uri_l = uri.rsplit(' as ', 1)
if len(uri_l) == 2:
credentials, mode = uri_l
mode = MODE[mode]
else:
credentials = uri_l[0]
mode = 0
userpass, hostportsid = credentials.split('@')
user, password = userpass.split('/')
hostport, sid = hostportsid.split('/')
hostport_l = hostport.split(':')
if len(hostport_l) == 2:
host, port = hostport_l
else:
host = hostport_l[0]
port = 1521
log.debug('connect: {0}'.format((user, password, host, port, sid, mode)))
# force UTF-8 client encoding
os.environ['NLS_LANG'] = '.AL32UTF8'
conn = cx_Oracle.connect(user, password,
cx_Oracle.makedsn(host, port, sid),
mode)
conn.outputtypehandler = _unicode_output
return conn
def show_dbs(*dbs):
'''
Show databases configuration from pillar. Filter by args
.. code-block:: bash
salt '*' oracle.show_dbs
salt '*' oracle.show_dbs my_db
'''
if dbs:
log.debug('get dbs from pillar: {0}'.format(dbs))
result = {}
for db in dbs:
result[db] = __salt__['pillar.get']('oracle:dbs:' + db)
return result
else:
pillar_dbs = __salt__['pillar.get']('oracle:dbs')
log.debug('get all ({0}) dbs from pillar'.format(len(pillar_dbs)))
return pillar_dbs
def show_pillar(item=None):
'''
Show Pillar segment oracle.* and subitem with notation "item:subitem"
CLI Example:
.. code-block:: bash
salt '*' oracle.show_pillar
salt '*' oracle.show_pillar dbs:my_db
'''
if item:
return __salt__['pillar.get']('oracle:' + item)
else:
return __salt__['pillar.get']('oracle')
def show_env():
'''
Show Environment used by Oracle Client
CLI Example:
.. code-block:: bash
salt '*' oracle.show_env
.. note::
at first _connect() ``NLS_LANG`` will forced to '.AL32UTF8'
'''
envs = ['PATH', 'ORACLE_HOME', 'TNS_ADMIN', 'NLS_LANG']
result = {}
for env in envs:
if env in os.environ:
result[env] = os.environ[env]
return result
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
7061,
6,
198,
48625,
6060,
14881,
4637,
8265,
198,
198,
25,
12417,
660,
7274,
25,
14840,
347,
579,
313,
709,
1279,
65,
579,
313,
709,
31,
14816,
13,
785,
29,
198,
19... | 2.269231 | 1,794 |
import base64
import operator
import struct
import time
import pytest
import six
import consul
import consul.std
Check = consul.Check
| [
11748,
2779,
2414,
198,
11748,
10088,
198,
11748,
2878,
198,
11748,
640,
198,
198,
11748,
12972,
9288,
198,
11748,
2237,
198,
198,
11748,
762,
377,
198,
11748,
762,
377,
13,
19282,
628,
198,
9787,
796,
762,
377,
13,
9787,
628,
198
] | 3.414634 | 41 |
import math
tot = 1
dia = 0
for side_length in range(3,100001,2):
hi = side_length**2
for i in range(4):
if is_prime(hi-i*side_length+i):
dia = dia+1
tot = tot+4
if dia/tot < 0.1:
print(side_length)
break
| [
11748,
10688,
201,
198,
201,
198,
83,
313,
796,
352,
201,
198,
67,
544,
796,
657,
201,
198,
1640,
1735,
62,
13664,
287,
2837,
7,
18,
11,
49388,
16,
11,
17,
2599,
201,
198,
197,
5303,
796,
1735,
62,
13664,
1174,
17,
201,
198,
197... | 1.886179 | 123 |
task = '''
.
() ,
. .
: ( ) ( ).
: V H, .
:
(V/6.5 + 0.5), (2 * H + 0.3).
.
.
: ,
@property.
'''
if __name__ == '__main__':
print(task)
objects = [
231,
22,
Coat(32),
'test',
True,
Costume(87),
Coat(32)
]
need_material = 0
for obj in objects:
if isinstance(obj, Clothes):
need_material += obj.need_material
print(need_material)
| [
35943,
796,
705,
7061,
198,
220,
220,
220,
220,
220,
220,
220,
764,
198,
220,
7499,
220,
220,
220,
837,
220,
220,
220,
198,
764,
220,
220,
220,
220,
220,
220,
220,
220,
220,
764,
628,
220,
220,
220,
220,
1058,
220,
357,
1267,
22... | 1.681818 | 308 |
import urllib2
import json
import time
from core.helpers.decorator import Cached
from core.helpers.config import config
from core.helpers.logger import log, LogLevel
| [
11748,
2956,
297,
571,
17,
198,
11748,
33918,
198,
11748,
640,
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198,
6738,
4755,
13,
16794,
364,
13,
12501,
273,
1352,
1330,
327,
2317,
198,
6738,
4755,
13,
16794,
364,
13,
11250,
1330,
4566,
198,
6738,
4755,
13,
16794,
364,
13,
... | 3.333333 | 51 |
from math import log10, sqrt
import cv2
import numpy as np
def PSNR(original, compressed):
'''
Calculates the Peak signal to noise ratio between a ground truth image and predicted image.
see https://www.geeksforgeeks.org/python-peak-signal-to-noise-ratio-psnr/
for reference
Parameters
----------
true image (cv2 image)
predicted image (cv2 image)
Returns
-------
PSNR score
'''
mse = np.mean((original - compressed) ** 2)
if(mse == 0): # MSE is zero means no noise is present in the signal .
# Therefore PSNR have no importance.
return 100
max_pixel = 255.0
psnr = 20 * log10(max_pixel / sqrt(mse))
return psnr
def colourfulnessMetric(img):
"""
Created on Mon Nov 15 10:55:16 2021
@author: Yucheng
Parameters
----------
img : cv2 RGB image
Returns
-------
M : colourness metric
-----------------------------
|not colourful | 0 |
|slightly colorful | 15 |
|moderately colourful | 33 |
|averagely colourful | 45 |
|quite colourful | 59 |
|highly colourful | 82 |
|extremely colourful | 109 |
-----------------------------
"""
# Get RGB components
R,G,B = cv2.split(img.astype("float"))
# colourfulness metric from Hasler et al., section 7
rg = R - G
yb = (1/2) * (R+G) - B
sigma_rgyb = np.sqrt(np.var(rg) + np.var(yb))
mu_rgyb = np.sqrt(np.mean(rg)**2 + np.mean(yb)**2)
M = sigma_rgyb + 0.3 * mu_rgyb
return M
if __name__ == "__main__":
main()
| [
6738,
10688,
1330,
2604,
940,
11,
19862,
17034,
198,
11748,
269,
85,
17,
198,
11748,
299,
32152,
355,
45941,
198,
198,
4299,
6599,
24723,
7,
14986,
11,
25388,
2599,
198,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
27131,
689,
262,
... | 2.285319 | 722 |
"""Test insight model is working the way it should."""
import pytest
from django.core.exceptions import ValidationError
from django.db import DataError
from .factories import InsightFactory
pytestmark = pytest.mark.django_db
| [
37811,
14402,
11281,
2746,
318,
1762,
262,
835,
340,
815,
526,
15931,
198,
11748,
12972,
9288,
198,
6738,
42625,
14208,
13,
7295,
13,
1069,
11755,
1330,
3254,
24765,
12331,
198,
6738,
42625,
14208,
13,
9945,
1330,
6060,
12331,
198,
198,
... | 3.530303 | 66 |
import os
import subprocess
test_suites_that_cant_run_in_parallel = [
"test-apis-dbgapTest", # not thread-safe
"test-google-googleDataAccessTest", # not thread-safe
"test-google-googleServiceAccountRemovalTest", # not thread-safe
"test-guppy-guppyTest", # not thread-safe
"test-smokeTests-brainTests", # manual (executable test)
"test-batch-GoogleBucketManifestGenerationTest", # @donot
"test-batch-S3BucketManifestGenerationTest", # @donot
"test-portal-dataguidOrgTest", # @donot
"test-mariner-marinerIntegrationTest", # @donot
"test-suites-fail", # special suite to force failures for invalid test labels
"test-portal-roleBasedUITest", # manual (executable test)
"test-portal-limitedFilePFBExportTestPlan", # manual (executable test)
"test-access-accessGUITest", # manual (executable test)
"test-portal-tieredAccessTest", # manual (executable test)
"test-portal-discoveryPageTestPlan", # manual (executable test)
"test-portal-dashboardReportsTest", # manual (executable test)
"test-guppy-nestedAggTest", # manual (executable test)
"test-portal-404pageTest", # manual (executable test)
"test-apis-dcfDataReplicationTest", # manual (executable test)
"test-portal-exportPfbToWorkspaceTest", # manual (executable test)
"test-portal-homepageChartNodesExecutableTestPlan",# manual (executable test)
"test-portal-profilePageTest", # manual (executable test)
"test-portal-terraExportWarningTestPlan", # manual (executable test)
"test-pelican-exportPfbTest", # not ready
"test-regressions-exportPerformanceTest", # legacy (disabled test)
"test-regressions-generateTestData", # legacy (disabled test)
"test-regressions-queryPerformanceTest", # legacy (disabled test)
"test-regressions-submissionPerformanceTest", # legacy (disabled test)
"test-dream-challenge-DCgen3clientTest", # legacy (disabled test)
"test-dream-challenge-synapaseLoginTest", # legacy (disabled test)
"test-prod-checkAllProjectsBucketAccessTest", # prod test
"test-portal-pfbExportTest", # nightly build test
"test-apis-etlTest", # long-running test
"test-apis-centralizedAuth", # long-running test
"test-google-googleServiceAccountTest", # long-running test
"test-google-googleServiceAccountKeyTest", # long-running test
"test-portal-dataUploadTest", # SUPER long-running test
"test-portal-indexingPageTest", # long-running test
"test-apis-metadataIngestionTest", # long-running test
"test-apis-auditServiceTest" # long-running test
]
if __name__ == "__main__":
main()
| [
11748,
28686,
198,
11748,
850,
14681,
198,
198,
9288,
62,
2385,
2737,
62,
5562,
62,
66,
415,
62,
5143,
62,
259,
62,
1845,
29363,
796,
685,
198,
220,
220,
220,
366,
9288,
12,
499,
271,
12,
9945,
43554,
14402,
1600,
220,
220,
220,
2... | 2.110092 | 1,526 |
from lib.utils.plugin import import_plugin
from .base_executor import parse_exception
from .executors.hive import HiveQueryExecutor
from .executors.presto import PrestoQueryExecutor
from .executors.sqlalchemy import (
MysqlQueryExecutor,
DruidQueryExecutor,
SqliteQueryExecutor,
SnowflakeQueryExecutor,
)
from .executors.bigquery import BigQueryQueryExecutor
ALL_PLUGIN_EXECUTORS = import_plugin("executor_plugin", "ALL_PLUGIN_EXECUTORS", [])
ALL_EXECUTORS = [
HiveQueryExecutor,
PrestoQueryExecutor,
MysqlQueryExecutor,
DruidQueryExecutor,
SqliteQueryExecutor,
BigQueryQueryExecutor,
SnowflakeQueryExecutor,
] + ALL_PLUGIN_EXECUTORS
# Re-export parse_exception
parse_exception
| [
6738,
9195,
13,
26791,
13,
33803,
1330,
1330,
62,
33803,
198,
6738,
764,
8692,
62,
18558,
38409,
1330,
21136,
62,
1069,
4516,
198,
198,
6738,
764,
18558,
315,
669,
13,
71,
425,
1330,
33235,
20746,
23002,
38409,
198,
6738,
764,
18558,
... | 2.713755 | 269 |
import itertools
import random
import re
from contextlib import suppress
from disnake import Embed, NotFound
from disnake.ext.commands import Cog, Context, command
from disnake.utils import escape_markdown
from bot.bot import Bot
from bot.constants import Colours, NEGATIVE_REPLIES, RedirectOutput
from bot.log import get_logger
from bot.utils.messages import wait_for_deletion
URL = "https://pypi.org/pypi/{package}/json"
PYPI_ICON = "https://cdn.discordapp.com/emojis/766274397257334814.png"
PYPI_COLOURS = itertools.cycle((Colours.yellow, Colours.blue, Colours.white))
ILLEGAL_CHARACTERS = re.compile(r"[^-_.a-zA-Z0-9]+")
INVALID_INPUT_DELETE_DELAY = RedirectOutput.delete_delay
log = get_logger(__name__)
def setup(bot: Bot) -> None:
"""Load the PyPi cog."""
bot.add_cog(PyPi(bot))
| [
11748,
340,
861,
10141,
198,
11748,
4738,
198,
11748,
302,
198,
6738,
4732,
8019,
1330,
18175,
198,
198,
6738,
595,
77,
539,
1330,
13302,
276,
11,
1892,
21077,
198,
6738,
595,
77,
539,
13,
2302,
13,
9503,
1746,
1330,
327,
519,
11,
3... | 2.598706 | 309 |
from functools import wraps
from flask import abort
from flask_login import current_user
from .models import Permission
<<<<<<< HEAD
=======
>>>>>>> 17-app-1
| [
6738,
1257,
310,
10141,
1330,
27521,
198,
6738,
42903,
1330,
15614,
198,
6738,
42903,
62,
38235,
1330,
1459,
62,
7220,
198,
6738,
764,
27530,
1330,
2448,
3411,
198,
198,
16791,
16791,
16791,
27,
39837,
198,
198,
1421,
18604,
198,
16471,
... | 3.404255 | 47 |
# !/usr/bin/env python3
# -*- coding:utf-8 -*-
# @Datetime : 2019/11/14 2:26
# @Author : Fangyang
# @Software : PyCharm
import sys
from PyQt5.QtWidgets import QApplication
import pyqtgraph as pg
import numpy as np
app = QApplication(sys.argv)
x = np.arange(1000)
y = np.random.normal(size=(3, 1000))
plotWidget = pg.plot(title='Three plot curves')
for i in range(3):
plotWidget.plot(x, y[i], pen=(i, 3))
status = app.exec_()
sys.exit(status)
if __name__ == '__main__':
pass
| [
2,
5145,
14,
14629,
14,
8800,
14,
24330,
21015,
18,
198,
2,
532,
9,
12,
19617,
25,
40477,
12,
23,
532,
9,
12,
198,
198,
2,
2488,
27354,
8079,
1058,
13130,
14,
1157,
14,
1415,
362,
25,
2075,
198,
2,
2488,
13838,
220,
220,
1058,
... | 2.420792 | 202 |
from .array import Array, wrap
from .ast_utils import (get_symbols, get_assigned,
get_unknown_names_and_calls, has_return, has_node)
from .config import get_config, set_config, use_config, Config
from .cython_generator import (
CythonGenerator, get_func_definition
)
from .ext_module import ExtModule
from .extern import Extern
from .low_level import Kernel, LocalMem, Cython, cast
from .parallel import (
Elementwise, Reduction, Scan, elementwise
)
from .profile import (
get_profile_info, named_profile, profile, profile_ctx, print_profile,
profile_kernel, ProfileContext, profile2csv
)
from .translator import (
CConverter, CStructHelper, OpenCLConverter, detect_type, ocl_detect_type,
py2c
)
from .types import KnownType, annotate, declare
from .utils import ArgumentParser
| [
6738,
764,
18747,
1330,
15690,
11,
14441,
198,
6738,
764,
459,
62,
26791,
1330,
357,
1136,
62,
1837,
2022,
10220,
11,
651,
62,
562,
3916,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220... | 2.929078 | 282 |