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#!/usr/bin/env python3
# Copyright (c) 2014-2017 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""Test mempool persistence.
By default, bwscoind will dump mempool on shutdown and
then reload it on startup. This can be overridden with
the -persistmempool=0 command line option.
Test is as follows:
- start node0, node1 and node2. node1 has -persistmempool=0
- create 5 transactions on node2 to its own address. Note that these
are not sent to node0 or node1 addresses because we don't want
them to be saved in the wallet.
- check that node0 and node1 have 5 transactions in their mempools
- shutdown all nodes.
- startup node0. Verify that it still has 5 transactions
in its mempool. Shutdown node0. This tests that by default the
mempool is persistent.
- startup node1. Verify that its mempool is empty. Shutdown node1.
This tests that with -persistmempool=0, the mempool is not
dumped to disk when the node is shut down.
- Restart node0 with -persistmempool=0. Verify that its mempool is
empty. Shutdown node0. This tests that with -persistmempool=0,
the mempool is not loaded from disk on start up.
- Restart node0 with -persistmempool. Verify that it has 5
transactions in its mempool. This tests that -persistmempool=0
does not overwrite a previously valid mempool stored on disk.
- Remove node0 mempool.dat and verify savemempool RPC recreates it
and verify that node1 can load it and has 5 transaction in its
mempool.
- Verify that savemempool throws when the RPC is called if
node1 can't write to disk.
"""
import os
import time
from test_framework.test_framework import BWScoinTestFramework
from test_framework.util import *
class MempoolPersistTest(BWScoinTestFramework):
def set_test_params(self):
self.num_nodes = 3
self.extra_args = [[], ["-persistmempool=0"], []]
def run_test(self):
chain_height = self.nodes[0].getblockcount()
assert_equal(chain_height, 200)
self.log.debug("Mine a single block to get out of IBD")
self.nodes[0].generate(1)
self.sync_all()
self.log.debug("Send 5 transactions from node2 (to its own address)")
for i in range(5):
self.nodes[2].sendtoaddress(self.nodes[2].getnewaddress(), Decimal("10"))
self.sync_all()
self.log.debug("Verify that node0 and node1 have 5 transactions in their mempools")
assert_equal(len(self.nodes[0].getrawmempool()), 5)
assert_equal(len(self.nodes[1].getrawmempool()), 5)
self.log.debug("Stop-start node0 and node1. Verify that node0 has the transactions in its mempool and node1 does not.")
self.stop_nodes()
self.start_node(0)
self.start_node(1)
# Give bwscoind a second to reload the mempool
time.sleep(1)
wait_until(lambda: len(self.nodes[0].getrawmempool()) == 5)
assert_equal(len(self.nodes[1].getrawmempool()), 0)
self.log.debug("Stop-start node0 with -persistmempool=0. Verify that it doesn't load its mempool.dat file.")
self.stop_nodes()
self.start_node(0, extra_args=["-persistmempool=0"])
# Give bwscoind a second to reload the mempool
time.sleep(1)
assert_equal(len(self.nodes[0].getrawmempool()), 0)
self.log.debug("Stop-start node0. Verify that it has the transactions in its mempool.")
self.stop_nodes()
self.start_node(0)
wait_until(lambda: len(self.nodes[0].getrawmempool()) == 5)
mempooldat0 = os.path.join(self.options.tmpdir, 'node0', 'regtest', 'mempool.dat')
mempooldat1 = os.path.join(self.options.tmpdir, 'node1', 'regtest', 'mempool.dat')
self.log.debug("Remove the mempool.dat file. Verify that savemempool to disk via RPC re-creates it")
os.remove(mempooldat0)
self.nodes[0].savemempool()
assert os.path.isfile(mempooldat0)
self.log.debug("Stop nodes, make node1 use mempool.dat from node0. Verify it has 5 transactions")
os.rename(mempooldat0, mempooldat1)
self.stop_nodes()
self.start_node(1, extra_args=[])
wait_until(lambda: len(self.nodes[1].getrawmempool()) == 5)
self.log.debug("Prevent bwscoind from writing mempool.dat to disk. Verify that `savemempool` fails")
# to test the exception we are setting bad permissions on a tmp file called mempool.dat.new
# which is an implementation detail that could change and break this test
mempooldotnew1 = mempooldat1 + '.new'
with os.fdopen(os.open(mempooldotnew1, os.O_CREAT, 0o000), 'w'):
pass
assert_raises_rpc_error(-1, "Unable to dump mempool to disk", self.nodes[1].savemempool)
os.remove(mempooldotnew1)
if __name__ == '__main__':
MempoolPersistTest().main()
| 44.558559 | 127 | 0.694703 | [
"MIT"
] | bwsnetwork/bwscoin | test/functional/mempool_persist.py | 4,946 | Python |
# Copyright (c) 2015 Nicolas JOUANIN
#
# See the file license.txt for copying permission.
import logging
from functools import partial
class EventLoggerPlugin:
def __init__(self, context):
self.context = context
async def log_event(self, *args, **kwargs):
self.context.logger.info("### '%s' EVENT FIRED ###", kwargs['event_name'].replace('old', ''))
def __getattr__(self, name):
if name.startswith("on_"):
return partial(self.log_event, event_name=name)
class PacketLoggerPlugin:
def __init__(self, context):
self.context = context
async def on_mqtt_packet_received(self, *args, **kwargs):
packet = kwargs.get('packet')
session = kwargs.get('session', None)
if self.context.logger.isEnabledFor(logging.DEBUG):
if session:
self.context.logger.debug("%s <-in-- %r", session.client_id, packet)
else:
self.context.logger.debug("<-in-- %r", packet)
async def on_mqtt_packet_sent(self, *args, **kwargs):
packet = kwargs.get('packet')
session = kwargs.get('session', None)
if self.context.logger.isEnabledFor(logging.DEBUG):
if session:
self.context.logger.debug("%s -out-> %r", session.client_id, packet)
else:
self.context.logger.debug("-out-> %r", packet)
| 32.325581 | 101 | 0.615827 | [
"MIT"
] | norwood867/distmqtt | distmqtt/plugins/logging.py | 1,390 | Python |
"""
WSGI config for share_all project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.11/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "share_all.settings")
application = get_wsgi_application()
| 23.294118 | 78 | 0.787879 | [
"MIT"
] | yingshaoxo/Share_All | share_all/wsgi.py | 396 | Python |
RAW_DATA_PATH = 'data/raw'
PROCESSED_DATA_PATH = 'data/processed'
OUTPUT_DATA_PATH = 'data/output'
MODELS_PATH = 'models'
REPORTS_PATH = 'reports'
continuous = ['temp', 'atemp','hum', 'windspeed', 'registered', 'cnt', 'casual', 'yr']
cyclic = ['season', 'mnth', 'hr', 'weekday']
categorical = ['holiday', 'workingday', 'weathersit']
drop = ['instant', 'dteday']
all_cols = continuous + cyclic + categorical + drop
| 32 | 86 | 0.685096 | [
"MIT"
] | carlomazzaferro/scikit-hts-examples | src/settings.py | 416 | Python |
# Copyright (C) 2019-2020 Intel Corporation
#
# SPDX-License-Identifier: MIT
import attr
from contextlib import ExitStack
from distutils.util import strtobool as str_to_bool # pylint: disable=unused-import
from functools import partial, wraps
from itertools import islice
from typing import Iterable, Tuple
NOTSET = object()
def find(iterable, pred=lambda x: True, default=None):
return next((x for x in iterable if pred(x)), default)
def cast(value, type_conv, default=None):
if value is None:
return default
try:
return type_conv(value)
except Exception:
return default
def to_snake_case(s):
if not s:
return ''
name = [s[0].lower()]
for idx, char in enumerate(s[1:]):
idx = idx + 1
if char.isalpha() and char.isupper():
prev_char = s[idx - 1]
if not (prev_char.isalpha() and prev_char.isupper()):
# avoid "HTML" -> "h_t_m_l"
name.append('_')
name.append(char.lower())
else:
name.append(char)
return ''.join(name)
def pairs(iterable):
a = iter(iterable)
return zip(a, a)
def take_by(iterable, count):
"""
Returns elements from the input iterable by batches of N items.
('abcdefg', 3) -> ['a', 'b', 'c'], ['d', 'e', 'f'], ['g']
"""
it = iter(iterable)
while True:
batch = list(islice(it, count))
if len(batch) == 0:
break
yield batch
def filter_dict(d, exclude_keys):
return { k: v for k, v in d.items() if k not in exclude_keys }
def parse_str_enum_value(value, enum_class, default=NOTSET,
unknown_member_error=None):
if value is None and default is not NOTSET:
value = default
elif isinstance(value, str):
try:
value = enum_class[value]
except KeyError:
raise ValueError((unknown_member_error or
"Unknown element of {cls} '{value}'. "
"The only known are: {available}") \
.format(
cls=enum_class.__name__,
value=value,
available=', '.join(e.name for e in enum_class)
)
)
elif isinstance(value, enum_class):
pass
else:
raise TypeError("Expected value type string or %s, but got %s" % \
(enum_class.__name__, type(value).__name__))
return value
def escape(s: str, escapes: Iterable[Tuple[str, str]]) -> str:
"""
'escapes' is an iterable of (pattern, substitute) pairs
"""
for pattern, sub in escapes:
s = s.replace(pattern, sub)
return s
def unescape(s: str, escapes: Iterable[Tuple[str, str]]) -> str:
"""
'escapes' is an iterable of (pattern, substitute) pairs
"""
for pattern, sub in escapes:
s = s.replace(sub, pattern)
return s
def optional_arg_decorator(fn):
@wraps(fn)
def wrapped_decorator(*args, **kwargs):
if len(args) == 1 and callable(args[0]) and not kwargs:
return fn(args[0], **kwargs)
else:
def real_decorator(decoratee):
return fn(decoratee, *args, **kwargs)
return real_decorator
return wrapped_decorator
class Rollback:
@attr.attrs
class Handler:
callback = attr.attrib()
enabled = attr.attrib(default=True)
ignore_errors = attr.attrib(default=False)
def __call__(self):
if self.enabled:
try:
self.callback()
except: # pylint: disable=bare-except
if not self.ignore_errors:
raise
def __init__(self):
self._handlers = {}
self._stack = ExitStack()
self.enabled = True
def add(self, callback, *args,
name=None, enabled=True, ignore_errors=False,
fwd_kwargs=None, **kwargs):
if args or kwargs or fwd_kwargs:
if fwd_kwargs:
kwargs.update(fwd_kwargs)
callback = partial(callback, *args, **kwargs)
name = name or hash(callback)
assert name not in self._handlers
handler = self.Handler(callback,
enabled=enabled, ignore_errors=ignore_errors)
self._handlers[name] = handler
self._stack.callback(handler)
return name
do = add # readability alias
def enable(self, name=None):
if name:
self._handlers[name].enabled = True
else:
self.enabled = True
def disable(self, name=None):
if name:
self._handlers[name].enabled = False
else:
self.enabled = False
def clean(self):
self.__exit__(None, None, None)
def __enter__(self):
return self
# pylint: disable=redefined-builtin
def __exit__(self, type=None, value=None, traceback=None):
if type is None:
return
if not self.enabled:
return
self._stack.__exit__(type, value, traceback)
# pylint: enable=redefined-builtin
@optional_arg_decorator
def error_rollback(func, arg_name='on_error', implicit=False):
@wraps(func)
def wrapped_func(*args, **kwargs):
with Rollback() as manager:
if implicit:
fglobals = func.__globals__
has_arg = arg_name in fglobals
old_val = fglobals.get(arg_name)
fglobals[arg_name] = manager
try:
func(*args, **kwargs)
finally:
if has_arg:
func.__globals__[arg_name] = old_val
else:
func.__globals__.pop(arg_name)
else:
kwargs[arg_name] = manager
func(*args, **kwargs)
return wrapped_func
| 28.444444 | 83 | 0.564708 | [
"MIT"
] | AdaptiveCity/datumaro | datumaro/util/__init__.py | 5,888 | Python |
from django import forms
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth.models import User
from django.forms.models import ModelForm
from .models import Image,Comment,Profile
class RegisterUserForm(UserCreationForm):
email = forms.EmailField()
first_name = forms.CharField(max_length=50)
last_name = forms.CharField(max_length=50)
class Meta:
model = User
fields = ('username','first_name','last_name','email','password1','password2')
class ImageForm(ModelForm):
class Meta:
model = Image
fields = ['image','name','caption','location' ]
class ProfileForm(ModelForm):
class Meta:
model = Profile
fields = ['bio','profile_pic' ]
class CommentForm(ModelForm):
class Meta:
model = Comment
fields =['content']
widgets = {
'content':forms.Textarea(attrs={'rows':2,'placeholder':'write comment'}),
} | 25.475 | 86 | 0.624141 | [
"MIT"
] | kipsang01/bonga-app | bongaapp/forms.py | 1,019 | Python |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: tensorflow/core/protobuf/saver.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='tensorflow/core/protobuf/saver.proto',
package='tensorflow',
syntax='proto3',
serialized_options=b'\n\023org.tensorflow.utilB\013SaverProtosP\001ZHgithub.com/tensorflow/tensorflow/tensorflow/go/core/core_protos_go_proto\370\001\001',
create_key=_descriptor._internal_create_key,
serialized_pb=b'\n$tensorflow/core/protobuf/saver.proto\x12\ntensorflow\"\x9e\x02\n\x08SaverDef\x12\x1c\n\x14\x66ilename_tensor_name\x18\x01 \x01(\t\x12\x18\n\x10save_tensor_name\x18\x02 \x01(\t\x12\x17\n\x0frestore_op_name\x18\x03 \x01(\t\x12\x13\n\x0bmax_to_keep\x18\x04 \x01(\x05\x12\x0f\n\x07sharded\x18\x05 \x01(\x08\x12%\n\x1dkeep_checkpoint_every_n_hours\x18\x06 \x01(\x02\x12=\n\x07version\x18\x07 \x01(\x0e\x32,.tensorflow.SaverDef.CheckpointFormatVersion\"5\n\x17\x43heckpointFormatVersion\x12\n\n\x06LEGACY\x10\x00\x12\x06\n\x02V1\x10\x01\x12\x06\n\x02V2\x10\x02\x42q\n\x13org.tensorflow.utilB\x0bSaverProtosP\x01ZHgithub.com/tensorflow/tensorflow/tensorflow/go/core/core_protos_go_proto\xf8\x01\x01\x62\x06proto3'
)
_SAVERDEF_CHECKPOINTFORMATVERSION = _descriptor.EnumDescriptor(
name='CheckpointFormatVersion',
full_name='tensorflow.SaverDef.CheckpointFormatVersion',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='LEGACY', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='V1', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='V2', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=286,
serialized_end=339,
)
_sym_db.RegisterEnumDescriptor(_SAVERDEF_CHECKPOINTFORMATVERSION)
_SAVERDEF = _descriptor.Descriptor(
name='SaverDef',
full_name='tensorflow.SaverDef',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='filename_tensor_name', full_name='tensorflow.SaverDef.filename_tensor_name', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='save_tensor_name', full_name='tensorflow.SaverDef.save_tensor_name', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='restore_op_name', full_name='tensorflow.SaverDef.restore_op_name', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='max_to_keep', full_name='tensorflow.SaverDef.max_to_keep', index=3,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='sharded', full_name='tensorflow.SaverDef.sharded', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='keep_checkpoint_every_n_hours', full_name='tensorflow.SaverDef.keep_checkpoint_every_n_hours', index=5,
number=6, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='version', full_name='tensorflow.SaverDef.version', index=6,
number=7, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
_SAVERDEF_CHECKPOINTFORMATVERSION,
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=53,
serialized_end=339,
)
_SAVERDEF.fields_by_name['version'].enum_type = _SAVERDEF_CHECKPOINTFORMATVERSION
_SAVERDEF_CHECKPOINTFORMATVERSION.containing_type = _SAVERDEF
DESCRIPTOR.message_types_by_name['SaverDef'] = _SAVERDEF
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
SaverDef = _reflection.GeneratedProtocolMessageType('SaverDef', (_message.Message,), {
'DESCRIPTOR' : _SAVERDEF,
'__module__' : 'tensorflow.core.protobuf.saver_pb2'
# @@protoc_insertion_point(class_scope:tensorflow.SaverDef)
})
_sym_db.RegisterMessage(SaverDef)
DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)
| 44.482993 | 727 | 0.768466 | [
"Apache-2.0"
] | alexeygrigorev/tensorflow-protobuf | tensorflow/core/protobuf/saver_pb2.py | 6,539 | Python |
import urllib.request, urllib.parse, urllib.error
import json
url = input('Web page: ')
print('Retrieving', url)
uh = urllib.request.urlopen(url)
data = uh.read().decode()
info = json.loads(data)
# info é um dict do tipo:
# {'note': 'This file contains the sample data for testing', 'comments': [{'name': 'Romina', 'cou ...
# * print(info['comments'])
# a primeira subdivisao é entre 'notes' e 'comments'
total = list()
for item in info['comments']:
# * print(item)
# cada item é um dict com 'name' e 'count'
total.append(int(item['count']))
total2 = [int(item['count']) for item in info['comments']]
# list Comprehensions é mais legal
print(sum(total2)) | 23.7 | 102 | 0.632911 | [
"MIT"
] | Felipe-Tommaselli/Python4everbody_Michigan | book3/s6_ex3.py | 715 | Python |
#Embedded file name: ACEStream\Player\BaseApp.pyo
import os
import sys
import time
import shutil
import urllib
import hashlib
import binascii
import random
import subprocess
import struct
import pickle
import cookielib
from operator import itemgetter
from base64 import b64encode, encodestring
from types import DictType, StringType
if sys.platform == 'win32':
import win32file
import win32api
from ACEStream.Core.Utilities.win32regchecker import Win32RegChecker, HKLM, HKCU
from threading import enumerate, currentThread, Lock, Timer
from traceback import print_stack, print_exc
from ACEStream.Video.utils import svcextdefaults
from ACEStream.Core.Utilities.odict import odict
from ACEStream.Core.Utilities.timeouturlopen import urlOpenTimeout
if sys.platform == 'darwin':
os.chdir(os.path.abspath(os.path.dirname(sys.argv[0])))
from ACEStream.__init__ import DEFAULT_SESSION_LISTENPORT
from ACEStream.version import VERSION, VERSION_REV
from ACEStream.env import TS_ENV_PLATFORM
from ACEStream.GlobalConfig import globalConfig
from ACEStream.Core.API import *
from ACEStream.Policies.RateManager import UserDefinedMaxAlwaysOtherwiseEquallyDividedRateManager
from ACEStream.Utilities.Instance2Instance import *
from ACEStream.Utilities.TimedTaskQueue import TimedTaskQueue
from ACEStream.Core.BitTornado.__init__ import createPeerID
from ACEStream.Video.utils import videoextdefaults
from ACEStream.Core.Utilities.logger import log, log_exc
from ACEStream.Core.Utilities.unicode import unicode2str_safe
from ACEStream.Core.Ads.Manager import AdManager
from ACEStream.Core.TS.Service import TSService
from ACEStream.Core.Utilities.mp4metadata import clear_mp4_metadata_tags_from_file
from ACEStream.Core.Statistics.GoogleAnalytics import GoogleAnalytics
from ACEStream.Core.Statistics.TNS import TNS, TNSNotAllowedException
from ACEStream.Core.Statistics.Settings import RemoteStatisticsSettings
from ACEStream.Core.Statistics.TrafficStatistics import TrafficStatistics
from ACEStream.Core.APIImplementation.FakeDownload import FakeDownload
from ACEStream.Utilities.HardwareIdentity import get_hardware_key
from ACEStream.Utilities.LSO import LSO
if sys.platform == 'win32':
TNS_ENABLED = True
else:
TNS_ENABLED = False
DEVELOPER_MODE = False
DEBUG = False
DEBUG_AD_STORAGE = False
DEBUG_HIDDEN_DOWNLOADS = False
DEBUG_SERVICE_REQUESTS = False
DEBUG_STATS_TO_FILE = False
DEBUG_PREMIUM = False
RATELIMITADSL = False
DOWNLOADSPEED = 300
DEFAULT_DISKSPACE_LIMIT = 10737418240L
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
downloadlimitvalue_file = os.path.join(os.path.split(current_file_path)[0],"values","downloadlimit.txt")
f = open(downloadlimitvalue_file, "r")
string = f.read()
if int(string) == 0:
DEFAULT_DOWNLOAD_RATE_LIMIT = 100000000000.0
else:
DEFAULT_DOWNLOAD_RATE_LIMIT = int(string)
DOWNLOAD_STATES_DISPLAY_INTERVAL = 600
SHOW_HIDDEN_DOWNLOADS_INFO = False
MIN_PROGRESS_KEEP = 0.001
DOWNLOAD_STATS_INTERVAL = 1800
PREMIUM_PREVIEW_TIMEOUT = 15
CHECK_AUTH_INTERVAL_REGULAR = 3600
CHECK_AUTH_INTERVAL_ERROR = 600
CHECK_AUTH_INTERVAL_PREMIUM = 60
CHECK_AUTH_MAX_ERRORS = 5
CHECK_PRELOAD_ADS_INTERVAL = 86400
CLEANUP_HIDDEN_DOWNLOADS_INTERVAL = 86400
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
vodbuffervalue_file = os.path.join(os.path.split(current_file_path)[0],"values","vodbuffer.txt")
f = open(vodbuffervalue_file, "r")
string = f.read()
DEFAULT_PLAYER_BUFFER_TIME = int(string)
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
livebuffervalue_file = os.path.join(os.path.split(current_file_path)[0],"values","livebuffer.txt")
f = open(livebuffervalue_file, "r")
string = f.read()
DEFAULT_LIVE_BUFFER_TIME = int(string)
CACHE_DIR_NAME = '_acestream_cache_'
DEFAULT_AD_STORAGE_LIMIT = 536870912L
AD_STORAGE_LIMIT_SMALL = 536870912L
AD_STORAGE_LIMIT_BIG = 1073741824L
AD_STORAGE_MIN_FREE_SPACE = 52428800L
AD_STORAGE_MAX_AGE = 2592000
class BaseApp(InstanceConnectionHandler):
def __init__(self, wrapper, redirectstderrout, appname, appversion, params, installdir, i2i_port, session_port):
self.apptype = globalConfig.get_value('apptype')
self.ext_version = self.check_integrity()
if DEVELOPER_MODE:
self.ext_version = True
debug_level = 0
skip_metadata = False
skip_mediainfo = False
use_libavi = False
if self.apptype == 'torrentstream':
encrypted_storage = True
self.registry_key = 'TorrentStream'
else:
encrypted_storage = True
self.registry_key = 'ACEStream'
ip = None
vod_live_max_pop_time = None
piece_picker_buffering_delay = None
try:
for param in params:
if param.startswith('--debug='):
_, level = param.split('=')
debug_level = int(level)
elif param == '--skip-metadata':
skip_metadata = True
elif param == '--skip-mediainfo':
skip_mediainfo = True
elif param == '--use-libavi':
use_libavi = True
elif param.startswith('--vod-live-max-pop-time='):
_, vod_live_max_pop_time = param.split('=')
elif param.startswith('--buffering-delay='):
_, piece_picker_buffering_delay = param.split('=')
except:
print_exc()
self.debug_systray = False
self.debug_level = 0
self.ip = ip
globalConfig.set_value('encrypted_storage', encrypted_storage)
globalConfig.set_value('use_libavi', use_libavi)
if vod_live_max_pop_time is not None:
try:
vod_live_max_pop_time = int(vod_live_max_pop_time)
if vod_live_max_pop_time < 1:
vod_live_max_pop_time = 1
globalConfig.set_value('vod_live_max_pop_time', vod_live_max_pop_time)
except:
pass
if piece_picker_buffering_delay is not None:
try:
a = piece_picker_buffering_delay.split(',')
if len(a) >= 2:
_min = int(a[0])
_max = int(a[1])
if len(a) >= 3:
_offset = int(a[2])
else:
_offset = 0
piece_picker_buffering_delay = (_min, _max, _offset)
if DEBUG:
log('baseapp::__init__: piece_picker_buffering_delay', piece_picker_buffering_delay)
globalConfig.set_value('piece_picker_buffering_delay', piece_picker_buffering_delay)
except:
pass
self.set_debug_level(debug_level)
ACEStream.Core.Video.VideoOnDemand.DEBUG_SKIP_METADATA = skip_metadata
ACEStream.Core.Video.VideoStatus.DEBUG_SKIP_METADATA = skip_metadata
ACEStream.Core.Video.VideoOnDemand.DO_MEDIAINFO_ANALYSIS = not skip_mediainfo
if DEBUG_STATS_TO_FILE:
self.debug_counter = 0
self.appname = appname
self.appversion = appversion
self.params = params
self.installdir = installdir
self.i2i_port = i2i_port
self.session_port = session_port
self.error = None
self.s = None
self.wrapper = wrapper
self.auth_data = {'last_success': None,
'errors': 0}
self.playerconfig = {}
self.download_states_display_counter = 0
self.user_profile = None
self.downloads_in_vodmode = {}
self.downloads_in_admode = {}
self.dlinfo_lock = Lock()
self.cleanup_hidden_downloads_lock = Lock()
self.check_preload_ads_lock = Lock()
self.timers = {}
self.playing_premium_content = False
self.download_stats = {}
self.last_download_stats = 0
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
downloadlimitvalue_file = os.path.join(os.path.split(current_file_path)[0],"values","downloadlimit.txt")
f = open(downloadlimitvalue_file, "r")
string = f.read()
self.max_download_rate = int(string)
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
uploadlimitvalue_file = os.path.join(os.path.split(current_file_path)[0],"values","uploadlimit.txt")
f = open(uploadlimitvalue_file, "r")
string = f.read()
self.max_upload_rate = int(string)
self.avg_download_rate = 0
self.avg_download_rate_sum = 0
self.avg_download_rate_count = 0
self.avg_upload_rate = 0
self.avg_upload_rate_sum = 0
self.avg_upload_rate_count = 0
self.ratelimiter = None
self.ratelimit_update_count = 0
self.playermode = DLSTATUS_DOWNLOADING
self.getpeerlistcount = 2
self.shuttingdown = False
self.tqueue = TimedTaskQueue(nameprefix='BGTaskQueue')
self.OnInitBase()
if self.i2i_port == 0:
port_file = os.path.join(self.installdir, 'acestream.port')
else:
port_file = None
self.i2i_listen_server = Instance2InstanceServer(self.i2i_port, self, timeout=86400.0, port_file=port_file)
self.i2i_listen_server.start()
InstanceConnectionHandler.__init__(self, self.i2ithread_readlinecallback)
self.check_license()
def check_license(self):
try:
path = os.path.join(self.installdir, '..', 'LICENSE.txt')
if not os.path.isfile(path):
return
size = os.path.getsize(path)
if size < 1024:
return
import locale
lang_code, encoding = locale.getdefaultlocale()
lang_code = lang_code.lower()
if lang_code.startswith('en'):
lang_code = 'en'
elif lang_code.startswith('ru'):
lang_code = 'ru'
else:
lang_code = 'en'
if lang_code == 'ru':
txt = '\xd0\x9b\xd0\xb8\xd1\x86\xd0\xb5\xd0\xbd\xd0\xb7\xd0\xb8\xd0\xbe\xd0\xbd\xd0\xbd\xd0\xbe\xd0\xb5 \xd1\x81\xd0\xbe\xd0\xb3\xd0\xbb\xd0\xb0\xd1\x88\xd0\xb5\xd0\xbd\xd0\xb8\xd0\xb5 \xd0\xbf\xd1\x80\xd0\xb5\xd0\xb4\xd1\x81\xd1\x82\xd0\xb0\xd0\xb2\xd0\xbb\xd0\xb5\xd0\xbd\xd0\xbe \xd0\xbd\xd0\xb0 \xd1\x81\xd0\xb0\xd0\xb9\xd1\x82\xd0\xb5: http://www.acestream.org/license'
else:
txt = 'License agreement presented on the site: http://www.acestream.org/license'
f = open(path, 'w')
f.write(txt)
f.close()
except:
pass
def test_ads(self):
affiliate_id = 0
zone_id = 0
developer_id = 0
include_interruptable_ads = False
provider_key = None
provider_content_id = None
content_ext = 'mp4'
content_duration = 3600
user_login = self.s.get_ts_login()
content_id = '1234567890123456789012345678901234567890'
ads = self.ad_manager.get_ads(device_id=self.device_id, user_login=user_login, user_level=2, content_type=DLTYPE_TORRENT, content_id=content_id, content_ext=content_ext, content_duration=content_duration, affiliate_id=affiliate_id, zone_id=zone_id, developer_id=developer_id, include_interruptable_ads=include_interruptable_ads, is_live=False, user_profile=self.user_profile, provider_key=provider_key, provider_content_id=provider_content_id)
print >> sys.stderr, '>>>test_ads:', ads
def set_debug_level(self, debug_level):
if not DEVELOPER_MODE:
return
if debug_level == self.debug_level:
return
self.debug_level = debug_level
log('set_debug_level:', debug_level)
ACEStream.Plugin.BackgroundProcess.DEBUG2 = debug_level == -1 or debug_level & 1 != 0
ACEStream.Player.BaseApp.DEBUG = debug_level == -1 or debug_level & 1 != 0
ACEStream.Core.Session.DEBUG = debug_level == -1 or debug_level & 1 != 0
ACEStream.Player.BaseApp.DEBUG_HIDDEN_DOWNLOADS = debug_level == -1 or debug_level & 1 != 0
ACEStream.Player.BaseApp.DEBUG_AD_STORAGE = debug_level == -1 or debug_level & 1 != 0
ACEStream.Player.BaseApp.DEBUG_SERVICE_REQUESTS = debug_level == -1 or debug_level & 1 != 0
ACEStream.Player.BaseApp.DEBUG_PREMIUM = debug_level == -1 or debug_level & 1 != 0
ACEStream.Player.BaseApp.SHOW_HIDDEN_DOWNLOADS_INFO = debug_level == -1 or debug_level & 1 != 0
ACEStream.Core.Ads.Manager.DEBUG = debug_level == -1 or debug_level & 1 != 0
ACEStream.Core.TS.Service.DEBUG = debug_level == -1 or debug_level & 1 != 0
ACEStream.Core.APIImplementation.DirectDownload.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.APIImplementation.DownloadImpl.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.APIImplementation.LaunchManyCore.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.APIImplementation.SingleDownload.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.BitTornado.download_bt1.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.BitTornado.BT1.GetRightHTTPDownloader.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.DirectDownload.Downloader.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.DirectDownload.Storage.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.DirectDownload.VODTransporter.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.Statistics.GoogleAnalytics.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.TorrentDef.DEBUG = False
ACEStream.Core.TS.domutils.DEBUG = False
ACEStream.Core.Utilities.mp4metadata.DEBUG = debug_level == -1 or debug_level & 2 != 0
ACEStream.Video.VideoServer.DEBUGLOCK = debug_level == -1 or debug_level & 2 != 0
ACEStream.Core.Video.PiecePickerStreaming.DEBUG = debug_level == -1 or debug_level & 4 != 0
ACEStream.Core.Video.PiecePickerStreaming.DEBUGPP = debug_level == -1 or debug_level & 4 != 0
ACEStream.Core.BitTornado.SocketHandler.DEBUG = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Choker.DEBUG = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Connecter.DEBUG = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Connecter.DEBUG_UT_PEX = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Downloader.DEBUG = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Uploader.DEBUG = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Encrypter.DEBUG = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Encrypter.DEBUG_CLOSE = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Rerequester.DEBUG = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Rerequester.DEBUG_DHT = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.BitTornado.BT1.Rerequester.DEBUG_CHECK_NETWORK_CONNECTION = debug_level == -1 or debug_level & 8 != 0
ACEStream.Core.Video.VideoOnDemand.DEBUG_HOOKIN = debug_level == -1 or debug_level & 16 != 0
ACEStream.Core.Video.LiveSourceAuth.DEBUG = debug_level == -1 or debug_level & 16 != 0
ACEStream.Core.Video.PiecePickerStreaming.DEBUG_LIVE = debug_level == -1 or debug_level & 16 != 0
ACEStream.Core.BitTornado.BT1.StorageWrapper.DEBUG_LIVE = debug_level == -1 or debug_level & 16 != 0
try:
ACEStream.Core.Video.VideoSource.DEBUG = debug_level == -1 or debug_level & 16 != 0
except:
pass
ACEStream.Core.Video.VideoOnDemand.DEBUG = debug_level == -1 or debug_level & 32 != 0
ACEStream.Core.Video.VideoStatus.DEBUG = debug_level == -1 or debug_level & 32 != 0
ACEStream.Video.VideoServer.DEBUG = debug_level == -1 or debug_level & 32 != 0
ACEStream.Video.VideoServer.DEBUGCONTENT = debug_level == -1 or debug_level & 32 != 0
ACEStream.Core.NATFirewall.NatCheck.DEBUG = debug_level == -1 or debug_level & 64 != 0
ACEStream.Core.NATFirewall.UPnPThread.DEBUG = debug_level == -1 or debug_level & 64 != 0
ACEStream.Core.NATFirewall.UDPPuncture.DEBUG = debug_level == -1 or debug_level & 64 != 0
ACEStream.Core.NATFirewall.upnp.DEBUG = debug_level == -1 or debug_level & 64 != 0
ACEStream.Core.NATFirewall.ConnectionCheck.DEBUG = debug_level == -1 or debug_level & 64 != 0
ACEStream.Core.BitTornado.natpunch.DEBUG = debug_level == -1 or debug_level & 64 != 0
ACEStream.Player.BaseApp.DEBUG_STATS_TO_FILE = debug_level == -1 or debug_level & 128 != 0
ACEStream.WebUI.WebUI.DEBUG = debug_level == -1 or debug_level & 256 != 0
ACEStream.Core.BitTornado.RawServer.DEBUG = debug_level == -1 or debug_level & 512 != 0
ACEStream.Core.BitTornado.RawServer.DEBUG2 = debug_level == -1 or debug_level & 512 != 0
ACEStream.Core.BitTornado.ServerPortHandler.DEBUG = debug_level == -1 or debug_level & 512 != 0
ACEStream.Core.BitTornado.ServerPortHandler.DEBUG2 = debug_level == -1 or debug_level & 512 != 0
ACEStream.Core.BitTornado.HTTPHandler.DEBUG = debug_level == -1 or debug_level & 512 != 0
ACEStream.Core.BitTornado.HTTPHandler.DEBUG2 = debug_level == -1 or debug_level & 512 != 0
ACEStream.Core.BitTornado.SocketHandler.DEBUG = debug_level == -1 or debug_level & 512 != 0
ACEStream.Core.BitTornado.SocketHandler.DEBUG2 = debug_level == -1 or debug_level & 512 != 0
ACEStream.Core.BitTornado.BT1.StorageWrapper.DEBUG = debug_level == -1 or debug_level & 8192 != 0
ACEStream.Core.BitTornado.BT1.StorageWrapper.DEBUG_WRITE = False
ACEStream.Core.BitTornado.BT1.StorageWrapper.DEBUG_HASHCHECK = debug_level == -1 or debug_level & 8192 != 0
ACEStream.Core.BitTornado.BT1.StorageWrapper.DEBUG_REQUESTS = debug_level == -1 or debug_level & 8192 != 0
ACEStream.Core.BitTornado.BT1.FileSelector.DEBUG = debug_level == -1 or debug_level & 8192 != 0
ACEStream.Core.BitTornado.download_bt1.DEBUG_ENCRYPTION = debug_level == -1 or debug_level & 8192 != 0
ACEStream.Core.BitTornado.BT1.Storage.DEBUG = debug_level == -1 or debug_level & 16384 != 0
ACEStream.Core.BitTornado.BT1.Storage.DEBUG_RESTORE = debug_level == -1 or debug_level & 16384 != 0
ACEStream.Core.Utilities.EncryptedStorage.DEBUG = debug_level == -1 or debug_level & 32768 != 0
ACEStream.Core.BitTornado.BT1.StorageWrapper.DEBUG_ENCRYPTED_STORAGE = debug_level == -1 or debug_level & 32768 != 0
ACEStream.Core.BitTornado.download_bt1.DEBUG_ENCRYPTION = debug_level == -1 or debug_level & 32768 != 0
ACEStream.Core.CacheDB.SqliteCacheDBHandler.DEBUG = debug_level == -1 or debug_level & 65536 != 0
ACEStream.Utilities.LSO.DEBUG = debug_level == -1 or debug_level & 131072 != 0
ACEStream.Core.Statistics.Settings.DEBUG = debug_level == -1 or debug_level & 131072 != 0
ACEStream.Core.Statistics.TNS.DEBUG = debug_level == -1 or debug_level & 131072 != 0
ACEStream.Core.Statistics.TrafficStatistics.DEBUG = debug_level == -1 or debug_level & 131072 != 0
def OnInitBase(self):
state_dir = Session.get_default_state_dir()
self.state_dir = state_dir
if DEBUG:
log('baseapp::init: state_dir', state_dir)
if globalConfig.get_mode() != 'client_console':
from ACEStream.Player.UtilityStub import UtilityStub
self.utility = UtilityStub(self.installdir, state_dir)
self.utility.app = self
log('build', VERSION_REV)
log('version', VERSION)
self.iconpath = os.path.join(self.installdir, 'data', 'images', 'engine.ico')
self.logopath = os.path.join(self.installdir, 'data', 'images', 'logo.png')
self.load_playerconfig(state_dir)
self.statFrame = None
self.live_frame = None
self.init_hardware_key()
cfgfilename = Session.get_default_config_filename(state_dir)
if DEBUG:
log('baseapp::init: session config', cfgfilename)
try:
self.sconfig = SessionStartupConfig.load(cfgfilename)
if self.session_port != DEFAULT_SESSION_LISTENPORT:
if DEBUG:
log('baseapp::init: non-default port specified, overwrite saved session port:', self.session_port)
self.sconfig.set_listen_port(self.session_port)
elif DEBUG:
log('baseapp::init: use session saved port', self.sconfig.get_listen_port())
except:
if DEBUG:
log('baseapp::init: cannot load config file', cfgfilename, 'Use default config')
self.sconfig = SessionStartupConfig()
self.sconfig.set_state_dir(state_dir)
self.sconfig.set_listen_port(self.session_port)
self.configure_session()
self.s = Session(self.sconfig, on_error=self.on_error)
self.s.set_download_states_callback(self.sesscb_states_callback)
self.device_id = b64encode(self.s.get_permid())
node_id = self.device_id
if self.hardware_key is not None:
node_id += ':' + self.hardware_key
self.node_id = hashlib.sha1(node_id).hexdigest()
self.traffic_stats = TrafficStatistics(TrafficStatistics.NODE_CLIENT, self.node_id)
if RATELIMITADSL:
self.ratelimiter = UserDefinedMaxAlwaysOtherwiseEquallyDividedRateManager()
self.ratelimiter.set_global_max_speed(DOWNLOAD, DOWNLOADSPEED)
self.ratelimiter.set_global_max_speed(UPLOAD, 90)
try:
self.s.load_checkpoint(initialdlstatus=DLSTATUS_STOPPED)
except:
log_exc()
ga = lambda : GoogleAnalytics.send_event('client', 'startup', VERSION)
self.run_delayed(ga)
self.tsservice = TSService(self)
self.run_delayed(self.check_auth_level, 0.1)
self.cookie_file = os.path.join(self.state_dir, 'cookies.pickle')
self.cookie_jar = cookielib.CookieJar()
self.load_cookies()
self.stat_settings = RemoteStatisticsSettings()
self.run_delayed(self.check_statistics_settings, 1)
if TNS_ENABLED:
try:
lso = LSO('source.mmi.bemobile.ua', 'mmi')
self.tns_uid = lso.get_uid()
except:
if DEBUG:
print_exc()
self.check_user_profile()
self.ad_manager = AdManager(self, self.cookie_jar)
if TS_ENV_PLATFORM == 'dune':
default_enabled = False
else:
default_enabled = True
preload_ads_enabled = self.get_preload_ads_enabled(default_enabled)
if DEBUG:
log('baseapp::init: preload_ads_enabled', preload_ads_enabled)
self.run_delayed(self.cleanup_hidden_downloads_task, 1.0)
self.run_delayed(self.remove_unknown_downloads, 20.0)
self.run_delayed(self.check_preload_ads, 1.0, 'check_preload_ads')
if sys.platform == 'win32':
self.run_delayed(self.start_updater, 60.0)
disk_cache_limit = self.get_playerconfig('disk_cache_limit')
if disk_cache_limit is None:
content_dir = self.get_default_destdir()
total, avail, used = self.get_disk_info(content_dir)
if total is not None:
disk_cache_limit = long(total * 0.5)
else:
disk_cache_limit = DEFAULT_DISKSPACE_LIMIT
self.set_playerconfig('disk_cache_limit', disk_cache_limit)
if DEBUG:
log('baseapp::init: set disk_cache_limit:', disk_cache_limit)
elif DEBUG:
log('baseapp::init: got disk_cache_limit:', disk_cache_limit)
ad_storage_limit = self.get_playerconfig('ad_storage_limit')
if ad_storage_limit is None:
ads_dir = self.s.get_ads_dir()
total, avail, used = self.get_disk_info(ads_dir)
if total is not None:
if avail < 10485760:
ad_storage_limit = AD_STORAGE_LIMIT_SMALL
else:
ad_storage_limit = AD_STORAGE_LIMIT_BIG
else:
ad_storage_limit = DEFAULT_AD_STORAGE_LIMIT
self.set_playerconfig('ad_storage_limit', ad_storage_limit)
if DEBUG:
log('baseapp::init: set ad_storage_limit:', ad_storage_limit)
elif DEBUG:
log('baseapp::init: got ad_storage_limit:', ad_storage_limit)
self.set_playerconfig('enable_http_support', True)
if DEBUG_STATS_TO_FILE:
try:
for f in os.listdir(self.installdir):
if f.startswith('stat_snapshot_'):
os.remove(os.path.join(self.installdir, f))
except:
pass
def on_error(self, exception):
try:
errmsg = str(exception)
except:
errmsg = 'Unexpected error'
try:
self.wrapper.on_error(errmsg, exit=True)
except:
print_exc()
def run_delayed(self, func, delay = 0.0, task_id = None, daemon = True, args = []):
if task_id is not None:
if self.timers.has_key(task_id):
self.timers[task_id].cancel()
t = Timer(delay, func, args)
if task_id is not None:
self.timers[task_id] = t
t.daemon = daemon
t.name = 'Timer-' + t.name
t.start()
return t
def start_updater(self):
if sys.platform != 'win32':
return
if self.apptype == 'torrentstream':
exename = 'tsupdate.exe'
else:
exename = 'ace_update.exe'
updater_path = os.path.join(self.installdir, '..', 'updater', exename)
if DEBUG:
log('baseapp::start_updater: updater_path', updater_path)
if os.path.exists(updater_path):
try:
subprocess.Popen(updater_path, close_fds=True)
except:
if DEBUG:
print_exc()
def remove_unknown_downloads(self):
try:
known_files = []
downloads = self.s.get_all_downloads()
for d in downloads:
if not d.is_hidden():
continue
destfiles = d.get_dest_files(get_all=True)
if destfiles:
for filename, savepath in destfiles:
known_files.append(savepath)
path = self.s.get_ads_dir()
filelist = os.listdir(path)
if DEBUG_AD_STORAGE:
log('baseapp::remove_unknown_downloads: known_files', known_files, 'filelist', filelist)
for basename in filelist:
filename = os.path.join(path, basename)
if filename not in known_files:
if DEBUG_AD_STORAGE:
log('baseapp::remove_unknown_downloads: remove: filename', filename)
os.remove(filename)
except:
if DEBUG:
print_exc()
def get_ad_storage_limit(self):
ad_storage_limit = self.get_playerconfig('ad_storage_limit', DEFAULT_AD_STORAGE_LIMIT)
ads_dir = self.s.get_ads_dir()
total, avail, used = self.get_disk_info(ads_dir)
if avail is None:
avail = ad_storage_limit + AD_STORAGE_MIN_FREE_SPACE
if DEBUG_AD_STORAGE:
log('baseapp::get_ad_storage_limit: failed to get disk info, set fake avail: avail', avail)
if avail < ad_storage_limit + AD_STORAGE_MIN_FREE_SPACE:
storage_limit = avail - AD_STORAGE_MIN_FREE_SPACE
else:
storage_limit = ad_storage_limit
if DEBUG_AD_STORAGE:
log('baseapp::get_ad_storage_limit: storage_limit', storage_limit, 'total', total, 'avail', avail, 'used', used)
return storage_limit
def cleanup_unused_ad_downloads(self, keep_hash_list):
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_unused_ad_downloads: keep_hash_list', keep_hash_list)
downloads = self.s.get_all_downloads()
for d in downloads:
if not d.is_hidden():
continue
if d.get_hash() not in keep_hash_list:
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_unused_ad_downloads: remove unused download: hash', binascii.hexlify(d.get_hash()))
self.s.remove_download(d, removecontent=True)
def cleanup_hidden_downloads_task(self):
self.cleanup_hidden_downloads()
self.run_delayed(self.cleanup_hidden_downloads_task, CLEANUP_HIDDEN_DOWNLOADS_INTERVAL)
def cleanup_hidden_downloads(self, needed = 0, priority = -1):
self.cleanup_hidden_downloads_lock.acquire()
try:
total_size = 0
dllist = []
downloads = self.s.get_all_downloads()
for d in downloads:
if not d.is_hidden():
continue
destfiles = d.get_dest_files(get_all=True)
download_priority = d.get_extra('priority', 0)
download_size = d.get_content_length()
download_last_access = 0
for filename, savepath in destfiles:
if os.path.exists(savepath):
stat = os.stat(savepath)
if stat.st_ctime > download_last_access:
download_last_access = stat.st_ctime
last_seen = self.get_ad_last_seen(d.get_hash())
if last_seen is not None:
download_last_access = last_seen
if download_size > 0:
total_size += download_size
dlinfo = (download_last_access,
download_priority,
download_size,
d)
dllist.append(dlinfo)
dllist.sort(key=itemgetter(1, 0))
storage_limit = self.get_ad_storage_limit()
free_up = total_size + needed - storage_limit
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_hidden_downloads: storage_limit', storage_limit, 'total_size', total_size, 'needed', needed, 'free_up', free_up, 'dllist', dllist)
for last_access, dlpriority, size, d in dllist:
remove = False
if priority != -1 and dlpriority >= priority:
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_hidden_downloads: do not remove download with higher priority: hash', binascii.hexlify(d.get_hash()), 'dlpriority', dlpriority, 'priority', priority)
continue
if d in self.downloads_in_vodmode:
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_hidden_downloads: do not remove playing download: hash', binascii.hexlify(d.get_hash()))
continue
is_ad = False
for maind_d, ads in self.downloads_in_admode.iteritems():
if d in ads:
is_ad = True
break
if is_ad:
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_hidden_downloads: do not remove download in admode: hash', binascii.hexlify(d.get_hash()))
continue
now = long(time.time())
if last_access < now - AD_STORAGE_MAX_AGE:
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_hidden_downloads: remove outdated download: hash', binascii.hexlify(d.get_hash()), 'last_access', last_access, 'now', now, 'max_age', AD_STORAGE_MAX_AGE)
remove = True
if not remove and free_up > 0:
remove = True
free_up -= size
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_hidden_downloads: remove download to free space: hash', binascii.hexlify(d.get_hash()), 'size', size, 'free_up', free_up)
if remove:
self.s.remove_download(d, removecontent=True)
if DEBUG_AD_STORAGE:
log('baseapp::cleanup_hidden_downloads: done: free_up', free_up)
return free_up <= 0
except:
log_exc()
finally:
self.cleanup_hidden_downloads_lock.release()
def check_preload_ads(self):
self.check_preload_ads_lock.acquire()
try:
preload_ads = self.ad_manager.get_preload_ads(self.device_id, self.s.get_ts_login(), self.get_playerconfig('enable_interruptable_ads', True), user_profile=self.user_profile)
if preload_ads == False:
return
self.add_preload_ads(preload_ads, True)
except:
log_exc()
finally:
self.check_preload_ads_lock.release()
self.run_delayed(self.check_preload_ads, CHECK_PRELOAD_ADS_INTERVAL, 'check_preload_ads')
def add_preload_ads(self, preload_ads, remove_unused):
dl_hash_list = []
for ad in preload_ads:
if ad['dltype'] == DLTYPE_TORRENT:
ad['dlhash'] = ad['tdef'].get_infohash()
ad['size'] = ad['tdef'].get_length()
elif ad['dltype'] == DLTYPE_DIRECT:
tdef = self.get_torrent_from_url(ad['url'])
if tdef is None:
ad['dlhash'] = hashlib.sha1(ad['url']).digest()
ad['size'] = 0
else:
ad['tdef'] = tdef
ad['dlhash'] = tdef.get_infohash()
ad['size'] = tdef.get_length()
ad['dltype'] = DLTYPE_TORRENT
if DEBUG:
log('baseapp::add_preload_ads: got torrent from url: url', ad['url'], 'infohash', binascii.hexlify(ad['dlhash']))
else:
raise ValueError('Unknown download type ' + str(ad['dltype']))
dl_hash_list.append(ad['dlhash'])
if remove_unused:
self.cleanup_unused_ad_downloads(dl_hash_list)
preload_ads.sort(key=lambda ad: ad['priority'], reverse=True)
for ad in preload_ads:
d = self.s.get_download(ad['dltype'], ad['dlhash'])
if d is not None:
pass
else:
if DEBUG:
log('baseapp::add_preload_ads: start new preload download: type', ad['dltype'], 'hash', binascii.hexlify(ad['dlhash']), 'priority', ad['priority'], 'size', ad['size'])
if not self.cleanup_hidden_downloads(needed=ad['size'], priority=ad['priority']):
if DEBUG:
log('baseapp::add_preload_ads: not enough space: hash', binascii.hexlify(ad['dlhash']), 'size', ad['size'])
continue
dcfg = DownloadStartupConfig()
dcfg.set_dest_dir(self.s.get_ads_dir())
dcfg.set_max_conns(10)
dcfg.set_max_conns_to_initiate(10)
dcfg.set_hidden(True)
dcfg.set_extra('priority', ad['priority'])
if ad['dltype'] == DLTYPE_TORRENT:
self.s.start_download(ad['tdef'], dcfg, initialdlstatus=DLSTATUS_STOPPED)
elif ad['dltype'] == DLTYPE_DIRECT:
self.s.start_direct_download(ad['url'], dcfg, initialdlstatus=DLSTATUS_STOPPED)
def check_auth_level(self, forceconnect = False):
got_error = False
try:
ts_login = unicode2str_safe(self.s.get_ts_login())
ts_password = unicode2str_safe(self.s.get_ts_password())
if len(ts_login) == 0 or len(ts_password) == 0:
self.s.set_authlevel(0)
return
if self.auth_data['last_success'] is None or forceconnect:
action = 'cn'
else:
action = 'chk'
new_authlevel = self.tsservice.get_user_level(ts_login, ts_password, action, self.device_id, self.hardware_key)
if new_authlevel is not None:
self.auth_data['last_success'] = time.time()
self.auth_data['errors'] = 0
if DEBUG:
log('baseapp::check_auth_level: got user level:', new_authlevel)
else:
got_error = True
self.auth_data['errors'] += 1
log('baseapp::check_auth_level: failed, error count', self.auth_data['errors'])
if self.auth_data['errors'] >= CHECK_AUTH_MAX_ERRORS:
log('baseapp::check_auth_level: max errors reached, reset user level')
new_authlevel = 0
if new_authlevel is not None:
current_authlevel = self.s.get_authlevel()
if new_authlevel != current_authlevel:
if DEBUG:
log('baseapp::check_auth_level: set new user level: current', current_authlevel, 'new', new_authlevel)
self.s.set_authlevel(new_authlevel)
for socket, ic in self.singsock2ic.iteritems():
ic.auth(new_authlevel)
except:
if DEBUG:
log_exc()
finally:
if got_error:
interval = CHECK_AUTH_INTERVAL_ERROR
if DEBUG:
log('baseapp::check_auth_level: got error, next try in', interval)
elif self.playing_premium_content:
interval = CHECK_AUTH_INTERVAL_PREMIUM
if DEBUG:
log('baseapp::check_auth_level: got premium, next try in', interval)
else:
interval = CHECK_AUTH_INTERVAL_REGULAR
if DEBUG:
log('baseapp::check_auth_level: regular next try in', interval)
self.run_delayed(self.check_auth_level, interval, task_id='check_auth_level')
def configure_session(self):
self.sconfig.set_install_dir(self.installdir)
if self.ip is not None:
if DEBUG:
log('baseapp::configure_session: set ip', self.ip)
self.sconfig.set_ip_for_tracker(self.ip)
self.sconfig.set_megacache(True)
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
maxconnections_file = os.path.join(os.path.split(current_file_path)[0],"values","maxconnections.txt")
f = open(maxconnections_file, "r")
self.sconfig.set_max_socket_connections(self.get_playerconfig('total_max_connects', int(string)))
self.sconfig.set_overlay(False)
self.sconfig.set_torrent_checking(False)
self.sconfig.set_buddycast(False)
self.sconfig.set_download_help(False)
self.sconfig.set_torrent_collecting(False)
self.sconfig.set_dialback(False)
self.sconfig.set_social_networking(False)
self.sconfig.set_remote_query(False)
self.sconfig.set_bartercast(False)
self.sconfig.set_crawler(False)
self.sconfig.set_multicast_local_peer_discovery(False)
self.sconfig.set_subtitles_collecting(False)
def _get_poa(self, tdef):
from ACEStream.Core.ClosedSwarm import ClosedSwarm, PaymentIntegration
print >> sys.stderr, 'Swarm_id:', encodestring(tdef.infohash).replace('\n', '')
try:
poa = ClosedSwarm.trivial_get_poa(self.s.get_state_dir(), self.s.get_permid(), tdef.infohash)
poa.verify()
if not poa.torrent_id == tdef.infohash:
raise Exception('Bad POA - wrong infohash')
print >> sys.stderr, 'Loaded poa from ', self.s.get_state_dir()
except:
swarm_id = encodestring(tdef.infohash).replace('\n', '')
my_id = encodestring(self.s.get_permid()).replace('\n', '')
try:
poa = PaymentIntegration.wx_get_poa(None, swarm_id, my_id, swarm_title=tdef.get_name())
except Exception as e:
print >> sys.stderr, 'Failed to get POA:', e
poa = None
try:
ClosedSwarm.trivial_save_poa(self.s.get_state_dir(), self.s.get_permid(), tdef.infohash, poa)
except Exception as e:
print >> sys.stderr, 'Failed to save POA', e
if poa:
if not poa.torrent_id == tdef.infohash:
raise Exception('Bad POA - wrong infohash')
return poa
def start_download(self, tdef, dlfile, extra_files_indexes = [], developer_id = 0, affiliate_id = 0, zone_id = 0, poa = None, supportedvodevents = None):
if poa:
from ACEStream.Core.ClosedSwarm import ClosedSwarm
if not poa.__class__ == ClosedSwarm.POA:
raise InvalidPOAException('Not a POA')
destdir = self.get_default_destdir()
try:
enough_space = True
length = tdef.get_length([dlfile])
if tdef.get_live():
length = long(length / 8 * 1.2)
if not self.free_up_diskspace_by_downloads(tdef.get_infohash(), length):
log('BaseApp::start_download: Not enough free diskspace')
enough_space = False
except:
log_exc()
if not enough_space:
raise Exception('Not enough disk space')
dcfg = DownloadStartupConfig()
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
maxconnectionsstream_file = os.path.join(os.path.split(current_file_path)[0],"values","maxconnectionsstream.txt")
f = open(maxconnectionsstream_file, "r")
string = f.read()
dcfg.set_max_conns(self.get_playerconfig('download_max_connects', int(string)))
if poa:
dcfg.set_poa(poa)
print >> sys.stderr, 'POA:', dcfg.get_poa()
else:
dcfg.set_poa(None)
if supportedvodevents is None:
supportedvodevents = self.get_supported_vod_events()
if DEBUG:
log('BaseApp::start_download: supportedvodevents', supportedvodevents)
dcfg.set_video_events(supportedvodevents)
prefix, ext = os.path.splitext(dlfile)
if ext != '' and ext[0] == '.':
content_ext = ext[1:]
else:
content_ext = ''
content_duration = None
if tdef.is_multifile_torrent():
svcdlfiles = self.is_svc(dlfile, tdef)
if svcdlfiles is not None:
dcfg.set_video_event_callback(self.sesscb_vod_event_callback, dlmode=DLMODE_SVC)
dcfg.set_selected_files(svcdlfiles)
else:
dcfg.set_video_event_callback(self.sesscb_vod_event_callback)
dcfg.set_selected_files([dlfile])
dcfg.set_extra_files(extra_files_indexes)
try:
p = [-1] * len(tdef.get_files())
total_duration = 0
content_length = 0
videofiles = tdef.get_files(exts=videoextdefaults)
for videofile in videofiles:
idx = tdef.get_index_of_file_in_files(videofile)
if videofile == dlfile or idx in extra_files_indexes:
p[idx] = 1
content_length += tdef.get_length(videofile)
duration = tdef.get_ts_duration(idx)
if duration is not None:
total_duration += duration
if total_duration > 0:
content_duration = total_duration
idx = tdef.get_index_of_file_in_files(dlfile)
if DEBUG:
log('BaseApp::start_download: bitrate', tdef.get_ts_bitrate(idx))
dcfg.set_files_priority(p)
if DEBUG:
log('BaseApp::start_download: got multi: dlfile', dlfile, 'priority', dcfg.get_files_priority, 'bitrate', tdef.get_ts_bitrate(idx), 'size', content_length, 'duration', content_duration, 'ext', content_ext)
except:
log_exc()
else:
dcfg.set_video_event_callback(self.sesscb_vod_event_callback)
content_duration = tdef.get_ts_duration()
content_length = tdef.get_length()
if DEBUG:
log('BaseApp::start_download: got single: bitrate', tdef.get_ts_bitrate(), 'size', content_length, 'duration', content_duration, 'ext', content_ext)
if content_duration is None:
content_duration = self.guess_duration_from_size(content_length)
if DEBUG:
log('baseapp::start_download: guess duration: size', content_length, 'duration', content_duration)
if tdef.get_live():
include_interruptable_ads = False
else:
include_interruptable_ads = self.get_playerconfig('enable_interruptable_ads', True)
newd_params = {}
provider_key = tdef.get_provider()
provider_content_id = tdef.get_content_id()
premium = tdef.get_premium()
if premium != 0 and provider_key is not None:
if DEBUG_PREMIUM:
log('baseapp::start_download: check premium status: provider_key', provider_key, 'content_id', provider_content_id)
if self.check_premium_status(provider_key, provider_content_id, tdef.get_infohash()):
newd_params['premium'] = True
newd_params['report_interval'] = 60
newd_params['user_check_interval'] = 60
auth_level = self.s.get_authlevel()
if DEBUG_PREMIUM:
log('baseapp::start_download: got premium content: provider_key', provider_key, 'content_id', provider_content_id, 'auth_level', auth_level)
if auth_level < 2:
newd_params['user_check_interval'] = 15
ads = self.ad_manager.get_ads(device_id=self.device_id, user_login=self.s.get_ts_login(), user_level=self.s.get_authlevel(), content_type=DLTYPE_TORRENT, content_id=binascii.hexlify(tdef.get_infohash()), content_ext=content_ext, content_duration=content_duration, affiliate_id=affiliate_id, zone_id=zone_id, developer_id=developer_id, include_interruptable_ads=include_interruptable_ads, is_live=tdef.get_live(), user_profile=self.user_profile, provider_key=provider_key, provider_content_id=provider_content_id)
if ads == False:
if DEBUG:
log('baseapp::start_download: failed to get ads, exit')
raise Exception, 'Cannot start playback'
dcfg.set_dest_dir(destdir)
rate = self.get_playerconfig('total_max_download_rate', DEFAULT_DOWNLOAD_RATE_LIMIT)
if DEBUG:
log('BaseApp::start_download: set download limit to', rate, 'Kb/s')
dcfg.set_max_speed(DOWNLOAD, rate, self.get_playerconfig('auto_download_limit', False))
dcfg.set_wait_sufficient_speed(self.get_playerconfig('wait_sufficient_speed', False))
dcfg.set_http_support(self.get_playerconfig('enable_http_support', True))
dcfg.set_player_buffer_time(self.get_playerconfig('player_buffer_time', DEFAULT_PLAYER_BUFFER_TIME))
dcfg.set_live_buffer_time(self.get_playerconfig('live_buffer_time', DEFAULT_LIVE_BUFFER_TIME))
infohash = tdef.get_infohash()
newd = None
for d in self.s.get_downloads():
if d.get_def().get_infohash() == infohash:
log('BaseApp::start_download: Reusing old duplicate download', infohash)
newd = d
if poa:
d.set_poa(poa)
self.s.lm.h4xor_reset_init_conn_counter()
initialdlstatus = None
got_uninterruptable_ad = False
if len(ads):
for ad in ads:
if not ad['interruptable']:
got_uninterruptable_ad = True
break
if got_uninterruptable_ad:
initialdlstatus = DLSTATUS_STOPPED
if newd is None:
log('BaseApp::start_download: starting new download: infohash', infohash, 'initialdlstatus', initialdlstatus)
newd = self.s.start_download(tdef, dcfg, initialdlstatus)
else:
newd.set_video_events(self.get_supported_vod_events())
newd.set_wait_sufficient_speed(dcfg.get_wait_sufficient_speed())
newd.set_http_support(dcfg.get_http_support())
newd.set_max_speed(UPLOAD, dcfg.get_max_speed(UPLOAD))
newd.set_max_speed(DOWNLOAD, dcfg.get_max_speed(DOWNLOAD), dcfg.get_auto_download_limit())
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
maxconnectionsstream_file = os.path.join(os.path.split(current_file_path)[0],"values","maxconnectionsstream.txt")
f = open(maxconnectionsstream_file, "r")
string = f.read()
newd.set_max_conns(self.get_playerconfig('download_max_connects', int(string)))
svcdlfiles = self.is_svc(dlfile, tdef)
if svcdlfiles is not None:
newd.set_video_event_callback(self.sesscb_vod_event_callback, dlmode=DLMODE_SVC)
newd.set_selected_files(svcdlfiles)
else:
newd.set_video_event_callback(self.sesscb_vod_event_callback)
newd.set_player_buffer_time(self.get_playerconfig('player_buffer_time', DEFAULT_PLAYER_BUFFER_TIME))
newd.set_live_buffer_time(self.get_playerconfig('live_buffer_time', DEFAULT_LIVE_BUFFER_TIME))
if tdef.is_multifile_torrent():
newd.set_selected_files([dlfile])
newd.set_extra_files(extra_files_indexes)
newd.set_files_priority(dcfg.get_files_priority())
if initialdlstatus is None:
if DEBUG:
log('BaseApp::start_download: restarting existing download: infohash', binascii.hexlify(infohash))
newd.restart(new_tdef=tdef)
else:
ds = newd.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
if ds.get_status != DLSTATUS_STOPPED:
if DEBUG:
log('BaseApp::start_download: existing download is active, stop it and wait for ads: infohash', binascii.hexlify(infohash))
newd.stop()
elif DEBUG:
log('BaseApp::start_download: skip restarting existing download, wait for ads: infohash', binascii.hexlify(infohash))
if DEBUG:
log('BaseApp::start_download: saving content to', newd.get_dest_files())
self.dlinfo_lock.acquire()
try:
if newd in self.downloads_in_vodmode:
self.downloads_in_vodmode[newd].update(newd_params)
else:
newd_params['start'] = time.time()
newd_params['download_id'] = hashlib.sha1(b64encode(self.s.get_ts_login()) + b64encode(tdef.get_infohash()) + str(time.time()) + str(random.randint(1, sys.maxint))).hexdigest()
if TNS_ENABLED:
if self.stat_settings.check_content('tns', tdef):
try:
newd_params['tns'] = TNS(self.stat_settings.get_url_list('tns'), self.stat_settings.get_options('tns'), self.tns_uid, self.cookie_jar, tdef)
newd_params['tns'].start()
except TNSNotAllowedException:
pass
except:
if DEBUG:
print_exc()
elif DEBUG:
log('baseapp::start_download: tns disabled: infohash', binascii.hexlify(tdef.get_infohash()))
self.downloads_in_vodmode[newd] = newd_params
if newd in self.downloads_in_admode:
if DEBUG:
log('baseapp::start_ad_downloads: remove old ad downloads on start')
del self.downloads_in_admode[newd]
if len(ads):
if got_uninterruptable_ad:
if DEBUG:
log('baseapp::start_download: got uninterruptable ad, start ads immediatelly')
start_ad_downloads_lambda = lambda : self.start_ad_downloads(newd, ads)
self.run_delayed(start_ad_downloads_lambda, 0.5)
else:
if DEBUG:
log('baseapp::start_download: no uninterruptable ad, start ads when main started')
start_ad_downloads_when_main_started_lambda = lambda : self.start_ad_downloads_when_main_started(newd, ads)
self.run_delayed(start_ad_downloads_when_main_started_lambda, 0.5)
finally:
self.dlinfo_lock.release()
func = lambda : GoogleAnalytics.send_event('client', 'play', VERSION)
self.run_delayed(func)
return newd
def start_ad_downloads_when_main_started(self, maind, ads):
self.dlinfo_lock.acquire()
try:
if maind not in self.downloads_in_vodmode:
if DEBUG:
log('baseapp::start_ad_downloads_when_main_started: download is not in vod mode, stop trying: hash', binascii.hexlify(maind.get_hash()))
return
ds = maind.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
dlstatus = ds.get_status()
is_vod = ds.is_vod()
playable = ds.get_vod_playable()
if DEBUG:
log('baseapp::start_ad_downloads_when_main_started: hash', binascii.hexlify(maind.get_hash()), 'status', dlstatus_strings[dlstatus], 'is_vod', is_vod, 'playable', playable)
if dlstatus == DLSTATUS_STOPPED_ON_ERROR:
if DEBUG:
log('baseapp::start_ad_downloads_when_main_started: download cannot start, stop trying: hash', binascii.hexlify(maind.get_hash()), 'error', ds.get_error())
return
start_ads = False
if dlstatus == DLSTATUS_DOWNLOADING and is_vod:
if playable:
if DEBUG:
log('baseapp::start_ad_downloads_when_main_started: download is playable, stop trying: hash', binascii.hexlify(maind.get_hash()))
return
if DEBUG:
log('baseapp::start_ad_downloads_when_main_started: download is not playable, start ads: hash', binascii.hexlify(maind.get_hash()))
start_ads = True
else:
if dlstatus == DLSTATUS_SEEDING:
if DEBUG:
log('baseapp::start_ad_downloads_when_main_started: download is finished, stop trying: hash', binascii.hexlify(maind.get_hash()))
return
if DEBUG:
log('baseapp::start_ad_downloads_when_main_started: keep trying: hash', binascii.hexlify(maind.get_hash()))
if start_ads:
self.start_ad_downloads(maind, ads)
else:
start_ad_downloads_when_main_started_lambda = lambda : self.start_ad_downloads_when_main_started(maind, ads)
self.run_delayed(start_ad_downloads_when_main_started_lambda, 1.0)
finally:
self.dlinfo_lock.release()
def start_ad_downloads(self, newd, ads):
ds = newd.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
main_dlstatus = ds.get_status()
main_progress = ds.get_progress()
if DEBUG:
log('baseapp::start_ad_downloads: start ads: main', binascii.hexlify(newd.get_hash()), 'status', dlstatus_strings[main_dlstatus], 'progress', main_progress)
noninterruptable_ads = []
noninterruptable_ads_hash_list = []
interruptable_ads = []
for ad in ads:
if ad['dltype'] == DLTYPE_TORRENT:
ad['dlhash'] = ad['tdef'].get_infohash()
ad['size'] = ad['tdef'].get_length()
elif ad['dltype'] == DLTYPE_DIRECT:
tdef = None
if tdef is None:
ad['dlhash'] = hashlib.sha1(ad['url']).digest()
ad['size'] = 0
else:
ad['tdef'] = tdef
ad['dlhash'] = tdef.get_infohash()
ad['size'] = tdef.get_length()
ad['dltype'] = DLTYPE_TORRENT
if DEBUG:
log('baseapp::start_ad_downloads: got torrent from url: url', ad['url'], 'infohash', binascii.hexlify(ad['dlhash']))
else:
raise ValueError('Unknown download type ' + str(ad['dltype']))
if not ad.has_key('priority'):
if ad['interruptable']:
ad['priority'] = 0
else:
ad['priority'] = -1
if ad['interruptable']:
interruptable_ads.append(ad)
else:
noninterruptable_ads.append(ad)
noninterruptable_ads_hash_list.append(ad['dlhash'])
def start_ad(ad):
d = self.s.get_download(ad['dltype'], ad['dlhash'])
if d is None:
if ad['interruptable'] or ad['wait_preload']:
if DEBUG:
log('baseapp::start_ad_downloads: interruptable or preload ad download is not in downloads, skip: dlhash', binascii.hexlify(ad['dlhash']))
return False
if DEBUG:
log('baseapp::start_ad_downloads: start new ad download: main', binascii.hexlify(newd.get_hash()), 'ad', binascii.hexlify(ad['dlhash']))
if not self.cleanup_hidden_downloads(needed=ad['size'], priority=ad['priority']):
if DEBUG:
log('baseapp::start_ad_downloads: not enough space: hash', binascii.hexlify(ad['dlhash']), 'size', ad['size'])
return False
dcfg = DownloadStartupConfig()
dcfg.set_video_event_callback(lambda d, event, params: self.sesscb_vod_event_callback(d, event, params, newd))
dcfg.set_dest_dir(self.s.get_ads_dir())
dcfg.set_player_buffer_time(1)
dcfg.set_max_conns(50)
dcfg.set_max_conns_to_initiate(60)
dcfg.set_hidden(True)
if ad['dltype'] == DLTYPE_TORRENT:
d = self.s.start_download(ad['tdef'], dcfg)
elif ad['dltype'] == DLTYPE_DIRECT:
dcfg.set_download_failed_callback(self.download_failed_callback)
if ad['predownload']:
dcfg.set_predownload(True)
d = self.s.start_direct_download(ad['url'], dcfg)
else:
if ad['interruptable']:
if main_progress == 1.0:
if DEBUG:
log('baseapp::start_ad_downloads: main content is completed, skip interruptable ad: dlhash', binascii.hexlify(ad['dlhash']))
return False
if ad['interruptable'] or ad['wait_preload']:
ds = d.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
progress = ds.get_progress()
if progress != 1.0:
if DEBUG:
log('baseapp::start_ad_downloads: interruptable or preload ad download is not completed, skip: dlhash', binascii.hexlify(ad['dlhash']), 'progress', progress)
return False
used_by_another_vod = False
for main_d, ads in self.downloads_in_admode.iteritems():
if d in ads:
used_by_another_vod = True
if DEBUG:
log('baseapp::start_ad_downloads: ad download is used by another vod: main', binascii.hexlify(newd.get_hash()), 'other', binascii.hexlify(main_d.get_hash()), 'ad', binascii.hexlify(d.get_hash()))
break
if used_by_another_vod:
start_ad_download_when_seeding_lambda = lambda d = d, newd = newd: self.start_ad_download_when_seeding(d, newd)
self.run_delayed(start_ad_download_when_seeding_lambda, 0.1)
else:
if DEBUG:
log('baseapp::start_ad_downloads: restart existing ad download: main', binascii.hexlify(newd.get_hash()), 'ad', binascii.hexlify(ad['dlhash']))
d.set_video_event_callback(lambda d, event, params: self.sesscb_vod_event_callback(d, event, params, newd))
d.set_player_buffer_time(1)
d.set_max_conns(10)
d.set_max_conns_to_initiate(10)
d.set_hidden(True)
if d.get_type() == DLTYPE_DIRECT:
d.set_download_failed_callback(self.download_failed_callback)
d.restart()
self.downloads_in_admode.setdefault(newd, odict())[d] = {'ad': ad,
'start_params': None,
'completed': False,
'started': None,
'finished': None,
'failed': False}
return True
started_noninterruptable_ads = 0
started_interruptable_ads = 0
for ad in noninterruptable_ads:
if start_ad(ad):
started_noninterruptable_ads += 1
for ad in interruptable_ads:
if start_ad(ad):
started_interruptable_ads += 1
if DEBUG:
log('baseapp::start_ad_downloads: started_noninterruptable_ads', started_noninterruptable_ads, 'started_interruptable_ads', started_interruptable_ads)
if started_noninterruptable_ads == 0 and started_interruptable_ads == 0 and main_dlstatus == DLSTATUS_STOPPED:
if DEBUG:
log('baseapp::start_ad_downloads: no ads started, start main download: main', binascii.hexlify(newd.get_hash()))
newd.restart()
self.add_preload_ads(interruptable_ads, False)
def guess_duration_from_size(self, content_length):
if content_length >= 734003200:
content_duration = 5400
elif content_length >= 314572800:
content_duration = 2700
elif content_length >= 104857600:
content_duration = 900
else:
content_duration = 300
return content_duration
def start_ad_download_when_seeding(self, d, main_d):
if DEBUG:
log('baseapp::start_ad_download_when_seeding: main', binascii.hexlify(main_d.get_hash()), 'ad', binascii.hexlify(d.get_hash()))
if main_d not in self.downloads_in_admode:
if DEBUG:
log('baseapp::start_ad_download_when_seeding: main download is not in admode, exit')
return
ds = d.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
dlstatus = ds.get_status()
if dlstatus != DLSTATUS_SEEDING:
if DEBUG:
log('baseapp::start_ad_download_when_seeding: not seeding, reschedule: dlstatus', dlstatus, 'main', binascii.hexlify(main_d.get_hash()), 'ad', binascii.hexlify(d.get_hash()))
start_ad_download_when_seeding_lambda = lambda : self.start_ad_download_when_seeding(d, main_d)
self.run_delayed(start_ad_download_when_seeding_lambda, 1.0)
return
if DEBUG:
log('baseapp::start_ad_download_when_seeding: ad download is seeding, restart')
d.set_video_event_callback(lambda d, event, params: self.sesscb_vod_event_callback(d, event, params, main_d))
d.set_player_buffer_time(1)
d.set_max_conns(10)
d.set_max_conns_to_initiate(10)
d.set_hidden(True)
d.restart()
def start_direct_download(self, main_url, download_url, developer_id, affiliate_id, zone_id):
destdir = self.get_default_destdir()
urlhash = hashlib.sha1(main_url).digest()
if DEBUG:
log('baseapp::start_direct_download: urlhash', binascii.hexlify(urlhash), 'main_url', main_url)
newd = self.s.get_download(DLTYPE_DIRECT, urlhash)
content_duration = 0
if newd is not None:
content_size = newd.get_content_length()
if content_size is not None:
content_duration = self.guess_duration_from_size(content_size)
ads = self.ad_manager.get_ads(device_id=self.device_id, user_login=self.s.get_ts_login(), user_level=self.s.get_authlevel(), content_type=DLTYPE_DIRECT, content_id=binascii.hexlify(urlhash), content_ext='', content_duration=content_duration, affiliate_id=affiliate_id, zone_id=zone_id, developer_id=developer_id, include_interruptable_ads=self.get_playerconfig('enable_interruptable_ads', True), user_profile=self.user_profile)
if ads == False:
if DEBUG:
log('baseapp::start_direct_download: failed to get ads, exit')
raise Exception, 'Cannot start playback'
initialdlstatus = None
got_uninterruptable_ad = False
if len(ads):
for ad in ads:
if not ad['interruptable']:
got_uninterruptable_ad = True
break
if got_uninterruptable_ad:
initialdlstatus = DLSTATUS_STOPPED
if newd is None:
dcfg = DownloadStartupConfig()
dcfg.set_dest_dir(destdir)
dcfg.set_wait_sufficient_speed(self.get_playerconfig('wait_sufficient_speed', False))
dcfg.set_player_buffer_time(self.get_playerconfig('player_buffer_time', DEFAULT_PLAYER_BUFFER_TIME))
dcfg.set_live_buffer_time(self.get_playerconfig('live_buffer_time', DEFAULT_LIVE_BUFFER_TIME))
dcfg.set_video_event_callback(self.sesscb_vod_event_callback)
dcfg.set_direct_download_url(download_url)
dcfg.set_download_finished_callback(lambda url, download_url, urlhash, fileinfo, developer_id = developer_id, affiliate_id = affiliate_id, zone_id = zone_id: self.direct_download_finished(url, download_url, urlhash, fileinfo, developer_id, affiliate_id, zone_id))
newd = self.s.start_direct_download(main_url, dcfg, initialdlstatus)
else:
newd.set_wait_sufficient_speed(self.get_playerconfig('wait_sufficient_speed', False))
newd.set_player_buffer_time(self.get_playerconfig('player_buffer_time', DEFAULT_PLAYER_BUFFER_TIME))
newd.set_live_buffer_time(self.get_playerconfig('live_buffer_time', DEFAULT_LIVE_BUFFER_TIME))
newd.set_video_event_callback(self.sesscb_vod_event_callback)
newd.set_direct_download_url(download_url)
newd.set_download_finished_callback(lambda url, download_url, urlhash, fileinfo, developer_id = developer_id, affiliate_id = affiliate_id, zone_id = zone_id: self.direct_download_finished(url, download_url, urlhash, fileinfo, developer_id, affiliate_id, zone_id))
if initialdlstatus is None:
if DEBUG:
log('BaseApp::start_direct_download: restarting existing download: urlhash', binascii.hexlify(urlhash))
newd.restart()
else:
ds = newd.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
if ds.get_status != DLSTATUS_STOPPED:
if DEBUG:
log('BaseApp::start_direct_download: existing download is active, stop it and wait for ads: urlhash', binascii.hexlify(urlhash))
newd.stop()
elif DEBUG:
log('BaseApp::start_direct_download: skip restarting existing download, wait for ads: urlhash', binascii.hexlify(urlhash))
self.dlinfo_lock.acquire()
try:
self.downloads_in_vodmode[newd] = {}
if newd in self.downloads_in_admode:
if DEBUG:
log('baseapp::start_ad_downloads: remove old ad downloads on start')
del self.downloads_in_admode[newd]
if len(ads):
if got_uninterruptable_ad:
if DEBUG:
log('baseapp::start_download: got uninterruptable ad, start ads immediatelly')
self.start_ad_downloads(newd, ads)
else:
if DEBUG:
log('baseapp::start_download: no uninterruptable ad, start ads when main started')
start_ad_downloads_when_main_started_lambda = lambda : self.start_ad_downloads_when_main_started(newd, ads)
self.run_delayed(start_ad_downloads_when_main_started_lambda, 0.5)
finally:
self.dlinfo_lock.release()
func = lambda : GoogleAnalytics.send_event('client', 'play', VERSION)
self.run_delayed(func)
return newd
def get_encrypted_file_metainfo(self, path):
f = None
try:
f = open(path, 'rb')
meta_len = f.read(4)
meta_len, = struct.unpack('l', meta_len)
if DEBUG:
log('baseapp::get_encrypted_file_metainfo: meta_len', meta_len)
meta = f.read(meta_len)
meta = pickle.loads(meta)
if DEBUG:
log('baseapp::get_encrypted_file_metainfo: meta', meta)
offset_fix = 4 + meta_len - meta['offset']
return (meta, offset_fix)
finally:
if f is not None:
f.close()
def play_encrypted_file(self, path, affiliate_id = 0, zone_id = 0, developer_id = 0):
if DEBUG:
log('baseapp::play_encrypted_file: path', path)
if not os.path.isfile(path):
if DEBUG:
log('baseapp::play_encrypted_file: play_encrypted_file')
meta, offset_fix = self.get_encrypted_file_metainfo(path)
content_duration = meta['duration']
if content_duration == 0:
content_duration = self.guess_duration_from_size(meta['file_length'])
ads = self.ad_manager.get_ads(device_id=self.device_id, user_login=self.s.get_ts_login(), user_level=self.s.get_authlevel(), content_type=DLTYPE_ENCRYPTED_FILE, content_id=binascii.hexlify(meta['hash']), content_ext='', content_duration=content_duration, affiliate_id=affiliate_id, zone_id=zone_id, developer_id=developer_id, include_interruptable_ads=False, provider_key=meta['provider'], user_profile=self.user_profile)
if ads == False:
if DEBUG:
log('baseapp::play_encrypted_file: failed to get ads, exit')
raise Exception, 'Cannot start playback'
got_uninterruptable_ad = False
if len(ads):
for ad in ads:
if not ad['interruptable']:
got_uninterruptable_ad = True
break
newd = FakeDownload(DLTYPE_ENCRYPTED_FILE, path, meta, offset_fix, self.sesscb_vod_event_callback)
self.dlinfo_lock.acquire()
try:
self.downloads_in_vodmode[newd] = {}
if newd in self.downloads_in_admode:
if DEBUG:
log('baseapp::play_encrypted_file: remove old ad downloads on start')
del self.downloads_in_admode[newd]
if len(ads) and got_uninterruptable_ad:
if DEBUG:
log('baseapp::play_encrypted_file: got uninterruptable ad, start ads immediatelly')
self.start_ad_downloads(newd, ads)
else:
newd.restart()
finally:
self.dlinfo_lock.release()
return newd
def direct_download_finished(self, url, download_url, urlhash, fileinfo, developer_id, affiliate_id, zone_id):
try:
if DEBUG:
log('baseapp::direct_download_finished: url', url, 'download_url', download_url, 'fileinfo', fileinfo, 'd', developer_id, 'a', affiliate_id, 'z', zone_id)
path = os.path.join(fileinfo['destdir'], fileinfo['filename'])
piecelen = 524288
tracker = 'http://tracker.publicbt.com:80/announce'
trackers = [['http://t1.torrentstream.net:2710/announce'],
['http://t2.torrentstream.net:2710/announce'],
['http://tracker.publicbt.com:80/announce'],
['http://tracker.openbittorrent.com:80/announce']]
if DEBUG:
log('baseapp::direct_download_finished: create torrent: path', path, 'piecelen', piecelen, 'trackers', trackers)
if fileinfo['mimetype'] == 'video/mp4':
cleared_mp4_metatags = clear_mp4_metadata_tags_from_file(path, ['gssd', 'gshh'])
if DEBUG:
log('baseapp::direct_download_finished: cleared_mp4_metatags', cleared_mp4_metatags)
else:
cleared_mp4_metatags = []
tdef = TorrentDef()
tdef.add_content(path)
tdef.set_piece_length(piecelen)
tdef.set_tracker(tracker)
tdef.set_tracker_hierarchy(trackers)
if download_url is None:
tdef.set_urllist([url])
if fileinfo.has_key('duration') and fileinfo['duration'] is not None:
tdef.set_ts_duration(0, fileinfo['duration'])
if len(cleared_mp4_metatags):
tdef.set_ts_replace_mp4_metatags(0, ','.join(cleared_mp4_metatags))
tdef.finalize()
infohash = tdef.get_infohash()
if not self.s.download_exists(DLTYPE_TORRENT, infohash):
if DEBUG:
log('baseapp::direct_download_finished: add new torrent to downloads')
dcfg = DownloadStartupConfig()
dcfg.set_dest_dir(fileinfo['destdir'])
d = self.s.start_download(tdef, dcfg)
elif DEBUG:
log('baseapp::direct_download_finished: torrent already exists in downloads: infohash', binascii.hexlify(infohash))
player_id, torrent_checksum = self.send_torrent_to_server(tdef, developer_id, affiliate_id, zone_id)
if player_id is not None:
self.save_player_data_to_db(player_id, torrent_checksum, infohash, developer_id, affiliate_id, zone_id)
self.save_url2torrent(url, infohash)
self.s.save_ts_metadata_db(infohash, tdef.get_ts_metadata())
except:
if DEBUG:
print_exc()
def got_ts_metadata(self, tdef, metadata):
if len(metadata) == 0:
return
if DEBUG:
log('baseapp::got_ts_metadata: infohash', binascii.hexlify(tdef.get_infohash()), 'metadata', metadata)
if metadata.has_key('duration'):
tdef.set_ts_duration(metadata['index'], metadata['duration'])
if metadata.has_key('prebuf_pieces'):
tdef.set_ts_prebuf_pieces(metadata['index'], metadata['prebuf_pieces'])
self.s.save_ts_metadata_db(tdef.get_infohash(), tdef.get_ts_metadata())
self.save_ts_metadata_server(tdef.get_infohash(), tdef.get_ts_metadata())
def save_ts_metadata_server(self, infohash, metadata):
if metadata is None:
return
if DEBUG:
log('baseapp::save_ts_metadata_server: infohash', binascii.hexlify(infohash), 'metadata', metadata)
lambda_save_ts_metadata_server = lambda : self._save_ts_metadata_server(infohash, metadata)
self.run_delayed(lambda_save_ts_metadata_server)
def _save_ts_metadata_server(self, infohash, metadata):
if DEBUG:
log('baseapp::_save_ts_metadata_server: infohash', binascii.hexlify(infohash), 'metadata', metadata)
try:
self.tsservice.send_metadata(infohash, metadata)
except:
if DEBUG:
log_exc()
def send_torrent_to_server(self, tdef, developer_id = 0, affiliate_id = 0, zone_id = 0):
if DEBUG_SERVICE_REQUESTS:
log('baseapp::send_torrent_to_server: infohash', binascii.hexlify(tdef.get_infohash()), 'd', developer_id, 'a', affiliate_id, 'z', zone_id)
torrent_data = tdef.save()
torrent_checksum = hashlib.sha1(torrent_data).digest()
protected = tdef.get_protected()
if protected:
infohash = tdef.get_infohash()
else:
infohash = None
player_id = self.tsservice.send_torrent(torrent_data, developer_id, affiliate_id, zone_id, protected, infohash)
if player_id is None:
return
if DEBUG_SERVICE_REQUESTS:
log('baseapp::send_torrent_to_server: torrent saved: infohash', binascii.hexlify(tdef.get_infohash()), 'checksum', binascii.hexlify(torrent_checksum), 'd', developer_id, 'a', affiliate_id, 'z', zone_id, 'player_id', player_id)
self.save_player_data_to_db(player_id, torrent_checksum, tdef.get_infohash(), developer_id, affiliate_id, zone_id)
return (player_id, torrent_checksum)
def update_torrent(self, tdef, developer_id = 0, affiliate_id = 0, zone_id = 0):
lambda_update_torrent = lambda : self._update_torrent(tdef, developer_id, affiliate_id, zone_id)
self.run_delayed(lambda_update_torrent)
def _update_torrent(self, tdef, developer_id, affiliate_id, zone_id):
try:
torrent_data = tdef.save()
torrent_checksum = hashlib.sha1(torrent_data).digest()
ret = self.tsservice.check_torrent(torrent_checksum=torrent_checksum, infohash=tdef.get_infohash(), developer_id=developer_id, affiliate_id=affiliate_id, zone_id=zone_id)
if ret is None:
if DEBUG_SERVICE_REQUESTS:
log('baseapp::_update_torrent: check_torrent failed')
return
player_id, metadata, http_seeds = ret
if DEBUG_SERVICE_REQUESTS:
log('baseapp::_update_torrent: torrent saved: infohash', binascii.hexlify(tdef.get_infohash()), 'checksum', binascii.hexlify(torrent_checksum), 'player_id', player_id, 'metadata', metadata, 'http_seeds', http_seeds)
if player_id is None:
player_id, torrent_checksum = self.send_torrent_to_server(tdef, developer_id, affiliate_id, zone_id)
else:
self.save_player_data_to_db(player_id, torrent_checksum, tdef.get_infohash(), developer_id, affiliate_id, zone_id)
if metadata is not None:
self.s.save_ts_metadata_db(tdef.get_infohash(), metadata)
try:
for d in self.s.get_downloads():
if d.get_hash() == tdef.get_infohash():
if DEBUG_SERVICE_REQUESTS:
log('baseapp::_update_torrent: send metadata to download: hash', binascii.hexlify(d.get_hash()), 'metadata', metadata)
d.got_metadata(metadata)
except:
pass
if http_seeds is not None:
self.s.set_ts_http_seeds(tdef.get_infohash(), http_seeds)
try:
for d in self.s.get_downloads():
if d.get_hash() == tdef.get_infohash():
if DEBUG_SERVICE_REQUESTS:
log('baseapp::_update_torrent: send http seeds to download: hash', binascii.hexlify(d.get_hash()), 'http_seeds', http_seeds)
d.got_http_seeds(http_seeds)
except:
pass
except:
if DEBUG:
log_exc()
def get_torrent_from_server(self, infohash = None, player_id = None):
if infohash is None and player_id is None:
raise ValueError, 'infohash or player id must be specified'
if infohash is not None and player_id is not None:
raise ValueError, 'Both infohash and player id cannot be specified at the same time'
if DEBUG_SERVICE_REQUESTS:
if infohash is not None:
log('baseapp::get_torrent_from_server: infohash', binascii.hexlify(infohash))
elif player_id is not None:
log('baseapp::get_torrent_from_server: player_id', player_id)
player_data = self.tsservice.get_torrent(infohash=infohash, player_id=player_id)
if player_data is None:
return
tdef = player_data['tdef']
self.s.save_torrent_local(tdef, player_data['checksum'])
self.s.save_ts_metadata_db(tdef.get_infohash(), tdef.get_ts_metadata())
return player_data
def get_torrent_from_adid(self, adid):
infohash = self.get_infohash_from_adid(adid)
if infohash is None:
return
ret = self.get_torrent_by_infohash(infohash)
if ret is None:
return
return ret['tdef']
def get_infohash_from_adid(self, adid):
infohash = None
infohash = self.get_infohash_from_adid_db(adid)
if infohash is not None:
return infohash
infohash = self.get_infohash_from_adid_server(adid)
if infohash is not None:
self.save_adid2infohash_db(adid, infohash)
return infohash
def get_infohash_from_adid_db(self, adid):
if DEBUG_SERVICE_REQUESTS:
t = time.time()
db = self.s.open_dbhandler(NTFY_ADID2INFOHASH)
if db is None:
return
infohash = db.get(adid)
self.s.close_dbhandler(db)
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_infohash_from_adid_db: adid', adid, 'infohash', infohash, 'time', time.time() - t)
return infohash
def get_ad_last_seen(self, infohash):
db = self.s.open_dbhandler(NTFY_ADID2INFOHASH)
if db is None:
return
last_seen = db.get_last_seen(infohash)
self.s.close_dbhandler(db)
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_ad_last_seen: infohash', binascii.hexlify(infohash), 'last_seen', last_seen)
return last_seen
def get_infohash_from_adid_server(self, adid):
return self.tsservice.get_infohash_from_adid(adid)
def save_adid2infohash_db(self, adid, infohash):
if DEBUG_SERVICE_REQUESTS:
log('baseapp::save_adid2infohash_db: adid', adid, 'infohash', binascii.hexlify(infohash))
db = self.s.open_dbhandler(NTFY_ADID2INFOHASH)
if db is None:
if DEBUG_SERVICE_REQUESTS:
log('baseapp::save_adid2infohash_db: no db')
return
db.put(adid, infohash)
self.s.close_dbhandler(db)
def get_torrent_from_url(self, url):
infohash = self.get_infohash_from_url(url)
if infohash is None:
return
ret = self.get_torrent_by_infohash(infohash)
if ret is None:
return
return ret['tdef']
def get_infohash_from_url(self, url):
infohash = None
infohash = self.get_infohash_from_url_db(url)
if infohash is not None:
return infohash
infohash = self.get_infohash_from_url_server(url)
if infohash is not None:
self.save_url2torrent_db(url, infohash)
return infohash
def get_infohash_from_url_db(self, url):
db = self.s.open_dbhandler(NTFY_URL2TORRENT)
if db is None:
return
infohash = db.get(url)
self.s.close_dbhandler(db)
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_infohash_from_url: url', url, 'infohash', infohash)
return infohash
def get_infohash_from_url_server(self, url):
return self.tsservice.get_infohash_from_url(url)
def save_url2torrent(self, url, infohash):
try:
self.save_url2torrent_db(url, infohash)
except:
log_exc()
try:
self.save_url2torrent_server(url, infohash)
except:
log_exc()
def save_url2torrent_db(self, url, infohash):
if DEBUG_SERVICE_REQUESTS:
log('baseapp::save_url2torrent: url', url, 'infohash', binascii.hexlify(infohash))
db = self.s.open_dbhandler(NTFY_URL2TORRENT)
if db is None:
if DEBUG_SERVICE_REQUESTS:
log('baseapp::save_url2torrent: no db')
return
db.put(url, infohash)
self.s.close_dbhandler(db)
def save_url2torrent_server(self, url, infohash):
self.tsservice.save_url2infohash(url, infohash)
def get_torrent_from_db(self, checksum = None, infohash = None):
if checksum is None and infohash is None:
return
torrent_db = None
tdef = None
try:
if DEBUG_SERVICE_REQUESTS:
t = time.time()
torrent_db = self.s.open_dbhandler(NTFY_TORRENTS)
if torrent_db is None:
return
if checksum is not None:
torrent = torrent_db.getTorrent(checksum=checksum, keys=['torrent_file_name'])
else:
torrent = torrent_db.getTorrent(infohash=infohash, keys=['torrent_file_name'])
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_torrent_from_db: infohash', infohash, 'checksum', checksum, 'torrent', torrent, 'time', time.time() - t)
if torrent is None:
return
torrent_dir = torrent_db.getTorrentDir()
path = os.path.join(torrent_dir, torrent['torrent_file_name'])
if os.path.exists(path):
if DEBUG_SERVICE_REQUESTS:
t = time.time()
tdef = TorrentDef.load(path)
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_torrent_from_db: load torrent from file: path', path, 'time', time.time() - t)
else:
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_torrent_from_db: torrent file removed, update db: path', path)
torrent_db.deleteTorrent(infohash)
return {'tdef': tdef,
'infohash': torrent['infohash'],
'checksum': torrent['checksum']}
except:
log_exc()
return
finally:
if torrent_db is not None:
self.s.close_dbhandler(torrent_db)
def get_torrent_by_infohash(self, infohash):
if DEBUG_SERVICE_REQUESTS:
t = time.time()
ret = self.get_torrent_from_db(infohash=infohash)
if ret is not None:
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_torrent_by_infohash: got from db: infohash', binascii.hexlify(infohash), 'time', time.time() - t)
return {'tdef': ret['tdef'],
'checksum': ret['checksum']}
if DEBUG_SERVICE_REQUESTS:
t = time.time()
player_data = self.get_torrent_from_server(infohash=infohash)
if player_data is not None:
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_torrent_by_infohash: got from server: infohash', binascii.hexlify(infohash), 'time', time.time() - t)
return {'tdef': player_data['tdef'],
'checksum': player_data['checksum']}
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_torrent_by_infohash: not found: infohash', binascii.hexlify(infohash))
def get_player_data_from_db(self, player_id):
try:
db = self.s.open_dbhandler(NTFY_TS_PLAYERS)
if db is None:
return
player_data = db.get(player_id)
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_player_data_from_db: player_id', player_id, 'player_data', player_data)
return player_data
except:
log_exc()
return
finally:
if db is not None:
self.s.close_dbhandler(db)
def get_player_id_from_db(self, checksum, infohash, developer_id, affiliate_id, zone_id):
try:
db = self.s.open_dbhandler(NTFY_TS_PLAYERS)
if db is None:
return
player_id = db.getPlayerId(checksum, infohash, developer_id, affiliate_id, zone_id)
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_player_id_from_db: player_id', player_id, 'checksum', checksum, 'infohash', binascii.hexlify(infohash), 'developer_id', developer_id, 'affiliate_id', affiliate_id, 'zone_id', zone_id)
return player_id
except:
log_exc()
return
finally:
if db is not None:
self.s.close_dbhandler(db)
def save_player_data_to_db(self, player_id, checksum, infohash, developer_id, affiliate_id, zone_id):
if DEBUG_SERVICE_REQUESTS:
log('baseapp::save_player_data_to_db: player_id', player_id, 'checksum', binascii.hexlify(checksum), 'infohash', binascii.hexlify(infohash), 'developer_id', developer_id, 'affiliate_id', affiliate_id, 'zone_id', zone_id)
try:
db = self.s.open_dbhandler(NTFY_TS_PLAYERS)
if db is None:
return
db.put(player_id, checksum, infohash, developer_id, affiliate_id, zone_id)
except:
log_exc()
finally:
if db is not None:
self.s.close_dbhandler(db)
def get_player_data(self, player_id):
player_data = self.get_player_data_from_db(player_id)
if player_data is not None:
ret = self.get_torrent_from_db(checksum=player_data['checksum'])
if ret is not None:
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_player_data: got from db: player_id', player_id, 'checksum', binascii.hexlify(player_data['checksum']), 'player_data', player_data)
player_data['tdef'] = ret['tdef']
return player_data
player_data = self.get_torrent_from_server(player_id=player_id)
if player_data is not None:
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_player_data: got from server: player_id', player_id, 'checksum', binascii.hexlify(player_data['checksum']), 'player_data', player_data)
self.save_player_data_to_db(player_id, player_data['checksum'], player_data['tdef'].get_infohash(), player_data['developer_id'], player_data['affiliate_id'], player_data['zone_id'])
return player_data
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_player_data: not found: player_id', player_id)
def check_user_profile(self):
if self.user_profile is None:
self.user_profile = self.get_user_profile()
return self.user_profile is not None
def get_user_profile(self):
db = None
try:
db = self.s.open_dbhandler(NTFY_USER_PROFILE)
if db is None:
return
profile = db.get_active_profile()
if DEBUG_SERVICE_REQUESTS:
log('baseapp::get_user_profile: profile', str(profile))
return profile
except:
log_exc()
return
finally:
if db is not None:
self.s.close_dbhandler(db)
def sesscb_vod_event_callback(self, d, event, params, main_download = None):
pass
def get_supported_vod_events(self):
pass
def get_drive_list(self):
try:
drives = win32api.GetLogicalDriveStrings()
drives = [ drivestr for drivestr in drives.split('\x00') if drivestr ]
return drives
except:
return []
def format_drive_name(self, drive):
if drive is None:
return ''
if len(drive) < 2:
return ''
drive = drive[:2].lower()
if not drive.endswith(':'):
return ''
return drive
def get_disk_info(self, path):
try:
folder = path
if sys.platform == 'win32':
free_bytes, total_bytes, _ = win32file.GetDiskFreeSpaceEx(folder)
used_bytes = total_bytes - free_bytes
else:
st = os.statvfs(folder)
free_bytes = st.f_bavail * st.f_frsize
total_bytes = st.f_blocks * st.f_frsize
used_bytes = (st.f_blocks - st.f_bfree) * st.f_frsize
return (total_bytes, free_bytes, used_bytes)
except:
if DEBUG:
log('baseapp::get_disk_info: cannot get disk info: path', path)
return (None, None, None)
def free_up_diskspace_by_downloads(self, infohash = None, needed = 0):
disk_cache_limit = self.get_playerconfig('disk_cache_limit', DEFAULT_DISKSPACE_LIMIT)
content_dir = self.get_default_destdir()
total, avail, used = self.get_disk_info(content_dir)
if avail is None:
if disk_cache_limit == 0:
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: cannot get disk info and disk cache is unlimited')
return True
avail = disk_cache_limit
if DEBUG:
log('BaseApp::free_up_diskspace_by_downloads: needed', needed, 'avail', avail, 'disk_cache_limit', disk_cache_limit)
if disk_cache_limit < needed < avail:
if DEBUG:
log('BaseApp::free_up_diskspace_by_downloads: no cleanup for bigguns')
return True
inuse = 0L
timelist = []
if self.apptype == 'acestream':
known_files = []
for d in self.s.get_downloads():
destfiles = d.get_dest_files(exts=videoextdefaults, get_all=True)
if self.apptype == 'acestream':
for filename, savepath in destfiles:
if os.path.exists(savepath):
known_files.append(savepath)
if infohash is not None and infohash == d.get_hash():
continue
if d in self.downloads_in_vodmode:
continue
if d.is_hidden():
continue
if DEBUG:
log('BaseApp::free_up_diskspace_by_downloads: downloaded content', destfiles)
dinuse = 0L
max_ctime = 0
for filename, savepath in destfiles:
dirname = os.path.dirname(savepath)
if dirname != content_dir:
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: skip dir:', dirname)
continue
if os.path.exists(savepath):
stat = os.stat(savepath)
dinuse += stat.st_size
if stat.st_ctime > max_ctime:
max_ctime = stat.st_ctime
if dinuse > 0:
inuse += dinuse
timerec = (max_ctime, dinuse, d)
timelist.append(timerec)
if self.apptype == 'acestream':
try:
filelist = os.listdir(content_dir)
except:
if DEBUG:
print_exc()
filelist = []
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: known_files', known_files, 'filelist', filelist)
for basename in filelist:
if basename == '.lock':
continue
if infohash is not None and basename == binascii.hexlify(infohash):
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: keep file: basename', basename, 'infohash', binascii.hexlify(infohash))
continue
filename = os.path.join(content_dir, basename)
if filename not in known_files:
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: remove unknown file: filename', filename)
try:
os.remove(filename)
except:
if DEBUG:
print_exc()
if disk_cache_limit == 0:
limit = avail
else:
limit = min(avail, disk_cache_limit)
if inuse + needed < limit:
if DEBUG:
log('BaseApp::free_up_diskspace_by_downloads: enough avail: inuse', inuse, 'needed', needed, 'limit', limit, 'avail', avail)
return True
timelist.sort()
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: timelist', timelist)
to_free = inuse + needed - limit
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: to_free', to_free, 'limit', limit, 'inuse', inuse, 'needed', needed)
for ctime, dinuse, d in timelist:
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: remove download: hash', binascii.hexlify(d.get_hash()), 'dinuse', dinuse, 'ctime', ctime)
self.s.remove_download(d, removecontent=True)
to_free -= dinuse
if DEBUG:
log('baseapp::free_up_diskspace_by_downloads: remove done: to_free', to_free, 'limit', limit, 'inuse', inuse, 'needed', needed)
if to_free <= 0:
return True
return False
def sesscb_states_callback(self, dslist):
if self.debug_systray:
getpeerlist = True
haspeerlist = True
else:
getpeerlist = False
haspeerlist = False
gui_states_callback_wrapper_lambda = lambda : self.gui_states_callback_wrapper(dslist, haspeerlist)
self.run_delayed(gui_states_callback_wrapper_lambda)
return (1.0, getpeerlist)
def gui_states_callback_wrapper(self, dslist, haspeerlist):
try:
self.gui_states_callback(dslist, haspeerlist)
except:
log_exc()
def gui_states_callback(self, dslist, haspeerlist):
if self.shuttingdown:
return ({},
[],
0,
0)
playermode = self.playermode
totalspeed = {UPLOAD: 0.0,
DOWNLOAD: 0.0}
totalhelping = 0
display_stats = self.download_states_display_counter % DOWNLOAD_STATES_DISPLAY_INTERVAL == 0
self.download_states_display_counter += 1
all_dslist = {}
playing_dslist = {}
hidden_dslist = {}
all_playing_are_seeding = True
playing_premium_content = False
self.dlinfo_lock.acquire()
try:
for ds in dslist:
d = ds.get_download()
all_dslist[d] = ds
is_vod_download = False
vod_download_params = None
if d.is_hidden():
hidden_dslist[d] = ds
if d in self.downloads_in_vodmode:
is_vod_download = True
vod_download_params = self.downloads_in_vodmode[d]
playing_dslist[d] = ds
if all_playing_are_seeding and ds.get_status() != DLSTATUS_SEEDING:
all_playing_are_seeding = False
if is_vod_download and vod_download_params.get('premium', False):
playing_premium_content = True
provider_key = d.get_def().get_provider()
provider_content_id = d.get_def().get_content_id()
if not self.report_premium_download(provider_key, provider_content_id, vod_download_params):
if time.time() > vod_download_params['start'] + PREMIUM_PREVIEW_TIMEOUT and not vod_download_params.has_key('stopped_preview'):
if DEBUG_PREMIUM:
log('baseapp::gui_states_callback: user auth failed for premium content, stop')
vod_download_params['stopped_preview'] = True
self.stop_download(d, 'http://acestream.net/embed/premium', 'This content is available for premium users only')
if DEBUG and display_stats:
log('baseapp::gui_states_callback: dlinfo: vod=%i type=%d hash=%s hidden=%i priority=%d status=%s paused=%i progress=%.1f%% error=%s' % (is_vod_download,
d.get_type(),
binascii.hexlify(d.get_hash()),
d.is_hidden(),
d.get_extra('priority', 0),
dlstatus_strings[ds.get_status()],
ds.get_paused(),
100.0 * ds.get_progress(),
ds.get_error()))
self.update_download_stats(ds)
if not d.is_hidden() or SHOW_HIDDEN_DOWNLOADS_INFO:
for dir in [UPLOAD, DOWNLOAD]:
totalspeed[dir] += ds.get_current_speed(dir)
totalhelping += ds.get_num_peers()
for main_download, ad_downloads in self.downloads_in_admode.iteritems():
if not playing_dslist.has_key(main_download):
if DEBUG:
log('baseapp::gui_states_callback: main download in ad mode is not in vod downloads: infohash', binascii.hexlify(main_download.get_hash()))
else:
main_ds = playing_dslist[main_download]
if main_ds.get_status() == DLSTATUS_STOPPED:
all_ads_completed = True
for d in ad_downloads.keys():
if not all_dslist.has_key(d):
if DEBUG:
log('baseapp::gui_states_callback: ad download not found in downloads: infohash', binascii.hexlify(d.get_hash()))
else:
ds = all_dslist[d]
if DEBUG:
log('baseapp::gui_states_callback: check ad download: main', binascii.hexlify(main_download.get_hash()), 'ad', binascii.hexlify(d.get_hash()), 'status', ds.get_status(), 'progress', ds.get_progress())
status = ds.get_status()
if status == DLSTATUS_STOPPED_ON_ERROR:
ad_downloads[d]['failed'] = True
elif status == DLSTATUS_STOPPED:
if DEBUG:
log('!!!! baseapp::gui_states_callback: ad download is stopped, mark as failed !!!!')
ad_downloads[d]['failed'] = True
elif status == DLSTATUS_SEEDING:
ad_downloads[d]['completed'] = True
else:
all_ads_completed = False
if all_ads_completed:
if DEBUG:
log('baseapp::gui_states_callback: all ads are completed, restart download: infohash', binascii.hexlify(main_download.get_hash()))
main_download.restart()
finally:
self.dlinfo_lock.release()
if haspeerlist:
try:
for ds in playing_dslist.values():
peerlist = ds.get_peerlist()
vodstats = ds.get_vod_stats()
stats = ds.get_stats()
if peerlist and self.statFrame:
self.statFrame.updateStats(spew=peerlist, statistics=stats, vod_stats=vodstats)
if DEBUG_STATS_TO_FILE:
self.save_state_to_file(spew=peerlist, statistics=stats, vod_stats=vodstats)
break
except:
log_exc()
if self.live_frame is not None:
try:
for ds in playing_dslist.values():
peerlist = ds.get_peerlist()
vodstats = ds.get_vod_stats()
stats = ds.get_stats()
self.live_frame.update(spew=peerlist, statistics=stats, vod_stats=vodstats)
break
except:
print_exc()
txt = self.appname + '\n\n'
txt += 'DL: %.1f\n' % totalspeed[DOWNLOAD]
txt += 'UL: %.1f\n' % totalspeed[UPLOAD]
txt += 'Helping: %d\n' % totalhelping
self.OnSetSysTrayTooltip(txt)
if totalspeed[DOWNLOAD] > self.max_download_rate:
self.max_download_rate = totalspeed[DOWNLOAD]
if totalspeed[UPLOAD] > self.max_upload_rate:
self.max_upload_rate = totalspeed[UPLOAD]
self.avg_download_rate_sum += totalspeed[DOWNLOAD]
self.avg_download_rate_count += 1
self.avg_download_rate = self.avg_download_rate_sum / float(self.avg_download_rate_count)
self.avg_upload_rate_sum += totalspeed[UPLOAD]
self.avg_upload_rate_count += 1
self.avg_upload_rate = self.avg_upload_rate_sum / float(self.avg_upload_rate_count)
if self.playing_premium_content != playing_premium_content:
if DEBUG_PREMIUM:
log('baseapp::gui_states_callback: playing_premium_content changed to', playing_premium_content)
self.playing_premium_content = playing_premium_content
if playing_premium_content:
self.run_delayed(self.check_auth_level, 1.0, task_id='check_auth_level')
if all_playing_are_seeding:
if self.get_playerconfig('enable_interruptable_ads', True):
max_progress = -1
max_priority = -1
download_to_restart = None
for d, ds in hidden_dslist.iteritems():
status = ds.get_status()
if status == DLSTATUS_STOPPED or status == DLSTATUS_STOPPED_ON_ERROR:
priority = d.get_extra('priority', 0)
if ds.get_progress() == 1.0:
if DEBUG_HIDDEN_DOWNLOADS:
log('baseapp::gui_states_callback: restart completed hidden download: hash', binascii.hexlify(d.get_hash()), 'status', dlstatus_strings[status], 'progress', ds.get_progress())
d.restart()
elif priority > max_priority:
download_to_restart = d
max_progress = ds.get_progress()
max_priority = priority
elif ds.get_progress() > max_progress:
download_to_restart = d
max_progress = ds.get_progress()
max_priority = priority
elif status == DLSTATUS_HASHCHECKING or ds.get_progress() != 1.0:
if DEBUG_HIDDEN_DOWNLOADS:
log('baseapp::gui_states_callback: got running hidden download: hash', binascii.hexlify(d.get_hash()), 'status', dlstatus_strings[status], 'progress', ds.get_progress())
download_to_restart = None
break
if download_to_restart is not None:
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
downloadlimitvalue_file = os.path.join(os.path.split(current_file_path)[0],"values","downloadlimit.txt")
f = open(downloadlimitvalue_file, "r")
string = f.read()
max_speed = self.get_playerconfig('max_download_rate', int(string))
if max_speed == 0:
max_speed = self.max_download_rate
limit_speed = max_speed / 3
download_to_restart.set_max_speed(DOWNLOAD, limit_speed)
if DEBUG_HIDDEN_DOWNLOADS:
ds = hidden_dslist[download_to_restart]
log('baseapp::gui_states_callback: restart hidden download: hash', binascii.hexlify(download_to_restart.get_hash()), 'status', dlstatus_strings[ds.get_status()], 'progress', ds.get_progress(), 'max_speed', max_speed, 'limit_speed', limit_speed)
download_to_restart.restart()
if playermode == DLSTATUS_DOWNLOADING:
if DEBUG:
log('BaseApp::gui_states_callback: all playing download are seeding, restart others')
t = time.time()
self.restart_other_downloads()
if DEBUG:
log('BaseApp::gui_states_callback: restart others: time', time.time() - t)
elif playermode == DLSTATUS_SEEDING:
if DEBUG:
log('BaseApp::gui_states_callback: not all playing download are seeding, stop others')
t = time.time()
self.stop_other_downloads()
if DEBUG:
log('BaseApp::gui_states_callback: stop others: time', time.time() - t)
if len(playing_dslist) == 0:
return ({},
[],
0,
0)
return (all_dslist,
playing_dslist.values(),
totalhelping,
totalspeed)
def update_download_stats(self, ds, force = False):
try:
if not force and time.time() - self.last_download_stats < DOWNLOAD_STATS_INTERVAL:
return
self.last_download_stats = time.time()
d = ds.get_download()
download_id = d.get_download_id()
if download_id is None:
return
if d.get_type() != DLTYPE_TORRENT:
return
tdef = d.get_def()
if not self.stat_settings.check_content('ts', tdef):
return
downloaded = ds.get_total_transferred(DOWNLOAD)
uploaded = ds.get_total_transferred(UPLOAD)
if not self.download_stats.has_key(download_id):
self.download_stats[download_id] = {'downloaded': 0,
'uploaded': 0}
if self.download_stats[download_id]['downloaded'] != downloaded or self.download_stats[download_id]['uploaded'] != uploaded:
self.download_stats[download_id]['downloaded'] = downloaded
self.download_stats[download_id]['uploaded'] = uploaded
infohash = binascii.hexlify(tdef.get_infohash())
provider_key = tdef.get_provider()
provider_content_id = tdef.get_content_id()
self.traffic_stats.send_event(download_id, 'keepalive', downloaded, uploaded, infohash, provider_key, provider_content_id)
except:
if DEBUG:
print_exc()
def save_state_to_file(self, spew, statistics = None, vod_stats = None):
info = ''
if spew is not None:
tot_uprate = 0.0
tot_downrate = 0.0
tot_downloaded = 0
for x in range(len(spew)):
peerdata = [''] * 17
if spew[x]['optimistic'] == 1:
a = '*'
else:
a = ' '
peerdata[0] = a
peerdata[2] = spew[x]['ip'].ljust(15)
peerdata[3] = spew[x]['direction']
peerdata[4] = ('%.0f kB/s' % (float(spew[x]['uprate']) / 1000)).ljust(10)
tot_uprate += spew[x]['uprate']
if spew[x]['uinterested'] == 1:
a = '*'
else:
a = ' '
peerdata[5] = a
if spew[x]['uchoked'] == 1:
a = '*'
else:
a = ' '
peerdata[6] = a
bitrate = None
if vod_stats['videostatus'] is not None:
bitrate = vod_stats['videostatus'].bitrate
str_downrate = '%.0f' % (spew[x]['downrate'] / 1024.0)
if 'short_downrate' in spew[x]:
if bitrate is None:
str_downrate += ' (%.0f)' % (spew[x]['short_downrate'] / 1024 / 0.0)
else:
str_downrate += ' (%.0f, %.1f)' % (spew[x]['short_downrate'] / 1024.0, spew[x]['short_downrate'] / float(bitrate))
peerdata[7] = str_downrate.ljust(15)
tot_downrate += spew[x]['downrate']
if spew[x]['dinterested'] == 1:
a = '*'
else:
a = ' '
peerdata[8] = a
if spew[x]['dchoked'] == 1:
a = '*'
else:
a = ' '
peerdata[9] = a
if spew[x]['snubbed'] == 1:
a = '*'
else:
a = ' '
peerdata[10] = a
tot_downloaded += spew[x]['dtotal']
peerdata[11] = ('%.2f MiB' % (float(spew[x]['dtotal']) / 1048576)).ljust(10)
if spew[x]['utotal'] is not None:
a = '%.2f MiB' % (float(spew[x]['utotal']) / 1048576)
else:
a = ''
peerdata[12] = a.ljust(10)
peerdata[13] = ('%.1f%%' % (float(int(spew[x]['completed'] * 1000)) / 10)).ljust(5)
if spew[x]['speed'] is not None:
a = '%.0f' % (float(spew[x]['speed']) / 1024)
if 'speed_proxy' in spew[x]:
a += ' | p:%.0f' % (float(spew[x]['speed_proxy']) / 1024)
if 'speed_non_proxy' in spew[x]:
a += ' | r:%.0f' % (float(spew[x]['speed_non_proxy']) / 1024)
else:
a = ''
peerdata[14] = a.ljust(15)
peerdata[15] = str(spew[x]['last_requested_piece']).ljust(4)
peerdata[16] = str(spew[x]['last_received_piece']).ljust(4)
info += '\t'.join(peerdata) + '\n'
info += '\n\nTOTALS: up=' + '%.0f kB/s' % (float(tot_uprate) / 1024) + ' down=' + '%.0f kB/s' % (float(tot_downrate) / 1024) + ' downloaded=' + '%.2f MiB' % (float(tot_downloaded) / 1048576) + '\n\n'
if vod_stats is not None:
for pos, data in vod_stats['proxybuf'].iteritems():
length = len(data)
info += str(pos) + ' '
for i in xrange(length / 131072):
info += '-'
info += str(pos + length - 1) + '\n'
info += 'buf: ' + str(vod_stats['outbuf']) + '\n'
if vod_stats['videostatus'] is not None:
vs = vod_stats['videostatus']
info += ' >> idx: ' + str(vs.fileindex)
info += ', br: ' + str(vs.bitrate / 1024)
info += ', len: ' + str(vs.piecelen / 1024)
info += ', first: ' + str(vs.first_piece)
info += ', last: ' + str(vs.last_piece)
info += ', have: ' + str(vs.numhave)
info += ', comp: %.2f' % vs.completed
info += ', prebuf: ' + str(vs.prebuffering)
info += ', pos: ' + str(vs.playback_pos)
info += ', hp: ' + str(vs.prebuf_high_priority_pieces)
info += ', pp: ' + str(vs.prebuf_missing_pieces)
have = vs.have[:]
have.sort()
info += ', pieces: ' + str(have)
for vs in vod_stats['extra_videostatus']:
info += '\n index: ' + str(vs.fileindex)
info += ', first piece: ' + str(vs.first_piece)
info += ', last piece: ' + str(vs.last_piece)
info += ', numhave: ' + str(vs.numhave)
info += ', completed: %.2f' % vs.completed
info += ', prebuf: ' + str(vs.prebuffering)
info += ', hp: ' + str(vs.prebuf_high_priority_pieces)
info += ', pp: ' + str(vs.prebuf_missing_pieces)
have = vs.have[:]
have.sort()
info += ', pieces: ' + str(have)
if statistics is not None:
for piece in xrange(len(statistics.storage_inactive_list)):
inactive = statistics.storage_inactive_list[piece]
if inactive is None:
inactive = 'all'
elif inactive == 1:
inactive = 'none'
else:
inactive = str(len(inactive))
info += '\n' + str(piece) + ': inactive=' + inactive + ' active=' + str(statistics.storage_active_list[piece]) + ' dirty=' + str(statistics.storage_dirty_list[piece])
if len(info):
self.debug_counter += 1
try:
filename = 'stat_snapshot_' + str(self.debug_counter).rjust(4, '0') + '_' + str(int(time.time())) + '.txt'
f = open(os.path.join(self.installdir, filename), 'w')
f.write(info)
f.close()
except:
raise
def OnSetSysTrayTooltip(self, txt):
try:
self.wrapper.set_icon_tooltip(txt)
except:
pass
def restart_other_downloads(self):
if self.shuttingdown:
return
if DEBUG:
log('baseapp::restart_other_downloads: ---')
self.playermode = DLSTATUS_SEEDING
self.ratelimiter = UserDefinedMaxAlwaysOtherwiseEquallyDividedRateManager()
self.set_ratelimits()
dlist = self.s.get_downloads()
for d in dlist:
if d.is_hidden():
ds = d.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
if ds.get_status() != DLSTATUS_STOPPED:
if DEBUG_HIDDEN_DOWNLOADS:
log('baseapp::restart_other_downloads: unpause hidden download: hash', binascii.hexlify(d.get_hash()))
d.pause(False)
continue
if d not in self.downloads_in_vodmode:
ds = d.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
if ds.get_status() == DLSTATUS_STOPPED and ds.get_progress() == 1.0:
if DEBUG:
log('baseapp::restart_other_downloads: start seeding: infohash', binascii.hexlify(d.get_hash()))
d.set_mode(DLMODE_NORMAL)
d.restart()
else:
d.pause(False)
def stop_other_downloads(self):
if self.shuttingdown:
return
if DEBUG:
log('baseapp::stop_other_downloads: ---')
self.playermode = DLSTATUS_DOWNLOADING
dlist = self.s.get_downloads()
for d in dlist:
if d in self.downloads_in_vodmode:
continue
is_ad = False
for maind_d, ads in self.downloads_in_admode.iteritems():
if d in ads:
is_ad = True
break
if is_ad:
continue
ds = d.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
if ds.get_status() == DLSTATUS_STOPPED:
continue
if DEBUG:
log('baseapp::stop_other_downloads: stop: infohash', binascii.hexlify(d.get_hash()), 'status', dlstatus_strings[ds.get_status()], 'progress', ds.get_progress())
if ds.get_status() == DLSTATUS_SEEDING:
d.pause(True, close_connections=True)
else:
d.stop()
def stop_hidden_downloads(self):
if self.shuttingdown:
return
if DEBUG_HIDDEN_DOWNLOADS:
log('baseapp::stop_hidden_downloads: ---')
dlist = self.s.get_downloads()
for d in dlist:
if not d.is_hidden():
continue
if d in self.downloads_in_vodmode:
continue
is_ad = False
for maind_d, ads in self.downloads_in_admode.iteritems():
if d in ads:
is_ad = True
break
if is_ad:
continue
ds = d.network_get_state(usercallback=None, getpeerlist=False, sessioncalling=True)
if ds.get_status() == DLSTATUS_STOPPED:
if ds.get_progress() == 0.0:
if DEBUG_HIDDEN_DOWNLOADS:
log('baseapp::stop_hidden_downloads: remove: infohash', binascii.hexlify(d.get_hash()), 'status', dlstatus_strings[ds.get_status()], 'progress', ds.get_progress())
self.s.remove_download(d, removecontent=True)
continue
if DEBUG_HIDDEN_DOWNLOADS:
log('baseapp::stop_hidden_downloads: stop: infohash', binascii.hexlify(d.get_hash()))
d.stop()
def remove_downloads_in_vodmode_if_not_complete(self):
if DEBUG:
log('BaseApp::remove_downloads_in_vodmode_if_not_complete: Removing playing download if not complete')
for d in self.downloads_in_vodmode:
d.set_state_callback(self.sesscb_remove_playing_callback)
def sesscb_remove_playing_callback(self, ds):
if DEBUG:
d = ds.get_download()
dlhash = binascii.hexlify(d.get_hash())
log('BaseApp::sesscb_remove_playing_callback: type', d.get_type(), 'hash', dlhash, 'status', dlstatus_strings[ds.get_status()], 'progress', ds.get_progress())
self.update_download_stats(ds, True)
d = ds.get_download()
if d.get_type() == DLTYPE_TORRENT:
live = d.get_def().get_live()
else:
live = False
if live:
remove_content = True
elif ds.get_status() == DLSTATUS_DOWNLOADING and ds.get_progress() >= MIN_PROGRESS_KEEP:
remove_content = False
elif ds.get_status() == DLSTATUS_SEEDING:
remove_content = False
elif ds.get_status() == DLSTATUS_HASHCHECKING:
remove_content = False
else:
remove_content = True
if not remove_content:
if ds.get_status() == DLSTATUS_SEEDING:
can_remove = self.can_remove_playing_download(d)
if can_remove:
self.remove_download_info(d)
if DEBUG:
log('baseapp::sesscb_remove_playing_callback: download is seeding, do not stop: dlhash', dlhash, 'remove_dlinfo', can_remove)
else:
if DEBUG:
log('BaseApp::sesscb_remove_playing_callback: keeping: dlhash', dlhash)
remove_playing_download_lambda = lambda : self.remove_playing_download(d, removecontent=False, stop=True)
self.run_delayed(remove_playing_download_lambda, 0.1)
else:
if DEBUG:
log('BaseApp::sesscb_remove_playing_callback: voting for removing: dlhash', dlhash)
if self.shuttingdown:
if DEBUG:
log('BaseApp::sesscb_remove_playing_callback: shuttingdown, call remove_playing_download immediately')
self.remove_playing_download(d, removecontent=True)
else:
if DEBUG:
log('BaseApp::sesscb_remove_playing_callback: schedule remove_playing_download')
remove_playing_download_lambda = lambda : self.remove_playing_download(d, removecontent=True)
self.run_delayed(remove_playing_download_lambda, 0.1)
return (-1.0, False)
def remove_playing_download(self, d, removecontent):
if self.s is not None:
if DEBUG:
log('BaseApp::remove_playing_download: dlhash', binascii.hexlify(d.get_hash()), 'removecontent', removecontent)
try:
self.s.remove_download(d, removecontent)
self.remove_download_info(d)
except:
log_exc()
elif DEBUG:
log('BaseApp::remove_playing_download: s is None')
def stop_playing_download(self, d):
if DEBUG:
log('BaseApp::stop_playing_download: dlhash', binascii.hexlify(d.get_hash()))
try:
d.stop()
self.remove_download_info(d)
except:
log_exc()
def remove_download_info(self, d):
if DEBUG:
log('baseapp::remove_download_info: remove download: hash', binascii.hexlify(d.get_hash()))
if d in self.downloads_in_vodmode:
params = self.downloads_in_vodmode[d]
if params.has_key('tns'):
if DEBUG:
log('baseapp::remove_download_info: stop tns: hash', binascii.hexlify(d.get_hash()))
params['tns'].stop()
del self.downloads_in_vodmode[d]
if d in self.downloads_in_admode:
del self.downloads_in_admode[d]
def set_ratelimits(self):
import os
current_file_path = os.path.dirname(os.path.realpath(__file__))
uploadlimitvalue_file = os.path.join(os.path.split(current_file_path)[0],"values","uploadlimit.txt")
f = open(uploadlimitvalue_file, "r")
uploadrate = float(self.get_playerconfig('total_max_upload_rate', int(string)))
if DEBUG:
log('BaseApp::set_ratelimits: Setting max upload rate to', uploadrate)
if self.ratelimiter is not None:
self.ratelimiter.set_global_max_speed(UPLOAD, uploadrate)
self.ratelimiter.set_global_max_seedupload_speed(uploadrate)
def ratelimit_callback(self, dslist):
if self.ratelimiter is None:
return
adjustspeeds = False
if self.ratelimit_update_count % 4 == 0:
adjustspeeds = True
self.ratelimit_update_count += 1
if adjustspeeds:
self.ratelimiter.add_downloadstatelist(dslist)
self.ratelimiter.adjust_speeds()
def load_playerconfig(self, state_dir):
self.playercfgfilename = os.path.join(state_dir, 'playerconf.pickle')
self.playerconfig = {}
if not os.path.isfile(self.playercfgfilename):
return
try:
f = open(self.playercfgfilename, 'rb')
self.playerconfig = pickle.load(f)
f.close()
except:
print_exc()
self.playerconfig = {}
def save_playerconfig(self):
try:
f = open(self.playercfgfilename, 'wb')
pickle.dump(self.playerconfig, f)
f.close()
except:
log_exc()
def set_playerconfig(self, key, value):
if self.playerconfig.has_key(key):
old_value = self.playerconfig[key]
else:
old_value = None
self.playerconfig[key] = value
if key == 'total_max_upload_rate':
try:
self.set_ratelimits()
except:
log_exc()
return old_value
def update_playerconfig(self, changed_config_params):
if 'enable_interruptable_ads' in changed_config_params:
value = self.get_playerconfig('enable_interruptable_ads')
if DEBUG:
log('baseapp::update_playerconfig: enable_interruptable_ads changed: value', value)
if value:
self.run_delayed(self.check_preload_ads, 3.0, 'check_preload_ads')
else:
self.run_delayed(self.stop_hidden_downloads, 3.0)
if 'disk_cache_limit' in changed_config_params:
if DEBUG:
log('baseapp::update_playerconfig: disk cache limit changed:', self.get_playerconfig('disk_cache_limit'))
self.free_up_diskspace_by_downloads()
for d in self.downloads_in_vodmode:
d.set_wait_sufficient_speed(self.get_playerconfig('wait_sufficient_speed'))
d.set_http_support(self.get_playerconfig('enable_http_support'))
d.set_player_buffer_time(self.get_playerconfig('player_buffer_time'))
d.set_live_buffer_time(self.get_playerconfig('live_buffer_time'))
d.set_max_speed(UPLOAD, self.get_playerconfig('total_max_upload_rate'))
d.set_max_speed(DOWNLOAD, self.get_playerconfig('total_max_download_rate'), self.get_playerconfig('auto_download_limit'))
d.set_max_conns(self.get_playerconfig('download_max_connects'))
def get_playerconfig(self, key, default = None):
if key in self.playerconfig:
return self.playerconfig[key]
return default
def OnExit(self):
log('BaseApp::OnExit:', currentThread().getName())
self.shuttingdown = True
self.remove_downloads_in_vodmode_if_not_complete()
if self.max_download_rate > 0:
if DEBUG:
log('baseapp::onexit: save max down rate:', self.max_download_rate)
self.set_playerconfig('max_download_rate', self.max_download_rate)
self.save_playerconfig()
self.i2i_listen_server.shutdown()
if globalConfig.get_mode() != 'client_console':
time.sleep(2)
if self.s is not None:
try:
state_dir = self.s.get_state_dir()
cfgfilename = Session.get_default_config_filename(state_dir)
if DEBUG:
log('baseapp::onexit: save SessionStartupConfig to', cfgfilename)
scfg = SessionStartupConfig.load(cfgfilename)
scfg.set_authlevel(self.s.get_authlevel())
scfg.save(cfgfilename)
except:
pass
self.s.shutdown(hacksessconfcheckpoint=False)
self.save_cookies()
if DEBUG:
self.debug_threads()
def debug_threads_task(self):
try:
self.debug_threads()
finally:
self.run_delayed(self.debug_threads_task, 600)
def debug_threads(self):
log('baseapp::debug_threads: ---')
count = 0
for t in enumerate():
log('baseapp::debug_threads: thread is running', t.name, 'daemon', t.daemon)
count += 1
log('baseapp::debug_threads: count', count)
def clear_session_state(self):
try:
if self.s is not None:
dlist = self.s.get_downloads(DLTYPE_TORRENT)
for d in dlist:
if not d.is_hidden():
self.s.remove_download(d, removecontent=True)
dlist = self.s.get_downloads(DLTYPE_DIRECT)
for d in dlist:
if not d.is_hidden():
self.s.remove_download(d, removecontent=True)
if self.apptype == 'acestream':
time.sleep(3)
path = self.get_default_destdir()
shutil.rmtree(path, True)
if DEBUG:
log('baseapp::clear_session_state: delete cache dir:', path)
except:
log_exc()
time.sleep(1)
def show_error(self, msg):
log('baseapp::show_error:', msg)
def get_default_destdir(self):
dest_dir = self.get_playerconfig('download_dir')
if dest_dir is not None:
if DEBUG:
print >> sys.stderr, 'get_default_destdir: get from config:', dest_dir, type(dest_dir)
elif sys.platform == 'win32':
registry = Win32RegChecker()
dest_dir = registry.readKey(HKCU, 'Software\\' + self.registry_key, 'DataDir', ignore_errors=True)
if dest_dir is None:
dest_dir = registry.readKey(HKLM, 'Software\\' + self.registry_key, 'DataDir', ignore_errors=True)
if DEBUG:
print >> sys.stderr, 'get_default_destdir: get from registry:', dest_dir, type(dest_dir)
if self.apptype == 'acestream':
if sys.platform == 'win32' and dest_dir is not None:
if len(dest_dir) < 2:
dest_dir = None
else:
drive = dest_dir[:2]
if not drive.endswith(':'):
dest_dir = None
else:
dest_dir = os.path.join(drive + '\\', CACHE_DIR_NAME)
if not self.check_dest_dir(dest_dir, make_hidden=True):
dest_dir = self.select_dest_dir()
if DEBUG:
log('baseapp::get_default_destdir: check_dest_dir() failed, selected:', dest_dir)
else:
if dest_dir is not None:
if not self.check_dest_dir(dest_dir, make_hidden=False):
dest_dir = None
if dest_dir is None:
state_dir = Session.get_default_state_dir()
dest_dir = os.path.join(state_dir, 'downloads')
if not self.check_dest_dir(dest_dir, make_hidden=False):
dest_dir = None
if dest_dir is None and sys.platform != 'win32':
dest_dir = os.path.join('/tmp', '.ACEStream', 'downloads')
if not self.check_dest_dir(dest_dir, make_hidden=False):
dest_dir = None
if dest_dir is None:
raise Exception, 'Cannot select dest dir'
self.set_playerconfig('download_dir', dest_dir)
return dest_dir
def check_dest_dir(self, dest_dir, make_hidden):
if dest_dir is None:
return False
if not os.path.isdir(dest_dir):
if DEBUG:
log('baseapp::check_dest_dir: dest dir is not a directory:', dest_dir)
try:
os.makedirs(dest_dir)
except:
if DEBUG:
log('baseapp::check_dest_dir: failed to create dest dir:', dest_dir)
return False
if make_hidden and sys.platform == 'win32':
try:
p = os.popen('attrib +h ' + dest_dir)
p.close()
except:
if DEBUG:
print_exc()
try:
lock = os.path.join(dest_dir, '.lock')
f = open(lock, 'w')
f.close()
except:
if DEBUG:
log('baseapp::check_dest_dir: cannot write to dest dir:', dest_dir)
return False
return True
def select_dest_dir(self):
dest_dir = None
if sys.platform == 'win32':
candidates = []
drive_list = self.get_drive_list()
if DEBUG:
log('>>>drive_list', drive_list)
for drive in drive_list:
if DEBUG:
log('>>>drive1', drive)
drive = self.format_drive_name(drive) + '\\'
if DEBUG:
log('>>>drive2', drive)
total, free, used = self.get_disk_info(drive)
if free is not None:
path = os.path.join(drive, CACHE_DIR_NAME)
candidates.append((free, path))
candidates.sort(reverse=True)
if DEBUG:
log('baseapp::select_dest_dir: candidates', candidates)
for free, path in candidates:
if self.check_dest_dir(path, True):
dest_dir = path
break
else:
state_dir = Session.get_default_state_dir()
path = os.path.join(state_dir, 'cache')
if self.check_dest_dir(path, True):
dest_dir = path
if dest_dir is None:
path = os.path.join('/tmp', '.ACEStream', 'cache')
if self.check_dest_dir(path, make_hidden=True):
dest_dir = path
if DEBUG:
log('baseapp::select_dest_dir: dest dir selected:', dest_dir)
return dest_dir
def get_preload_ads_enabled(self, default_value = True):
enabled = self.get_playerconfig('enable_interruptable_ads')
if enabled is None:
if sys.platform == 'win32':
registry = Win32RegChecker()
enabled = registry.readKey(HKCU, 'Software\\' + self.registry_key, 'EnablePreload', ignore_errors=True)
if DEBUG:
log('baseapp::get_preload_ads_enabled: get from registry HKCU:', enabled)
if enabled is None:
enabled = registry.readKey(HKLM, 'Software\\' + self.registry_key, 'EnablePreload', ignore_errors=True)
if DEBUG:
log('baseapp::get_preload_ads_enabled: get from registry HKLM:', enabled)
if enabled is None:
enabled = default_value
else:
try:
enabled = int(enabled)
enabled = enabled != 0
except:
enabled = default_value
else:
enabled = default_value
self.set_playerconfig('enable_interruptable_ads', enabled)
elif DEBUG:
log('baseapp::get_preload_ads_enabled: get from config:', enabled)
return enabled
def is_svc(self, dlfile, tdef):
svcfiles = None
if tdef.is_multifile_torrent():
enhancement = tdef.get_files(exts=svcextdefaults)
if enhancement:
enhancement.sort()
if tdef.get_length(enhancement[0]) == tdef.get_length(dlfile):
svcfiles = [dlfile]
svcfiles.extend(enhancement)
return svcfiles
def i2ithread_readlinecallback(self, ic, cmd):
pass
def make_provider_stream_cache_key(self, provider_key, infohash, device_id, user_login, user_password, user_key):
return '-'.join([provider_key,
binascii.hexlify(infohash),
device_id,
user_login,
hashlib.sha1(user_password).hexdigest(),
user_key])
def update_provider_stream_cache(self, provider_key, infohash, device_id, user_login, user_password, user_key):
key = self.make_provider_stream_cache_key(provider_key, infohash, device_id, user_login, user_password, user_key)
if DEBUG:
log('baseapp::update_provider_stream_cache: save data to provider stream cache: key', key)
self.provider_stream_cache.setdefault(key, {'last_success': 0})
self.provider_stream_cache[key]['last_success'] = time.time()
def check_provider_stream_cache(self, provider_key, infohash, device_id, user_login, user_password, user_key):
key = self.make_provider_stream_cache_key(provider_key, infohash, device_id, user_login, user_password, user_key)
if key not in self.provider_stream_cache:
return False
else:
last_success = self.provider_stream_cache[key]['last_success']
if DEBUG:
log('baseapp::check_provider_stream_cache: got data from provider stream cache: key', key, 'last_success', last_success)
if time.time() - last_success > STREAM_CACHE_TTL:
if DEBUG:
log('baseapp::check_provider_stream_cache: data from provider stream cache expired: key', key, 'last_success', last_success)
del self.provider_stream_cache[key]
return False
if DEBUG:
log('baseapp::check_provider_stream_cache: got valid data from provider stream cache: key', key, 'last_success', last_success)
return True
def load_cookies(self):
try:
f = open(self.cookie_file, 'r')
data = pickle.load(f)
f.close()
for c in data:
if DEBUG:
log('baseapp::load_cookies: add cookie:', c)
self.cookie_jar.set_cookie(c)
return True
except:
if DEBUG:
log('baseapp::load_cookies: cannot load cookies file:', self.cookie_file)
return False
def save_cookies(self):
try:
cookies = []
for c in self.cookie_jar:
cookies.append(c)
if DEBUG:
log('baseapp::save_cookies: file', self.cookie_file, 'cookies', cookies)
f = open(self.cookie_file, 'w')
pickle.dump(cookies, f)
f.close()
return True
except:
if DEBUG:
log('baseapp::save_cookies: cannot save to file', self.cookie_file)
return False
def check_premium_status(self, provider_key, content_id, infohash):
if content_id is None:
if DEBUG_PREMIUM:
log('baseapp::check_premium_status: empty content id')
return False
status = self.tsservice.check_premium_status(provider_key, content_id, infohash)
if DEBUG_PREMIUM:
log('baseapp::check_premium_status: provider_key', provider_key, 'content_id', content_id, 'status', status)
if status is None:
if DEBUG_PREMIUM:
log('baseapp::check_premium_status: request failed, consider premium: provider_key', provider_key, 'content_id', content_id)
return True
return status == 1
def report_premium_download(self, provider_key, content_id, params):
report = False
check_user = False
user_ok = True
if not params.has_key('last_report'):
if DEBUG_PREMIUM:
log('baseapp::report_premium_download: not yet reported')
report = True
elif params['last_report'] < time.time() - params['report_interval']:
if DEBUG_PREMIUM:
log('baseapp::report_premium_download: time to report: last', params['last_report'], 'now', time.time(), 'interval', params['report_interval'])
report = True
if not params.has_key('last_user_check'):
if DEBUG_PREMIUM:
log('baseapp::report_premium_download: user not checked')
check_user = True
elif params['last_user_check'] < time.time() - params['user_check_interval']:
if DEBUG_PREMIUM:
log('baseapp::report_premium_download: time to check user: last', params['last_user_check'], 'now', time.time(), 'interval', params['user_check_interval'])
check_user = True
if report:
params['last_report'] = time.time()
user_login = self.s.get_ts_login()
self.tsservice.report_premium_download(params['download_id'], provider_key, content_id, user_login)
if check_user:
params['last_user_check'] = time.time()
user_level = self.s.get_authlevel()
if user_level != 2:
if DEBUG_PREMIUM:
log('baseapp::report_premium_download: user auth failed: level', user_level)
user_ok = False
return user_ok
def check_statistics_settings(self):
if DEBUG:
log('baseapp::check_statistics_settings: ---')
try:
timeout = self.stat_settings.check_settings()
self.traffic_stats.set_url_list(self.stat_settings.get_url_list('ts'))
except:
if DEBUG:
print_exc()
timeout = 3600
finally:
if DEBUG:
log('baseapp::check_statistics_settings: next run in', timeout)
self.run_delayed(self.check_statistics_settings, timeout)
def tns_send_event(self, d, event, event_data = None, delay = 0):
try:
if d in self.downloads_in_vodmode:
dparams = self.downloads_in_vodmode[d]
if dparams.has_key('tns'):
dparams['tns'].send_event(event, event_data, delay)
except:
print_exc()
def init_hardware_key(self):
try:
self.hardware_key = get_hardware_key()
if DEBUG:
log('baseapp::init_hardware_key: got key:', self.hardware_key)
except:
if DEBUG:
print_exc()
self.hardware_key = None
def check_integrity(self):
if sys.platform != 'win32':
return True
if not self.check_string('.Torrent Stream', '64048011141141110101' + '1611230380611411101790901'):
if DEVELOPER_MODE:
log('string failed')
return False
selfpath = sys.argv[0]
exename = os.path.basename(selfpath)
if self.apptype == 'torrentstream':
check_exe1 = 'tsengine.exe'
check_exe2 = 'tsengine'
check_exe3 = '61151110101130150' + '1011101640101021101'
check_exe4 = '61151110' + '1011301501011101'
else:
check_exe1 = 'ace_engine.exe'
check_exe2 = 'ace_engine'
check_exe3 = '79099010159' + '0101011301501011101640101021101'
check_exe4 = '790990101590101011' + '301501011101'
if exename != check_exe1 and exename != check_exe2:
if DEVELOPER_MODE:
log('exename failed:', exename)
return False
if not (self.check_string(exename, check_exe3) or self.check_string(exename, check_exe4)):
if DEVELOPER_MODE:
log('exename failed 2')
return False
base = os.path.abspath(os.path.dirname(selfpath))
if DEVELOPER_MODE:
log('selfpath', selfpath, 'exename', exename, 'base', base)
files = []
files.append({'path': 'lib\\pycompat27.pyd',
'path2': '801501890290211121990111901211790611050550640211121001'})
files.append({'path': '..\\updater\\tsupdate.exe',
'path2': '640640290711211001790611101411290611511711211001790611101640101021101'})
files.append({'path': '..\\player\\npts_plugin.dll',
'path2': '640640290211801790121101411290011211611511590211801711301501011640001801801'})
files.append({'path': '..\\player\\tsplayer.exe',
'path2': '640640290211801790121101411290611511211801790121101411640101021101'})
files.append({'path': 'python27.dll',
'path2': '211121611401111011050550640001801801',
'check': '4cad50ea762261d7f1361f7095cc6c740c2aa1b6',
'check2': '250990790001350840101790550450050050450940001550201940150450940201550840750350990990450990550250840990050790790940890450'})
files.append({'path': 'lib\\_ctypes.pyd',
'path2': '801501890290590990611121211101511640211121001',
'check': '616293e45730b2d4b49002d65cac9fb319c44aa2',
'check2': '450940450050750150101250350550150840890050001250890250750840840050001450350990790990750201890150940750990250250790790050'})
files.append({'path': 'lib\\_hashlib.pyd',
'path2': '801501890290590401790511401801501890640211121001',
'check': '3e5e42e2ff2bfdfa36fad0a14d18a5508717ee47',
'check2': '150101350101250050101050201201050890201001201790150450201790001840790940250001940650790350350840650550940550101101250550'})
files.append({'path': 'lib\\_socket.pyd',
'path2': '801501890290590511111990701101611640211121001',
'check': '95deea9dbbf5c19d8042439bd676c2c3e6b47328',
'check2': '750350001101101790750001890890201350990940750001650840250050250150750890001450550450990050990150101450890250550150050650'})
files.append({'path': 'lib\\_sqlite3.pyd',
'path2': '801501890290590511311801501611101150640211121001',
'check': 'dc0dadc7e0a73ca83c7f6fa21e807b5eb8ff67e1',
'check2': '001990840001790001990550101840790550150990790650150990550201450201790050940101650840550890350101890650201201450550101940'})
files.append({'path': 'lib\\_ssl.pyd',
'path2': '801501890290590511511801640211121001',
'check': '7d656f10b4d9d7f6d55caaa626e5975422637466',
'check2': '550001450350450201940840890250001750001550201450001350350990790790790450050450101350750550350250050050450150550250450450'})
files.append({'path': 'lib\\LIBEAY32.dll',
'path2': '801501890290670370660960560980150050640001801801',
'check': '3fc80784b3f0714a1859521f990965b949a71536',
'check2': '150201990650840550650250890150201840550940250790940650350750350050940201750750840750450350890750250750790550940350150450'})
files.append({'path': 'lib\\M2Crypto.__m2crypto.pyd',
'path2': '801501890290770050760411121211611111640590590901050990411121211611111640211121001',
'check': '01a2dbcfe59602b45fa9c389cb604570ca71dbf1',
'check2': '840940790050001890990201101350750450840050890250350201790750990150650750990890450840250350550840990790550940001890201940'})
files.append({'path': 'lib\\pycompat.pyd',
'path2': '801501890290211121990111901211790611640211121001',
'check': 'e282471605acb12f842fe1047ca445e819297762',
'check2': '101050650050250550940450840350790990890940050201650250050201101940840250550990790250250350101650940750050750550550450050'})
files.append({'path': 'lib\\SSLEAY32.dll',
'path2': '801501890290380380670960560980150050640001801801',
'check': '42323e4435bc986c45c9a2b841e7da7b6a98b228',
'check2': '250050150050150101250250150350890990750650450990250350990750790050890650250940101550001790550890450790750650890050050650'})
files.append({'path': 'lib\\wxbase28uh_vc.dll',
'path2': '801501890290911021890790511101050650711401590811990640001801801',
'check': '22a7683af988f5d0bef8abe4934dba03a093f21d',
'check2': '050050790550450650150790201750650650201350001840890101201650790890101250750150250001890790840150790840750150201050940001'})
files.append({'path': 'lib\\wxmsw28uh_adv_vc.dll',
'path2': '801501890290911021901511911050650711401590790001811590811990640001801801',
'check': 'd0aac3f14afe9c0bedc9a906b4dd6981597a8685',
'check2': '001840790790990150201940250790201101750990840890101001990750790750840450890250001001450750650940350750550790650450650350'})
files.append({'path': 'tsengine.exe',
'path2': '611511101011301501011101640101021101',
'check': '1a77f3cf03b882514683af1d6d2f9f0480a4bf2e',
'check2': '940790550550201150990201840150890650650050350940250450650150790201940001450001050201750201840250650840790250890201050101'})
files.append({'path': 'tsengine_stream.exe',
'path2': '611511101011301501011101590511611411101790901640101021101',
'check': '0c28965c60bae004e0c8a0a79f070dce266f6e33',
'check2': '840990050650750450350990450840890790101840840250101840990650790840790550750201840550840001990101050450450201450101150150'})
return self.check_files(base, files)
def check_files(self, base, files):
for f in files:
do_check = f.has_key('check')
if not self.check_string(f['path'], f['path2']):
if DEVELOPER_MODE:
log('path failed:', f['path'])
return False
if do_check and not self.check_string(f['check'], f['check2']):
if DEVELOPER_MODE:
log('check failed:', f['check'])
return False
path = os.path.join(base, f['path'])
if not self.file_exists(path):
if DEVELOPER_MODE:
log('not found:', path)
return False
if do_check:
check = self.file_checksum(path)
if check != f['check']:
if DEVELOPER_MODE:
log('checksum failed:', path, f['check'], check)
return False
return True
def check_string(self, s, check):
s1 = self.get_string(check)
if s1 != s:
if DEVELOPER_MODE:
log('check string failed:', s, s1)
return False
return True
def get_string(self, s, padding = 3):
return ''.join([ chr(int(s[i:i + padding][::-1])) for i in xrange(0, len(s), padding) ])
def file_exists(self, path):
if not os.path.isfile(path):
return False
try:
f = open(path, 'rb')
f.close()
except:
return False
return True
def file_checksum(self, path):
f = None
try:
f = open(path, 'rb')
h = hashlib.sha1()
got_data = False
while True:
buf = f.read(4096)
if not buf:
break
got_data = True
h.update(buf)
if not got_data:
return ''
return h.hexdigest()
except:
if DEBUG:
print_exc()
return ''
finally:
if f is not None:
f.close()
| 48.625198 | 520 | 0.598059 | [
"Apache-2.0"
] | C6SUMMER/allinclusive-kodi-pi | .kodi/userdata/addon_data/plugin.video.p2p-streams/acestream/ace/ACEStream/Player/BaseApp.py | 153,607 | Python |
"""
Support for Homekit number ranges.
These are mostly used where a HomeKit accessory exposes additional non-standard
characteristics that don't map to a Home Assistant feature.
"""
from aiohomekit.model.characteristics import Characteristic, CharacteristicsTypes
from homeassistant.components.number import NumberEntity
from homeassistant.core import callback
from . import KNOWN_DEVICES, CharacteristicEntity
NUMBER_ENTITIES = {
CharacteristicsTypes.Vendor.VOCOLINC_HUMIDIFIER_SPRAY_LEVEL: {
"name": "Spray Quantity",
"icon": "mdi:water",
},
CharacteristicsTypes.Vendor.EVE_DEGREE_ELEVATION: {
"name": "Elevation",
"icon": "mdi:elevation-rise",
},
}
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up Homekit numbers."""
hkid = config_entry.data["AccessoryPairingID"]
conn = hass.data[KNOWN_DEVICES][hkid]
@callback
def async_add_characteristic(char: Characteristic):
kwargs = NUMBER_ENTITIES.get(char.type)
if not kwargs:
return False
info = {"aid": char.service.accessory.aid, "iid": char.service.iid}
async_add_entities([HomeKitNumber(conn, info, char, **kwargs)], True)
return True
conn.add_char_factory(async_add_characteristic)
class HomeKitNumber(CharacteristicEntity, NumberEntity):
"""Representation of a Number control on a homekit accessory."""
def __init__(
self,
conn,
info,
char,
device_class=None,
icon=None,
name=None,
**kwargs,
):
"""Initialise a HomeKit number control."""
self._device_class = device_class
self._icon = icon
self._name = name
super().__init__(conn, info, char)
def get_characteristic_types(self):
"""Define the homekit characteristics the entity is tracking."""
return [self._char.type]
@property
def device_class(self):
"""Return type of sensor."""
return self._device_class
@property
def icon(self):
"""Return the sensor icon."""
return self._icon
@property
def min_value(self) -> float:
"""Return the minimum value."""
return self._char.minValue
@property
def max_value(self) -> float:
"""Return the maximum value."""
return self._char.maxValue
@property
def step(self) -> float:
"""Return the increment/decrement step."""
return self._char.minStep
@property
def value(self) -> float:
"""Return the current characteristic value."""
return self._char.value
async def async_set_value(self, value: float):
"""Set the characteristic to this value."""
await self.async_put_characteristics(
{
self._char.type: value,
}
)
| 27.653846 | 81 | 0.644645 | [
"Apache-2.0"
] | 0xFEEDC0DE64/homeassistant-core | homeassistant/components/homekit_controller/number.py | 2,876 | Python |
# -*- coding: utf-8 -*-
from django.conf.urls import url
from django.views.generic import TemplateView
from . import views
app_name = 'services_communicator'
urlpatterns = [
url(
regex="^ServiceList/~create/$",
view=views.ServiceListCreateView.as_view(),
name='ServiceList_create',
),
url(
regex="^ServiceList/(?P<pk>\d+)/~delete/$",
view=views.ServiceListDeleteView.as_view(),
name='ServiceList_delete',
),
url(
regex="^ServiceList/(?P<pk>\d+)/$",
view=views.ServiceListDetailView.as_view(),
name='ServiceList_detail',
),
url(
regex="^ServiceList/(?P<pk>\d+)/~update/$",
view=views.ServiceListUpdateView.as_view(),
name='ServiceList_update',
),
url(
regex="^ServiceList/$",
view=views.ServiceListListView.as_view(),
name='ServiceList_list',
),
]
| 25.305556 | 51 | 0.601537 | [
"MIT"
] | eshafik/services_communicator | services_communicator/urls.py | 911 | Python |
#
# Copyright 2018 EveryUP Srl
#
# 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 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 unicode_literals
from django.db import models
from django.utils.translation import ugettext_lazy as _
from django.contrib.auth.models import User, AbstractBaseUser, BaseUserManager, PermissionsMixin
from django.utils import timezone
from authosm.exceptions import OSMAuthException
from lib.osm.osmclient.clientv2 import Client
import utils
class OsmUserManager(BaseUserManager):
"""Custom manager for OsmUser."""
def _create_user(self, username, password, is_staff, is_superuser, **extra_fields):
"""Create and save a CustomUser with the given username and password. """
now = timezone.now()
if not username:
raise ValueError('The given username must be set')
is_active = extra_fields.pop("is_active", True)
user = self.model(username=username, is_staff=is_staff, is_active=is_active,
is_superuser=is_superuser, last_login=now,
date_joined=now, **extra_fields)
user.set_password(password)
user.save(using=self._db)
return user
"""Create and save an OsmUser with the given username and password."""
def create_superuser(self, username, password, **extra_fields):
return self._create_user(username, password, True, True, is_admin=True,
**extra_fields)
class AbstractOsmUser(AbstractBaseUser, PermissionsMixin):
"""Abstract User with the same behaviour as Django's default User.
Inherits from both the AbstractBaseUser and PermissionMixin.
The following attributes are inherited from the superclasses:
* password
* last_login
* is_superuser
"""
username = models.CharField(_('username'), primary_key=True, max_length=255, unique=True, db_index=True)
is_admin = models.BooleanField(_('admin status'), default=False)
is_basic_user = models.BooleanField(_('basic_user status'), default=False)
current_project = models.CharField(_('project_id'), max_length=255)
psw = models.CharField(_('psw'), max_length=36)
token = models.CharField(_('token'), max_length=36)
project_id = models.CharField(_('project_id'), max_length=36)
token_expires = models.FloatField(_('token_expires'), max_length=36)
objects = OsmUserManager()
USERNAME_FIELD = 'username'
REQUIRED_FIELDS = []
@property
def is_authenticated(self):
"""Checks for a valid authentication."""
if self.token is not None and utils.is_token_valid({'expires': self.token_expires}):
return True
else:
return False
def get_token(self):
if self.is_authenticated:
return {'id': self.token, 'expires': self.token_expires, 'project_id': self.project_id}
return None
def get_projects(self):
client = Client()
result = client.get_user_info(self.get_token(), self.username)
if 'error' in result and result['error'] is True:
return []
else:
return result['data']['projects']
def switch_project(self, project_id):
client = Client()
result = client.switch_project({'project_id': project_id, 'username': self.username, 'password': self.psw})
if 'error' in result and result['error'] is True:
raise OSMAuthException(result['data'])
else:
self.token = result['data']['id']
self.project_id = result['data']['project_id']
self.token_expires = result['data']['expires']
self.save()
return True
return False
class Meta:
verbose_name = _('custom user')
verbose_name_plural = _('custom users')
abstract = True
class OsmUser(AbstractOsmUser):
"""
Concrete class of AbstractCustomUser.
Use this if you don't need to extend CustomUser.
"""
class Meta(AbstractOsmUser.Meta):
swappable = 'AUTH_USER_MODEL'
| 35.138462 | 115 | 0.669002 | [
"Apache-2.0"
] | 5g-media/OIDC_ON_OSMr5 | LW-UI/authosm/models.py | 4,568 | Python |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
feedforward neural network
"""
import mindspore.nn as nn
from mindelec.architecture import get_activation, LinearBlock
class FFNN(nn.Cell):
"""
Full-connect networks.
Args:
input_dim (int): the input dimensions.
output_dim (int): the output dimensions.
hidden_layer (int): number of hidden layers.
activation (str or Cell): activation functions.
"""
def __init__(self, input_dim, output_dim, hidden_layer=64, activation="sin"):
super(FFNN, self).__init__()
self.activation = get_activation(activation)
self.fc1 = LinearBlock(input_dim, hidden_layer)
self.fc2 = LinearBlock(hidden_layer, hidden_layer)
self.fc3 = LinearBlock(hidden_layer, hidden_layer)
self.fc4 = LinearBlock(hidden_layer, hidden_layer)
self.fc5 = LinearBlock(hidden_layer, output_dim)
def construct(self, *inputs):
"""fc network"""
x = inputs[0]
out = self.fc1(x)
out = self.activation(out)
out = self.fc2(out)
out = self.activation(out)
out = self.fc3(out)
out = self.activation(out)
out = self.fc4(out)
out = self.activation(out)
out = self.fc5(out)
return out
| 33.946429 | 81 | 0.652814 | [
"Apache-2.0"
] | mindspore-ai/mindscience | MindElec/examples/physics_driven/frequency_domain_maxwell/src/model.py | 1,901 | Python |
# !/usr/bin/python3
# coding: utf_8
""" Config your app """
import os
from hal.files.parsers import JSONParser
from hal.files.save_as import write_dicts_to_json
from .config import APP_FOLDER, API_FOLDER, DATA_FOLDER
from .data.coins import CryptoCoin, CRYPTO_COINS
class ConfigManager:
""" Manages config files for app """
def __init__(self, config_file):
create_workplace()
self.config_file = config_file
self.raw = None
self.data = {}
self._check()
self._read_config()
def _read_config(self):
"""
:return: {}
Config data
"""
self.raw = JSONParser(self.config_file).get_content()
for key, value in self.raw.items():
self.data[key] = value
def _check(self):
if not os.path.exists(self.config_file):
raise ValueError(
"Empty config file! Please write your settings "
"and store at " + self.config_file
)
def create_config(self):
"""
:return: void
Creates config file
"""
if os.path.exists(self.config_file):
raise ValueError("Creating new config will erase previous data!")
write_dicts_to_json({}, self.config_file) # empty data
def get(self, key):
"""
:param key: str
What you want
:return: {}
Item you want
"""
return self.data[key]
def save(self):
"""
:return: void
Saves app data to local config file
"""
write_dicts_to_json(self.data, self.config_file)
def create_workplace():
"""
:return: void
Creates folder
"""
for directory in [APP_FOLDER, API_FOLDER, DATA_FOLDER]:
if not os.path.exists(directory):
os.makedirs(directory)
def get_coin(symbol):
"""
:param symbol: str
Symbol of coin
:return: CryptoCoin
Coin if a crypto-coin exists with that name
"""
candidate = CryptoCoin(symbol, symbol)
for coin in CRYPTO_COINS:
if coin.symbol == candidate:
return coin
| 22.163265 | 77 | 0.575967 | [
"MIT"
] | sirfoga/pyhodl | pyhodl/app.py | 2,172 | Python |
"""is_sqllab_view
Revision ID: 130915240929
Revises: f231d82b9b26
Create Date: 2018-04-03 08:19:34.098789
"""
import sqlalchemy as sa
from alembic import op
from sqlalchemy.ext.declarative import declarative_base
from rabbitai import db
# revision identifiers, used by Alembic.
revision = "130915240929"
down_revision = "f231d82b9b26"
Base = declarative_base()
class Table(Base):
"""Declarative class to do query in upgrade"""
__tablename__ = "tables"
id = sa.Column(sa.Integer, primary_key=True)
sql = sa.Column(sa.Text)
is_sqllab_view = sa.Column(sa.Boolean())
def upgrade():
bind = op.get_bind()
op.add_column(
"tables",
sa.Column(
"is_sqllab_view",
sa.Boolean(),
nullable=True,
default=False,
server_default=sa.false(),
),
)
session = db.Session(bind=bind)
# Use Slice class defined here instead of models.Slice
for tbl in session.query(Table).all():
if tbl.sql:
tbl.is_sqllab_view = True
session.commit()
db.session.close()
def downgrade():
op.drop_column("tables", "is_sqllab_view")
| 20.785714 | 58 | 0.648625 | [
"Apache-2.0"
] | psbsgic/rabbitai | rabbitai/migrations/versions/130915240929_is_sqllab_viz_flow.py | 1,164 | Python |
from collections import OrderedDict
from . import util
from ..errors import ModelInfoLookupError
class ModelInfo:
def __init__(self, pairs=[], default_fields=None):
"""
Constructs a mapping of information about a model.
:class:`~revscoring.scoring.ModelInfo` objects are usually nested
within each other to provide a convenient tree structure for
:func:`~revscoring.scoring.ModelInfo.lookup` and
:func:`~revscoring.scoring.ModelInfo.format`.
"""
self._data = OrderedDict(pairs)
self._default_fields = set(default_fields) \
if default_fields is not None else None
def __len__(self):
return len(self.keys())
def __getitem__(self, key):
return self._data[key]
def __setitem__(self, key, value):
self._data[key] = value
def __contains__(self, key):
try:
return (key in self._data or
key in ('true', 'false') and key == 'true' in self._data or
int(key) in self._data)
except ValueError:
return False
def keys(self):
return self._data.keys()
def get(self, key, default=None):
return self._data.get(key, default)
def values(self):
return self._data.values()
def items(self):
return self._data.items()
def __iter__(self):
return iter(self._data)
def move_to_end(self, key, last=True):
return self._data.move_to_end(key, last=last)
def lookup(self, path=None):
"""
Looks up a specific information value based on either a string pattern
or a path.
For example, the pattern "stats.roc_auc.labels.true" is the same as
the path ``['stats', 'roc_auc', 'labels', True]``.
:Parameters:
path : `str` | `list`
The location of the information to lookup.
"""
if isinstance(path, str):
path = util.parse_pattern(path)
elif path is None:
path = []
d = self
remaining_path = list(path) # Make sure we don't overwrite the input
while len(path) > 0:
key = path.pop(0)
d = try_key(key, d)
if hasattr(d, "lookup"):
return d.lookup(remaining_path)
else:
continue
return d
def format(self, paths=None, formatting="str", **kwargs):
"""
Format a representation of the model information in a useful way.
:Parameters:
paths : `iterable` ( `str` | [`str`] )
A set of paths to use when selecting which information should
formatted. Everything beneath a provided path in the tree
will be formatted. E.g. `statistics.roc_auc` and `statistics`
will format redundantly because `roc_auc` is already within
`statistics`. Alternatively `statistics.roc_auc` and
`statistics.pr_auc` will format only those two specific
bits of information.
formatting : "json" or "str"
Which output formatting do you want? "str" returns something
nice to show on the command-line. "json" returns something
that will pass through :func:`json.dump` without error.
"""
paths = paths or []
_paths = [
util.parse_pattern(path) if isinstance(path, str) else path
for path in paths]
path_tree = util.treeify(_paths)
if formatting == "str":
return self.format_str(path_tree, **kwargs)
elif formatting == "json":
return self.format_json(path_tree, **kwargs)
else:
raise ValueError("Formatting {0} is not available for {1}."
.format(formatting, self.__class__.__name__))
def format_str(self, path_tree, **kwargs):
formatted = "Model Information:\n"
for key in self.normalize_fields(path_tree):
key_val = try_key(key, self)
if hasattr(key_val, "format_str"):
sub_tree = path_tree.get(key, {})
formatted += util.tab_it_in(
key_val.format_str(sub_tree, **kwargs))
else:
formatted += util.tab_it_in(" - {0}: {1}"
.format(key, key_val))
return formatted
def format_json(self, path_tree, **kwargs):
d = OrderedDict()
for key in self.normalize_fields(path_tree):
key_val = try_key(key, self)
if hasattr(key_val, "format_json"):
sub_tree = path_tree.get(key, {})
d[key] = key_val.format_json(sub_tree, **kwargs)
else:
d[key] = key_val
return d
def normalize_fields(self, path_tree):
if len(path_tree) > 0:
yield from path_tree.keys()
else:
for field in self.keys():
if self._default_fields is None or \
field in self._default_fields:
yield field
def try_key(key, d):
try:
return d[key]
except KeyError:
try:
if key in ("true", "false"):
return d[key == 'true']
else:
try:
return d[int(key)]
except ValueError:
raise ModelInfoLookupError(key)
except KeyError:
raise ModelInfoLookupError(key)
| 33.335329 | 79 | 0.553979 | [
"MIT"
] | leojoubert/revscoring | revscoring/scoring/model_info.py | 5,567 | Python |
# -*- coding: utf-8 -*-
import cv2
import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
# Define a class to receive the characteristics of each line detection
class Lane():
def __init__(self):
# 当前的图像
self.current_warped_binary = None
# 当前图片的尺寸
self.current_warped_binary_shape = []
# 检测到的车道线像素的横坐标 x values for detected line pixels
self.allx = None
# 检测到的车道线像素的纵坐标 y values for detected line pixels
self.ally = None
# 以纵坐标为自变量，取值空间
self.ploty = None
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# 是否检测到车道线 was the line detected in the last iteration?
self.detected = False
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# 保存的数据量
self.n = 5
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# 最近n个帧的拟合曲线 x values of the last n fits of the line
self.recent_fitted_xs = []
# 最近n个帧的平均拟合曲线 average x values of the fitted line over the last n iterations
self.average_fitted_x = []
# 当前帧的拟合曲线
self.current_fitted_x = []
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# 最近n个帧的拟合函数
self.recent_fits = []
# 最近n个帧的拟合函数 polynomial coefficients averaged over the last n iterations
self.average_fit = []
# 当前帧的拟合函数 polynomial coefficients for the most recent fit
self.current_fit = [np.array([False])]
# 拟合函数的误差 difference in fit coefficients between last and new fits
self.diffs = np.array([0, 0, 0], dtype='float')
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# 半径 radius of curvature of the line in some units
self.radius_of_curvature = []
# 车辆在车道线之间距离 distance in meters of vehicle center from the line
self.line_base_pos = None
# 对全新的帧进行车道线像素检测
def find_lane_pixels(self, binary_warped, location):
self.current_warped_binary = binary_warped
self.current_warped_binary_shape = binary_warped.shape
self.ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0] // 2:, :], axis=0)
# Create an output image to draw on and visualize the result
# out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0] // 2)
if location == "left":
base = np.argmax(histogram[:midpoint])
elif location == "right":
base = np.argmax(histogram[midpoint:]) + midpoint
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 9
# Set the width of the windows +/- margin
margin = 80
# Set minimum number of pixels found to recenter window
minpix = 50
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0] // nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero() # 扁平化后非零值点的列表
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
current = base
# Create empty lists to receive left and right lane pixel indices
lane_inds = []
# right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window + 1) * window_height
win_y_high = binary_warped.shape[0] - window * window_height
win_x_low = current - margin
win_x_high = current + margin
# # Draw the windows on the visualization image
# cv2.rectangle(out_img, (win_xleft_low, win_y_low),
# (win_xleft_high, win_y_high), (0, 255, 0), 2)
# cv2.rectangle(out_img, (win_xright_low, win_y_low),
# (win_xright_high, win_y_high), (0, 255, 0), 2)
# 形成对每个像素的bool值
# Identify the nonzero pixels in x and y within the window #
good_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_x_low) & (nonzerox < win_x_high)).nonzero()[0]
# Append these indices to the lists
lane_inds.append(good_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_inds) > minpix:
current = np.int(np.mean(nonzerox[good_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
lane_inds = np.concatenate(lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
x = nonzerox[lane_inds]
y = nonzeroy[lane_inds]
self.allx = x
self.ally = y
return x, y
# 在之前的plot基础上找车道线
def search_pixel_around_poly(self, binary_warped):
self.current_warped_binary = binary_warped
self.current_warped_binary_shape = binary_warped.shape
self.ploty = np.linspace(0, binary_warped.shape[0] - 1, binary_warped.shape[0])
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 80
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
fit = self.recent_fits[-1]
### TO-DO: Set the area of search based on activated x-values ###
### within the +/- margin of our polynomial function ###
### Hint: consider the window areas for the similarly named variables ###
### in the previous quiz, but change the windows to our new search area ###
lane_inds = ((nonzerox > (fit[0] * (nonzeroy ** 2) + fit[1] * nonzeroy + fit[2] - margin)) & (
nonzerox < (fit[0] * (nonzeroy ** 2) + fit[1] * nonzeroy + fit[2] + margin)))
# Again, extract left and right line pixel positions
x = nonzerox[lane_inds]
y = nonzeroy[lane_inds]
self.allx = x
self.ally = y
return x, y
def fit_polynomial(self):
ploty = self.ploty
# Fit a second order polynomial to each using `np.polyfit`
fit = np.polyfit(self.ally, self.allx, 2)
# 存储当前结果
self.current_fit = fit
# 计算误差
if len(self.recent_fits) == 0:
self.diffs = [0,0,0]
else:
new = np.array(self.current_fit)
old = np.array(self.recent_fits[-1])
self.diffs = new - old
# 存储为历史结果
if len(self.recent_fits) < self.n:
self.recent_fits.append(self.current_fit)
elif len(self.recent_fits) == self.n:
self.recent_fits.pop(0)
self.recent_fits.append(self.current_fit)
else:
self.recent_fits.append(self.current_fit)
self.recent_fits = self.recent_fits[-self.n:] # 后面n个
# 计算当前平均
self.average_fit = np.array(self.recent_fits).mean(axis=0)
try:
x_fitted = self.average_fit[0] * ploty ** 2 + self.average_fit[1] * ploty + self.average_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
x_fitted = 1 * ploty ** 2 + 1 * ploty
self.detected = False
else:
self.detected = True
self.current_fitted_x = x_fitted
# 存储为历史结果
if len(self.recent_fitted_xs) < self.n:
self.recent_fitted_xs.append(self.current_fitted_x)
elif len(self.recent_fitted_xs) == self.n:
self.recent_fitted_xs.pop(0)
self.recent_fitted_xs.append(self.current_fitted_x)
else:
self.recent_fitted_xs.append(self.current_fitted_x)
self.recent_fitted_xs = self.recent_fitted_xs[-self.n:] # 后面n个
self.average_fitted_x = np.array(self.recent_fitted_xs).mean(axis=0)
return self.average_fitted_x
def fit(self, binary_warped,location,sequence=True):
if sequence:
if not self.detected:
# 没有检测到，重新开始检测
self.find_lane_pixels(binary_warped,location)
else:
# 从上一次周围开始检测
self.search_pixel_around_poly(binary_warped)
# TODO 如果两次检测的误差较大怎么办？
# TODO 是否存在
self.fit_polynomial()
# if np.abs(self.diffs).sum() > 20:
# self.current_fit = np.array(self.recent_fits[:-1]).mean(axis=0)
# self.recent_fits[-1] = self.current_fit
# self.average_fit = np.array(self.recent_fits).mean(axis=0)
#
# self.current_fitted_x = np.array(self.recent_fitted_xs[:-1]).mean(axis=0)
# self.recent_fitted_xs[-1] = self.current_fitted_x
# self.average_fitted_x = np.array(self.recent_fitted_xs).mean(axis=0)
else:
self.find_lane_pixels(binary_warped, location)
self.fit_polynomial()
def measure_curvature_real(self,ploty, x, y):
'''
Calculates the curvature of polynomial functions in meters.
'''
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30 / 720 # meters per pixel in y dimension
xm_per_pix = 3.7 / 700 # meters per pixel in x dimension
fit_cr = np.polyfit(y * ym_per_pix, x * xm_per_pix, 2)
# Define y-value where we want radius of curvature
# We'll choose the maximum y-value, corresponding to the bottom of the image
y_eval = np.max(ploty)
# Calculation of R_curve (radius of curvature)
curverad = ((1 + (2 * fit_cr[0] * y_eval * ym_per_pix + fit_cr[1]) ** 2) ** 1.5) / np.absolute(2 * fit_cr[0])
self.radius_of_curvature = curverad
return curverad
if __name__ == "__main__":
from lane.perspective import perspective,src,dst
from lane.gaussian_blur import gaussian_blur
from lane.combined_threshold import combined_threshold
from lane.measure_vehicle_pos import measure_vehicle_pos
from lane.draw_lane import draw_lane
image = mpimg.imread('../output_images/undistorted/straight_lines1-undistorted.jpg')
image = gaussian_blur(image, 3)
combined = combined_threshold(image, ksize=3,
th=[[20, 100], [25, 254], [100, 250], [0.6, 1.2], [180, 254], [250, 0]])
combined = gaussian_blur(combined, 3)
perspectived_img = perspective(combined,src,dst)
# plt.imshow(perspectived_img,cmap="gray")
# plt.show()
left_lane = Lane()
left_lane.fit(perspectived_img,"left")
right_lane = Lane()
right_lane.fit(perspectived_img, "right")
result = left_lane.visual(perspectived_img,"left")
plt.imshow(result)
result = right_lane.visual(perspectived_img, "right")
plt.imshow(result)
plt.show()
# # 计算曲率
# left_r = left_lane.measure_curvature_real(left_lane.ploty, left_lane.average_fitted_x, left_lane.ploty)
# right_r = left_lane.measure_curvature_real(right_lane.ploty, right_lane.average_fitted_x, right_lane.ploty)
#
# # 计算偏移值
# v = measure_vehicle_pos(left_lane.average_fitted_x, right_lane.average_fitted_x,left_lane.current_warped_binary_shape[1])
#
# # 绘制车道线
# img = draw_lane(image, combined, dst, src,left_lane.current_fitted_x, right_lane.current_fitted_x, right_lane.ploty)
# plt.imshow(img)
# # 打印文字
# plt.text(0,60,"Radius of Curvature = %d(m)"%int(r),fontdict={'size': 20, 'color': 'w'})
# plt.text(0,120, "Vehicle is %.2f(m) left of center" % v, fontdict={'size': 20, 'color': 'w'})
# plt.show()
| 36.617391 | 127 | 0.592654 | [
"MIT"
] | jo-ny/CarND-Advanced-Lane-Lines | lane/Lane.py | 13,189 | Python |
import ctypes as C
import numpy as np
from math import log,e
import hankelmatrixcreator
import sys
import time
import iohelpers
import math
import modelconversion
from scipy.sparse.linalg import lsqr
from scipy import sparse
import copy
DEBUG = False
VERBOSE = True
FAILURE_CONST = -100000.0
class TensorWFA:
def __init__(self, n_symbols):
self.n_symbols = n_symbols
self.hankels_learnt = False
def _estimate_hankels(self, data, prefixdict, suffixdict):
self.h_pands,self.symbol_hankels,self.hp_pandsigma,self.hbar_pands,self.hbar_pandsigma,self.hbar_sigmaands = hankelmatrixcreator.construct_tensor_hankels(data, prefixdict, suffixdict, self.n_symbols, 100)
if VERBOSE:
print "Finished Hankel estimation"
def compute_XY(self, hbar_pands, hbar_pandsigma, hbar_sigmaands, symbol_hankels, num_symbols, num_components):
if VERBOSE:
print "Constructing Q matrices.."
self.qp,_,self.qs = sparse.linalg.svds(hbar_pands,num_components)
self.qs_constructed = True
qp = sparse.csr_matrix(np.mat((self.qp[:,0:num_components]).T))
qs = sparse.csr_matrix(np.mat((self.qs[0:num_components, :])))
qp.eliminate_zeros()
qp.prune()
qs.eliminate_zeros()
qs.prune()
if VERBOSE:
print "Computing N.."
hsquiggleps = qp*hbar_pands*qs.T
hsquiggleps = sparse.csr_matrix(np.linalg.inv(hsquiggleps.A))
N = qs.T*(hsquiggleps)*qp
N.eliminate_zeros()
N.prune()
if VERBOSE:
print "Computing X.."
Xs = hbar_sigmaands*N*hbar_pandsigma
X = np.empty((num_symbols, num_symbols), order='F', dtype=np.float64)
X[:,:] = Xs.A
self.rank = np.linalg.matrix_rank(X)
if self.rank < num_components:
print "Rank Deficient!!"
return [],[], False
if VERBOSE:
print "Computing Y..."
leftside = hbar_sigmaands*N
leftside.eliminate_zeros()
leftside.prune()
rightside = N*hbar_pandsigma
rightside.eliminate_zeros()
rightside.prune()
Y = np.empty((num_symbols, num_symbols, num_symbols))
for sym in range(num_symbols):
Y[:,sym,:] = (leftside*(symbol_hankels[sym])*rightside).A
return X,Y, True
def learn_tensor(self, data, prefixdict, suffixdict, num_components):
starttime = time.clock()
if not self.hankels_learnt:
begintime = time.clock()
self._estimate_hankels(data, prefixdict, suffixdict)
self.inittime = time.clock()-begintime
self.hankels_learnt = True
#adding aliases with "self" prefix for readability.
h_pands = self.h_pands
symbol_hankels = self.symbol_hankels
hp_pandsigma = self.hp_pandsigma
hbar_pands = self.hbar_pands
hbar_pandsigma = self.hbar_pandsigma
hbar_sigmaands = self.hbar_sigmaands
num_symbols = self.n_symbols
X,Y, success = self.compute_XY(hbar_pands, hbar_pandsigma, hbar_sigmaands, symbol_hankels, num_symbols, num_components)
if not success:
return success
if VERBOSE:
print "Performing tensor decomposition.."
lib = C.CDLL('./cpp/libTensor.so')
f_learn_tensor = lib.learn_tensor
f_learn_tensor.argtypes = [np.ctypeslib.ndpointer(dtype = np.float64), np.ctypeslib.ndpointer(dtype = np.float64), C.c_int, np.ctypeslib.ndpointer(dtype = np.float64), np.ctypeslib.ndpointer(dtype = np.float64), C.c_int, C.c_int]
f_learn_tensor.restype = C.c_double
Otilde = np.empty((num_symbols, num_components), order='F', dtype=np.float64)
gamma = np.empty((num_components), order='F', dtype=np.float64)
res = f_learn_tensor(X,Y,num_components,Otilde,gamma,num_symbols, 1)
if res == FAILURE_CONST:
return False
if VERBOSE:
print "Building unnormalized model.."
Otilde = np.mat(Otilde)
# hbar_pandsigma = np.mat(self.hbar_pandsigma.toarray())
# hbar_sigmaands = np.mat(self.hbar_sigmaands.toarray())
# hbar_pands = np.mat(self.hbar_pands.toarray())
Otildepinv = np.linalg.pinv(Otilde)
spOtildepinv = sparse.csr_matrix(Otildepinv)
spOtildepinv.eliminate_zeros()
spOtildepinv.prune()
Otildep = hbar_pandsigma*Otildepinv.T
Otildes = Otildepinv*hbar_sigmaands
alphatilde = np.mat(Otildep[0,:]).T
stopvec = np.mat(Otildes[:,0])
spOtildeppinv = sparse.csr_matrix(np.linalg.pinv(Otildep))
spOtildeppinv.eliminate_zeros()
spOtildeppinv.prune()
Ttilde = self._compute_T(spOtildeppinv, num_components)
Dgamma = np.mat(np.diag(gamma))
Dgamma = Dgamma*Dgamma
Ds = spOtildeppinv*hp_pandsigma*spOtildepinv.T
Dspinv = np.mat(Ds.A)
Ds = np.linalg.pinv(np.mat(Ds.A))
Dsigma = np.eye(alphatilde.shape[0])-np.diag(stopvec)
Dsigmapinv = np.linalg.pinv(Dsigma)
Beta = Ds*(np.linalg.pinv(Ttilde))*Dgamma*stopvec
for i in range(stopvec.shape[0]):
stopvec[i] = Beta[i]/(1+Beta[i])
alpha = np.empty((num_components), order='F', dtype=np.float64)
alpha[:] = (alphatilde.T*Dsigmapinv*Dspinv).A
O = np.empty((num_components, num_symbols), order='F', dtype=np.float64)
O[:,:] = (Otilde*Dsigma).A.T
T = np.empty((num_components, num_components), order='F', dtype=np.float64)
T[:] = (Ds*Ttilde*Dspinv*Dsigmapinv).A
if DEBUG:
print "O before PNFA projection: ", O
print "T before PNFA projection: ", T
print "ainf before simplex projection: ", stopvec
print "a before simplex projection: ", alpha
O,T,alpha, stopvec = self._project_to_probabilities(O,T,alpha, stopvec.T.A[0], num_components, num_symbols)
stopvec = np.mat(stopvec).T
alpha = np.mat(alpha).T
O = O.T
if DEBUG:
print "O after PNFA projection: ", O
print "T after PNFA projection: ", T
print "ainf after simplex projection: ", stopvec
print "a after simplex projection: ", alpha
self.initvec, self.ainf, self.wordstopvec, self.As = self.convert_hmm_to_wfa(O, T, alpha, stopvec)
self.a = self.initvec.copy()
self.buildtime = time.clock() - starttime
return True
def _compute_sparse_pinv(self, sparsemat, rank):
u,s,v = sparse.linalg.svds(sparsemat, rank)
u = np.mat(u)
s = np.mat(np.diag(s))
v = np.mat(v)
pinv = v.T*np.linalg.inv(s)*u.T
return np.mat(pinv)
def _compute_T(self, Otildepinv, num_components):
A = (Otildepinv*self.h_pands).T
A.eliminate_zeros()
A.prune()
B = (Otildepinv*self.hbar_pands).T
B.eliminate_zeros()
B.prune()
T = np.empty((num_components, num_components))
for i in range(num_components):
T[i,:] = lsqr(A,B[:,i].A)[0]
return T
def _project_to_probabilities(self, O, T, alpha, ainf, num_components, num_symbols):
lib = C.CDLL('../cpp/libTensor.so')
f_simplex_proj = lib.do_simplex_projection
f_simplex_proj.argtypes = [np.ctypeslib.ndpointer(dtype = np.float64), C.c_double, C.c_int]
f_simplex_proj.restype = C.c_int
f_probmat_proj = lib.do_probmat_projection
f_probmat_proj.argtypes = [np.ctypeslib.ndpointer(dtype = np.float64), np.ctypeslib.ndpointer(dtype = np.float64), C.c_int, C.c_int]
f_probmat_proj.restype = C.c_int
f_simplex_proj(alpha, 1.0, num_components)
zeros = np.zeros((num_components), order='F', dtype=np.float64)
probO = np.zeros((num_components, num_symbols+1), order='F', dtype=np.float64)
probO[:,0:num_symbols] = O
probO[:,num_symbols:num_symbols+1] = np.mat(ainf).T
f_probmat_proj(probO, zeros, num_components, num_symbols)
O = probO[:,0:num_symbols]
ainf = probO[:,num_symbols:num_symbols+1]
ainf = ainf.T
f_probmat_proj(T, zeros, num_components, num_components)
return O, T, alpha, ainf
def get_symbol_prediction(self):
predictedsymbol = -1
maxscore = np.finfo(float).eps
for symbol in range(self.n_symbols):
symbolscore = self.get_obs_prob(symbol)
if symbolscore > maxscore:
predictedsymbol = symbol
maxscore = symbolscore
stopscore = float(self.a.T*self.wordstopvec)
if stopscore > maxscore:
predictedsymbol = self.n_symbols
return predictedsymbol
def get_WER(self, testdata):
errors = 0
numpredictions = 0
for seq in testdata:
for obs in seq:
numpredictions += 1
predsymbol = self.get_symbol_prediction()
self.update(obs)
if predsymbol != obs:
errors += 1
predsymbol = self.get_symbol_prediction()
numpredictions += 1
if predsymbol != self.n_symbols:
errors += 1
self.reset()
return float(errors)/float(numpredictions)
#provides average log-likelihood score
def score(self, data):
loglike = 0
for seq in data:
seqloglike = 0
self.reset()
for obs in seq:
seqloglike = seqloglike + log(self.get_obs_prob(obs))
self.update(obs)
loglike += seqloglike
return loglike/(float(len(data)))
#updates a/start/state vector after seeing symbol
def update(self, obs):
bomat = self.As[obs]
numerator = self.a.T*bomat
denom = numerator*self.ainf
self.a = (numerator/denom).T
#resets state vector
def reset(self):
self.a = self.initvec.copy()
#returns the probablilty of a particular observation given current state
def get_obs_prob(self, obs):
prob = (self.a.T)*(self.As[obs])*self.ainf
prob = min(prob,1)
prob = max(prob,np.finfo(float).eps)
return prob
#returns the probability of an entire sequence, or "word"
def get_word_prob(self,seq):
seqprob = 0
for obs in seq:
prob = self.get_obs_prob(obs)
if prob <= np.finfo(float).eps:
return np.finfo(float).eps
seqprob += log(prob)
self.update(obs)
endprob = float(self.a.T*self.wordstopvec)
if endprob <= np.finfo(float).eps:
return np.finfo(float).eps
seqprob += log(endprob)
self.reset()
if not math.isnan(seqprob):
return e**seqprob
else:
return np.finfo(float).eps
def scorepautomac(self, testdata, truprobs):
modelprobs = np.zeros((len(truprobs)))
probsum = 0
i = 0
for seq in testdata:
prob = self.get_word_prob(seq)
modelprobs[i] = prob
probsum += prob
i += 1
modelprobs /= float(probsum)
i = 0
scoresum = 0
for truprob in truprobs:
if modelprobs[i] < np.finfo(float).eps:
modelprobs[i] = np.finfo(float).eps
scoresum += truprob*log(modelprobs[i],2)
i += 1
return 2.0**(-1.0*float(scoresum))
def get_perplexity(self, testdata):
modelprobs = np.zeros((len(testdata)))
probsum = 0
i = 0
for seq in testdata:
prob = self.get_word_prob(seq)
modelprobs[i] = prob
probsum += prob
i += 1
scoresum = 0
for i in range(len(modelprobs)):
if modelprobs[i] < np.finfo(float).eps:
modelprobs[i] = np.finfo(float).eps
scoresum += log(modelprobs[i],2)
scoresum /= float(len(testdata))
return 2.0**(-1.0*float(scoresum))
def convert_hmm_to_wfa(self, O, T, alpha, stopvec):
As = {}
for symbol in range(O.shape[0]):
As[symbol] = np.mat(np.diag(O[symbol,:]))*T
return alpha, np.mat(np.ones(T.shape[0])).T, stopvec, As
if __name__ == '__main__':
PAUTOMACPATH = "/home/williamleif/Dropbox/icml2014-experiments/datasets/PAutomaC-competition_sets/"
RESULTS_DIR = "/home/williamleif/Dropbox/icml2014-experiments/results/tensor/"
MODEL_DIR = "/home/williamleif/Dropbox/icml2014-experiments/models/"
metric = sys.argv[1]
problem = sys.argv[2]
n_symbols = sys.argv[3]
n_symbols = int(n_symbols)
if problem != "tree" and problem != "timeseries":
traindata = iohelpers.parse_file(PAUTOMACPATH+problem+".pautomac.train")
testdata = iohelpers.parse_file(PAUTOMACPATH+problem+".pautomac.test")
if metric == "KL":
groundtruth = iohelpers.parse_groundtruth_file(PAUTOMACPATH+problem+".pautomac_solution.txt")
else:
validdata = traindata[15000:20000]
traindata = traindata[0:15000]
maxbasissize = int(sys.argv[4])
prefixdict, suffixdict = hankelmatrixcreator.top_k_string_bases(traindata,maxbasissize,n_symbols)
basislength = len(prefixdict)
bestsize = 0
avruntime = 0
nummodelsmade = 0
bestscore = 0
wfa = TensorWFA(n_symbols)
begintime = time.clock()
for i in range(3, 6):
success = wfa.learn_tensor(traindata, prefixdict, suffixdict, i)
if i == 3:
inittime = wfa.inittime
if not success:
break
if metric == "WER":
score = wfa.get_WER(validdata)
else:
score = wfa.scorepautomac(testdata,groundtruth)
if bestsize == 0:
bestscore = score
bestsize = i
bestwfa = copy.deepcopy(wfa)
elif score < bestscore and abs(score-1000) > 0.1:
bestscore = score
bestsize = i
bestwfa = copy.deepcopy(wfa)
print "Model size: ", i, " Score: ", score
avruntime += wfa.buildtime
nummodelsmade += 1
# if metric == "WER":
# bestscore = bestwfa.get_WER(testdata)
# iohelpers.write_results(RESULTS_DIR+"tensor-pautomac="+problem+"-"+metric+".txt", problem,"size= "+str(bestsize)+", basis size="+str(basislength), metric, bestscore, avruntime/float(nummodelsmade))
# iohelpers.write_pnfa_model(MODEL_DIR+"tensor-"+str(bestsize)+"-pautomac="+problem+"-"+metric+".fsm", bestwfa)
runtime = time.clock()-begintime
fp = open("/home/williamleif/Dropbox/icml2014-experiments/results/runtimes/tensor", "w")
fp.write("Init time: "+str(inittime)+" Runtime: "+str(runtime))
else:
RESULTS_DIR = "/home/williamleif/Dropbox/icml2014-experiments/results/real/"
if problem == "tree":
traindata = iohelpers.parse_file("/home/williamleif/Dropbox/icml2014-experiments/datasets/treebankdata.obs")
validdata = traindata[0:5000]
testdata = traindata[5000:10000]
traindata = traindata[10000:len(traindata)]
maxbasissize = int(sys.argv[4])
prefixdict, suffixdict = hankelmatrixcreator.top_k_string_bases(traindata,maxbasissize,n_symbols)
basislength = len(prefixdict)
bestsize = 0
avruntime = 0
nummodelsmade = 0
bestscore = 0
wfa = TensorWFA(n_symbols)
for i in range(3, n_symbols+1):
success = wfa.learn_tensor(traindata, prefixdict, suffixdict, i)
if not success:
break
if metric == "WER":
score = wfa.get_WER(validdata)
else:
score = wfa.get_perplexity(validdata)
if bestsize == 0:
bestscore = score
bestsize = i
bestwfa = copy.deepcopy(wfa)
elif score < bestscore and abs(score-1000) > 0.1:
bestscore = score
bestsize = i
bestwfa = copy.deepcopy(wfa)
print "Model size: ", i, " Score: ", score
avruntime += wfa.buildtime
nummodelsmade += 1
if metric == "WER":
bestscore = bestwfa.get_WER(testdata)
else:
bestscore = bestwfa.get_perplexity(testdata)
iohelpers.write_results(RESULTS_DIR+"tensor-"+metric+".txt", problem,"size= "+str(bestsize)+", basis size="+str(basislength), metric, bestscore, 0)
iohelpers.write_pnfa_model(MODEL_DIR+"tensor-"+str(bestsize)+"-pautomac="+problem+"-"+metric+".fsm", bestwfa)
| 33.281496 | 237 | 0.597741 | [
"Apache-2.0"
] | ICML14MoMCompare/MoMs-for-StochasticLanguages | code/tensor/wfatensorlearn.py | 16,907 | Python |
from django.contrib.auth import get_user_model
from django.contrib.auth.hashers import make_password
from django.db import transaction
from rest_framework import exceptions, serializers
from care.facility.models import Facility, FacilityUser, READ_ONLY_USER_TYPES
from care.users.api.serializers.lsg import DistrictSerializer, LocalBodySerializer, StateSerializer, BlockSerializer
from care.users.models import GENDER_CHOICES
from care.utils.serializer.phonenumber_ispossible_field import PhoneNumberIsPossibleField
from config.serializers import ChoiceField
User = get_user_model()
class SignUpSerializer(serializers.ModelSerializer):
user_type = ChoiceField(choices=User.TYPE_CHOICES)
gender = ChoiceField(choices=GENDER_CHOICES)
password = serializers.CharField(write_only=True)
phone_number = PhoneNumberIsPossibleField()
alt_phone_number = PhoneNumberIsPossibleField(required=False, allow_blank=True)
class Meta:
model = User
fields = (
"id",
"username",
"first_name",
"last_name",
"email",
"password",
"user_type",
"ward",
"local_body",
"district",
"state",
"phone_number",
"alt_phone_number",
"gender",
"age",
)
def create(self, validated_data):
validated_data["password"] = make_password(validated_data.get("password"))
return super().create(validated_data)
class UserCreateSerializer(SignUpSerializer):
password = serializers.CharField(required=False)
facilities = serializers.ListSerializer(
child=serializers.UUIDField(), required=False, allow_empty=True, write_only=True
)
class Meta:
model = User
include = ("facilities",)
exclude = (
"is_superuser",
"is_staff",
"is_active",
"last_login",
"date_joined",
"verified",
"deleted",
"groups",
"user_permissions",
)
def validate_facilities(self, facility_ids):
if facility_ids:
if len(facility_ids) != Facility.objects.filter(external_id__in=facility_ids).count():
available_facility_ids = Facility.objects.filter(external_id__in=facility_ids).values_list(
"external_id", flat=True
)
not_found_ids = list(set(facility_ids) - set(available_facility_ids))
raise serializers.ValidationError(
f"Some facilities are not available - {', '.join([str(_id) for _id in not_found_ids])}"
)
return facility_ids
def validate_ward(self, value):
if (
value is not None
and value != self.context["created_by"].ward
and not self.context["created_by"].is_superuser
and not self.context["created_by"].user_type >= User.TYPE_VALUE_MAP["LocalBodyAdmin"]
):
raise serializers.ValidationError("Cannot create for a different Ward")
return value
def validate_local_body(self, value):
if (
value is not None
and value != self.context["created_by"].local_body
and not self.context["created_by"].is_superuser
and not self.context["created_by"].user_type >= User.TYPE_VALUE_MAP["DistrictAdmin"]
):
raise serializers.ValidationError("Cannot create for a different local body")
return value
def validate_district(self, value):
if (
value is not None
and value != self.context["created_by"].district
and not self.context["created_by"].is_superuser
and not self.context["created_by"].user_type >= User.TYPE_VALUE_MAP["StateAdmin"]
):
raise serializers.ValidationError("Cannot create for a different state")
return value
def validate_state(self, value):
if (
value is not None
and value != self.context["created_by"].state
and not self.context["created_by"].is_superuser
):
raise serializers.ValidationError("Cannot create for a different state")
return value
def validate(self, attrs):
validated = super(UserCreateSerializer, self).validate(attrs)
if self.context["created_by"].user_type in READ_ONLY_USER_TYPES:
if validated["user_type"] not in READ_ONLY_USER_TYPES:
raise exceptions.ValidationError(
{"user_type": ["Read only users can create other read only users only"]}
)
if (
validated["user_type"] > self.context["created_by"].user_type
and not self.context["created_by"].is_superuser
):
raise exceptions.ValidationError(
{"user_type": ["User cannot create another user with higher permissions"]}
)
if (
not validated.get("ward")
and not validated.get("local_body")
and not validated.get("district")
and not validated.get("state")
):
raise exceptions.ValidationError({"__all__": ["One of ward, local body, district or state is required"]})
if validated.get("user_type") == User.TYPE_VALUE_MAP["BlockAdmin"]:
local_body_object = validated.get("local_body")
if local_body_object.block is None:
raise exceptions.ValidationError({"__all__": ["The local_body doesn't have a Block associated with it"]})
return validated
def facility_query(self, user):
queryset = Facility.objects.all()
if user.is_superuser:
pass
elif user.user_type >= User.TYPE_VALUE_MAP["StateLabAdmin"]:
queryset = queryset.filter(state=user.state)
elif user.user_type >= User.TYPE_VALUE_MAP["DistrictLabAdmin"]:
queryset = queryset.filter(district=user.district)
elif user.user_type >= User.TYPE_VALUE_MAP["BlockAdmin"]:
queryset = queryset.filter(block=user.block)
elif user.user_type >= User.TYPE_VALUE_MAP["LocalBodyAdmin"]:
queryset = queryset.filter(local_body=user.local_body)
else:
queryset = queryset.filter(users__id__exact=user.id)
return queryset
def create(self, validated_data):
with transaction.atomic():
facilities = validated_data.pop("facilities", [])
user = User.objects.create_user(**{**validated_data, "verified": True})
user.set_password(validated_data["password"])
facility_query = self.facility_query(self.context["created_by"])
if facilities:
facility_objs = facility_query.filter(external_id__in=facilities)
facility_user_objs = [
FacilityUser(facility=facility, user=user, created_by=self.context["created_by"])
for facility in facility_objs
]
FacilityUser.objects.bulk_create(facility_user_objs)
return user
class UserSerializer(SignUpSerializer):
user_type = ChoiceField(choices=User.TYPE_CHOICES, read_only=True)
is_superuser = serializers.BooleanField(read_only=True)
local_body_object = LocalBodySerializer(source="local_body", read_only=True)
block_object = BlockSerializer(source="local_body__block", read_only=True)
district_object = DistrictSerializer(source="district", read_only=True)
state_object = StateSerializer(source="state", read_only=True)
alt_phone_number = PhoneNumberIsPossibleField(required=False, allow_blank=True)
class Meta:
model = User
fields = (
"id",
"username",
"first_name",
"last_name",
"email",
"user_type",
"local_body",
"block",
"district",
"state",
"phone_number",
"alt_phone_number",
"gender",
"age",
"is_superuser",
"verified",
"local_body_object",
"block_object",
"district_object",
"state_object",
"pf_endpoint",
"pf_p256dh",
"pf_auth",
)
read_only_fields = (
"is_superuser",
"verified",
"user_type",
"ward",
"local_body",
"block",
"district",
"state",
"pf_endpoint",
"pf_p256dh",
"pf_auth",
)
extra_kwargs = {"url": {"lookup_field": "username"}}
class UserBaseMinimumSerializer(serializers.ModelSerializer):
user_type = ChoiceField(choices=User.TYPE_CHOICES, read_only=True)
class Meta:
model = User
fields = (
"id",
"first_name",
"username",
"email",
"last_name",
"user_type",
"last_login",
)
class UserListSerializer(serializers.ModelSerializer):
local_body_object = LocalBodySerializer(source="local_body", read_only=True)
block_object = BlockSerializer(source="local_body__block", read_only=True)
district_object = DistrictSerializer(source="district", read_only=True)
state_object = StateSerializer(source="state", read_only=True)
user_type = ChoiceField(choices=User.TYPE_CHOICES, read_only=True)
class Meta:
model = User
fields = (
"id",
"first_name",
"last_name",
"username",
"local_body_object",
"block_object",
"district_object",
"state_object",
"user_type",
"last_login",
)
| 36.316176 | 121 | 0.604778 | [
"MIT"
] | CoronaSafeUP/care | care/users/api/serializers/user.py | 9,878 | Python |
talks = [
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/11nelson",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/12ballard",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/13aburto",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/14bednar",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/15andersen",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/16gavarret",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/17kacher",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/18eyring",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/23holland",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/24kearon",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/25aidukaitis",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/26gong",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/27ochoa",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/28hamilton",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/29cook",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/31oaks",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/32porter",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/33craven",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/34video",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/35bingham",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/36renlund",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/41christofferson",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/42wright",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/43stevenson",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/44ringwood",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/45rasband",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/46martinez",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/47nelson",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/51oaks",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/52ojediran",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/53klebingat",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/54pace",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/55soares",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/56funk",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/57uchtdorf",
"https://www.churchofjesuschrist.org/study/general-conference/2022/04/58nelson",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/11nelson",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/12holland",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/13cordon",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/14soares",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/15christofferson",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/16gilbert",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/17giuffra",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/18oaks",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/21eyring",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/22bednar",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/23schmeil",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/24porter",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/25kopischke",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/26rasband",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/27golden",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/28villanueva",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/29stevenson",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/31ballard",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/32eubank",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/33nielson",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/34valenzuela",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/35wilcox",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/36kyungu",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/37nash",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/38eyring",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/41uchtdorf",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/42johnson",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/43renlund",
"https://www.churchofjesuschrist.org/study/general-conference/2021/10/44sikahema",
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"https://www.churchofjesuschrist.org/study/general-conference/1971/04/great-experiences",
"https://www.churchofjesuschrist.org/study/general-conference/1971/04/except-the-lord-build-the-house",
"https://www.churchofjesuschrist.org/study/general-conference/1971/04/eternal-joy-is-eternal-growth",
"https://www.churchofjesuschrist.org/study/general-conference/1971/04/drink-of-the-pure-water",
"https://www.churchofjesuschrist.org/study/general-conference/1971/04/choose-you-this-day",
"https://www.churchofjesuschrist.org/study/general-conference/1971/04/be-slow-to-anger",
"https://www.churchofjesuschrist.org/study/general-conference/1971/04/all-may-share-in-adams-blessing",
"https://www.churchofjesuschrist.org/study/general-conference/1971/04/a-witness-and-a-blessing"
]
for talk in talks:
split = talk.split('/')
year = split[-3]
month = split[-2]
print(year + '\t' + month) | 102.074423 | 159 | 0.779159 | [
"MIT"
] | thetaylors/pyConferenceTalkRandomizer | playground.py | 389,516 | Python |
"""\
Perl code generator
@copyright: 2002-2004 D.H. aka crazyinsomniac on sourceforge.net
@copyright: 2012-2016 Carsten Grohmann
@copyright: 2017-2020 Dietmar Schwertberger
@license: MIT (see LICENSE.txt) - THIS PROGRAM COMES WITH NO WARRANTY
"""
import os, os.path, re
from codegen import BaseLangCodeWriter, BaseSourceFileContent
import wcodegen, compat
import logging
class SourceFileContent(BaseSourceFileContent):
rec_block_start = re.compile(
r'^(?P<spaces>\s*)' # leading spaces
r'#\s*' # comment sign
r'begin\s+wxGlade:\s*' # "begin wxGlade:" statement and tailing spaces
r'(?P<classname>[a-zA-Z_]+[\w:]*?)??' # class or function name (non-greedy)
r'(?::{2}|\s*)' # separator between class and function / block (non-greedy)
r'(?P<block>\w+)' # function / block name
r'\s*$' # tailing spaces
)
rec_block_end = re.compile(
r'^\s*' # leading spaces
r'#\s*' # comment sign
r'end\s+wxGlade' # "end exGlade" statement
r'\s*$' # tailing spaces
)
# Less precise regex, but working :-P
# Should match: package Foo; or package Foo::bar::baz ;
rec_class_decl = re.compile(
r'^\s*' # leading spaces
r'package\s+([a-zA-Z_][\w:]*)\s*;' # "package <name>" statement
r'.*$' # any character till eol
)
rec_event_handler = re.compile(
r'^\s*' # leading spaces
r'#\s*wxGlade:\s*(?P<class>[\w:]+)::(?P<handler>\w+) <event_handler>' # wxGlade event handler
# statement with class and
# event handler name
r'\s*$' # tailing spaces
)
# Regexp to match Perl's Plain Old Documentation format; see: manpage perlpod
rec_pod = re.compile(
r'^\s*' # leading spaces
r'=[A-Za-z_]+\w*' # match POD statement
r'.*$' # any character till eol
)
def build_untouched_content(self):
"""\
Builds a string with the contents of the file that must be left as is,
and replaces the wxGlade blocks with tags that in turn will be replaced
by the new wxGlade blocks
WARNING: NOT YET COMPLETE -- crazyinsomniac
alb - almost done :)
WARNING: There is *NO* support for here documents: if you put wxGlade
blocks inside a here document, you're likely going into troubles...
"""
BaseSourceFileContent.build_untouched_content(self)
inside_block = False
inside_pod = False
tmp_in = self._load_file(self.name)
out_lines = []
check_old_methods = [] # list of indices with set_properties or do_layout
for line in tmp_in:
result = self.rec_pod.match(line)
if result:
inside_pod = True
if inside_pod:
out_lines.append(line)
if line.startswith('=cut'):
inside_pod = False
continue
result = self.rec_class_decl.match(line)
if result:
if not self.class_name:
# this is the first class declared in the file: insert the new ones before this
out_lines.append( '<%swxGlade insert new_classes>' % self.nonce )
self.new_classes_inserted = True
self.class_name = result.group(1)
self.class_name = self.format_classname(self.class_name)
self.classes.add( self.class_name ) # add the found class to the list of classes of this module
out_lines.append(line)
elif not inside_block:
result = self.rec_block_start.match(line)
if result:
# replace the lines inside a wxGlade block with a tag that will be used later by add_class
spaces = result.group('spaces')
which_class = result.group('classname')
which_block = result.group('block')
if not which_class:
which_class = self.class_name
else:
which_class = self.format_classname(which_class)
self.spaces[which_class] = spaces
inside_block = True
if not self.class_name:
out_lines.append( '<%swxGlade replace %s>' % (self.nonce, which_block) )
else:
if which_block in ("__do_layout","__set_properties"):
# probably to be removed
check_old_methods.append( len(out_lines) )
out_lines.append( '<%swxGlade replace %s %s>' % (self.nonce, which_class, which_block) )
else:
result = self.rec_event_handler.match(line)
if result:
which_handler = result.group('handler')
which_class = self.format_classname(result.group('class'))
self.event_handlers.setdefault( which_class, set() ).add( which_handler )
if self.class_name and self.is_end_of_class(line):
# add extra event handlers here...
out_lines.append( '<%swxGlade event_handlers %s>' % (self.nonce, self.class_name) )
out_lines.append(line)
else:
# ignore all the lines inside a wxGlade block
if self.rec_block_end.match(line):
inside_block = False
if not self.new_classes_inserted:
# if we are here, the previous ``version'' of the file did not contain any class, so we must add the
# new_classes tag at the end of the file
out_lines.append( '<%swxGlade insert new_classes>' % self.nonce )
# when moving from 0.9 to 1.0: remove empty methods "do_layout" and "set_properties"
while check_old_methods:
i = check_old_methods.pop(-1)
if out_lines[i+1].strip()=='}': # just end of block -> remove incl. trailing empty lines
self._remove_method(out_lines, i-2, i+1)
# set the ``persistent'' content of the file
self.content = out_lines
class PerlCodeWriter(BaseLangCodeWriter, wcodegen.PerlMixin):
"Code writer class for writing Perl code out of the designed GUI elements; see: BaseLangCodeWriter"
_code_statements = {
'backgroundcolour': "%(objname)s->SetBackgroundColour(%(value)s);\n",
'disabled': "%(objname)s->Enable(0);\n",
'extraproperties': "%(objname)s->Set%(propname_cap)s(%(value)s);\n",
'focused': "%(objname)s->SetFocus();\n",
'foregroundcolour': "%(objname)s->SetForegroundColour(%(value)s);\n",
'hidden': "%(objname)s->Show(0);\n",
'setfont': "%(objname)s->SetFont(Wx::Font->new(%(size)s, %(family)s, "
"%(style)s, %(weight)s, %(underlined)s, %(face)s));\n",
'tooltip': "%(objname)s->SetToolTipString(%(tooltip)s);\n",
'tooltip_3': "%(objname)s->SetToolTip(%(tooltip)s);\n",
'wxcolour': "Wx::Colour->new(%(value)s)",
'wxnullcolour': "Wx::NullColour",
'wxsystemcolour': "Wx::SystemSettings::GetColour(%(value)s)",
}
class_separator = '::'
classattr_always = ['wxBoxSizer', 'wxStaticBoxSizer', 'wxGridSizer', 'wxFlexGridSizer']
indent_amount = 1
indent_symbol = '\t'
indent_level_func_body = 1
language_note = '# To get wxPerl visit http://www.wxperl.it\n' \
'#\n'
name_ctor = 'new'
new_defaults = [] # Default class members, will be initialised during new_project()
shebang = '#!/usr/bin/perl -w -- \n#\n'
SourceFileContent = SourceFileContent
tmpl_cfunc_end = '%(tab)sreturn $self;\n' \
'\n' \
'}\n' \
'\n'
tmpl_class_end = '\n%(comment)s end of class %(klass)s\n\n1;\n\n'
tmpl_class_end_nomarker = '\n\n1;\n\n'
tmpl_func_event_stub = """\
sub %(handler)s {
%(tab)smy ($self, $event) = @_;
%(tab)s# wxGlade: %(klass)s::%(handler)s <event_handler>
%(tab)swarn "Event handler (%(handler)s) not implemented";
%(tab)s$event->Skip;
%(tab)s# end wxGlade
}
"""
tmpl_func_empty = '%(tab)sreturn;\n'
tmpl_sizeritem = '%s->Add(%s, %s, %s, %s);\n'
tmpl_gridbagsizeritem = '%s->Add(%s, %s, %s, %s, %s);\n'
tmpl_gridbagsizerspacer = '%s->Add(%s, %s, %s, %s, %s, %s);\n'
tmpl_spacersize = '%s, %s'
tmpl_style = \
'%(tab)s$style = %(style)s\n' \
'%(tab)s%(tab)sunless defined $style;\n' \
'\n'
tmpl_toplevel_style = tmpl_style
tmpl_appfile = """%(overwrite)s%(header_lines)s"""
def _get_app_template(self, app, top_win):
'build template string for application'
if not self.app_name: return None
# XXX use Show() for frames/panels and ShowModal()/Destroy for dialogs
klass = app.klass
if self._use_gettext:
gettext1 = ['%(tab)smy $local = Wx::Locale->new("English", "en", "en"); # replace with ??',
'%(tab)s$local->AddCatalog("%(textdomain)s"); # replace with the appropriate catalog name\n']
else:
gettext1 = []
if klass:
ret = [ 'package %(klass)s;',
'',
'use base qw(Wx::App);',
'use strict;',
'%(pl_import)s',
'sub OnInit {',
'%(tab)smy( $self ) = shift;',
'',
'%(tab)sWx::InitAllImageHandlers();',
'',
'%(tab)smy $%(top_win)s = %(top_win_class)s->new();',
'',
'%(tab)s$self->SetTopWindow($%(top_win)s);',
'%(tab)s$%(top_win)s->Show(1);',
'',
'%(tab)sreturn 1;',
'}']
if self._mark_blocks:
ret.append('# end of class %(klass)s')
ret += ['',
'package main;',
'',
'unless(caller){'] + gettext1 + [
'%(tab)smy $%(name)s = %(klass)s->new();',
'%(tab)s$%(name)s->MainLoop();',
'}', '']
else:
ret = ['1;',
'',
'package main;',
'%(pl_import)s',
'unless(caller){'] + gettext1 + [
'%(tab)slocal *Wx::App::OnInit = sub{1};',
'%(tab)smy $%(name)s = Wx::App->new();',
'%(tab)sWx::InitAllImageHandlers();',
'',
'%(tab)smy $%(top_win)s = %(top_win_class)s->new();',
'',
'%(tab)s$%(name)s->SetTopWindow($%(top_win)s);',
'%(tab)s$%(top_win)s->Show(1);',
'%(tab)s$%(name)s->MainLoop();',
'}', '']
return '\n'.join(ret)
def init_lang(self, app_attrs):
# initial new defaults late to use the proper indent characters
tab = self.tabs(1)
self.new_defaults = {
'$parent' : '%s$parent = undef unless defined $parent;\n' % tab,
'$id' : '%s$id = -1 unless defined $id;\n' % tab,
'$title' : '%s$title = "" unless defined $title;\n' % tab,
'$pos' : '%s$pos = wxDefaultPosition unless defined $pos;\n' % tab,
'$size' : '%s$size = wxDefaultSize unless defined $size;\n' % tab,
'$name' : '%s$name = "" unless defined $name;\n\n' % tab,
#'$style' is a special case
}
self.header_lines = [
'use Wx qw[:allclasses];\n',
'use strict;\n'
]
def add_app(self, app_attrs, top_win):
# add language specific mappings
if self.multiple_files:
self.lang_mapping['pl_import'] = "\nuse %s;\n" % top_win.klass
else:
self.lang_mapping['pl_import'] = ''
BaseLangCodeWriter.add_app(self, app_attrs, top_win)
def generate_code_ctor(self, code_obj, is_new, tab):
code_lines = []
write = code_lines.append
builder = self.obj_builders[code_obj.WX_CLASS]
mycn = getattr(builder, 'cn', self.cn)
mycn_f = getattr(builder, 'cn_f', self.cn_f)
# custom base classes support
custom_base = code_obj.check_prop_nodefault('custom_base') and code_obj.custom_base.strip() or None
new_signature = getattr(builder, 'new_signature', [])
# generate constructor code
if is_new:
write('package %s;\n\n' % code_obj.klass)
write('use Wx qw[:everything];\nuse base qw(%s);\nuse strict;\n\n' % code_obj.WX_CLASS.replace('wx', 'Wx::', 1))
if self._use_gettext:
if self.multiple_files:
self.classes[code_obj].dependencies.add( "use Wx::Locale gettext => '_T';\n" )
else:
write("use Wx::Locale gettext => '_T';\n")
# The dependencies have to add to the package block too because global imports are not visible inside the
# package block
# TODO: Don't add dependencies twice with Perl
# write the module dependencies for this class (package)
dep_list = sorted( self.classes[code_obj].dependencies )
if dep_list:
code = self._tagcontent('dependencies', dep_list, True)
write(code)
write('sub new {\n')
write(tab + "my( $self, %s ) = @_;\n" % ", ".join(new_signature))
if new_signature:
for k in new_signature:
if k in self.new_defaults:
write(self.new_defaults[k])
else:
new_signature = ['@_[1 .. $#_]'] # shift(@_)->SUPER::new(@_);
logging.info( "%s did not declare self.new_defaults ", code_obj.klass )
elif custom_base:
# custom base classes set, but "overwrite existing sources" not set. Issue a warning about this
self.warning( '%s has custom base classes, but you are not overwriting existing sources: '
'please check that the resulting code is correct!' % code_obj.name )
if self._mark_blocks:
# __init__ begin tag
write(self.tmpl_block_begin % {'class_separator':self.class_separator, 'comment_sign':self.comment_sign,
'function':self.name_ctor, 'klass':self.cn_class(code_obj.klass),
'tab':tab} )
# the optional initial code from the code properties
if not self.preview and code_obj.check_prop("extracode_pre"):
for l in code_obj.properties["extracode_pre"].get_lines():
write(tab + l)
style_p = code_obj.properties.get("style")
if style_p and style_p.value_set != style_p.default_value:
style = style_p.get_string_value()
m_style = mycn_f( style )
if m_style:
stmt_style = self._format_style(style, code_obj)
write( stmt_style % {'style':m_style, 'tab':tab} )
# class parent constructor
write(tab + '$self = $self->SUPER::new( %s );\n' % ", ".join(new_signature))
# set size here to avoid problems with splitter windows
if code_obj.check_prop('size'):
write( tab + self.generate_code_size(code_obj) )
for l in builder.get_properties_code(code_obj):
write(tab + l)
if code_obj.check_prop_truth('extraproperties'):
for l in builder.generate_code_extraproperties(code_obj):
write(tab + l)
# the initial and final code for the contained elements
for l in self.classes[code_obj].init:
write(tab + l)
if self.classes[code_obj].final:
write(tab + "\n")
for l in self.classes[code_obj].final:
write(tab + l)
# now check if there is initial and final code for the element itself
for l in builder.get_init_code(code_obj):
write(tab+l)
for l in builder.get_layout_code(code_obj):
write(tab + l)
# the optional final code from the code properties
if not self.preview and code_obj.check_prop("extracode_post"):
for l in code_obj.properties["extracode_post"].get_lines():
write(tab + l)
return code_lines
def generate_code_event_bind(self, code_obj, tab, event_handlers):
code_lines = []
for obj, event, handler, unused in event_handlers:
if obj.name:
obj_id = '%s->GetId'%self.format_generic_access(obj) # e.g. '$self->{button_1}->GetId' or '$self->GetId'
else:
obj_id = self.generate_code_id(None, obj.id)[1] or '-1' # but this is wrong anyway...
if 'EVT_NAVIGATION_KEY' in event:
tmpl = '''%(tab)s%(event)s($self, $self->can('%(handler)s'));\n'''
else:
tmpl = '''%(tab)s%(event)s($self, %(obj_id)s, $self->can('%(handler)s'));\n'''
code_lines.append( tmpl % {'tab': tab, 'event': self.cn(event), 'handler': handler, 'obj_id': obj_id} )
if event_handlers:
code_lines.append('\n')
return code_lines
def generate_code_id(self, obj, id=None):
if id is None:
id = obj.window_id
if not id:
if obj is not None and obj.check_prop_truth("stockitem"):
return '', self.cn("wxID_" + obj.stockitem)
return '', self.cn('wxID_ANY')
id = str(id)
tokens = id.split('=', 1)
if len(tokens) != 2:
return '', self.cn(tokens[0]) # we assume name is declared elsewhere
name, val = tokens
if not name:
return '', self.cn(val)
name = name.strip()
val = val.strip()
if val == '?':
val = self.cn('wxNewId()')
else:
val = self.cn(val)
# check to see if we have to make the var global or not...
return 'use constant %s => %s;\n' % (name, val), name
def generate_code_size(self, obj):
objname = self.format_generic_access(obj)
size = obj.properties["size"].get_string_value()
use_dialog_units = (size[-1] == 'd')
method = 'SetMinSize' if obj.parent_window else 'SetSize'
if use_dialog_units:
return '%s->%s(%s->ConvertDialogSizeToPixels(Wx::Size->new(%s)));\n' % (objname, method, objname, size[:-1])
return '%s->%s(Wx::Size->new(%s));\n' % (objname, method, size)
def _quote_str(self, s):
"""Escape all unicode characters to there unicode code points in form of \\uxxxx.
The returned string is a pure ascii string.
Normal ascii characters like \\n or \\t won't be escaped.
note: wxGlade don't handles file encoding well currently. Thereby
we escape all unicode characters.
note: The string 's' is encoded with self.app_encoding already.
see: BaseLangCodeWriter._quote_str for additional details
see: _recode_x80_xff()"""
s = s.replace('$', r'\$')
s = s.replace('@', r'\@')
# convert all strings to unicode first
if not isinstance(s, compat.unicode):
s = s.decode(self.app_encoding)
# check if it's pure ascii
try:
dummy = s.encode('ascii')
if self._use_gettext:
return '_T("%s")' % s
else:
return '"%s"' % s
except UnicodeError:
pass
# convert unicode strings to pure ascii
# use "raw-unicode-escape" just escaped unicode characters and not default escape sequences
s = s.encode('raw-unicode-escape')
s = self._recode_x80_xff(s)
if compat.PYTHON3:
# convert back to str (unicode)
s = s.decode("ASCII")
# convert Python style to Perl style
s = re.sub(r'\\u([0-9a-f]{4})', r'\\N{U+\1}', s)
if self._use_gettext:
return '_T("%s")' % s
else:
return '"%s"' % s
def add_object_format_name(self, name):
return '#$self->%s' % name
def _format_classattr(self, obj):
res = BaseLangCodeWriter._format_classattr(self, obj)
if not res:
return res
elif obj.name.startswith('$self->'):
return obj.name
elif obj.name.startswith('$'):
return obj.name
# spacer.name is "<width>, <height>" already
elif obj.WX_CLASS == 'spacer':
return obj.name
# Perl stores sizers always in class attributes
elif self.store_as_attr(obj) or obj.IS_SIZER:
return '$self->{%s}' % obj.name
return '$%s' % obj.name
def _format_import(self, klass):
return 'use %s;\n' % klass
def _get_class_filename(self, klass):
"Returns the name for a Perl module (.pm) to store a single class in multi file projects"
return os.path.join( self.out_dir, klass.replace('::', os.sep) + '.pm' )
def format_generic_access(self, obj):
if obj.IS_CLASS:
return '$self'
return self._format_classattr(obj)
writer = PerlCodeWriter() # the code writer instance
language = writer.language # Language generated by this code generator
| 41.484288 | 124 | 0.524841 | [
"MIT"
] | ardovm/wxGlade | codegen/perl_codegen.py | 22,443 | Python |
test = {
'name': 'rle',
'points': 1,
'suites': [
{
'cases': [
{
'code': r"""
scm> (rle '())
()
scm> (stream-to-list (rle (list-to-stream '(1 2 3))))
((1 1) (2 1) (3 1))
scm> (stream-to-list (rle (list-to-stream '(1 1 2 2 3 3))))
((1 2) (2 2) (3 2))
scm> (define s (rle (list-to-stream '(1 1 1 1 1 6 6 6 6 2 5 5 5))))
s
scm> (stream-to-list s)
((1 5) (6 4) (2 1) (5 3))
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': r"""
scm> (load 'lab13_extra)
""",
'teardown': '',
'type': 'scheme'
}
]
}
| 22.151515 | 77 | 0.352941 | [
"MIT"
] | MassimoWu88/MassimoWu88 | labs/lab13/tests/rle.py | 731 | Python |
# Auto generated from meta.yaml by namespacegen.py version: 0.4.0
# Generation date: 2020-08-25 16:45
# Schema: metamodel
#
# id: https://w3id.org/biolink/biolinkml/meta
# description: A metamodel for defining biolink related schemas
# license: https://creativecommons.org/publicdomain/zero/1.0/
from collections import defaultdict
from typing import Iterable, Dict, Tuple
from biolinkml.utils.curienamespace import CurieNamespace
GENE = 'gene'
DISEASE = 'disease'
CHEMICAL_SUBSTANCE = 'chemical substance'
SYMBOL = 'Approved_Symbol'
class IdentifierResolverException(RuntimeError):
pass
class BiolinkNameSpace:
"""
Map of BioLink Model registered URI Namespaces
"""
_namespaces = [
CurieNamespace('OIO', 'http://www.geneontology.org/formats/oboInOwl#'),
CurieNamespace('bibo', 'http://purl.org/ontology/bibo/'),
CurieNamespace('biolinkml', 'https://w3id.org/biolink/biolinkml/'),
CurieNamespace('dcterms', 'http://purl.org/dc/terms/'),
CurieNamespace('meta', 'https://w3id.org/biolink/biolinkml/meta/'),
CurieNamespace('oslc', 'http://open-services.net/ns/core#'),
CurieNamespace('owl', 'http://www.w3.org/2002/07/owl#'),
CurieNamespace('pav', 'http://purl.org/pav/'),
CurieNamespace('rdf', 'http://www.w3.org/1999/02/22-rdf-syntax-ns#'),
CurieNamespace('rdfs', 'http://www.w3.org/2000/01/rdf-schema#'),
CurieNamespace('schema', 'http://schema.org/'),
CurieNamespace('skos', 'http://www.w3.org/2004/02/skos/core#'),
CurieNamespace('xsd', 'http://www.w3.org/2001/XMLSchema#'),
]
# class level dictionaries
_prefix_map: Dict[str, CurieNamespace] = {}
@classmethod
def _get_prefix_map(cls):
if not cls._prefix_map:
for ns in cls._namespaces:
# index by upper case for uniformity of search
cls._prefix_map[ns.prefix.upper()] = ns
return cls._prefix_map
@classmethod
def parse_curie(cls, curie: str) -> Tuple[CurieNamespace, str]:
"""
Parse a candidate CURIE
:param curie: candidate curie string
:return: CURIE namespace and object_id
"""
found = CurieNamespace("", ""), curie # default value if not a CURIE or unknown XMLNS prefix
if ':' in curie:
part = curie.split(":")
# Normalize retrieval with upper case of prefix for lookup
prefix = part[0].upper()
if prefix in cls._get_prefix_map():
found = cls._prefix_map[prefix], part[1]
return found
@classmethod
def parse_uri(cls, uri: str) -> Tuple[CurieNamespace, str]:
"""
Parse a candidate URI
:param uri: candidate URI string
:return: namespace and object_id
"""
found = CurieNamespace("", ""), uri # default value returned if unknown URI namespace
# TODO: is there a more efficient lookup scheme here than a linear search of namespaces?
for ns in cls._namespaces:
base_uri = str(ns)
if uri.startswith(base_uri):
# simple minded deletion of base_uri to give the object_id
object_id = uri.replace(base_uri, "")
found = ns, object_id
break
return found
@classmethod
def parse_identifier(cls, identifier: str) -> Tuple[CurieNamespace, str]:
# trivial case of a null identifier?
if not identifier:
return CurieNamespace("", ""), ""
# check if this is a candidate URI...
if identifier.lower().startswith("http"):
# guess that perhaps it is, so try to parse it
return cls.parse_uri(identifier)
else: # attempt to parse as a CURIE
return cls.parse_curie(identifier)
def object_id(identifier, keep_version=False) -> str:
"""
Returns the core object_id of a CURIE, with or without the version suffix.
Note: not designed to be used with a URI (will give an invalid outcome)
:param identifier: candidate CURIE identifier for processing
:param keep_version: True if the version string suffix is to be retained in the identifier
:return:
"""
# trivial case: null input value?
if not identifier:
return identifier
if ':' in identifier:
identifier = identifier.split(":")[1]
if not keep_version and '.' in identifier:
identifier = identifier.split(".")[0]
return identifier
def fix_curies(identifiers, prefix=''):
"""
Applies the specified XMLNS prefix to (an) identifier(s) known
to be "raw" IDs as keys in a dictionary or elements in a list (or a simple string)
:param identifiers:
:param prefix:
:return:
"""
if not prefix:
# return identifiers without modification
# Caller may already consider them in curie format
return identifiers
if isinstance(identifiers, dict):
curie_dict = defaultdict(dict)
for key in identifiers.keys():
curie_dict[prefix + ':' + object_id(key, keep_version=True)] = identifiers[key]
return curie_dict
# identifiers assumed to be just a single object identifier
elif isinstance(identifiers, str):
# single string to convert
return prefix + ':' + object_id(identifiers, keep_version=True)
elif isinstance(identifiers, Iterable):
return [prefix + ':' + object_id(x, keep_version=True) for x in identifiers]
else:
raise RuntimeError("fix_curie() is not sure how to fix an instance of data type '", type(identifiers))
def curie(identifier) -> str:
# Ignore enpty strings
if not identifier:
return ""
else:
namespace: CurieNamespace
identifier_object_id: str
namespace, identifier_object_id = BiolinkNameSpace.parse_identifier(identifier)
return namespace.curie(identifier_object_id)
| 34.836257 | 110 | 0.642605 | [
"CC0-1.0"
] | hsolbrig/biolinkml | tests/test_scripts/output/gennamespace/meta_namespaces.py | 5,957 | Python |
from typing import Sequence, Dict, List, Optional
from abc import ABC, abstractmethod
# from nltk.corpus import wordnet
from pymagnitude import Magnitude
from .utils import (
UPPERCASE_RE,
LOWERCASE_RE,
DIGIT_RE,
PUNC_REPEAT_RE,
)
class FeatureExtractor(ABC):
@abstractmethod
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
raise NotImplementedError
class BiasFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
features["bias"] = 1.0
class TokenFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
features["tok[%d]=%s" % (relative_idx, token)] = 1.0
class UppercaseFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
if str.isupper(token):
features["uppercase[%d]" % (relative_idx)] = 1.0
class TitlecaseFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
if str.istitle(token):
features["titlecase[%d]" % (relative_idx)] = 1.0
class InitialTitlecaseFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
if str.istitle(token) and current_idx + relative_idx == 0:
features["initialtitlecase[%d]" % (relative_idx)] = 1.0
class PunctuationFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
if PUNC_REPEAT_RE.fullmatch(token):
features["punc[%d]" % (relative_idx)] = 1.0
class DigitFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
if DIGIT_RE.search(token):
features["digit[%d]" % (relative_idx)] = 1.0
class WordShapeFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
shape_str = token
shape_str = UPPERCASE_RE.sub("X", shape_str)
shape_str = LOWERCASE_RE.sub("x", shape_str)
shape_str = DIGIT_RE.sub("0", shape_str)
features["shape[%d]=%s" % (relative_idx, shape_str)] = 1.0
"""
class LikelyAdjectiveFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
for synset in wordnet.synsets(token):
if synset.name().split('.')[0] == token:
if synset.pos() == 's':
features["adj[%d]" % (relative_idx)] = 1.0
class AfterVerbFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
if relative_idx != -1:
return
for synset in wordnet.synsets(token):
if synset.name().split('.')[0] == token:
if synset.pos() == 'v':
features["afterverb[%d]" % (relative_idx)] = 1.0
class PosFeature(FeatureExtractor):
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
for synset in wordnet.synsets(token):
if synset.name().split('.')[0] == token:
features[f"adj[{relative_idx}]={synset.pos()}"] = 1.0
"""
class WordVectorFeature(FeatureExtractor):
def __init__(self, vectors_path: str, scaling: float = 1.0) -> None:
self.vectors = Magnitude(vectors_path, normalized=False)
self.scaling = scaling
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
vector = self.vectors.query(token)
keys = ["v" + str(i) for i in range(len(vector))]
values = vector * self.scaling
features.update(zip(keys, values))
class BrownClusterFeature(FeatureExtractor):
def __init__(
self,
clusters_path: str,
*,
use_full_paths: bool = False,
use_prefixes: bool = False,
prefixes: Optional[Sequence[int]] = None,
):
if not use_full_paths and not use_prefixes:
raise ValueError
self.prefix_dict = {}
self.full_path_dict = {}
self.use_full_paths = use_full_paths
with open(clusters_path, "r", encoding='utf-8') as cluster_file:
for line in cluster_file:
cluster, word, _frequency = line.split("\t")
if use_full_paths:
self.full_path_dict[word] = cluster
elif use_prefixes:
if prefixes is not None:
self.prefix_dict[word] = [
cluster[:prefix]
for prefix in prefixes
if prefix <= len(cluster)
]
else:
self.prefix_dict[word] = [
cluster[:prefix] for prefix in range(1, len(cluster) + 1)
]
def extract(
self,
token: str,
current_idx: int,
relative_idx: int,
tokens: Sequence[str],
features: Dict[str, float],
) -> None:
if relative_idx != 0:
return
if self.use_full_paths and token in self.full_path_dict:
features[f"cpath={self.full_path_dict[token]}"] = 1.0
elif token in self.prefix_dict:
for prefix in self.prefix_dict[token]:
features[f"cprefix{len(prefix)}={prefix}"] = 1.0
class WindowedTokenFeatureExtractor:
def __init__(self, feature_extractors: Sequence[FeatureExtractor], window_size: int):
self.feature_extractors = feature_extractors
self.window_size = window_size
def extract(self, tokens: Sequence[str]) -> List[Dict[str, float]]:
features = []
for i, _ in enumerate(tokens):
current_feature = {}
start = max(0, i - self.window_size)
end = min(len(tokens) - 1, i + self.window_size) + 1
for j in range(start, end):
relative_idx = j - i
for feature_extractor in self.feature_extractors:
feature_extractor.extract(
tokens[j], i, relative_idx, tokens, current_feature
)
features.append(current_feature)
return features
| 30.137255 | 89 | 0.554066 | [
"MIT"
] | YifanLeng/Esports_NER | CRF/feature_extractors.py | 7,685 | Python |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
from functools import partial
import tensorflow as tf
import tensorflow.contrib.eager as tfe
from general.utilTF1.utils import session
from general.kneeOsteoarthritisDataset.KneeOsteoarthritsDataset import KneeOsteoarthritsDataset
_N_CPU = multiprocessing.cpu_count()
def batch_dataset(dataset, batch_size, prefetch_batch=_N_CPU + 1, drop_remainder=True, filter=None,
map_func=None, num_threads=_N_CPU, shuffle=True, buffer_size=4096, repeat=-1):
if filter:
dataset = dataset.filter(filter)
if map_func:
dataset = dataset.map(map_func, num_parallel_calls=num_threads)
if shuffle:
dataset = dataset.shuffle(buffer_size)
if drop_remainder:
dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(batch_size))
else:
dataset = dataset.batch(batch_size)
dataset = dataset.repeat(repeat).prefetch(prefetch_batch)
return dataset
class Dataset(object):
def __init__(self):
self._dataset = None
self._iterator = None
self._batch_op = None
self._sess = None
self._is_eager = tf.executing_eagerly()
self._eager_iterator = None
def __del__(self):
if self._sess:
self._sess.close()
def __iter__(self):
return self
def __next__(self):
try:
b = self.get_next()
except:
raise StopIteration
else:
return b
next = __next__
def get_next(self):
if self._is_eager:
return self._eager_iterator.get_next()
else:
return self._sess.run(self._batch_op)
def reset(self, feed_dict={}):
if self._is_eager:
self._eager_iterator = tfe.Iterator(self._dataset)
else:
self._sess.run(self._iterator.initializer, feed_dict=feed_dict)
def _bulid(self, dataset, sess=None):
self._dataset = dataset
if self._is_eager:
self._eager_iterator = tfe.Iterator(dataset)
else:
self._iterator = dataset.make_initializable_iterator()
self._batch_op = self._iterator.get_next()
if sess:
self._sess = sess
else:
self._sess = session()
try:
self.reset()
except:
pass
@property
def dataset(self):
return self._dataset
@property
def iterator(self):
return self._iterator
@property
def batch_op(self):
return self._batch_op
def get_dataset(data_set_path,shuffle=True):
# shape
img_shape = [256,256, 1]
# dataset
def _map_func(img,label):
img = tf.image.resize_images(img, [img_shape[0], img_shape[1]], method=tf.image.ResizeMethod.BICUBIC)
img = tf.clip_by_value(tf.cast(img, tf.float32), 0, 255) / 255 # / 127.5 - 1
return img,label
# get image pathes
#
kneeosteo_train = KneeOsteoarthritsDataset(data_path=data_set_path)
labels = list(kneeosteo_train.dict_url_class.values())
paths = list(kneeosteo_train.dict_url_class.keys())
assert (len(paths) == len(labels))
print('The dataset %s has %f elements. ' % (data_set_path, len(labels)))
Dataset = partial(DiskImageData, img_paths=paths,labels=labels, repeat=1, map_func=_map_func,shuffle=shuffle)
# index func
def get_imgs(batch):
return batch
return Dataset, img_shape, get_imgs
def disk_image_batch_dataset(img_paths, batch_size, labels=None, prefetch_batch=_N_CPU + 1, drop_remainder=True, filter=None,
map_func=None, num_threads=_N_CPU, shuffle=True, buffer_size=4096, repeat=-1):
"""Disk image batch dataset.
This function is suitable for jpg and png files
Arguments:
img_paths : String list or 1-D tensor, each of which is an iamge path
labels : Label list/tuple_of_list or tensor/tuple_of_tensor, each of which is a corresponding label
"""
if labels is None:
dataset = tf.data.Dataset.from_tensor_slices(img_paths)
elif isinstance(labels, tuple):
dataset = tf.data.Dataset.from_tensor_slices((img_paths,) + tuple(labels))
else:
dataset = tf.data.Dataset.from_tensor_slices((img_paths, labels))
def parse_func(path, *label):
img = tf.read_file(path)
img = tf.image.decode_png(img, 1)
return (img,) + label
if map_func:
def map_func_(*args):
return map_func(*parse_func(*args))
else:
map_func_ = parse_func
# dataset = dataset.map(parse_func, num_parallel_calls=num_threads) is slower
dataset = batch_dataset(dataset, batch_size, prefetch_batch, drop_remainder, filter,
map_func_, num_threads, shuffle, buffer_size, repeat)
return dataset
class DiskImageData(Dataset):
"""DiskImageData.
This class is suitable for jpg and png files
Arguments:
img_paths : String list or 1-D tensor, each of which is an iamge path
labels : Label list or tensor, each of which is a corresponding label
"""
def __init__(self, img_paths, batch_size, labels=None, prefetch_batch=_N_CPU + 1, drop_remainder=True, filter=None,
map_func=None, num_threads=_N_CPU, shuffle=True, buffer_size=4096, repeat=-1, sess=None):
super(DiskImageData, self).__init__()
dataset = disk_image_batch_dataset(img_paths, batch_size, labels, prefetch_batch, drop_remainder, filter,
map_func, num_threads, shuffle, buffer_size, repeat)
self._bulid(dataset, sess)
self._n_data = len(img_paths)
def __len__(self):
return self._n_data
| 30.489583 | 125 | 0.65562 | [
"BSD-3-Clause"
] | duennbart/masterthesis_VAE | general/utilTF1/dataset.py | 5,854 | Python |
#!/usr/bin/env python
from django.contrib.auth import get_user_model
import django
import configparser
import sys
import os
django_project_name = os.environ.get('DJANGO_PROJECT_NAME')
sys.path.append('/app')
os.environ.setdefault("DJANGO_SETTINGS_MODULE", django_project_name+'.settings')
django.setup()
def django_superuser_creation(**kwargs):
User = get_user_model()
if User.objects.filter(username=kwargs['username']).count()==0:
User.objects.create_superuser(**kwargs)
print('[django] Superuser created.')
else:
print('[django] Superuser creation skipped.')
config = configparser.ConfigParser()
config.read('/run/secrets/djangodb.cnf')
try:
user_detail = dict(config['superuser'])
except KeyError as e:
print('[django] Config file for django does not has superuser detail.')
sys.exit(1)
else:
django_superuser_creation(**user_detail)
| 27.151515 | 80 | 0.736607 | [
"MIT"
] | narendra-cs/DjangoApp | scripts/create_django_superuser.py | 896 | Python |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
from flask import Flask
from flask import request
from flask import jsonify
from line_sdk import Linebot
LINE_ACCESS_TOKEN = ""
LINE_CHANNEL_SECRET = ""
app = Flask(__name__)
@app.route("/", methods=["GET", "POST"])
def webhook():
# GET
if request.method == "GET":
return "Line Webhook Success."
# POST
elif request.method == "POST":
body = request.get_data(as_text=True)
signature = request.headers["X-Line-Signature"]
# Line
linebot = Linebot(LINE_ACCESS_TOKEN, LINE_CHANNEL_SECRET)
# dataLIST = [{status, type, message, userID, replyToken, timestamp}]
# replyToken = 回覆需要的ID , message = 使用者輸入的內容
dataLIST = linebot.parse(body, signature)
for dataDICT in dataLIST:
if dataDICT["status"]:
if dataDICT["type"] == "message":
try:
# 這裡輸入客製化內容
# dataDICT["message"] => 使用者輸入的內容
msgSTR = "[MSG] {}".format(dataDICT["message"])
except Exception as e:
msgSTR = "[ERROR] {}".format(str(e))
# respText(聊天室ID, 要回覆的內容)
linebot.respText(dataDICT["replyToken"], msgSTR)
return jsonify({"status": True, "msg": "Line Webhook Success."})
# OTHER
else:
return "HTTP_405_METHOD_NOT_ALLOWED"
if __name__ == "__main__":
app.run() | 28.132075 | 77 | 0.560027 | [
"MIT"
] | RobinJLin/LokiHub | Heroku/Heroku-Linebot-Github/line_app.py | 1,569 | Python |
# All paths are relative to train_val.py file
config = {
'images_path': 'train_val_data/Flicker8k_Dataset/', #Make sure you put that last slash(/)
'train_data_path': 'train_val_data/Flickr8k_text/Flickr_8k.trainImages.txt',
'val_data_path': 'train_val_data/Flickr8k_text/Flickr_8k.devImages.txt',
'captions_path': 'train_val_data/Flickr8k_text/Flickr8k.token.txt',
'tokenizer_path': 'model_data/tokenizer.pkl',
'model_data_path': 'model_data/', #Make sure you put that last slash(/)
'model_load_path': 'model_data/model_vgg16_epoch-17_train_loss-2.2955_val_loss-3.1448.hdf5',
'num_of_epochs': 18,
'max_length': 40, #This is set manually after training of model and required for test.py
'batch_size': 64,
'beam_search_k':3,
'test_data_tuple': [
"https://images.unsplash.com/photo-1502673530728-f79b4cab31b1?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1050&q=80", # Photo by John Price (https://unsplash.com/@johnprice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on Unsplash (https://unsplash.com/s/photos/dog?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
"https://images.unsplash.com/photo-1576941089067-2de3c901e126?ixlib=rb-1.2.1&auto=format&fit=crop&w=1443&q=80", # Photo by Jacques Bopp (https://unsplash.com/@jacquesbopp?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on Unsplash (https://unsplash.com/s/photos/house?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
"https://images.unsplash.com/photo-1542103749-8ef59b94f47e?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1050&q=80" # Photo by Dan (https://unsplash.com/@dann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on Unsplash (https://unsplash.com/s/photos/person?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
], #sample images
'test_data_path': 'test_data/', #Make sure you put that last slash(/)
'model_type': 'vgg16', # inceptionv3 or vgg16
'random_seed': 1035
}
rnnConfig = {
'embedding_size': 300,
'LSTM_units': 256,
'dense_units': 256,
'dropout': 0.3
}
| 71.466667 | 378 | 0.772854 | [
"MIT"
] | saharshxyz/altML | ml/config.py | 2,144 | Python |
"""
---
title: Train Feedback Transformer
summary: This is training code with notes for a feedback transformer.
---
# Train Feedback Transformer
This trains a [feedback transformer](index.html) model for auto-regression.
You can pick the original feedback transformer or the new version
where the keys and values are precalculated.
Here's a Colab notebook for training a feedback transformer on Tiny Shakespeare dataset.
[](https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/transformers/feedback/experiment.ipynb)
[](https://web.lab-ml.com/run?uuid=d8eb9416530a11eb8fb50242ac1c0002)
"""
import torch
from torch import nn
from labml import experiment
from labml.configs import option
from labml.utils.pytorch import get_modules
from labml_helpers.module import Module
from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
from labml_nn.transformers import Encoder, Generator, TransformerConfigs
from labml_nn.transformers.utils import subsequent_mask
class AutoregressiveModel(Module):
"""
## Auto regressive model
"""
def __init__(self, n_vocab: int, d_model: int, transformer: Module):
super().__init__()
# Token embedding module
self.src_embed = nn.Embedding(n_vocab, d_model)
self.transformer = transformer
self.generator = nn.Linear(d_model, n_vocab)
def __call__(self, x: torch.Tensor):
# Embed the tokens
x = self.src_embed(x)
# Run it through the the transformer
res = self.transformer(x)
# Generate logits of the next token
return self.generator(res), None
class Configs(NLPAutoRegressionConfigs):
"""
## Configurations
The default configs can and will be over-ridden when we start the experiment
"""
model: AutoregressiveModel
d_model: int = 512
heads: int = 8
dropout: float = 0.0
d_ff: int = 2048
n_layers: int = 6
@option(Configs.model)
def feedback_transformer(c: Configs):
"""
Create [original feedback transformer](index.html).
"""
from labml_nn.transformers.feedback import FeedbackTransformer, FeedbackTransformerLayer, \
FeedbackAttention, FeedForward
return AutoregressiveModel(
c.n_tokens, c.d_model,
FeedbackTransformer(
FeedbackTransformerLayer(d_model=c.d_model,
attn=FeedbackAttention(c.heads, c.d_model, c.dropout),
feed_forward=FeedForward(c.d_model, c.d_ff, c.dropout),
dropout_prob=c.dropout),
c.n_layers)).to(c.device)
@option(Configs.model)
def feedback_transformer_kv(c: Configs):
"""
Create [updated feedback transformer](index.html#kv_shared), with precalculated keys and values.
"""
from labml_nn.transformers.feedback import FeedbackTransformerKV, FeedbackTransformerLayer, \
FeedbackAttention, FeedForward
return AutoregressiveModel(
c.n_tokens, c.d_model,
FeedbackTransformerKV(
FeedbackTransformerLayer(d_model=c.d_model,
attn=FeedbackAttention(c.heads, c.d_model, c.dropout,
is_kv_precomputed=True),
feed_forward=FeedForward(c.d_model, c.d_ff, c.dropout),
dropout_prob=c.dropout),
c.n_layers, c.d_model, c.heads)).to(c.device)
def main():
# Create experiment
experiment.create(name="feedback_transformer")
# Create configs
conf = Configs()
# Load configurations
experiment.configs(conf,
# A dictionary of configurations to override
{'tokenizer': 'character',
'text': 'tiny_shakespeare',
'optimizer.learning_rate': 1.0,
'optimizer.optimizer': 'Noam',
'prompt': 'It is',
'prompt_separator': '',
# Use `feedback_transformer` for original feedback transformer
'model': 'feedback_transformer_kv',
'train_loader': 'shuffled_train_loader',
'valid_loader': 'shuffled_valid_loader',
'seq_len': 128,
'epochs': 128,
'batch_size': 64,
'inner_iterations': 25})
# Set models for saving and loading
experiment.add_pytorch_models(get_modules(conf))
# Start the experiment
with experiment.start():
# Run the training loop
conf.run()
if __name__ == '__main__':
main()
| 34.216783 | 188 | 0.628858 | [
"MIT"
] | drpraneshkrishnan/nn | labml_nn/transformers/feedback/experiment.py | 4,893 | Python |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
from pathlib import Path
from typing import cast
import toml
sys.path.insert(0, os.path.abspath("../../"))
# -- Project information -----------------------------------------------------
project = "dataprep"
copyright = "2020, SFU Database System Lab"
author = "SFU Database System Lab"
# The full version, including alpha/beta/rc tags
def get_version() -> str:
"""
Get the library version from pyproject.toml
"""
path = Path(__file__).resolve().parents[2] / "pyproject.toml"
pyproject = toml.loads(open(str(path)).read())
return cast(str, pyproject["tool"]["poetry"]["version"])
release = get_version()
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.todo",
"sphinx.ext.viewcode",
"sphinx.ext.autodoc",
"sphinx.ext.napoleon",
"nbsphinx",
"sphinx_autodoc_typehints",
]
autodoc_typehints = "description"
# Napoleon settings
napoleon_google_docstring = False
napoleon_numpy_docstring = True
napoleon_include_init_with_doc = False
napoleon_include_private_with_doc = False
napoleon_include_special_with_doc = False
napoleon_use_admonition_for_examples = False
napoleon_use_admonition_for_notes = False
napoleon_use_admonition_for_references = False
napoleon_use_ivar = False
napoleon_use_param = True
napoleon_use_rtype = True
napoleon_use_keyword = True
napoleon_custom_sections = None
# autodoc_default_options = {
# "members": True,
# "member-order": "bysource",
# "special-members": "__init__",
# }
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = []
master_doc = "index"
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "nature"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
| 31.27551 | 79 | 0.6969 | [
"MIT"
] | Sanjana12111994/dataprep | docs/source/conf.py | 3,065 | Python |
import os
from os import path
import stat
import mmap
import directio
from setting import (
LONGHORN_SOCKET_DIR, LONGHORN_DEV_DIR, PAGE_SIZE,
)
def readat_direct(dev, offset, length):
pg = offset / PAGE_SIZE
in_page_offset = offset % PAGE_SIZE
# either read less than a page, or whole pages
if in_page_offset != 0:
assert pg == (offset + length) / PAGE_SIZE
to_read = PAGE_SIZE
else:
assert length % PAGE_SIZE == 0
to_read = length
pg_offset = pg * PAGE_SIZE
f = os.open(dev, os.O_DIRECT | os.O_RDONLY)
try:
os.lseek(f, pg_offset, os.SEEK_SET)
ret = directio.read(f, to_read)
finally:
os.close(f)
return ret[in_page_offset: in_page_offset + length]
def writeat_direct(dev, offset, data):
pg = offset / PAGE_SIZE
# don't support across page write
assert pg == (offset + len(data)) / PAGE_SIZE
pg_offset = pg * PAGE_SIZE
f = os.open(dev, os.O_DIRECT | os.O_RDWR)
m = mmap.mmap(-1, PAGE_SIZE)
try:
os.lseek(f, pg_offset, os.SEEK_SET)
pg_data = readat_direct(dev, pg_offset, PAGE_SIZE)
m.write(pg_data)
m.seek(offset % PAGE_SIZE)
m.write(data)
ret = directio.write(f, m)
finally:
m.close()
os.close(f)
return ret
def get_socket_path(volume):
return path.join(LONGHORN_SOCKET_DIR, "longhorn-" + volume + ".sock")
class blockdev:
def __init__(self, volume):
self.dev = path.join(LONGHORN_DEV_DIR, volume)
def readat(self, offset, length):
with open(self.dev, 'r') as f:
f.seek(offset)
ret = f.read(length)
return ret
# return readat_direct(self.dev, offset, length)
def writeat(self, offset, data):
return writeat_direct(self.dev, offset, data)
def ready(self):
if not os.path.exists(self.dev):
return False
mode = os.stat(self.dev).st_mode
if not stat.S_ISBLK(mode):
return False
return True
| 25.525 | 73 | 0.617042 | [
"Apache-2.0"
] | MalibuKoKo/longhorn-engine | integration/data/frontend.py | 2,042 | Python |
import datetime
import string
from collections import namedtuple
from distutils.version import LooseVersion
from random import choices
from typing import Optional, Type
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from pandas.tests.extension.base import (
BaseArithmeticOpsTests,
BaseBooleanReduceTests,
BaseCastingTests,
BaseComparisonOpsTests,
BaseConstructorsTests,
BaseDtypeTests,
BaseGetitemTests,
BaseGroupbyTests,
BaseInterfaceTests,
BaseMethodsTests,
BaseMissingTests,
BaseNoReduceTests,
BaseNumericReduceTests,
BaseParsingTests,
BasePrintingTests,
BaseReshapingTests,
BaseSetitemTests,
)
from fletcher import FletcherBaseDtype
if LooseVersion(pd.__version__) >= "0.25.0":
# imports of pytest fixtures needed for derived unittest classes
from pandas.tests.extension.conftest import ( # noqa: F401
as_array, # noqa: F401
use_numpy, # noqa: F401
groupby_apply_op, # noqa: F401
as_frame, # noqa: F401
as_series, # noqa: F401
)
PANDAS_GE_1_1_0 = LooseVersion(pd.__version__) >= "1.1.0"
FletcherTestType = namedtuple(
"FletcherTestType",
[
"dtype",
"data",
"data_missing",
"data_for_grouping",
"data_for_sorting",
"data_missing_for_sorting",
"data_repeated",
],
)
def is_arithmetic_type(arrow_dtype: pa.DataType) -> bool:
"""Check whether this is a type that support arithmetics."""
return (
pa.types.is_integer(arrow_dtype)
or pa.types.is_floating(arrow_dtype)
or pa.types.is_decimal(arrow_dtype)
)
skip_non_artithmetic_type = pytest.mark.skip_by_type_filter(
[lambda x: not is_arithmetic_type(x)]
)
xfail_list_scalar_constuctor_not_implemented = pytest.mark.xfail_by_type_filter(
[pa.types.is_list], "constructor from scalars is not implemented for lists"
)
xfail_list_equals_not_implemented = pytest.mark.xfail_by_type_filter(
[pa.types.is_list], "== is not implemented for lists"
)
xfail_list_setitem_not_implemented = pytest.mark.xfail_by_type_filter(
[pa.types.is_list], "__setitem__ is not implemented for lists"
)
xfail_missing_list_dict_encode = pytest.mark.xfail_by_type_filter(
[pa.types.is_list],
"ArrowNotImplementedError: dictionary-encode not implemented for list<item: string>",
)
xfail_bool_too_few_uniques = pytest.mark.xfail_by_type_filter(
[pa.types.is_boolean], "Test requires at least 3 unique values"
)
test_types = [
FletcherTestType(
pa.string(),
["🙈", "Ö", "Č", "a", "B"] * 20,
[None, "A"],
["B", "B", None, None, "A", "A", "B", "C"],
["B", "C", "A"],
["B", None, "A"],
lambda: choices(list(string.ascii_letters), k=10),
),
FletcherTestType(
pa.bool_(),
[True, False, True, True, False] * 20,
[None, False],
[True, True, None, None, False, False, True, False],
[True, False, False],
[True, None, False],
lambda: choices([True, False], k=10),
),
FletcherTestType(
pa.int8(),
# Use small values here so that np.prod stays in int32
[2, 1, 1, 2, 1] * 20,
[None, 1],
[2, 2, None, None, -100, -100, 2, 100],
[2, 100, -10],
[2, None, -10],
lambda: choices(list(range(100)), k=10),
),
FletcherTestType(
pa.int16(),
# Use small values here so that np.prod stays in int32
[2, 1, 3, 2, 1] * 20,
[None, 1],
[2, 2, None, None, -100, -100, 2, 100],
[2, 100, -10],
[2, None, -10],
lambda: choices(list(range(100)), k=10),
),
FletcherTestType(
pa.int32(),
# Use small values here so that np.prod stays in int32
[2, 1, 3, 2, 1] * 20,
[None, 1],
[2, 2, None, None, -100, -100, 2, 100],
[2, 100, -10],
[2, None, -10],
lambda: choices(list(range(100)), k=10),
),
FletcherTestType(
pa.int64(),
# Use small values here so that np.prod stays in int64
[2, 1, 3, 2, 1] * 20,
[None, 1],
[2, 2, None, None, -100, -100, 2, 100],
[2, 100, -10],
[2, None, -10],
lambda: choices(list(range(100)), k=10),
),
FletcherTestType(
pa.float64(),
[2, 1.0, 1.0, 5.5, 6.6] * 20,
[None, 1.1],
[2.5, 2.5, None, None, -100.1, -100.1, 2.5, 100.1],
[2.5, 100.99, -10.1],
[2.5, None, -10.1],
lambda: choices([2.5, 1.0, -1.0, 0, 66.6], k=10),
),
# Most of the tests fail as assert_extension_array_equal casts to numpy object
# arrays and on them equality is not defined.
pytest.param(
FletcherTestType(
pa.list_(pa.string()),
[["B", "C"], ["A"], [None], ["A", "A"], []] * 20,
[None, ["A"]],
[["B"], ["B"], None, None, ["A"], ["A"], ["B"], ["C"]],
[["B"], ["C"], ["A"]],
[["B"], None, ["A"]],
lambda: choices([["B", "C"], ["A"], [None], ["A", "A"]], k=10),
)
),
FletcherTestType(
pa.date64(),
[
datetime.date(2015, 1, 1),
datetime.date(2010, 12, 31),
datetime.date(1970, 1, 1),
datetime.date(1900, 3, 31),
datetime.date(1999, 12, 31),
]
* 20,
[None, datetime.date(2015, 1, 1)],
[
datetime.date(2015, 2, 2),
datetime.date(2015, 2, 2),
None,
None,
datetime.date(2015, 1, 1),
datetime.date(2015, 1, 1),
datetime.date(2015, 2, 2),
datetime.date(2015, 3, 3),
],
[
datetime.date(2015, 2, 2),
datetime.date(2015, 3, 3),
datetime.date(2015, 1, 1),
],
[datetime.date(2015, 2, 2), None, datetime.date(2015, 1, 1)],
lambda: choices(list(pd.date_range("2010-1-1", "2011-1-1").date), k=10),
),
]
@pytest.fixture(params=[True, False])
def box_in_series(request):
"""Whether to box the data in a Series."""
return request.param
@pytest.fixture(params=test_types)
def fletcher_type(request):
return request.param
@pytest.fixture(autouse=True)
def skip_by_type_filter(request, fletcher_type):
if request.node.get_closest_marker("skip_by_type_filter"):
for marker in request.node.iter_markers("skip_by_type_filter"):
for func in marker.args[0]:
if func(fletcher_type.dtype):
pytest.skip(f"skipped for type: {fletcher_type}")
@pytest.fixture(autouse=True)
def xfail_by_type_filter(request, fletcher_type):
if request.node.get_closest_marker("xfail_by_type_filter"):
for marker in request.node.iter_markers("xfail_by_type_filter"):
for func in marker.args[0]:
if func(fletcher_type.dtype):
pytest.xfail(f"XFAIL for type: {fletcher_type}")
@pytest.fixture
def dtype(fletcher_type, fletcher_dtype):
return fletcher_dtype(fletcher_type.dtype)
@pytest.fixture
def data(fletcher_type, fletcher_array):
return fletcher_array(fletcher_type.data, dtype=fletcher_type.dtype)
@pytest.fixture
def data_for_twos(dtype, fletcher_type, fletcher_array):
if dtype._is_numeric:
return fletcher_array([2] * 100, dtype=fletcher_type.dtype)
else:
return None
@pytest.fixture
def data_missing(fletcher_type, fletcher_array):
return fletcher_array(fletcher_type.data_missing, dtype=fletcher_type.dtype)
@pytest.fixture
def data_repeated(fletcher_type, fletcher_array):
"""Return different versions of data for count times."""
pass # noqa
def gen(count):
for _ in range(count):
yield fletcher_array(
fletcher_type.data_repeated(), dtype=fletcher_type.dtype
)
yield gen
@pytest.fixture
def data_for_grouping(fletcher_type, fletcher_array):
"""Fixture with data for factorization, grouping, and unique tests.
Expected to be like [B, B, NA, NA, A, A, B, C]
Where A < B < C and NA is missing
"""
return fletcher_array(fletcher_type.data_for_grouping, dtype=fletcher_type.dtype)
@pytest.fixture
def data_for_sorting(fletcher_type, fletcher_array):
"""Length-3 array with a known sort order.
This should be three items [B, C, A] with
A < B < C
"""
return fletcher_array(fletcher_type.data_for_sorting, dtype=fletcher_type.dtype)
@pytest.fixture
def data_missing_for_sorting(fletcher_type, fletcher_array):
"""Length-3 array with a known sort order.
This should be three items [B, NA, A] with
A < B and NA missing.
"""
return fletcher_array(
fletcher_type.data_missing_for_sorting, dtype=fletcher_type.dtype
)
@pytest.fixture(params=[None, lambda x: x])
def sort_by_key(request):
"""
Return a simple fixture for festing keys in sorting methods.
Tests None (no key) and the identity key.
"""
return request.param
class TestBaseCasting(BaseCastingTests):
pass
class TestBaseConstructors(BaseConstructorsTests):
def test_from_dtype(self, data):
if pa.types.is_string(data.dtype.arrow_dtype):
pytest.xfail(
"String construction is failing as Pandas wants to pass the FletcherChunkedDtype to NumPy"
)
BaseConstructorsTests.test_from_dtype(self, data)
@xfail_list_scalar_constuctor_not_implemented
def test_series_constructor_scalar_with_index(self, data, dtype):
if PANDAS_GE_1_1_0:
BaseConstructorsTests.test_series_constructor_scalar_with_index(
self, data, dtype
)
class TestBaseDtype(BaseDtypeTests):
pass
class TestBaseGetitemTests(BaseGetitemTests):
def test_loc_iloc_frame_single_dtype(self, data):
if pa.types.is_string(data.dtype.arrow_dtype):
pytest.mark.xfail(
reason="https://github.com/pandas-dev/pandas/issues/27673"
)
else:
BaseGetitemTests.test_loc_iloc_frame_single_dtype(self, data)
class TestBaseGroupbyTests(BaseGroupbyTests):
@xfail_bool_too_few_uniques
@xfail_missing_list_dict_encode
@pytest.mark.parametrize("as_index", [True, False])
def test_groupby_extension_agg(self, as_index, data_for_grouping):
BaseGroupbyTests.test_groupby_extension_agg(self, as_index, data_for_grouping)
@xfail_bool_too_few_uniques
@xfail_missing_list_dict_encode
def test_groupby_extension_no_sort(self, data_for_grouping):
BaseGroupbyTests.test_groupby_extension_no_sort(self, data_for_grouping)
@xfail_missing_list_dict_encode
def test_groupby_extension_transform(self, data_for_grouping):
if pa.types.is_boolean(data_for_grouping.dtype.arrow_dtype):
valid = data_for_grouping[~data_for_grouping.isna()]
df = pd.DataFrame({"A": [1, 1, 3, 3, 1, 4], "B": valid})
result = df.groupby("B").A.transform(len)
# Expected grouping is different as we only have two non-null values
expected = pd.Series([3, 3, 3, 3, 3, 3], name="A")
self.assert_series_equal(result, expected)
else:
BaseGroupbyTests.test_groupby_extension_transform(self, data_for_grouping)
@xfail_missing_list_dict_encode
def test_groupby_extension_apply(
self, data_for_grouping, groupby_apply_op # noqa: F811
):
BaseGroupbyTests.test_groupby_extension_apply(
self, data_for_grouping, groupby_apply_op
)
class TestBaseInterfaceTests(BaseInterfaceTests):
@pytest.mark.xfail(
reason="view or self[:] returns a shallow copy in-place edits are not backpropagated"
)
def test_view(self, data):
BaseInterfaceTests.test_view(self, data)
def test_array_interface(self, data):
if pa.types.is_list(data.dtype.arrow_dtype):
pytest.xfail("Not sure whether this test really holds for list")
else:
BaseInterfaceTests.test_array_interface(self, data)
@xfail_list_setitem_not_implemented
def test_copy(self, data):
BaseInterfaceTests.test_array_interface(self, data)
class TestBaseMethodsTests(BaseMethodsTests):
# https://github.com/pandas-dev/pandas/issues/22843
@pytest.mark.skip(reason="Incorrect expected")
@pytest.mark.parametrize("dropna", [True, False])
def test_value_counts(self, all_data, dropna, dtype):
pass
@xfail_list_equals_not_implemented
@pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
def test_equals(self, data, na_value, as_series, box): # noqa: F811
if PANDAS_GE_1_1_0:
BaseMethodsTests.test_equals(self, data, na_value, as_series, box)
@xfail_missing_list_dict_encode
def test_value_counts_with_normalize(self, data):
if PANDAS_GE_1_1_0:
BaseMethodsTests.test_value_counts_with_normalize(self, data)
def test_combine_le(self, data_repeated):
# GH 20825
# Test that combine works when doing a <= (le) comparison
# Fletcher returns 'fletcher_chunked[bool]' instead of np.bool as dtype
orig_data1, orig_data2 = data_repeated(2)
if pa.types.is_list(orig_data1.dtype.arrow_dtype):
return pytest.skip("__le__ not implemented for list scalars with None")
s1 = pd.Series(orig_data1)
s2 = pd.Series(orig_data2)
result = s1.combine(s2, lambda x1, x2: x1 <= x2)
expected = pd.Series(
orig_data1._from_sequence(
[a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))]
)
)
self.assert_series_equal(result, expected)
val = s1.iloc[0]
result = s1.combine(val, lambda x1, x2: x1 <= x2)
expected = pd.Series(
orig_data1._from_sequence([a <= val for a in list(orig_data1)])
)
self.assert_series_equal(result, expected)
def test_combine_add(self, data_repeated, dtype):
if dtype.name in [
"fletcher_chunked[date64[ms]]",
"fletcher_continuous[date64[ms]]",
]:
pytest.skip(
"unsupported operand type(s) for +: 'datetime.date' and 'datetime.date"
)
else:
BaseMethodsTests.test_combine_add(self, data_repeated)
@xfail_bool_too_few_uniques
def test_argsort(self, data_for_sorting):
BaseMethodsTests.test_argsort(self, data_for_sorting)
@xfail_bool_too_few_uniques
def test_argmin_argmax(self, data_for_sorting, data_missing_for_sorting, na_value):
if PANDAS_GE_1_1_0:
BaseMethodsTests.test_argmin_argmax(
self, data_for_sorting, data_missing_for_sorting, na_value
)
else:
pass
@pytest.mark.parametrize("ascending", [True, False])
@xfail_bool_too_few_uniques
@xfail_missing_list_dict_encode
def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
if PANDAS_GE_1_1_0:
BaseMethodsTests.test_sort_values(
self, data_for_sorting, ascending, sort_by_key
)
else:
BaseMethodsTests.test_sort_values(self, data_for_sorting, ascending)
@pytest.mark.parametrize("na_sentinel", [-1, -2])
@xfail_bool_too_few_uniques
@xfail_missing_list_dict_encode
def test_factorize(self, data_for_grouping, na_sentinel):
BaseMethodsTests.test_factorize(self, data_for_grouping, na_sentinel)
@pytest.mark.parametrize("na_sentinel", [-1, -2])
@xfail_bool_too_few_uniques
@xfail_missing_list_dict_encode
def test_factorize_equivalence(self, data_for_grouping, na_sentinel):
BaseMethodsTests.test_factorize_equivalence(
self, data_for_grouping, na_sentinel
)
@pytest.mark.parametrize("ascending", [True, False])
@xfail_missing_list_dict_encode
def test_sort_values_frame(self, data_for_sorting, ascending):
BaseMethodsTests.test_sort_values_frame(self, data_for_sorting, ascending)
@xfail_bool_too_few_uniques
def test_searchsorted(self, data_for_sorting, as_series): # noqa: F811
BaseMethodsTests.test_searchsorted(self, data_for_sorting, as_series)
@pytest.mark.parametrize("box", [pd.Series, lambda x: x])
@pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
@xfail_missing_list_dict_encode
def test_unique(self, data, box, method):
BaseMethodsTests.test_unique(self, data, box, method)
@xfail_missing_list_dict_encode
def test_factorize_empty(self, data):
BaseMethodsTests.test_factorize_empty(self, data)
def test_fillna_copy_frame(self, data_missing):
if pa.types.is_list(data_missing.dtype.arrow_dtype):
pytest.xfail("pandas' fillna cannot cope with lists as a scalar")
else:
BaseMethodsTests.test_fillna_copy_frame(self, data_missing)
def test_fillna_copy_series(self, data_missing):
if pa.types.is_list(data_missing.dtype.arrow_dtype):
pytest.xfail("pandas' fillna cannot cope with lists as a scalar")
else:
BaseMethodsTests.test_fillna_copy_series(self, data_missing)
@xfail_list_setitem_not_implemented
def test_combine_first(self, data):
BaseMethodsTests.test_combine_first(self, data)
@xfail_list_setitem_not_implemented
def test_shift_0_periods(self, data):
if PANDAS_GE_1_1_0:
BaseMethodsTests.test_shift_0_periods(self, data)
def test_shift_fill_value(self, data):
if pa.types.is_list(data.dtype.arrow_dtype):
pytest.xfail("pandas' isna cannot cope with lists")
else:
BaseMethodsTests.test_shift_fill_value(self, data)
def test_hash_pandas_object_works(self, data, as_frame): # noqa: F811
if pa.types.is_list(data.dtype.arrow_dtype):
pytest.xfail("Fails on hashing ndarrays")
else:
BaseMethodsTests.test_hash_pandas_object_works(self, data, as_frame)
@xfail_list_setitem_not_implemented
def test_where_series(self, data, na_value, as_frame): # noqa: F811
BaseMethodsTests.test_where_series(self, data, na_value, as_frame)
class TestBaseMissingTests(BaseMissingTests):
@pytest.mark.parametrize("method", ["ffill", "bfill"])
def test_fillna_series_method(self, data_missing, method):
BaseMissingTests.test_fillna_series_method(self, data_missing, method)
def test_fillna_frame(self, data_missing):
if pa.types.is_list(data_missing.dtype.arrow_dtype):
pytest.xfail("pandas' fillna cannot cope with lists as a scalar")
else:
BaseMissingTests.test_fillna_frame(self, data_missing)
def test_fillna_scalar(self, data_missing):
if pa.types.is_list(data_missing.dtype.arrow_dtype):
pytest.xfail("pandas' fillna cannot cope with lists as a scalar")
else:
BaseMissingTests.test_fillna_scalar(self, data_missing)
def test_fillna_series(self, data_missing):
if pa.types.is_list(data_missing.dtype.arrow_dtype):
pytest.xfail("pandas' fillna cannot cope with lists as a scalar")
else:
BaseMissingTests.test_fillna_series(self, data_missing)
class TestBaseReshapingTests(BaseReshapingTests):
def test_concat_mixed_dtypes(self, data, dtype):
arrow_dtype = data.dtype.arrow_dtype
if (
pa.types.is_integer(arrow_dtype)
or pa.types.is_floating(arrow_dtype)
or pa.types.is_boolean(arrow_dtype)
):
# https://github.com/pandas-dev/pandas/issues/21792
pytest.skip("pd.concat(int64, fletcher_chunked[int64] yields int64")
elif pa.types.is_temporal(arrow_dtype):
# https://github.com/pandas-dev/pandas/issues/33331
pytest.xfail("pd.concat(temporal, categorical) mangles dates")
else:
BaseReshapingTests.test_concat_mixed_dtypes(self, data)
def test_merge_on_extension_array(self, data):
if pa.types.is_list(data.dtype.arrow_dtype):
pytest.xfail("pandas tries to hash scalar lists")
else:
BaseReshapingTests.test_merge_on_extension_array(self, data)
def test_merge_on_extension_array_duplicates(self, data):
if pa.types.is_list(data.dtype.arrow_dtype):
pytest.xfail("pandas tries to hash scalar lists")
else:
BaseReshapingTests.test_merge_on_extension_array_duplicates(self, data)
@xfail_list_setitem_not_implemented
def test_ravel(self, data):
BaseReshapingTests.test_ravel(self, data)
@xfail_list_setitem_not_implemented
@pytest.mark.xfail(reason="Views don't update their parent #96")
def test_transpose(self, data):
if hasattr(BaseReshapingTests, "test_transpose"):
BaseReshapingTests.test_transpose(self, data)
class TestBaseSetitemTests(BaseSetitemTests):
@xfail_list_setitem_not_implemented
def test_setitem_scalar_series(self, data, box_in_series):
BaseSetitemTests.test_setitem_scalar_series(self, data, box_in_series)
@xfail_list_setitem_not_implemented
def test_setitem_sequence(self, data, box_in_series):
BaseSetitemTests.test_setitem_sequence(self, data, box_in_series)
@xfail_list_setitem_not_implemented
def test_setitem_empty_indxer(self, data, box_in_series):
BaseSetitemTests.test_setitem_empty_indxer(self, data, box_in_series)
@xfail_list_setitem_not_implemented
def test_setitem_sequence_broadcasts(self, data, box_in_series):
BaseSetitemTests.test_setitem_sequence_broadcasts(self, data, box_in_series)
@pytest.mark.parametrize("setter", ["loc", "iloc"])
@xfail_list_setitem_not_implemented
def test_setitem_scalar(self, data, setter):
BaseSetitemTests.test_setitem_scalar(self, data, setter)
@xfail_list_setitem_not_implemented
def test_setitem_loc_scalar_mixed(self, data):
BaseSetitemTests.test_setitem_loc_scalar_mixed(self, data)
@xfail_list_setitem_not_implemented
def test_setitem_loc_scalar_single(self, data):
BaseSetitemTests.test_setitem_loc_scalar_single(self, data)
@xfail_list_setitem_not_implemented
def test_setitem_loc_scalar_multiple_homogoneous(self, data):
BaseSetitemTests.test_setitem_loc_scalar_multiple_homogoneous(self, data)
@xfail_list_setitem_not_implemented
def test_setitem_iloc_scalar_mixed(self, data):
BaseSetitemTests.test_setitem_iloc_scalar_mixed(self, data)
@xfail_list_setitem_not_implemented
def test_setitem_iloc_scalar_single(self, data):
BaseSetitemTests.test_setitem_iloc_scalar_single(self, data)
@xfail_list_setitem_not_implemented
def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
BaseSetitemTests.test_setitem_iloc_scalar_multiple_homogoneous(self, data)
@xfail_list_setitem_not_implemented
def test_setitem_nullable_mask(self, data):
if not PANDAS_GE_1_1_0:
BaseSetitemTests.test_setitem_nullable_mask(self, data)
@pytest.mark.parametrize("as_callable", [True, False])
@pytest.mark.parametrize("setter", ["loc", None])
@xfail_list_setitem_not_implemented
def test_setitem_mask_aligned(self, data, as_callable, setter):
BaseSetitemTests.test_setitem_mask_aligned(self, data, as_callable, setter)
@pytest.mark.parametrize("setter", ["loc", None])
@xfail_list_setitem_not_implemented
def test_setitem_mask_broadcast(self, data, setter):
BaseSetitemTests.test_setitem_mask_broadcast(self, data, setter)
@xfail_list_setitem_not_implemented
def test_setitem_slice(self, data, box_in_series):
if PANDAS_GE_1_1_0:
BaseSetitemTests.test_setitem_slice(self, data, box_in_series)
@xfail_list_setitem_not_implemented
def test_setitem_loc_iloc_slice(self, data):
if PANDAS_GE_1_1_0:
BaseSetitemTests.test_setitem_loc_iloc_slice(self, data)
@xfail_list_setitem_not_implemented
def test_setitem_slice_array(self, data):
BaseSetitemTests.test_setitem_slice_array(self, data)
@xfail_list_setitem_not_implemented
@pytest.mark.parametrize(
"mask",
[
np.array([True, True, True, False, False]),
pd.array([True, True, True, False, False], dtype="boolean"),
pd.array([True, True, True, pd.NA, pd.NA], dtype="boolean"),
],
ids=["numpy-array", "boolean-array", "boolean-array-na"],
)
def test_setitem_mask(self, data, mask, box_in_series):
if PANDAS_GE_1_1_0:
BaseSetitemTests.test_setitem_mask(self, data, mask, box_in_series)
@pytest.mark.xfail(reason="Views don't update their parent #96")
def test_setitem_preserves_views(self, data):
pass
@xfail_list_setitem_not_implemented
def test_setitem_mask_boolean_array_with_na(self, data, box_in_series):
if PANDAS_GE_1_1_0:
BaseSetitemTests.test_setitem_mask_boolean_array_with_na(
self, data, box_in_series
)
@pytest.mark.parametrize(
"idx",
[[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
ids=["list", "integer-array", "numpy-array"],
)
@pytest.mark.xfail(reason="https://github.com/xhochy/fletcher/issues/110")
def test_setitem_integer_array(self, data, idx, box_in_series):
if PANDAS_GE_1_1_0:
BaseSetitemTests.test_setitem_integer_array(self, data, idx, box_in_series)
class TestBaseParsingTests(BaseParsingTests):
@pytest.mark.parametrize("engine", ["c", "python"])
def test_EA_types(self, engine, data):
pytest.mark.xfail(
"pandas doesn't yet support registering ExtentionDtypes via a pattern"
)
class TestBasePrintingTests(BasePrintingTests):
pass
class TestBaseBooleanReduceTests(BaseBooleanReduceTests):
@pytest.mark.parametrize("skipna", [True, False])
def test_reduce_series(self, data, all_boolean_reductions, skipna):
if pa.types.is_boolean(data.dtype.arrow_dtype):
BaseBooleanReduceTests.test_reduce_series(
self, data, all_boolean_reductions, skipna
)
else:
pytest.skip("Boolean reductions are only tested with boolean types")
class TestBaseNoReduceTests(BaseNoReduceTests):
@pytest.mark.parametrize("skipna", [True, False])
def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
arrow_dtype = data.dtype.arrow_dtype
if (
pa.types.is_integer(arrow_dtype)
or pa.types.is_floating(arrow_dtype)
or pa.types.is_decimal(arrow_dtype)
):
pytest.skip("Numeric arrays implement reductions, so don't raise")
else:
BaseNoReduceTests.test_reduce_series_numeric(
self, data, all_numeric_reductions, skipna
)
@pytest.mark.parametrize("skipna", [True, False])
def test_reduce_series_boolean(self, data, all_boolean_reductions, skipna):
if pa.types.is_boolean(data.dtype.arrow_dtype):
pytest.skip("BooleanArray does define boolean reductions, so don't raise")
else:
BaseNoReduceTests.test_reduce_series_boolean(
self, data, all_boolean_reductions, skipna
)
class TestBaseNumericReduceTests(BaseNumericReduceTests):
@pytest.mark.parametrize("skipna", [True, False])
def test_reduce_series(self, data, all_numeric_reductions, skipna):
if all_numeric_reductions == "prod":
# Shorten in the case of prod to avoid overflows
data = data[:2]
arrow_dtype = data.dtype.arrow_dtype
if (
pa.types.is_integer(arrow_dtype)
or pa.types.is_floating(arrow_dtype)
or pa.types.is_decimal(arrow_dtype)
):
BaseNumericReduceTests.test_reduce_series(
self, data, all_numeric_reductions, skipna
)
else:
pytest.skip("Reduce not implemented on non-numeric types")
class TestBaseComparisonOpsTests(BaseComparisonOpsTests):
def check_opname(self, s, op_name, other, exc=None):
super().check_opname(s, op_name, other, exc=None)
def _compare_other(self, s, data, op_name, other):
self.check_opname(s, op_name, other)
def test_compare_scalar(self, data, all_compare_operators):
if pa.types.is_list(data.dtype.arrow_dtype):
pytest.xfail("pandas cannot cope with lists as scalar")
else:
# FIXME: Upstream always compares againt 0
op_name = all_compare_operators
s = pd.Series(data)
self._compare_other(s, data, op_name, data[0])
def test_compare_array(self, data, all_compare_operators):
if pa.types.is_list(data.dtype.arrow_dtype):
pytest.xfail("Comparision of list not implemented yet")
else:
BaseComparisonOpsTests.test_compare_array(self, data, all_compare_operators)
def _check_op(self, s, op, other, op_name, exc=NotImplementedError):
if exc is None:
result = op(s, other)
# We return fletcher booleans to support nulls
expected = s.combine(other, op)
if not isinstance(expected.dtype, FletcherBaseDtype):
expected = pd.Series(type(s.values)(expected.values))
self.assert_series_equal(result, expected)
else:
with pytest.raises(exc):
op(s, other)
class TestBaseArithmeticOpsTests(BaseArithmeticOpsTests):
# TODO: Instead of skipping other types, we should set the correct exceptions here
series_scalar_exc: Optional[Type[TypeError]] = None
frame_scalar_exc: Optional[Type[TypeError]] = None
series_array_exc: Optional[Type[TypeError]] = None
divmod_exc: Optional[Type[TypeError]] = None
def _check_op(self, s, op, other, op_name, exc=NotImplementedError):
if exc is None:
result = op(s, other)
expected = s.combine(other, op)
# Combine always returns an int64 for integral arrays but for
# operations on smaller integer types, we expect also smaller int types
# in the result of the non-combine operations.
if hasattr(expected.dtype, "arrow_dtype"):
arrow_dtype = expected.dtype.arrow_dtype
if pa.types.is_integer(arrow_dtype):
# In the case of an operand with a higher bytesize, we also expect the
# output to be int64.
other_is_np_int64 = (
isinstance(other, pd.Series)
and isinstance(other.values, np.ndarray)
and other.dtype.char in ("q", "l")
)
if (
pa.types.is_integer(arrow_dtype)
and pa.types.is_integer(s.dtype.arrow_dtype)
and not other_is_np_int64
):
expected = expected.astype(s.dtype)
self.assert_series_equal(result, expected)
else:
with pytest.raises(exc):
op(s, other)
@skip_non_artithmetic_type
def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
BaseArithmeticOpsTests.test_arith_series_with_scalar(
self, data, all_arithmetic_operators
)
@skip_non_artithmetic_type
def test_arith_series_with_array(self, data, all_arithmetic_operators):
BaseArithmeticOpsTests.test_arith_series_with_array(
self, data, all_arithmetic_operators
)
@skip_non_artithmetic_type
def test_divmod(self, data):
BaseArithmeticOpsTests.test_divmod(self, data)
@skip_non_artithmetic_type
def test_divmod_series_array(self, data, data_for_twos):
BaseArithmeticOpsTests.test_divmod(self, data)
@skip_non_artithmetic_type
def test_add_series_with_extension_array(self, data):
BaseArithmeticOpsTests.test_add_series_with_extension_array(self, data)
def test_error(self, data, all_arithmetic_operators):
arrow_dtype = data.dtype.arrow_dtype
if (
pa.types.is_integer(arrow_dtype)
or pa.types.is_floating(arrow_dtype)
or pa.types.is_decimal(arrow_dtype)
):
pytest.skip("Numeric does not error on ops")
else:
pytest.xfail("Should error here")
def _check_divmod_op(self, s, op, other, exc=None):
super()._check_divmod_op(s, op, other, None)
| 36.478357 | 106 | 0.664953 | [
"MIT"
] | artemru/fletcher | tests/test_pandas_extension.py | 32,872 | Python |
# -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2017-06-09 13:02
from __future__ import unicode_literals
from django.db import migrations
def load_continents(apps, schema_editor):
continent = apps.get_model("country_name_tool", "Continent")
newcontinents = [['NA', 'North America'], ['AS', 'Asia'], ['AF', 'Africa'], ['EU', 'Europe'], ['SA', 'South America'], ['OC', 'Oceania'], ['AN', 'Antarctica']]
for each in newcontinents:
toinsert = continent(continent_code=each[0], continent_name=each[1])
toinsert.save()
class Migration(migrations.Migration):
dependencies = [
('country_name_tool', '0002_auto_20170523_1825'),
]
operations = [
migrations.RunPython(load_continents)
]
| 29.96 | 163 | 0.658211 | [
"MIT"
] | owid/owid-importer | country_name_tool/migrations/0003_auto_20170609_1302.py | 749 | Python |
import os,sys,json,cv2
from nima.inference.inference_model import InferenceModel
import opt4 as opt
from PIL import Image
# write to result file
def write_json(args,score):
try:
outfile =open(args[3],'w')
#print('saving test json at '+args[3])
except IndexError:
print('output_location not found')
print('saving test json at "./test_data/data.json"')
outfile = open('./test_data/data.json','w+')
finally:
#print(score)
json.dump(score,outfile)
outfile.close
#get score by testing
def test(model,image):
score = {'results':[]}
for i in image:
#'''
img = cv2.imread(image.get(i))
img = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
r = model.predict_from_pil_image(img)
'''
r = model.predict_from_file(image.get(i))
'''
score['results'].append({'name':i, 'score':r['mean_score'], 'b':0, 'c':0})
#print(r['mean_score'],type(r['mean_score']))
score['results'] = sorted(score['results'], key= lambda x : x['score'], reverse= True)
return score
#switch mode and execute test
def start(mode,model,image):
if mode == 'test':
score = test(model,image)
'''
print(str(len(image))+' pictures found!')
print('=======================')
print('test_json')
print(score)
print('=======================')
'''
elif mode == 'adjust':
#print(image)
if len(image) > 1:
print('error adjust more than 1 image')
sys.exit(0)
score = test(model,image)
'''
print('=======================')
print('test_json')
print(score)
print('=======================')
'''
for i in image:
results = opt.starts(image.get(i))
#print(i,results)
filepath, ext = os.path.splitext(image.get(i))
img_dir = filepath+'_'+ext
results[2].save(img_dir)
score['results'].append({'name':i.split('.')[-2]+'_.'+i.split('.')[-1], 'score':results[1], 'b':results[0][0], 'c':results[0][1], 'img_dir':img_dir})
'''
print('adjsut_json')
print(score)
print('=======================')
'''
else:
print('error select mode')
sys.exit(0)
return score
#get image from a folder
def handle_folder(pth):
image = {}
for i in os.listdir(pth):
if i.endswith('.jpeg') or i.endswith('.jpg'):
image[i] = pth + i
return image
# detect test or adjust
def detect_mode(args):
target = args[1]
if target == 'test':
return 'test'
elif target == 'adjust':
return 'adjust'
else:
print('error mode in args[1]')
sys.exit(0)
# detect folder or a picture
def detect_pth(args):
target = args[2]
if target.endswith('.jpeg') or target.endswith('.jpg'):
try:
_=open(target)
except:
print('error pic')
sys.exit(0)
else:
return 'pic',target
else:
try:
#print(target)
os.stat(target)
except:
print('error folder')
sys.exit(0)
else:
if not target.endswith('/'):
target += '/'
try:
os.stat(target)
except:
print('error detecting the path')
sys.exit(0)
return 'folder',target
# main
def mynima(args):
model_pth = './tmp3/emd_loss_epoch_49_train_0.05391903784470127_0.12613263790013726.pth'
#model_pth = './tmp/emd_loss_epoch_49_train_0.03547421253612805_0.08993149643023331.pth'
#model_pth = './tmp0710/emd_loss_epoch_49_train_0.06696929275146844_0.1384279955681362.pth'
model = InferenceModel(path_to_model=model_pth)
mode = detect_mode(args)
method,pth = detect_pth(args)
#print(method)
if method == 'pic':
name = os.path.basename(pth)
#print('name '+name) #get pic name
image = {name:pth} #make image dict.
elif method == 'folder':
image = handle_folder(pth) #get image dict.
score = start(mode,model,image)
write_json(args,score)
return
#arg[1]: (adjust/test) arg[2]: (folder_path/img_pth) arg[3]: (output_file)
mynima(sys.argv) | 28.025641 | 161 | 0.539341 | [
"MIT"
] | jjmao-cs/nima.pytorch | nima/nima_new.py | 4,372 | Python |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from functorch import grad, nnc_jit, make_fx, make_nnc
import torch
import time
def f(x):
return torch.sin(x).sum()
inp = torch.randn(100)
grad_pt = grad(f)
grad_fx = make_fx(grad_pt)(inp)
grad_nnc = nnc_jit(grad_pt, skip_specialization=True)
loopnest = make_nnc(grad_pt)(inp)
print(loopnest)
def bench(name, f, iters=10000, warmup=3):
for _ in range(warmup):
f()
begin = time.time()
for _ in range(iters):
f()
print(f"{name}: ", time.time() - begin)
bench("Pytorch: ", lambda: grad_pt(inp))
bench("FX: ", lambda: grad_fx(inp))
bench("NNC: ", lambda: grad_nnc(inp))
| 22.694444 | 71 | 0.682987 | [
"BSD-3-Clause"
] | ConnollyLeon/functorch | examples/compilation/simple_function.py | 817 | Python |
#!/usr/bin/env python3
#
# This file is part of LiteX-Boards.
#
# Copyright (c) 2021 Florent Kermarrec <[email protected]>
# Copyright (c) 2021 Greg Davill <[email protected]>
# SPDX-License-Identifier: BSD-2-Clause
# Build/Use:
# ./gsd_butterstick.py --uart-name=crossover --with-etherbone --csr-csv=csr.csv --build --load
# litex_server --udp
# litex_term bridge
import os
import sys
import argparse
from migen import *
from migen.genlib.resetsync import AsyncResetSynchronizer
from litex_boards.platforms import butterstick
from litex.build.lattice.trellis import trellis_args, trellis_argdict
from litex.soc.cores.clock import *
from litex.soc.integration.soc_core import *
from litex.soc.integration.builder import *
from litex.soc.cores.led import LedChaser
from litedram.modules import MT41K64M16,MT41K128M16,MT41K256M16,MT41K512M16
from litedram.phy import ECP5DDRPHY
from liteeth.phy.ecp5rgmii import LiteEthPHYRGMII
# CRG ---------------------------------------------------------------------------------------------
class _CRG(Module):
def __init__(self, platform, sys_clk_freq):
self.rst = Signal()
self.clock_domains.cd_init = ClockDomain()
self.clock_domains.cd_por = ClockDomain(reset_less=True)
self.clock_domains.cd_sys = ClockDomain()
self.clock_domains.cd_sys2x = ClockDomain()
self.clock_domains.cd_sys2x_i = ClockDomain(reset_less=True)
# # #
self.stop = Signal()
self.reset = Signal()
# Clk / Rst
clk30 = platform.request("clk30")
rst_n = platform.request("user_btn", 0)
# Power on reset
por_count = Signal(16, reset=2**16-1)
por_done = Signal()
self.comb += self.cd_por.clk.eq(clk30)
self.comb += por_done.eq(por_count == 0)
self.sync.por += If(~por_done, por_count.eq(por_count - 1))
# PLL
self.submodules.pll = pll = ECP5PLL()
self.comb += pll.reset.eq(~por_done | ~rst_n | self.rst)
pll.register_clkin(clk30, 30e6)
pll.create_clkout(self.cd_sys2x_i, 2*sys_clk_freq)
pll.create_clkout(self.cd_init, 25e6)
self.specials += [
Instance("ECLKSYNCB",
i_ECLKI = self.cd_sys2x_i.clk,
i_STOP = self.stop,
o_ECLKO = self.cd_sys2x.clk),
Instance("CLKDIVF",
p_DIV = "2.0",
i_ALIGNWD = 0,
i_CLKI = self.cd_sys2x.clk,
i_RST = self.reset,
o_CDIVX = self.cd_sys.clk),
AsyncResetSynchronizer(self.cd_sys, ~pll.locked | self.reset),
AsyncResetSynchronizer(self.cd_sys2x, ~pll.locked | self.reset),
]
# BaseSoC ------------------------------------------------------------------------------------------
class BaseSoC(SoCCore):
def __init__(self, revision="1.0", device="85F", sdram_device="MT41K64M16", sys_clk_freq=int(60e6),
toolchain="trellis", with_ethernet=False, with_etherbone=False, eth_ip="192.168.1.50",
eth_dynamic_ip=False,
with_spi_flash=False,
with_led_chaser=True,
**kwargs) :
platform = butterstick.Platform(revision=revision, device=device ,toolchain=toolchain)
# SoCCore ----------------------------------------------------------------------------------
if kwargs["uart_name"] == "serial":
kwargs["uart_name"] = "crossover"
SoCCore.__init__(self, platform, sys_clk_freq,
ident = "LiteX SoC on ButterStick",
ident_version = True,
**kwargs)
# CRG --------------------------------------------------------------------------------------
self.submodules.crg = _CRG(platform, sys_clk_freq)
# DDR3 SDRAM -------------------------------------------------------------------------------
if not self.integrated_main_ram_size:
available_sdram_modules = {
"MT41K64M16": MT41K64M16,
"MT41K128M16": MT41K128M16,
"MT41K256M16": MT41K256M16,
"MT41K512M16": MT41K512M16,
}
sdram_module = available_sdram_modules.get(sdram_device)
self.submodules.ddrphy = ECP5DDRPHY(
platform.request("ddram"),
sys_clk_freq=sys_clk_freq)
self.comb += self.crg.stop.eq(self.ddrphy.init.stop)
self.comb += self.crg.reset.eq(self.ddrphy.init.reset)
self.add_sdram("sdram",
phy = self.ddrphy,
module = sdram_module(sys_clk_freq, "1:2"),
l2_cache_size = kwargs.get("l2_size", 8192)
)
# Ethernet / Etherbone ---------------------------------------------------------------------
if with_ethernet or with_etherbone:
self.submodules.ethphy = LiteEthPHYRGMII(
clock_pads = self.platform.request("eth_clocks"),
pads = self.platform.request("eth"))
if with_ethernet:
self.add_ethernet(phy=self.ethphy, dynamic_ip=eth_dynamic_ip)
if with_etherbone:
self.add_etherbone(phy=self.ethphy, ip_address=eth_ip)
# SPI Flash --------------------------------------------------------------------------------
if with_spi_flash:
from litespi.modules import W25Q128JV
from litespi.opcodes import SpiNorFlashOpCodes as Codes
self.add_spi_flash(mode="4x", module=W25Q128JV(Codes.READ_1_1_4), with_master=False)
# Leds -------------------------------------------------------------------------------------
if with_led_chaser:
self.comb += platform.request("user_led_color").eq(0b010) # Blue.
self.submodules.leds = LedChaser(
pads = platform.request_all("user_led"),
sys_clk_freq = sys_clk_freq)
# Build --------------------------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser(description="LiteX SoC on ButterStick")
parser.add_argument("--build", action="store_true", help="Build bitstream.")
parser.add_argument("--load", action="store_true", help="Load bitstream.")
parser.add_argument("--toolchain", default="trellis", help="FPGA toolchain (trellis or diamond).")
parser.add_argument("--sys-clk-freq", default=75e6, help="System clock frequency.")
parser.add_argument("--revision", default="1.0", help="Board Revision (1.0).")
parser.add_argument("--device", default="85F", help="ECP5 device (25F, 45F, 85F).")
parser.add_argument("--sdram-device", default="MT41K64M16", help="SDRAM device (MT41K64M16, MT41K128M16, MT41K256M16 or MT41K512M16).")
ethopts = parser.add_mutually_exclusive_group()
ethopts.add_argument("--with-ethernet", action="store_true", help="Add Ethernet.")
ethopts.add_argument("--with-etherbone", action="store_true", help="Add EtherBone.")
parser.add_argument("--eth-ip", default="192.168.1.50", help="Ethernet/Etherbone IP address.")
parser.add_argument("--eth-dynamic-ip", action="store_true", help="Enable dynamic Ethernet IP addresses setting.")
parser.add_argument("--with-spi-flash", action="store_true", help="Enable SPI Flash (MMAPed).")
sdopts = parser.add_mutually_exclusive_group()
sdopts.add_argument("--with-spi-sdcard", action="store_true", help="Enable SPI-mode SDCard support.")
sdopts.add_argument("--with-sdcard", action="store_true", help="Enable SDCard support.")
builder_args(parser)
soc_core_args(parser)
trellis_args(parser)
args = parser.parse_args()
assert not (args.with_etherbone and args.eth_dynamic_ip)
soc = BaseSoC(
toolchain = args.toolchain,
revision = args.revision,
device = args.device,
sdram_device = args.sdram_device,
sys_clk_freq = int(float(args.sys_clk_freq)),
with_ethernet = args.with_ethernet,
with_etherbone = args.with_etherbone,
eth_ip = args.eth_ip,
eth_dynamic_ip = args.eth_dynamic_ip,
with_spi_flash = args.with_spi_flash,
**soc_core_argdict(args))
if args.with_spi_sdcard:
soc.add_spi_sdcard()
if args.with_sdcard:
soc.add_sdcard()
builder = Builder(soc, **builder_argdict(args))
builder_kargs = trellis_argdict(args) if args.toolchain == "trellis" else {}
builder.build(**builder_kargs, run=args.build)
if args.load:
prog = soc.platform.create_programmer()
prog.load_bitstream(os.path.join(builder.gateware_dir, soc.build_name + ".bit"))
if __name__ == "__main__":
main()
| 43.916256 | 144 | 0.573864 | [
"BSD-2-Clause"
] | Logicwax/litex-boards | litex_boards/targets/gsd_butterstick.py | 8,915 | Python |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Provides standard metric evaluations for dialog.
Uses locking and shared memory when ``numthreads`` is set to >1 to share metrics between
processes.
"""
import re
from abc import ABC, abstractmethod
from collections import Counter
import queue
import functools
import datetime
from typing import Union, List, Optional, Tuple, Set, Any, Dict
import torch
from parlai.core.message import Message
from parlai.utils.misc import warn_once
from parlai.utils.typing import TScalar, TVector
try:
import torch.multiprocessing as multiprocessing
except ImportError:
import multiprocessing # type: ignore
DEFAULT_METRICS = {'bleu-4', 'accuracy', 'f1'}
ROUGE_METRICS = {'rouge-1', 'rouge-2', 'rouge-L'}
BLEU_METRICS = {'bleu-1', 'bleu-2', 'bleu-3', 'bleu-4'}
ALL_METRICS = DEFAULT_METRICS | ROUGE_METRICS | BLEU_METRICS
try:
from nltk.translate import bleu_score as nltkbleu
except ImportError:
# User doesn't have nltk installed, so we can't use it for bleu
# We'll just turn off things, but we might want to warn the user
nltkbleu = None
try:
from fairseq import bleu as fairseqbleu
except ImportError:
fairseqbleu = None
try:
import rouge
except ImportError:
# User doesn't have py-rouge installed, so we can't use it.
# We'll just turn off rouge computations
rouge = None
re_art = re.compile(r'\b(a|an|the)\b')
re_punc = re.compile(r'[!"#$%&()*+,-./:;<=>?@\[\]\\^`{|}~_\']')
@functools.total_ordering
class Metric(ABC):
"""
Base class for storing metrics.
Subclasses should define .value(). Examples are provided for each subclass.
"""
@property
def is_global(self) -> bool:
"""
Indicates whether this metric should be reported globally or per-task.
"""
return False
@property
def macro_average(self) -> bool:
"""
Indicates whether this metric should be macro-averaged when globally reported.
"""
return False
@abstractmethod
def value(self) -> float:
"""
Return the value of the metric as a float.
"""
pass
@abstractmethod
def __add__(self, other: Any) -> 'Metric':
raise NotImplementedError
def __iadd__(self, other):
return self.__radd__(other)
def __radd__(self, other: Any):
if other is None:
return self
return self.__add__(other)
def __str__(self) -> str:
return f'{self.value():.4g}'
def __repr__(self) -> str:
return f'{self.__class__.__name__}({self.value():.4g})'
def __float__(self) -> float:
return float(self.value())
def __int__(self) -> int:
return int(self.value())
def __eq__(self, other: Any) -> bool:
if isinstance(other, Metric):
return self.value() == other.value()
else:
return self.value() == other
def __lt__(self, other: Any) -> bool:
if isinstance(other, Metric):
return self.value() < other.value()
else:
return self.value() < other
def __sub__(self, other: Any) -> float:
"""
Used heavily for assertAlmostEqual.
"""
if not isinstance(other, float):
raise TypeError('Metrics.__sub__ is intentionally limited to floats.')
return self.value() - other
def __rsub__(self, other: Any) -> float:
"""
Used heavily for assertAlmostEqual.
NOTE: This is not necessary in python 3.7+.
"""
if not isinstance(other, float):
raise TypeError('Metrics.__rsub__ is intentionally limited to floats.')
return other - self.value()
@classmethod
def as_number(cls, obj: TScalar) -> Union[int, float]:
if isinstance(obj, torch.Tensor):
obj_as_number: Union[int, float] = obj.item()
else:
obj_as_number = obj # type: ignore
assert isinstance(obj_as_number, int) or isinstance(obj_as_number, float)
return obj_as_number
@classmethod
def as_float(cls, obj: TScalar) -> float:
return float(cls.as_number(obj))
@classmethod
def as_int(cls, obj: TScalar) -> int:
return int(cls.as_number(obj))
@classmethod
def many(cls, *objs: List[TVector]) -> List['Metric']:
"""
Construct many of a Metric from the base parts.
Useful if you separately compute numerators and denomenators, etc.
"""
lengths = [len(o) for o in objs]
if len(set(lengths)) != 1:
raise IndexError(f'Uneven {cls.__name__} constructions: {lengths}')
return [cls(*items) for items in zip(*objs)]
class FixedMetric(Metric):
"""
Fixed metrics are verified to be the same when combined, or throw an error.
FixedMetric is used for things like total_train_updates, which should not be
combined across different multitasks or different workers.
"""
__slots__ = ('_value',)
def __init__(self, value: TScalar):
self._value = self.as_number(value)
def __add__(self, other: Optional['FixedMetric']) -> 'FixedMetric':
if other is None:
return self
if self != other:
raise ValueError(f"FixedMetrics not the same: {self} and {other}")
return self
def value(self) -> float:
return self._value
class SumMetric(Metric):
"""
Class that keeps a running sum of some metric.
Examples of SumMetric include things like "exs", the number of examples seen since
the last report, which depends exactly on a teacher.
"""
__slots__ = ('_sum',)
def __init__(self, sum_: TScalar = 0):
if isinstance(sum_, torch.Tensor):
self._sum = sum_.item()
else:
assert isinstance(sum_, (int, float))
self._sum = sum_
def __add__(self, other: Optional['SumMetric']) -> 'SumMetric':
# NOTE: hinting can be cleaned up with "from __future__ import annotations" when
# we drop Python 3.6
if other is None:
return self
full_sum = self._sum + other._sum
# always keep the same return type
return type(self)(sum_=full_sum)
def value(self) -> float:
return self._sum
class AverageMetric(Metric):
"""
Class that keeps a running average of some metric.
Examples of AverageMetrics include hits@1, F1, accuracy, etc. These metrics all have
per-example values that can be directly mapped back to a teacher.
"""
__slots__ = ('_numer', '_denom')
@property
def macro_average(self) -> bool:
"""
Indicates whether this metric should be macro-averaged when globally reported.
"""
return True
def __init__(self, numer: TScalar, denom: TScalar = 1):
self._numer = self.as_number(numer)
self._denom = self.as_number(denom)
def __add__(self, other: Optional['AverageMetric']) -> 'AverageMetric':
# NOTE: hinting can be cleaned up with "from __future__ import annotations" when
# we drop Python 3.6
if other is None:
return self
full_numer: TScalar = self._numer + other._numer
full_denom: TScalar = self._denom + other._denom
# always keep the same return type
return type(self)(numer=full_numer, denom=full_denom)
def value(self) -> float:
if self._numer == 0 and self._denom == 0:
# don't nan out if we haven't counted anything
return 0.0
if self._denom == 0:
return float('nan')
return self._numer / self._denom
class MacroAverageMetric(Metric):
"""
Class that represents the macro average of several numbers.
Used for aggregating task level metrics. It is only used for things that are
AverageMetrics already.
"""
__slots__ = '_values'
def __init__(self, metrics: Dict[str, Metric]) -> None:
self._values = metrics
def __add__(self, other: Optional['MacroAverageMetric']) -> 'MacroAverageMetric':
if other is None:
return self
output = dict(**self._values)
for k, v in other._values.items():
output[k] = output.get(k, None) + v
return MacroAverageMetric(output)
def value(self) -> float:
sum_ = sum(v.value() for v in self._values.values())
n = len(self._values)
return sum_ / n
class TimerMetric(Metric):
"""
A timer metric keep tracks of the first/last times it was used.
"""
__slots__ = ('_value', '_start', '_end')
@classmethod
def _now(cls) -> int:
return datetime.datetime.utcnow().timestamp()
def __init__(
self,
value: TScalar,
start_time: Optional[int] = None,
end_time: Optional[int] = None,
):
self._value = self.as_number(value)
if start_time is None:
start_time = self._now()
if end_time is None:
end_time = self._now()
self._start = start_time
self._end = end_time
def __add__(self, other: Optional['TimerMetric']) -> 'TimerMetric':
# NOTE: hinting can be cleaned up with "from __future__ import annotations" when
# we drop Python 3.6
if other is None:
return self
total: TScalar = self._value + other._value
start: int = min(self._start, other._start)
end: int = max(self._start, other._end)
return type(self)(total, start, end)
def value(self) -> float:
if self._value == 0 or self._end == self._start:
return 0
return self._value / (self._end - self._start)
class GlobalMetric:
"""
A global metric is one that should not be aggregated across different tasks.
Examples of global metric include things like learning rate and updates.
These need to be accumulated or averaged over multiple parleys, but cannot
be correlated with a single task.
Key to it is the notion that any one worker or any one task already has a global
view of the value, and so no combinations should be done. Note this is different
then a FixedMetric, in that a GlobalMetric can be still averaged across multiple
parleys(), but a FixedMetric is always fixed.
"""
@property
def is_global(self) -> bool:
return True
class GlobalFixedMetric(GlobalMetric, FixedMetric):
"""
Global fixed metric.
Used for things like total_train_updates.
"""
pass
class GlobalSumMetric(GlobalMetric, SumMetric):
"""
Global sum metric.
Used for 'exs' and 'updates'.
"""
pass
class GlobalAverageMetric(GlobalMetric, AverageMetric):
"""
Global Average metric.
Used for things like learning rate, and many agent-specific metrics.
"""
pass
class LegacyMetric(GlobalAverageMetric):
"""
Legacy Metrics are reported by agent as float.
"""
pass
class GlobalTimerMetric(GlobalMetric, TimerMetric):
pass
class F1Metric(AverageMetric):
"""
Helper class which computes token-level F1.
"""
@staticmethod
def _prec_recall_f1_score(pred_items, gold_items):
"""
Compute precision, recall and f1 given a set of gold and prediction items.
:param pred_items: iterable of predicted values
:param gold_items: iterable of gold values
:return: tuple (p, r, f1) for precision, recall, f1
"""
common = Counter(gold_items) & Counter(pred_items)
num_same = sum(common.values())
if num_same == 0:
return 0, 0, 0
precision = 1.0 * num_same / len(pred_items)
recall = 1.0 * num_same / len(gold_items)
f1 = (2 * precision * recall) / (precision + recall)
return precision, recall, f1
@staticmethod
def compute(guess: str, answers: List[str]) -> 'F1Metric':
if guess is None or answers is None:
return AverageMetric(0, 0)
g_tokens = normalize_answer(guess).split()
scores = [
F1Metric._prec_recall_f1_score(g_tokens, normalize_answer(a).split())
for a in answers
]
return F1Metric(max(f1 for p, r, f1 in scores), 1)
class ExactMatchMetric(AverageMetric):
@staticmethod
def compute(guess: str, answers: List[str]) -> 'ExactMatchMetric':
if guess is None or answers is None:
return None
guess = normalize_answer(guess)
for a in answers:
if guess == normalize_answer(a):
return ExactMatchMetric(1)
return ExactMatchMetric(0)
class BleuMetric(AverageMetric):
@staticmethod
def compute(guess: str, answers: List[str], k: int = 4) -> Optional['BleuMetric']:
"""
Compute approximate BLEU score between guess and a set of answers.
"""
if nltkbleu is None:
# bleu library not installed, just return a default value
return None
# Warning: BLEU calculation *should* include proper tokenization and
# punctuation etc. We're using the normalize_answer for everything though,
# so we're over-estimating our BLEU scores. Also note that NLTK's bleu is
# going to be slower than fairseq's (which is written in C), but fairseq's
# requires that everything be in arrays of ints (i.e. as tensors). NLTK's
# works with strings, which is better suited for this module.
weights = [1 / k for _ in range(k)]
score = nltkbleu.sentence_bleu(
[normalize_answer(a).split(" ") for a in answers],
normalize_answer(guess).split(" "),
smoothing_function=nltkbleu.SmoothingFunction(epsilon=1e-12).method1,
weights=weights,
)
return BleuMetric(score)
class FairseqBleuMetric(AverageMetric):
@staticmethod
def compute_many(
guess: torch.Tensor, answers: torch.Tensor, pad_idx, end_idx, unk_idx
):
"""
Return BLEU-1..4 using fairseq and tokens.
"""
if fairseqbleu is None:
return None
scorer = fairseqbleu.Scorer(pad_idx, end_idx, unk_idx)
answers = answers.cpu().int()
guess = guess.cpu().int()
scorer.add(answers, guess)
return [FairseqBleuMetric(scorer.score(i) / 100.0) for i in range(1, 5)]
class RougeMetric(AverageMetric):
_evaluator = None
@staticmethod
def compute_many(
guess: str, answers: List[str]
) -> Tuple[
Optional['RougeMetric'], Optional['RougeMetric'], Optional['RougeMetric']
]:
"""
Compute ROUGE score between guess and *any* answer.
Done with compute_many due to increased efficiency.
:return: (rouge-1, rouge-2, rouge-L)
"""
# possible global initialization
global rouge
if rouge is None:
return None, None, None
if RougeMetric._evaluator is None:
RougeMetric._evaluator = rouge.Rouge(
metrics=['rouge-n', 'rouge-l'], max_n=2
)
try:
scores = [
RougeMetric._evaluator.get_scores(
normalize_answer(guess), normalize_answer(a)
)
for a in answers
]
except LookupError:
warn_once(
'ROUGE requires nltk punkt tokenizer. Please run '
'`python -c "import nltk; nltk.download(\'punkt\')`'
)
return None, None, None
scores_rouge1 = max(score['rouge-1']['r'] for score in scores)
scores_rouge2 = max(score['rouge-2']['r'] for score in scores)
scores_rougeL = max(score['rouge-l']['r'] for score in scores)
return (
RougeMetric(scores_rouge1),
RougeMetric(scores_rouge2),
RougeMetric(scores_rougeL),
)
def normalize_answer(s):
"""
Lower text and remove punctuation, articles and extra whitespace.
"""
s = s.lower()
s = re_punc.sub(' ', s)
s = re_art.sub(' ', s)
# TODO: this could almost certainly be faster with a regex \s+ -> ' '
s = ' '.join(s.split())
return s
def aggregate_named_reports(
named_reports: Dict[str, Dict[str, Metric]], micro_average: bool = False
) -> Dict[str, Metric]:
"""
Aggregate metrics from multiple reports.
:param reports:
Dict of tasks -> metrics.
:param micro_average:
If true, top level metrics will be the micro average. By default, we
use macro average.
:return:
The aggregated report
"""
if len(named_reports) == 0:
raise ValueError("Cannot aggregate empty reports.")
if len(named_reports) == 1:
# no real aggregation to be done
return next(iter(named_reports.values()))
# reporters is a list of teachers or worlds
m: Dict[str, Metric] = {}
macro_averages: Dict[str, Dict[str, Metric]] = {}
for task_id, task_report in named_reports.items():
for each_metric, value in task_report.items():
if value.is_global:
# just take the first one we saw
if each_metric not in m:
m[each_metric] = value
else:
task_metric = f'{task_id}/{each_metric}'
m[task_metric] = m.get(task_metric) + value
if micro_average or not value.macro_average:
# none + a => a from implementation of Metric.__add__
m[each_metric] = m.get(each_metric) + value
else:
# macro average
if each_metric not in macro_averages:
macro_averages[each_metric] = {}
macro_averages[each_metric][task_id] = value
for key, values in macro_averages.items():
m[key] = MacroAverageMetric(values)
return m
def aggregate_unnamed_reports(reports: List[Dict[str, Metric]]) -> Dict[str, Metric]:
"""
Combines metrics without regard for tracking provenence.
"""
m: Dict[str, Metric] = {}
for task_report in reports:
for each_metric, value in task_report.items():
m[each_metric] = m.get(each_metric) + value
return m
class Metrics(object):
"""
Threadsafe metrics container focused on aggregation.
"""
def __init__(self, threadsafe=False, shared=None):
self._threadsafe = threadsafe
if self._threadsafe and shared is None:
# Threadsafe metrics tracking works by keeping a queue that workers can
# push updates to. the main worker works through the queue at report
# time. We could add some buffering to improve performance, but we
# are deprioritizing hogwild performance at this time.
self._buffer = None
self._queue = multiprocessing.SimpleQueue()
self._worker = False
self._data = {}
elif shared and 'queue' in shared:
# This is a clone, in threadsafe mode
self._buffer = {}
self._queue = shared['queue']
self._worker = True
self._data = None
elif shared and 'data' in shared:
# This is a clone, in non-threadsafe mode
self._buffer = None
self._queue = None
self._worker = False
self._data = shared['data']
else:
# The original in non-threadsafe mode
self._buffer = None
self._queue = None
self._worker = False
self._data = {}
def __str__(self):
return str(self._data)
def __repr__(self):
return f'Metrics({repr(self._data)})'
def add(self, key: str, value: Optional[Metric]) -> None:
"""
Record an accumulation to a metric.
"""
if self._threadsafe and self._worker:
self._buffer[key] = self._buffer.get(key) + value
else:
self._data[key] = self._data.get(key) + value
def flush(self):
"""
Clear the local buffer and push it on.
"""
if self._threadsafe and self._buffer:
self._queue.put(self._buffer)
self._buffer.clear()
def report(self):
"""
Report the metrics over all data seen so far.
"""
self.sync()
return {k: v for k, v in self._data.items()}
def sync(self):
"""
Process all items on the queue to ensure it is up to date.
"""
if self._worker:
self.flush()
elif self._threadsafe and not self._worker:
for buffer_ in self._drain_queue():
for key, value in buffer_.items():
self._data[key] = self._data.get(key) + value
def _drain_queue(self):
"""
Drain the queue, yielding all items in it.
"""
while not self._queue.empty():
try:
yield self._queue.get()
except queue.Empty:
break
def clear(self):
"""
Clear all the metrics.
"""
if self._worker:
self._buffer.clear()
elif self._threadsafe and not self._worker:
for _ in self._drain_queue():
pass
if self._data:
self._data.clear()
def share(self):
if self._threadsafe:
return {'queue': self._queue}
else:
return {'data': self._data}
class TeacherMetrics(Metrics):
"""
Helper container which encapsulates standard metrics (F1, BLEU, ...).
"""
def __init__(
self,
threadsafe: bool = False,
metrics_list: str = "default",
shared: Dict[str, Any] = None,
) -> None:
super().__init__(threadsafe=threadsafe, shared=shared)
self._metrics_list = self._infer_metrics(metrics_list)
self.eval_pr = [1, 5, 10, 100]
@staticmethod
def _infer_metrics(cli_arg: str) -> Set[str]:
"""
Parse the CLI metric into a list of metrics we wish to compute.
"""
col: Set[str] = set()
names = cli_arg.split(",")
for n in names:
if n == 'default':
col |= DEFAULT_METRICS
elif n == 'rouge':
col |= ROUGE_METRICS
elif n == 'bleu':
col |= BLEU_METRICS
elif n == 'all':
col |= ALL_METRICS
else:
col.add(n)
return col
def _update_ranking_metrics(self, observation, labels):
text_cands = observation.get('text_candidates', None)
if text_cands is None:
return
# Now loop through text candidates, assuming they are sorted.
# If any of them is a label then score a point.
# maintain hits@1, 5, 10, 50, 100, etc.
label_set = set(normalize_answer(l) for l in labels)
cnts = {k: 0 for k in self.eval_pr}
cnt = 0
for c in text_cands:
cnt += 1
if normalize_answer(c) in label_set:
for k in self.eval_pr:
if cnt <= k:
cnts[k] += 1
# hits metric is 1 if cnts[k] > 0.
# (other metrics such as p@k and r@k take
# the value of cnt into account.)
for k in self.eval_pr:
self.add(f'hits@{k}', AverageMetric(cnts[k] > 0))
def evaluate_response(self, observation: Message, labels: List[str]) -> None:
"""
Compute all required text-based metrics based on an observation and labels.
"""
prediction = observation.get('text', None)
self.add('exs', SumMetric(1))
if prediction is not None:
self.add('accuracy', ExactMatchMetric.compute(prediction, labels))
self.add('f1', F1Metric.compute(prediction, labels))
for k in range(1, 5): # 1..4
if f'bleu-{k}' in self._metrics_list:
self.add(f'bleu-{k}', BleuMetric.compute(prediction, labels, k))
# if any of the rouges are in the list
if self._metrics_list & ROUGE_METRICS:
r1, r2, rL = RougeMetric.compute_many(prediction, labels)
if 'rouge-1' in self._metrics_list:
self.add('rouge_1', r1)
if 'rouge-2' in self._metrics_list:
self.add('rouge_2', r2)
if 'rouge-L' in self._metrics_list:
self.add('rouge_L', rL)
# Ranking metrics.
self._update_ranking_metrics(observation, labels)
# User-reported metrics
if 'metrics' in observation:
for uk, v in observation['metrics'].items():
if uk in ALL_METRICS:
# don't let the user override our metrics
uk = f'USER_{uk}'
assert isinstance(uk, str), type(k)
if not isinstance(v, Metric):
warn_once(f'Metric {uk} is assumed to be averaged per example.')
v = AverageMetric(v)
assert isinstance(v, Metric)
self.add(uk, v)
# always flush at the end of processing this response
self.flush()
| 31.426634 | 88 | 0.59395 | [
"MIT"
] | Totoola-Kehinde/ParlAI | parlai/core/metrics.py | 25,487 | Python |
from abcunits import ConversionUnit
KFACTOR = 273.15 #Difference Kelvin, C (how precise is this known?)
class TempUnit(ConversionUnit):
""" Temperature units. ALl conversions go through Kelvin. """
#http://www.metric-conversions.org/temperature/fahrenheit-to-kelvin.htm
class Kelvin(TempUnit):
short = 'K'
full = 'Kelvin' #Proper names, keep this way?
symbol = 'r$^{\deg}K$' #Isn't degree Kelvin technially wrong?
_canonical = True
@staticmethod
def to_canonical(x):
return x
@staticmethod
def from_canonical(x):
return x
class Celsius(TempUnit):
short = 'C'
full = 'Celsius'
symbol = 'r$^{\deg}C$'
@staticmethod
def to_canonical(x):
return x + KFACTOR
@staticmethod
def from_canonical(x):
return x - KFACTOR
class Farenheiht(TempUnit):
short = 'F'
full = 'Farenheiht'
symbol = 'r$^{\deg}F$'
@staticmethod
def to_canonical(x):
return ((x - 32.00)/1.80) + 273.15
@staticmethod
def from_canonical(x):
return ((x - KFACTOR) * 1.80) + 32.00
_tempunits = (Kelvin(),
Celsius(),
Farenheiht(),
ConversionUnit() #For null case
)
TEMPUNITS = dict((obj.short, obj) for obj in _tempunits) | 22.137931 | 71 | 0.610592 | [
"BSD-3-Clause"
] | hugadams/scikit-spectra | skspec/units/tempunits.py | 1,284 | Python |
##
##
# File auto-generated against equivalent DynamicSerialize Java class
class DeleteRequest(object):
def __init__(self):
self.datasets = None
self.groups = None
self.filename = None
def getDatasets(self):
return self.datasets
def setDatasets(self, datasets):
self.datasets = datasets
def getGroups(self):
return self.groups
def setGroups(self, groups):
self.groups = groups
def getFilename(self):
return self.filename
def setFilename(self, filename):
self.filename = filename
| 19 | 68 | 0.641766 | [
"BSD-3-Clause"
] | mjames-upc/python-awips | dynamicserialize/dstypes/com/raytheon/uf/common/pypies/request/DeleteRequest.py | 589 | Python |
# -*- coding: utf-8 -*-
{
"name": """Preview Media Files""",
"summary": """Open attached images in popup""",
"category": "Web",
"images": ["images/screenshot-1.png"],
"vesion": "10.0.1.0.0",
"application": False,
"author": "IT-Projects LLC, Dinar Gabbasov",
"support": "[email protected]",
"website": "https://twitter.com/gabbasov_dinar",
"license": "OPL-1",
"price": 19.00,
"currency": "EUR",
"depends": ["ir_attachment_url"],
"external_dependencies": {"python": [], "bin": []},
"data": ["views/web_preview_template.xml"],
"qweb": ["static/src/xml/media_tree_view_widget.xml"],
"demo": [],
"post_load": None,
"pre_init_hook": None,
"post_init_hook": None,
"auto_install": False,
"installable": True,
}
| 30.269231 | 58 | 0.579416 | [
"MIT"
] | ShaheenHossain/itpp-labs-misc-addons13 | web_preview/__manifest__.py | 787 | Python |
"""Conftests
"""
| 5.666667 | 12 | 0.529412 | [
"MIT"
] | Azure-Samples/azure-intelligent-edge-patterns | factory-ai-vision/EdgeSolution/modules/WebModule/backend/vision_on_edge/streams/tests/conftest.py | 17 | Python |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# convert / import osm xml .osm file into a Shapefile
import subprocess
import os
import shutil
# specify output format
output_format = "ESRI Shapefile"
# complete path to input OSM xml file .osm
input_osm = '../geodata/OSM_san_francisco_westbluff.osm'
# Windows users can uncomment these two lines if needed
# ogr2ogr = r"c:/OSGeo4W/bin/ogr2ogr.exe"
# ogr_info = r"c:/OSGeo4W/bin/ogrinfo.exe"
# view what geometry types are available in our OSM file
subprocess.call([ogr_info, input_osm])
destination_dir = os.path.realpath('../geodata/temp')
if os.path.isdir(destination_dir):
# remove output folder if it exists
shutil.rmtree(destination_dir)
print("removing existing directory : " + destination_dir)
# create new output folder
os.mkdir(destination_dir)
print("creating new directory : " + destination_dir)
# list of geometry types to convert to Shapefile
geom_types = ["lines", "points", "multilinestrings", "multipolygons"]
# create a new Shapefile for each geometry type
for g_type in geom_types:
subprocess.call([ogr2ogr,
"-skipfailures", "-f", output_format,
destination_dir, input_osm,
"layer", g_type,
"--config","OSM_USE_CUSTOM_INDEXING", "NO"])
print("done creating " + g_type)
# if you like to export to SPATIALITE from .osm
# subprocess.call([ogr2ogr, "-skipfailures", "-f",
# "SQLITE", "-dsco", "SPATIALITE=YES",
# "my2.sqlite", input_osm])
| 30.45098 | 73 | 0.676111 | [
"MIT"
] | MatthiasWunsch/python-geospatial-analysis-cookbook | ch03/code/ch03-04_osm2shp.py | 1,553 | Python |
from flask_wtf import FlaskForm
from flask_wtf.file import FileRequired
from wtforms import (
StringField,
SubmitField,
PasswordField,
FileField,
SelectField,
TextAreaField,
BooleanField,
)
from wtforms.validators import DataRequired, Length, ValidationError
from models import RoomBGMTypes
"""
Reference:
movie_id = form.movie_id.data
place = form.place.data
imageid = form.imageid.data
title = form.title.data
"""
class RoomMovieForm(FlaskForm):
movie_id = StringField("Movie ID")
place = StringField("Place in Room")
imageid = StringField("Image ID")
title = StringField("Movie Title")
submit = SubmitField("Done!")
class LoginForm(FlaskForm):
username = StringField("Username")
password = PasswordField("Password")
submit = SubmitField("Enter the underground")
class NewsForm(FlaskForm):
news = TextAreaField("News Contents", validators=[DataRequired()])
create = SubmitField("Create!")
class MiiUploadForm(FlaskForm):
mii = FileField("Mii Selection", validators=[FileRequired()])
name = StringField("Mii Name", validators=[DataRequired()])
color1 = StringField("Shirt Color (Hex)", validators=[DataRequired()])
color2 = StringField("Pants Color (Hex)", validators=[DataRequired()])
upload = SubmitField("Add Mii")
class NewUserForm(FlaskForm):
username = StringField("Username", validators=[DataRequired()])
password1 = PasswordField("Password", validators=[DataRequired()])
password2 = PasswordField("Confirm Password", validators=[DataRequired()])
upload = SubmitField("Complete")
def validate_password1(self, _):
if self.password1.data != self.password2.data:
return ValidationError("New passwords must be the same")
class ChangePasswordForm(FlaskForm):
current_password = PasswordField("Password", validators=[DataRequired()])
new_password = PasswordField("New Password", validators=[DataRequired()])
new_password_confirmation = PasswordField(
"Confirm New Password", validators=[DataRequired()]
)
complete = SubmitField("Complete")
def validate_current_password(self, _):
if self.current_password.data == self.new_password.data:
return ValidationError("New password cannot be the same as current!")
def validate_new_password(self, _):
if self.new_password.data != self.new_password_confirmation.data:
return ValidationError("New passwords must be the same")
class MovieUploadForm(FlaskForm):
movie = FileField("Movie", validators=[FileRequired()])
title = StringField("Movie title", validators=[DataRequired(), Length(max=48)])
thumbnail = FileField("Movie thumbnail", validators=[FileRequired()])
# Choices for the select field are only evaluated once, so we must set it when necessary.
category = SelectField("Movie category", validators=[DataRequired()])
upload = SubmitField("Add Movie")
class CategoryAddForm(FlaskForm):
category_name = StringField("Category Name", validators=[DataRequired()])
thumbnail = FileField("Movie thumbnail", validators=[FileRequired()])
submit = SubmitField("Add")
class CategoryEditForm(FlaskForm):
category_name = StringField("Category Name", validators=[DataRequired()])
thumbnail = FileField("Movie thumbnail")
submit = SubmitField("Edit")
class ParadeForm(FlaskForm):
news = StringField("News", validators=[DataRequired()])
company = StringField("Company", validators=[DataRequired()])
image = FileField("Parade Banner", validators=[FileRequired()])
submit = SubmitField("Create")
class RoomForm(FlaskForm):
bgm = SelectField(
"Background Music",
choices=RoomBGMTypes.choices(),
coerce=RoomBGMTypes.coerce,
)
room_logo = FileField("Room Logo", validators=[FileRequired()])
has_mascot = BooleanField("Mascot Enabled")
has_contact = BooleanField("Show Contact Information")
intro_msg = StringField("Intro Message", validators=[DataRequired()])
mii_msg = StringField("Mii Message", validators=[DataRequired()])
submit = SubmitField("Create")
class KillMii(FlaskForm):
given_id = StringField("Item ID", validators=[DataRequired()])
submit = SubmitField("Delete!")
class ConciergeForm(FlaskForm):
prof = StringField("Profession", validators=[DataRequired()])
message1 = StringField("Message 1", validators=[DataRequired()])
message2 = StringField("Message 2", validators=[DataRequired()])
message3 = StringField("Message 3", validators=[DataRequired()])
message4 = StringField("Message 4", validators=[DataRequired()])
message5 = StringField("Message 5", validators=[DataRequired()])
message6 = StringField("Message 6", validators=[DataRequired()])
message7 = StringField("Message 7", validators=[DataRequired()])
movieid = StringField("Movie ID", validators=[DataRequired()])
submit = SubmitField("Create!")
class PosterForm(FlaskForm):
file = FileField("Poster Image", validators=[FileRequired()])
title = StringField("Title", validators=[DataRequired()])
msg = StringField("Message", validators=[DataRequired()])
upload = SubmitField("Create Poster!")
| 36.213793 | 93 | 0.708246 | [
"MPL-2.0",
"MPL-2.0-no-copyleft-exception"
] | dhtdht020/room-server | theunderground/forms.py | 5,251 | Python |
#!/usr/bin/env python3
# Copyright (c) 2010 ArtForz -- public domain half-a-node
# Copyright (c) 2012 Jeff Garzik
# Copyright (c) 2010-2020 The UFO Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
"""UFO test framework primitive and message structures
CBlock, CTransaction, CBlockHeader, CTxIn, CTxOut, etc....:
data structures that should map to corresponding structures in
UFO/primitives
msg_block, msg_tx, msg_headers, etc.:
data structures that represent network messages
ser_*, deser_*: functions that handle serialization/deserialization.
Classes use __slots__ to ensure extraneous attributes aren't accidentally added
by tests, compromising their intended effect.
"""
from codecs import encode
import copy
import hashlib
from io import BytesIO
import math
import random
import socket
import struct
import time
from test_framework.siphash import siphash256
from test_framework.util import hex_str_to_bytes, assert_equal
MAX_LOCATOR_SZ = 101
MAX_BLOCK_BASE_SIZE = 1000000
MAX_BLOOM_FILTER_SIZE = 36000
MAX_BLOOM_HASH_FUNCS = 50
COIN = 100000000 # 1 btc in satoshis
MAX_MONEY = 21000000 * COIN
BIP125_SEQUENCE_NUMBER = 0xfffffffd # Sequence number that is BIP 125 opt-in and BIP 68-opt-out
MAX_PROTOCOL_MESSAGE_LENGTH = 4000000 # Maximum length of incoming protocol messages
MAX_HEADERS_RESULTS = 2000 # Number of headers sent in one getheaders result
MAX_INV_SIZE = 50000 # Maximum number of entries in an 'inv' protocol message
NODE_NETWORK = (1 << 0)
NODE_BLOOM = (1 << 2)
NODE_WITNESS = (1 << 3)
NODE_COMPACT_FILTERS = (1 << 6)
NODE_NETWORK_LIMITED = (1 << 10)
MSG_TX = 1
MSG_BLOCK = 2
MSG_FILTERED_BLOCK = 3
MSG_CMPCT_BLOCK = 4
MSG_WTX = 5
MSG_WITNESS_FLAG = 1 << 30
MSG_TYPE_MASK = 0xffffffff >> 2
MSG_WITNESS_TX = MSG_TX | MSG_WITNESS_FLAG
FILTER_TYPE_BASIC = 0
WITNESS_SCALE_FACTOR = 4
# Serialization/deserialization tools
def sha256(s):
return hashlib.new('sha256', s).digest()
def hash256(s):
return sha256(sha256(s))
def ser_compact_size(l):
r = b""
if l < 253:
r = struct.pack("B", l)
elif l < 0x10000:
r = struct.pack("<BH", 253, l)
elif l < 0x100000000:
r = struct.pack("<BI", 254, l)
else:
r = struct.pack("<BQ", 255, l)
return r
def deser_compact_size(f):
nit = struct.unpack("<B", f.read(1))[0]
if nit == 253:
nit = struct.unpack("<H", f.read(2))[0]
elif nit == 254:
nit = struct.unpack("<I", f.read(4))[0]
elif nit == 255:
nit = struct.unpack("<Q", f.read(8))[0]
return nit
def deser_string(f):
nit = deser_compact_size(f)
return f.read(nit)
def ser_string(s):
return ser_compact_size(len(s)) + s
def deser_uint256(f):
r = 0
for i in range(8):
t = struct.unpack("<I", f.read(4))[0]
r += t << (i * 32)
return r
def ser_uint256(u):
rs = b""
for _ in range(8):
rs += struct.pack("<I", u & 0xFFFFFFFF)
u >>= 32
return rs
def uint256_from_str(s):
r = 0
t = struct.unpack("<IIIIIIII", s[:32])
for i in range(8):
r += t[i] << (i * 32)
return r
def uint256_from_compact(c):
nbytes = (c >> 24) & 0xFF
v = (c & 0xFFFFFF) << (8 * (nbytes - 3))
return v
# deser_function_name: Allow for an alternate deserialization function on the
# entries in the vector.
def deser_vector(f, c, deser_function_name=None):
nit = deser_compact_size(f)
r = []
for _ in range(nit):
t = c()
if deser_function_name:
getattr(t, deser_function_name)(f)
else:
t.deserialize(f)
r.append(t)
return r
# ser_function_name: Allow for an alternate serialization function on the
# entries in the vector (we use this for serializing the vector of transactions
# for a witness block).
def ser_vector(l, ser_function_name=None):
r = ser_compact_size(len(l))
for i in l:
if ser_function_name:
r += getattr(i, ser_function_name)()
else:
r += i.serialize()
return r
def deser_uint256_vector(f):
nit = deser_compact_size(f)
r = []
for _ in range(nit):
t = deser_uint256(f)
r.append(t)
return r
def ser_uint256_vector(l):
r = ser_compact_size(len(l))
for i in l:
r += ser_uint256(i)
return r
def deser_string_vector(f):
nit = deser_compact_size(f)
r = []
for _ in range(nit):
t = deser_string(f)
r.append(t)
return r
def ser_string_vector(l):
r = ser_compact_size(len(l))
for sv in l:
r += ser_string(sv)
return r
# Deserialize from a hex string representation (eg from RPC)
def FromHex(obj, hex_string):
obj.deserialize(BytesIO(hex_str_to_bytes(hex_string)))
return obj
# Convert a binary-serializable object to hex (eg for submission via RPC)
def ToHex(obj):
return obj.serialize().hex()
# Objects that map to UFOd objects, which can be serialized/deserialized
class CAddress:
__slots__ = ("net", "ip", "nServices", "port", "time")
# see https://github.com/UFO/bips/blob/master/bip-0155.mediawiki
NET_IPV4 = 1
ADDRV2_NET_NAME = {
NET_IPV4: "IPv4"
}
ADDRV2_ADDRESS_LENGTH = {
NET_IPV4: 4
}
def __init__(self):
self.time = 0
self.nServices = 1
self.net = self.NET_IPV4
self.ip = "0.0.0.0"
self.port = 0
def deserialize(self, f, *, with_time=True):
"""Deserialize from addrv1 format (pre-BIP155)"""
if with_time:
# VERSION messages serialize CAddress objects without time
self.time = struct.unpack("<I", f.read(4))[0]
self.nServices = struct.unpack("<Q", f.read(8))[0]
# We only support IPv4 which means skip 12 bytes and read the next 4 as IPv4 address.
f.read(12)
self.net = self.NET_IPV4
self.ip = socket.inet_ntoa(f.read(4))
self.port = struct.unpack(">H", f.read(2))[0]
def serialize(self, *, with_time=True):
"""Serialize in addrv1 format (pre-BIP155)"""
assert self.net == self.NET_IPV4
r = b""
if with_time:
# VERSION messages serialize CAddress objects without time
r += struct.pack("<I", self.time)
r += struct.pack("<Q", self.nServices)
r += b"\x00" * 10 + b"\xff" * 2
r += socket.inet_aton(self.ip)
r += struct.pack(">H", self.port)
return r
def deserialize_v2(self, f):
"""Deserialize from addrv2 format (BIP155)"""
self.time = struct.unpack("<I", f.read(4))[0]
self.nServices = deser_compact_size(f)
self.net = struct.unpack("B", f.read(1))[0]
assert self.net == self.NET_IPV4
address_length = deser_compact_size(f)
assert address_length == self.ADDRV2_ADDRESS_LENGTH[self.net]
self.ip = socket.inet_ntoa(f.read(4))
self.port = struct.unpack(">H", f.read(2))[0]
def serialize_v2(self):
"""Serialize in addrv2 format (BIP155)"""
assert self.net == self.NET_IPV4
r = b""
r += struct.pack("<I", self.time)
r += ser_compact_size(self.nServices)
r += struct.pack("B", self.net)
r += ser_compact_size(self.ADDRV2_ADDRESS_LENGTH[self.net])
r += socket.inet_aton(self.ip)
r += struct.pack(">H", self.port)
return r
def __repr__(self):
return ("CAddress(nServices=%i net=%s addr=%s port=%i)"
% (self.nServices, self.ADDRV2_NET_NAME[self.net], self.ip, self.port))
class CInv:
__slots__ = ("hash", "type")
typemap = {
0: "Error",
MSG_TX: "TX",
MSG_BLOCK: "Block",
MSG_TX | MSG_WITNESS_FLAG: "WitnessTx",
MSG_BLOCK | MSG_WITNESS_FLAG: "WitnessBlock",
MSG_FILTERED_BLOCK: "filtered Block",
MSG_CMPCT_BLOCK: "CompactBlock",
MSG_WTX: "WTX",
}
def __init__(self, t=0, h=0):
self.type = t
self.hash = h
def deserialize(self, f):
self.type = struct.unpack("<I", f.read(4))[0]
self.hash = deser_uint256(f)
def serialize(self):
r = b""
r += struct.pack("<I", self.type)
r += ser_uint256(self.hash)
return r
def __repr__(self):
return "CInv(type=%s hash=%064x)" \
% (self.typemap[self.type], self.hash)
def __eq__(self, other):
return isinstance(other, CInv) and self.hash == other.hash and self.type == other.type
class CBlockLocator:
__slots__ = ("nVersion", "vHave")
def __init__(self):
self.vHave = []
def deserialize(self, f):
struct.unpack("<i", f.read(4))[0] # Ignore version field.
self.vHave = deser_uint256_vector(f)
def serialize(self):
r = b""
r += struct.pack("<i", 0) # UFO Core ignores version field. Set it to 0.
r += ser_uint256_vector(self.vHave)
return r
def __repr__(self):
return "CBlockLocator(vHave=%s)" % (repr(self.vHave))
class COutPoint:
__slots__ = ("hash", "n")
def __init__(self, hash=0, n=0):
self.hash = hash
self.n = n
def deserialize(self, f):
self.hash = deser_uint256(f)
self.n = struct.unpack("<I", f.read(4))[0]
def serialize(self):
r = b""
r += ser_uint256(self.hash)
r += struct.pack("<I", self.n)
return r
def __repr__(self):
return "COutPoint(hash=%064x n=%i)" % (self.hash, self.n)
class CTxIn:
__slots__ = ("nSequence", "prevout", "scriptSig")
def __init__(self, outpoint=None, scriptSig=b"", nSequence=0):
if outpoint is None:
self.prevout = COutPoint()
else:
self.prevout = outpoint
self.scriptSig = scriptSig
self.nSequence = nSequence
def deserialize(self, f):
self.prevout = COutPoint()
self.prevout.deserialize(f)
self.scriptSig = deser_string(f)
self.nSequence = struct.unpack("<I", f.read(4))[0]
def serialize(self):
r = b""
r += self.prevout.serialize()
r += ser_string(self.scriptSig)
r += struct.pack("<I", self.nSequence)
return r
def __repr__(self):
return "CTxIn(prevout=%s scriptSig=%s nSequence=%i)" \
% (repr(self.prevout), self.scriptSig.hex(),
self.nSequence)
class CTxOut:
__slots__ = ("nValue", "scriptPubKey")
def __init__(self, nValue=0, scriptPubKey=b""):
self.nValue = nValue
self.scriptPubKey = scriptPubKey
def deserialize(self, f):
self.nValue = struct.unpack("<q", f.read(8))[0]
self.scriptPubKey = deser_string(f)
def serialize(self):
r = b""
r += struct.pack("<q", self.nValue)
r += ser_string(self.scriptPubKey)
return r
def __repr__(self):
return "CTxOut(nValue=%i.%08i scriptPubKey=%s)" \
% (self.nValue // COIN, self.nValue % COIN,
self.scriptPubKey.hex())
class CScriptWitness:
__slots__ = ("stack",)
def __init__(self):
# stack is a vector of strings
self.stack = []
def __repr__(self):
return "CScriptWitness(%s)" % \
(",".join([x.hex() for x in self.stack]))
def is_null(self):
if self.stack:
return False
return True
class CTxInWitness:
__slots__ = ("scriptWitness",)
def __init__(self):
self.scriptWitness = CScriptWitness()
def deserialize(self, f):
self.scriptWitness.stack = deser_string_vector(f)
def serialize(self):
return ser_string_vector(self.scriptWitness.stack)
def __repr__(self):
return repr(self.scriptWitness)
def is_null(self):
return self.scriptWitness.is_null()
class CTxWitness:
__slots__ = ("vtxinwit",)
def __init__(self):
self.vtxinwit = []
def deserialize(self, f):
for i in range(len(self.vtxinwit)):
self.vtxinwit[i].deserialize(f)
def serialize(self):
r = b""
# This is different than the usual vector serialization --
# we omit the length of the vector, which is required to be
# the same length as the transaction's vin vector.
for x in self.vtxinwit:
r += x.serialize()
return r
def __repr__(self):
return "CTxWitness(%s)" % \
(';'.join([repr(x) for x in self.vtxinwit]))
def is_null(self):
for x in self.vtxinwit:
if not x.is_null():
return False
return True
class CTransaction:
__slots__ = ("hash", "nLockTime", "nVersion", "sha256", "vin", "vout",
"wit")
def __init__(self, tx=None):
if tx is None:
self.nVersion = 1
self.vin = []
self.vout = []
self.wit = CTxWitness()
self.nLockTime = 0
self.sha256 = None
self.hash = None
else:
self.nVersion = tx.nVersion
self.vin = copy.deepcopy(tx.vin)
self.vout = copy.deepcopy(tx.vout)
self.nLockTime = tx.nLockTime
self.sha256 = tx.sha256
self.hash = tx.hash
self.wit = copy.deepcopy(tx.wit)
def deserialize(self, f):
self.nVersion = struct.unpack("<i", f.read(4))[0]
self.vin = deser_vector(f, CTxIn)
flags = 0
if len(self.vin) == 0:
flags = struct.unpack("<B", f.read(1))[0]
# Not sure why flags can't be zero, but this
# matches the implementation in UFOd
if (flags != 0):
self.vin = deser_vector(f, CTxIn)
self.vout = deser_vector(f, CTxOut)
else:
self.vout = deser_vector(f, CTxOut)
if flags != 0:
self.wit.vtxinwit = [CTxInWitness() for _ in range(len(self.vin))]
self.wit.deserialize(f)
else:
self.wit = CTxWitness()
self.nLockTime = struct.unpack("<I", f.read(4))[0]
self.sha256 = None
self.hash = None
def serialize_without_witness(self):
r = b""
r += struct.pack("<i", self.nVersion)
r += ser_vector(self.vin)
r += ser_vector(self.vout)
r += struct.pack("<I", self.nLockTime)
return r
# Only serialize with witness when explicitly called for
def serialize_with_witness(self):
flags = 0
if not self.wit.is_null():
flags |= 1
r = b""
r += struct.pack("<i", self.nVersion)
if flags:
dummy = []
r += ser_vector(dummy)
r += struct.pack("<B", flags)
r += ser_vector(self.vin)
r += ser_vector(self.vout)
if flags & 1:
if (len(self.wit.vtxinwit) != len(self.vin)):
# vtxinwit must have the same length as vin
self.wit.vtxinwit = self.wit.vtxinwit[:len(self.vin)]
for _ in range(len(self.wit.vtxinwit), len(self.vin)):
self.wit.vtxinwit.append(CTxInWitness())
r += self.wit.serialize()
r += struct.pack("<I", self.nLockTime)
return r
# Regular serialization is with witness -- must explicitly
# call serialize_without_witness to exclude witness data.
def serialize(self):
return self.serialize_with_witness()
def getwtxid(self):
return hash256(self.serialize())[::-1].hex()
# Recalculate the txid (transaction hash without witness)
def rehash(self):
self.sha256 = None
self.calc_sha256()
return self.hash
# We will only cache the serialization without witness in
# self.sha256 and self.hash -- those are expected to be the txid.
def calc_sha256(self, with_witness=False):
if with_witness:
# Don't cache the result, just return it
return uint256_from_str(hash256(self.serialize_with_witness()))
if self.sha256 is None:
self.sha256 = uint256_from_str(hash256(self.serialize_without_witness()))
self.hash = hash256(self.serialize_without_witness())[::-1].hex()
def is_valid(self):
self.calc_sha256()
for tout in self.vout:
if tout.nValue < 0 or tout.nValue > 21000000 * COIN:
return False
return True
# Calculate the virtual transaction size using witness and non-witness
# serialization size (does NOT use sigops).
def get_vsize(self):
with_witness_size = len(self.serialize_with_witness())
without_witness_size = len(self.serialize_without_witness())
return math.ceil(((WITNESS_SCALE_FACTOR - 1) * without_witness_size + with_witness_size) / WITNESS_SCALE_FACTOR)
def __repr__(self):
return "CTransaction(nVersion=%i vin=%s vout=%s wit=%s nLockTime=%i)" \
% (self.nVersion, repr(self.vin), repr(self.vout), repr(self.wit), self.nLockTime)
class CBlockHeader:
__slots__ = ("hash", "hashMerkleRoot", "hashPrevBlock", "nufos", "nNonce",
"nTime", "nVersion", "sha256")
def __init__(self, header=None):
if header is None:
self.set_null()
else:
self.nVersion = header.nVersion
self.hashPrevBlock = header.hashPrevBlock
self.hashMerkleRoot = header.hashMerkleRoot
self.nTime = header.nTime
self.nufos = header.nufos
self.nNonce = header.nNonce
self.sha256 = header.sha256
self.hash = header.hash
self.calc_sha256()
def set_null(self):
self.nVersion = 1
self.hashPrevBlock = 0
self.hashMerkleRoot = 0
self.nTime = 0
self.nufos = 0
self.nNonce = 0
self.sha256 = None
self.hash = None
def deserialize(self, f):
self.nVersion = struct.unpack("<i", f.read(4))[0]
self.hashPrevBlock = deser_uint256(f)
self.hashMerkleRoot = deser_uint256(f)
self.nTime = struct.unpack("<I", f.read(4))[0]
self.nufos = struct.unpack("<I", f.read(4))[0]
self.nNonce = struct.unpack("<I", f.read(4))[0]
self.sha256 = None
self.hash = None
def serialize(self):
r = b""
r += struct.pack("<i", self.nVersion)
r += ser_uint256(self.hashPrevBlock)
r += ser_uint256(self.hashMerkleRoot)
r += struct.pack("<I", self.nTime)
r += struct.pack("<I", self.nufos)
r += struct.pack("<I", self.nNonce)
return r
def calc_sha256(self):
if self.sha256 is None:
r = b""
r += struct.pack("<i", self.nVersion)
r += ser_uint256(self.hashPrevBlock)
r += ser_uint256(self.hashMerkleRoot)
r += struct.pack("<I", self.nTime)
r += struct.pack("<I", self.nufos)
r += struct.pack("<I", self.nNonce)
self.sha256 = uint256_from_str(hash256(r))
self.hash = encode(hash256(r)[::-1], 'hex_codec').decode('ascii')
def rehash(self):
self.sha256 = None
self.calc_sha256()
return self.sha256
def __repr__(self):
return "CBlockHeader(nVersion=%i hashPrevBlock=%064x hashMerkleRoot=%064x nTime=%s nufos=%08x nNonce=%08x)" \
% (self.nVersion, self.hashPrevBlock, self.hashMerkleRoot,
time.ctime(self.nTime), self.nufos, self.nNonce)
BLOCK_HEADER_SIZE = len(CBlockHeader().serialize())
assert_equal(BLOCK_HEADER_SIZE, 80)
class CBlock(CBlockHeader):
__slots__ = ("vtx",)
def __init__(self, header=None):
super().__init__(header)
self.vtx = []
def deserialize(self, f):
super().deserialize(f)
self.vtx = deser_vector(f, CTransaction)
def serialize(self, with_witness=True):
r = b""
r += super().serialize()
if with_witness:
r += ser_vector(self.vtx, "serialize_with_witness")
else:
r += ser_vector(self.vtx, "serialize_without_witness")
return r
# Calculate the merkle root given a vector of transaction hashes
@classmethod
def get_merkle_root(cls, hashes):
while len(hashes) > 1:
newhashes = []
for i in range(0, len(hashes), 2):
i2 = min(i+1, len(hashes)-1)
newhashes.append(hash256(hashes[i] + hashes[i2]))
hashes = newhashes
return uint256_from_str(hashes[0])
def calc_merkle_root(self):
hashes = []
for tx in self.vtx:
tx.calc_sha256()
hashes.append(ser_uint256(tx.sha256))
return self.get_merkle_root(hashes)
def calc_witness_merkle_root(self):
# For witness root purposes, the hash of the
# coinbase, with witness, is defined to be 0...0
hashes = [ser_uint256(0)]
for tx in self.vtx[1:]:
# Calculate the hashes with witness data
hashes.append(ser_uint256(tx.calc_sha256(True)))
return self.get_merkle_root(hashes)
def is_valid(self):
self.calc_sha256()
target = uint256_from_compact(self.nufos)
if self.sha256 > target:
return False
for tx in self.vtx:
if not tx.is_valid():
return False
if self.calc_merkle_root() != self.hashMerkleRoot:
return False
return True
def solve(self):
self.rehash()
target = uint256_from_compact(self.nufos)
while self.sha256 > target:
self.nNonce += 1
self.rehash()
def __repr__(self):
return "CBlock(nVersion=%i hashPrevBlock=%064x hashMerkleRoot=%064x nTime=%s nufos=%08x nNonce=%08x vtx=%s)" \
% (self.nVersion, self.hashPrevBlock, self.hashMerkleRoot,
time.ctime(self.nTime), self.nufos, self.nNonce, repr(self.vtx))
class PrefilledTransaction:
__slots__ = ("index", "tx")
def __init__(self, index=0, tx = None):
self.index = index
self.tx = tx
def deserialize(self, f):
self.index = deser_compact_size(f)
self.tx = CTransaction()
self.tx.deserialize(f)
def serialize(self, with_witness=True):
r = b""
r += ser_compact_size(self.index)
if with_witness:
r += self.tx.serialize_with_witness()
else:
r += self.tx.serialize_without_witness()
return r
def serialize_without_witness(self):
return self.serialize(with_witness=False)
def serialize_with_witness(self):
return self.serialize(with_witness=True)
def __repr__(self):
return "PrefilledTransaction(index=%d, tx=%s)" % (self.index, repr(self.tx))
# This is what we send on the wire, in a cmpctblock message.
class P2PHeaderAndShortIDs:
__slots__ = ("header", "nonce", "prefilled_txn", "prefilled_txn_length",
"shortids", "shortids_length")
def __init__(self):
self.header = CBlockHeader()
self.nonce = 0
self.shortids_length = 0
self.shortids = []
self.prefilled_txn_length = 0
self.prefilled_txn = []
def deserialize(self, f):
self.header.deserialize(f)
self.nonce = struct.unpack("<Q", f.read(8))[0]
self.shortids_length = deser_compact_size(f)
for _ in range(self.shortids_length):
# shortids are defined to be 6 bytes in the spec, so append
# two zero bytes and read it in as an 8-byte number
self.shortids.append(struct.unpack("<Q", f.read(6) + b'\x00\x00')[0])
self.prefilled_txn = deser_vector(f, PrefilledTransaction)
self.prefilled_txn_length = len(self.prefilled_txn)
# When using version 2 compact blocks, we must serialize with_witness.
def serialize(self, with_witness=False):
r = b""
r += self.header.serialize()
r += struct.pack("<Q", self.nonce)
r += ser_compact_size(self.shortids_length)
for x in self.shortids:
# We only want the first 6 bytes
r += struct.pack("<Q", x)[0:6]
if with_witness:
r += ser_vector(self.prefilled_txn, "serialize_with_witness")
else:
r += ser_vector(self.prefilled_txn, "serialize_without_witness")
return r
def __repr__(self):
return "P2PHeaderAndShortIDs(header=%s, nonce=%d, shortids_length=%d, shortids=%s, prefilled_txn_length=%d, prefilledtxn=%s" % (repr(self.header), self.nonce, self.shortids_length, repr(self.shortids), self.prefilled_txn_length, repr(self.prefilled_txn))
# P2P version of the above that will use witness serialization (for compact
# block version 2)
class P2PHeaderAndShortWitnessIDs(P2PHeaderAndShortIDs):
__slots__ = ()
def serialize(self):
return super().serialize(with_witness=True)
# Calculate the BIP 152-compact blocks shortid for a given transaction hash
def calculate_shortid(k0, k1, tx_hash):
expected_shortid = siphash256(k0, k1, tx_hash)
expected_shortid &= 0x0000ffffffffffff
return expected_shortid
# This version gets rid of the array lengths, and reinterprets the differential
# encoding into indices that can be used for lookup.
class HeaderAndShortIDs:
__slots__ = ("header", "nonce", "prefilled_txn", "shortids", "use_witness")
def __init__(self, p2pheaders_and_shortids = None):
self.header = CBlockHeader()
self.nonce = 0
self.shortids = []
self.prefilled_txn = []
self.use_witness = False
if p2pheaders_and_shortids is not None:
self.header = p2pheaders_and_shortids.header
self.nonce = p2pheaders_and_shortids.nonce
self.shortids = p2pheaders_and_shortids.shortids
last_index = -1
for x in p2pheaders_and_shortids.prefilled_txn:
self.prefilled_txn.append(PrefilledTransaction(x.index + last_index + 1, x.tx))
last_index = self.prefilled_txn[-1].index
def to_p2p(self):
if self.use_witness:
ret = P2PHeaderAndShortWitnessIDs()
else:
ret = P2PHeaderAndShortIDs()
ret.header = self.header
ret.nonce = self.nonce
ret.shortids_length = len(self.shortids)
ret.shortids = self.shortids
ret.prefilled_txn_length = len(self.prefilled_txn)
ret.prefilled_txn = []
last_index = -1
for x in self.prefilled_txn:
ret.prefilled_txn.append(PrefilledTransaction(x.index - last_index - 1, x.tx))
last_index = x.index
return ret
def get_siphash_keys(self):
header_nonce = self.header.serialize()
header_nonce += struct.pack("<Q", self.nonce)
hash_header_nonce_as_str = sha256(header_nonce)
key0 = struct.unpack("<Q", hash_header_nonce_as_str[0:8])[0]
key1 = struct.unpack("<Q", hash_header_nonce_as_str[8:16])[0]
return [ key0, key1 ]
# Version 2 compact blocks use wtxid in shortids (rather than txid)
def initialize_from_block(self, block, nonce=0, prefill_list=None, use_witness=False):
if prefill_list is None:
prefill_list = [0]
self.header = CBlockHeader(block)
self.nonce = nonce
self.prefilled_txn = [ PrefilledTransaction(i, block.vtx[i]) for i in prefill_list ]
self.shortids = []
self.use_witness = use_witness
[k0, k1] = self.get_siphash_keys()
for i in range(len(block.vtx)):
if i not in prefill_list:
tx_hash = block.vtx[i].sha256
if use_witness:
tx_hash = block.vtx[i].calc_sha256(with_witness=True)
self.shortids.append(calculate_shortid(k0, k1, tx_hash))
def __repr__(self):
return "HeaderAndShortIDs(header=%s, nonce=%d, shortids=%s, prefilledtxn=%s" % (repr(self.header), self.nonce, repr(self.shortids), repr(self.prefilled_txn))
class BlockTransactionsRequest:
__slots__ = ("blockhash", "indexes")
def __init__(self, blockhash=0, indexes = None):
self.blockhash = blockhash
self.indexes = indexes if indexes is not None else []
def deserialize(self, f):
self.blockhash = deser_uint256(f)
indexes_length = deser_compact_size(f)
for _ in range(indexes_length):
self.indexes.append(deser_compact_size(f))
def serialize(self):
r = b""
r += ser_uint256(self.blockhash)
r += ser_compact_size(len(self.indexes))
for x in self.indexes:
r += ser_compact_size(x)
return r
# helper to set the differentially encoded indexes from absolute ones
def from_absolute(self, absolute_indexes):
self.indexes = []
last_index = -1
for x in absolute_indexes:
self.indexes.append(x-last_index-1)
last_index = x
def to_absolute(self):
absolute_indexes = []
last_index = -1
for x in self.indexes:
absolute_indexes.append(x+last_index+1)
last_index = absolute_indexes[-1]
return absolute_indexes
def __repr__(self):
return "BlockTransactionsRequest(hash=%064x indexes=%s)" % (self.blockhash, repr(self.indexes))
class BlockTransactions:
__slots__ = ("blockhash", "transactions")
def __init__(self, blockhash=0, transactions = None):
self.blockhash = blockhash
self.transactions = transactions if transactions is not None else []
def deserialize(self, f):
self.blockhash = deser_uint256(f)
self.transactions = deser_vector(f, CTransaction)
def serialize(self, with_witness=True):
r = b""
r += ser_uint256(self.blockhash)
if with_witness:
r += ser_vector(self.transactions, "serialize_with_witness")
else:
r += ser_vector(self.transactions, "serialize_without_witness")
return r
def __repr__(self):
return "BlockTransactions(hash=%064x transactions=%s)" % (self.blockhash, repr(self.transactions))
class CPartialMerkleTree:
__slots__ = ("nTransactions", "vufos", "vHash")
def __init__(self):
self.nTransactions = 0
self.vHash = []
self.vufos = []
def deserialize(self, f):
self.nTransactions = struct.unpack("<i", f.read(4))[0]
self.vHash = deser_uint256_vector(f)
vBytes = deser_string(f)
self.vufos = []
for i in range(len(vBytes) * 8):
self.vufos.append(vBytes[i//8] & (1 << (i % 8)) != 0)
def serialize(self):
r = b""
r += struct.pack("<i", self.nTransactions)
r += ser_uint256_vector(self.vHash)
vBytesArray = bytearray([0x00] * ((len(self.vufos) + 7)//8))
for i in range(len(self.vufos)):
vBytesArray[i // 8] |= self.vufos[i] << (i % 8)
r += ser_string(bytes(vBytesArray))
return r
def __repr__(self):
return "CPartialMerkleTree(nTransactions=%d, vHash=%s, vufos=%s)" % (self.nTransactions, repr(self.vHash), repr(self.vufos))
class CMerkleBlock:
__slots__ = ("header", "txn")
def __init__(self):
self.header = CBlockHeader()
self.txn = CPartialMerkleTree()
def deserialize(self, f):
self.header.deserialize(f)
self.txn.deserialize(f)
def serialize(self):
r = b""
r += self.header.serialize()
r += self.txn.serialize()
return r
def __repr__(self):
return "CMerkleBlock(header=%s, txn=%s)" % (repr(self.header), repr(self.txn))
# Objects that correspond to messages on the wire
class msg_version:
__slots__ = ("addrFrom", "addrTo", "nNonce", "relay", "nServices",
"nStartingHeight", "nTime", "nVersion", "strSubVer")
msgtype = b"version"
def __init__(self):
self.nVersion = 0
self.nServices = 0
self.nTime = int(time.time())
self.addrTo = CAddress()
self.addrFrom = CAddress()
self.nNonce = random.getrandufos(64)
self.strSubVer = ''
self.nStartingHeight = -1
self.relay = 0
def deserialize(self, f):
self.nVersion = struct.unpack("<i", f.read(4))[0]
self.nServices = struct.unpack("<Q", f.read(8))[0]
self.nTime = struct.unpack("<q", f.read(8))[0]
self.addrTo = CAddress()
self.addrTo.deserialize(f, with_time=False)
self.addrFrom = CAddress()
self.addrFrom.deserialize(f, with_time=False)
self.nNonce = struct.unpack("<Q", f.read(8))[0]
self.strSubVer = deser_string(f).decode('utf-8')
self.nStartingHeight = struct.unpack("<i", f.read(4))[0]
if self.nVersion >= 70001:
# Relay field is optional for version 70001 onwards
try:
self.relay = struct.unpack("<b", f.read(1))[0]
except:
self.relay = 0
else:
self.relay = 0
def serialize(self):
r = b""
r += struct.pack("<i", self.nVersion)
r += struct.pack("<Q", self.nServices)
r += struct.pack("<q", self.nTime)
r += self.addrTo.serialize(with_time=False)
r += self.addrFrom.serialize(with_time=False)
r += struct.pack("<Q", self.nNonce)
r += ser_string(self.strSubVer.encode('utf-8'))
r += struct.pack("<i", self.nStartingHeight)
r += struct.pack("<b", self.relay)
return r
def __repr__(self):
return 'msg_version(nVersion=%i nServices=%i nTime=%s addrTo=%s addrFrom=%s nNonce=0x%016X strSubVer=%s nStartingHeight=%i relay=%i)' \
% (self.nVersion, self.nServices, time.ctime(self.nTime),
repr(self.addrTo), repr(self.addrFrom), self.nNonce,
self.strSubVer, self.nStartingHeight, self.relay)
class msg_verack:
__slots__ = ()
msgtype = b"verack"
def __init__(self):
pass
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_verack()"
class msg_addr:
__slots__ = ("addrs",)
msgtype = b"addr"
def __init__(self):
self.addrs = []
def deserialize(self, f):
self.addrs = deser_vector(f, CAddress)
def serialize(self):
return ser_vector(self.addrs)
def __repr__(self):
return "msg_addr(addrs=%s)" % (repr(self.addrs))
class msg_addrv2:
__slots__ = ("addrs",)
msgtype = b"addrv2"
def __init__(self):
self.addrs = []
def deserialize(self, f):
self.addrs = deser_vector(f, CAddress, "deserialize_v2")
def serialize(self):
return ser_vector(self.addrs, "serialize_v2")
def __repr__(self):
return "msg_addrv2(addrs=%s)" % (repr(self.addrs))
class msg_sendaddrv2:
__slots__ = ()
msgtype = b"sendaddrv2"
def __init__(self):
pass
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_sendaddrv2()"
class msg_inv:
__slots__ = ("inv",)
msgtype = b"inv"
def __init__(self, inv=None):
if inv is None:
self.inv = []
else:
self.inv = inv
def deserialize(self, f):
self.inv = deser_vector(f, CInv)
def serialize(self):
return ser_vector(self.inv)
def __repr__(self):
return "msg_inv(inv=%s)" % (repr(self.inv))
class msg_getdata:
__slots__ = ("inv",)
msgtype = b"getdata"
def __init__(self, inv=None):
self.inv = inv if inv is not None else []
def deserialize(self, f):
self.inv = deser_vector(f, CInv)
def serialize(self):
return ser_vector(self.inv)
def __repr__(self):
return "msg_getdata(inv=%s)" % (repr(self.inv))
class msg_getblocks:
__slots__ = ("locator", "hashstop")
msgtype = b"getblocks"
def __init__(self):
self.locator = CBlockLocator()
self.hashstop = 0
def deserialize(self, f):
self.locator = CBlockLocator()
self.locator.deserialize(f)
self.hashstop = deser_uint256(f)
def serialize(self):
r = b""
r += self.locator.serialize()
r += ser_uint256(self.hashstop)
return r
def __repr__(self):
return "msg_getblocks(locator=%s hashstop=%064x)" \
% (repr(self.locator), self.hashstop)
class msg_tx:
__slots__ = ("tx",)
msgtype = b"tx"
def __init__(self, tx=CTransaction()):
self.tx = tx
def deserialize(self, f):
self.tx.deserialize(f)
def serialize(self):
return self.tx.serialize_with_witness()
def __repr__(self):
return "msg_tx(tx=%s)" % (repr(self.tx))
class msg_wtxidrelay:
__slots__ = ()
msgtype = b"wtxidrelay"
def __init__(self):
pass
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_wtxidrelay()"
class msg_no_witness_tx(msg_tx):
__slots__ = ()
def serialize(self):
return self.tx.serialize_without_witness()
class msg_block:
__slots__ = ("block",)
msgtype = b"block"
def __init__(self, block=None):
if block is None:
self.block = CBlock()
else:
self.block = block
def deserialize(self, f):
self.block.deserialize(f)
def serialize(self):
return self.block.serialize()
def __repr__(self):
return "msg_block(block=%s)" % (repr(self.block))
# for cases where a user needs tighter control over what is sent over the wire
# note that the user must supply the name of the msgtype, and the data
class msg_generic:
__slots__ = ("data")
def __init__(self, msgtype, data=None):
self.msgtype = msgtype
self.data = data
def serialize(self):
return self.data
def __repr__(self):
return "msg_generic()"
class msg_no_witness_block(msg_block):
__slots__ = ()
def serialize(self):
return self.block.serialize(with_witness=False)
class msg_getaddr:
__slots__ = ()
msgtype = b"getaddr"
def __init__(self):
pass
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_getaddr()"
class msg_ping:
__slots__ = ("nonce",)
msgtype = b"ping"
def __init__(self, nonce=0):
self.nonce = nonce
def deserialize(self, f):
self.nonce = struct.unpack("<Q", f.read(8))[0]
def serialize(self):
r = b""
r += struct.pack("<Q", self.nonce)
return r
def __repr__(self):
return "msg_ping(nonce=%08x)" % self.nonce
class msg_pong:
__slots__ = ("nonce",)
msgtype = b"pong"
def __init__(self, nonce=0):
self.nonce = nonce
def deserialize(self, f):
self.nonce = struct.unpack("<Q", f.read(8))[0]
def serialize(self):
r = b""
r += struct.pack("<Q", self.nonce)
return r
def __repr__(self):
return "msg_pong(nonce=%08x)" % self.nonce
class msg_mempool:
__slots__ = ()
msgtype = b"mempool"
def __init__(self):
pass
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_mempool()"
class msg_notfound:
__slots__ = ("vec", )
msgtype = b"notfound"
def __init__(self, vec=None):
self.vec = vec or []
def deserialize(self, f):
self.vec = deser_vector(f, CInv)
def serialize(self):
return ser_vector(self.vec)
def __repr__(self):
return "msg_notfound(vec=%s)" % (repr(self.vec))
class msg_sendheaders:
__slots__ = ()
msgtype = b"sendheaders"
def __init__(self):
pass
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_sendheaders()"
# getheaders message has
# number of entries
# vector of hashes
# hash_stop (hash of last desired block header, 0 to get as many as possible)
class msg_getheaders:
__slots__ = ("hashstop", "locator",)
msgtype = b"getheaders"
def __init__(self):
self.locator = CBlockLocator()
self.hashstop = 0
def deserialize(self, f):
self.locator = CBlockLocator()
self.locator.deserialize(f)
self.hashstop = deser_uint256(f)
def serialize(self):
r = b""
r += self.locator.serialize()
r += ser_uint256(self.hashstop)
return r
def __repr__(self):
return "msg_getheaders(locator=%s, stop=%064x)" \
% (repr(self.locator), self.hashstop)
# headers message has
# <count> <vector of block headers>
class msg_headers:
__slots__ = ("headers",)
msgtype = b"headers"
def __init__(self, headers=None):
self.headers = headers if headers is not None else []
def deserialize(self, f):
# comment in UFOd indicates these should be deserialized as blocks
blocks = deser_vector(f, CBlock)
for x in blocks:
self.headers.append(CBlockHeader(x))
def serialize(self):
blocks = [CBlock(x) for x in self.headers]
return ser_vector(blocks)
def __repr__(self):
return "msg_headers(headers=%s)" % repr(self.headers)
class msg_merkleblock:
__slots__ = ("merkleblock",)
msgtype = b"merkleblock"
def __init__(self, merkleblock=None):
if merkleblock is None:
self.merkleblock = CMerkleBlock()
else:
self.merkleblock = merkleblock
def deserialize(self, f):
self.merkleblock.deserialize(f)
def serialize(self):
return self.merkleblock.serialize()
def __repr__(self):
return "msg_merkleblock(merkleblock=%s)" % (repr(self.merkleblock))
class msg_filterload:
__slots__ = ("data", "nHashFuncs", "nTweak", "nFlags")
msgtype = b"filterload"
def __init__(self, data=b'00', nHashFuncs=0, nTweak=0, nFlags=0):
self.data = data
self.nHashFuncs = nHashFuncs
self.nTweak = nTweak
self.nFlags = nFlags
def deserialize(self, f):
self.data = deser_string(f)
self.nHashFuncs = struct.unpack("<I", f.read(4))[0]
self.nTweak = struct.unpack("<I", f.read(4))[0]
self.nFlags = struct.unpack("<B", f.read(1))[0]
def serialize(self):
r = b""
r += ser_string(self.data)
r += struct.pack("<I", self.nHashFuncs)
r += struct.pack("<I", self.nTweak)
r += struct.pack("<B", self.nFlags)
return r
def __repr__(self):
return "msg_filterload(data={}, nHashFuncs={}, nTweak={}, nFlags={})".format(
self.data, self.nHashFuncs, self.nTweak, self.nFlags)
class msg_filteradd:
__slots__ = ("data")
msgtype = b"filteradd"
def __init__(self, data):
self.data = data
def deserialize(self, f):
self.data = deser_string(f)
def serialize(self):
r = b""
r += ser_string(self.data)
return r
def __repr__(self):
return "msg_filteradd(data={})".format(self.data)
class msg_filterclear:
__slots__ = ()
msgtype = b"filterclear"
def __init__(self):
pass
def deserialize(self, f):
pass
def serialize(self):
return b""
def __repr__(self):
return "msg_filterclear()"
class msg_feefilter:
__slots__ = ("feerate",)
msgtype = b"feefilter"
def __init__(self, feerate=0):
self.feerate = feerate
def deserialize(self, f):
self.feerate = struct.unpack("<Q", f.read(8))[0]
def serialize(self):
r = b""
r += struct.pack("<Q", self.feerate)
return r
def __repr__(self):
return "msg_feefilter(feerate=%08x)" % self.feerate
class msg_sendcmpct:
__slots__ = ("announce", "version")
msgtype = b"sendcmpct"
def __init__(self, announce=False, version=1):
self.announce = announce
self.version = version
def deserialize(self, f):
self.announce = struct.unpack("<?", f.read(1))[0]
self.version = struct.unpack("<Q", f.read(8))[0]
def serialize(self):
r = b""
r += struct.pack("<?", self.announce)
r += struct.pack("<Q", self.version)
return r
def __repr__(self):
return "msg_sendcmpct(announce=%s, version=%lu)" % (self.announce, self.version)
class msg_cmpctblock:
__slots__ = ("header_and_shortids",)
msgtype = b"cmpctblock"
def __init__(self, header_and_shortids = None):
self.header_and_shortids = header_and_shortids
def deserialize(self, f):
self.header_and_shortids = P2PHeaderAndShortIDs()
self.header_and_shortids.deserialize(f)
def serialize(self):
r = b""
r += self.header_and_shortids.serialize()
return r
def __repr__(self):
return "msg_cmpctblock(HeaderAndShortIDs=%s)" % repr(self.header_and_shortids)
class msg_getblocktxn:
__slots__ = ("block_txn_request",)
msgtype = b"getblocktxn"
def __init__(self):
self.block_txn_request = None
def deserialize(self, f):
self.block_txn_request = BlockTransactionsRequest()
self.block_txn_request.deserialize(f)
def serialize(self):
r = b""
r += self.block_txn_request.serialize()
return r
def __repr__(self):
return "msg_getblocktxn(block_txn_request=%s)" % (repr(self.block_txn_request))
class msg_blocktxn:
__slots__ = ("block_transactions",)
msgtype = b"blocktxn"
def __init__(self):
self.block_transactions = BlockTransactions()
def deserialize(self, f):
self.block_transactions.deserialize(f)
def serialize(self):
r = b""
r += self.block_transactions.serialize()
return r
def __repr__(self):
return "msg_blocktxn(block_transactions=%s)" % (repr(self.block_transactions))
class msg_no_witness_blocktxn(msg_blocktxn):
__slots__ = ()
def serialize(self):
return self.block_transactions.serialize(with_witness=False)
class msg_getcfilters:
__slots__ = ("filter_type", "start_height", "stop_hash")
msgtype = b"getcfilters"
def __init__(self, filter_type, start_height, stop_hash):
self.filter_type = filter_type
self.start_height = start_height
self.stop_hash = stop_hash
def deserialize(self, f):
self.filter_type = struct.unpack("<B", f.read(1))[0]
self.start_height = struct.unpack("<I", f.read(4))[0]
self.stop_hash = deser_uint256(f)
def serialize(self):
r = b""
r += struct.pack("<B", self.filter_type)
r += struct.pack("<I", self.start_height)
r += ser_uint256(self.stop_hash)
return r
def __repr__(self):
return "msg_getcfilters(filter_type={:#x}, start_height={}, stop_hash={:x})".format(
self.filter_type, self.start_height, self.stop_hash)
class msg_cfilter:
__slots__ = ("filter_type", "block_hash", "filter_data")
msgtype = b"cfilter"
def __init__(self, filter_type=None, block_hash=None, filter_data=None):
self.filter_type = filter_type
self.block_hash = block_hash
self.filter_data = filter_data
def deserialize(self, f):
self.filter_type = struct.unpack("<B", f.read(1))[0]
self.block_hash = deser_uint256(f)
self.filter_data = deser_string(f)
def serialize(self):
r = b""
r += struct.pack("<B", self.filter_type)
r += ser_uint256(self.block_hash)
r += ser_string(self.filter_data)
return r
def __repr__(self):
return "msg_cfilter(filter_type={:#x}, block_hash={:x})".format(
self.filter_type, self.block_hash)
class msg_getcfheaders:
__slots__ = ("filter_type", "start_height", "stop_hash")
msgtype = b"getcfheaders"
def __init__(self, filter_type, start_height, stop_hash):
self.filter_type = filter_type
self.start_height = start_height
self.stop_hash = stop_hash
def deserialize(self, f):
self.filter_type = struct.unpack("<B", f.read(1))[0]
self.start_height = struct.unpack("<I", f.read(4))[0]
self.stop_hash = deser_uint256(f)
def serialize(self):
r = b""
r += struct.pack("<B", self.filter_type)
r += struct.pack("<I", self.start_height)
r += ser_uint256(self.stop_hash)
return r
def __repr__(self):
return "msg_getcfheaders(filter_type={:#x}, start_height={}, stop_hash={:x})".format(
self.filter_type, self.start_height, self.stop_hash)
class msg_cfheaders:
__slots__ = ("filter_type", "stop_hash", "prev_header", "hashes")
msgtype = b"cfheaders"
def __init__(self, filter_type=None, stop_hash=None, prev_header=None, hashes=None):
self.filter_type = filter_type
self.stop_hash = stop_hash
self.prev_header = prev_header
self.hashes = hashes
def deserialize(self, f):
self.filter_type = struct.unpack("<B", f.read(1))[0]
self.stop_hash = deser_uint256(f)
self.prev_header = deser_uint256(f)
self.hashes = deser_uint256_vector(f)
def serialize(self):
r = b""
r += struct.pack("<B", self.filter_type)
r += ser_uint256(self.stop_hash)
r += ser_uint256(self.prev_header)
r += ser_uint256_vector(self.hashes)
return r
def __repr__(self):
return "msg_cfheaders(filter_type={:#x}, stop_hash={:x})".format(
self.filter_type, self.stop_hash)
class msg_getcfcheckpt:
__slots__ = ("filter_type", "stop_hash")
msgtype = b"getcfcheckpt"
def __init__(self, filter_type, stop_hash):
self.filter_type = filter_type
self.stop_hash = stop_hash
def deserialize(self, f):
self.filter_type = struct.unpack("<B", f.read(1))[0]
self.stop_hash = deser_uint256(f)
def serialize(self):
r = b""
r += struct.pack("<B", self.filter_type)
r += ser_uint256(self.stop_hash)
return r
def __repr__(self):
return "msg_getcfcheckpt(filter_type={:#x}, stop_hash={:x})".format(
self.filter_type, self.stop_hash)
class msg_cfcheckpt:
__slots__ = ("filter_type", "stop_hash", "headers")
msgtype = b"cfcheckpt"
def __init__(self, filter_type=None, stop_hash=None, headers=None):
self.filter_type = filter_type
self.stop_hash = stop_hash
self.headers = headers
def deserialize(self, f):
self.filter_type = struct.unpack("<B", f.read(1))[0]
self.stop_hash = deser_uint256(f)
self.headers = deser_uint256_vector(f)
def serialize(self):
r = b""
r += struct.pack("<B", self.filter_type)
r += ser_uint256(self.stop_hash)
r += ser_uint256_vector(self.headers)
return r
def __repr__(self):
return "msg_cfcheckpt(filter_type={:#x}, stop_hash={:x})".format(
self.filter_type, self.stop_hash)
| 28.754199 | 262 | 0.604537 | [
"MIT"
] | UFO-ETL/ufo | test/functional/test_framework/messages.py | 51,355 | Python |
def main():
def Problem1():
def Problem2():
def Problem3():
def Problem4():
def Problem1():
def countFunction
for .count(0 <= NUMBER)
def Problem2():
userInput = ""
while(userInput != 'q'):
userInput = input("Enter something")
def Problem3():
def addFunction
def subtractFunction
def multiplyFunction
def divideFunction
userInput = input("Enter two numbers")
.run(addFunction)
.run(subtractFunction)
.run(multiplyFunction)
.run(divideFunction)
def Problem4():
userInput = input("Give me a number")
def twoNumbersFunction
userInput2 = input("Do you want to add, subtract, multiply, or divide the two numbers?")
return str
print(userInput)
print(userInput2)
print(result)
| 20.171429 | 88 | 0.719547 | [
"Apache-2.0"
] | cs-fullstack-2019-spring/python-functions-cw-rdunavant | CW.py | 706 | Python |
# -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = u'Messages'
copyright = u'2018, Tim Phillips'
author = u'Tim Phillips, Tasha Chin'
# The short X.Y version
version = u''
# The full version, including alpha/beta/rc tags
release = u''
# -- General configuration ---------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The master toctree document.
master_doc = 'index'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store']
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = None
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'alabaster'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# The default sidebars (for documents that don't match any pattern) are
# defined by theme itself. Builtin themes are using these templates by
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
# 'searchbox.html']``.
#
# html_sidebars = {}
# -- Options for HTMLHelp output ---------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'Messagesdoc'
# -- Options for LaTeX output ------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'Messages.tex', u'Messages Documentation',
u'Tasha Chin', 'manual'),
]
# -- Options for manual page output ------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'messages', u'Messages Documentation',
[author], 1)
]
# -- Options for Texinfo output ----------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'Messages', u'Messages Documentation',
author, 'Messages', 'One line description of project.',
'Miscellaneous'),
]
# -- Options for Epub output -------------------------------------------------
# Bibliographic Dublin Core info.
epub_title = project
# The unique identifier of the text. This can be a ISBN number
# or the project homepage.
#
# epub_identifier = ''
# A unique identification for the text.
#
# epub_uid = ''
# A list of files that should not be packed into the epub file.
epub_exclude_files = ['search.html'] | 29.809249 | 79 | 0.653481 | [
"MIT"
] | Ipv6Python/messages | docs/conf.py | 5,157 | Python |
from unittest import TestCase, mock
from bulksms.sms import send_single, send_bulk
class BulkSMSTestCase(TestCase):
def test_send_single_sms(self):
# Mock send single sms function.
mock_send_single = mock.create_autospec(send_single, return_value='results')
mock_send_single('0831234567', 'Message.')
mock_send_single.assert_called_once_with('0831234567', 'Message.')
self.assertEqual(mock_send_single.call_count, 1)
def test_send_bulk_sms(self):
# Mock send bulk sms function.
mock_send_bulk = mock.create_autospec(send_bulk, return_value='results')
mock_send_bulk('test.txt')
mock_send_bulk.assert_called_once_with('test.txt')
self.assertEqual(mock_send_bulk.call_count, 1)
| 38.4 | 84 | 0.729167 | [
"MIT"
] | tsotetsi/django-bulksms | tests/unit/test_bulksms.py | 768 | Python |
import os
from options.train_options import TrainOptions
from models import create_model
from util.visualizer import save_images
from util import html
from PIL import Image
import string
import torch
import torchvision
import torchvision.transforms as transforms
import coremltools as ct
from util import util
import numpy as np
opt = TrainOptions().gather_options()
opt.isTrain = True
opt.name = "siggraph_caffemodel"
opt.mask_cent = 0
# opt.name = "siggraph_retrained"
opt.gpu_ids = []
opt.load_model = True
opt.num_threads = 1 # test code only supports num_threads = 1
opt.batch_size = 1 # test code only supports batch_size = 1
opt.display_id = -1 # no visdom display
opt.phase = 'val'
opt.dataroot = './dataset/ilsvrc2012/%s/' % opt.phase
opt.serial_batches = True
opt.aspect_ratio = 1.
# process opt.suffix
if opt.suffix:
suffix = ('_' + opt.suffix.format(**vars(opt))
) if opt.suffix != '' else ''
opt.name = opt.name + suffix
opt.A = 2 * opt.ab_max / opt.ab_quant + 1
opt.B = opt.A
class Colorization(torch.nn.Module):
def __init__(self):
super(Colorization, self).__init__()
model = create_model(opt)
model.setup(opt)
model.eval()
self.model = model
def forward(self, image, hint):
data = {
"A": image[:, 0:1, :, :],
"B": image[:, 1:3, :, :],
"hint_B": hint[:, 0:2, :, :],
"mask_B": hint[:, 2:3, :, :]
}
# with torch.no_grad():
self.model.set_input(data)
self.model.forward()
fake_reg = torch.cat((self.model.real_A, self.model.fake_B_reg), dim=1)
return fake_reg
image_path = "./large.JPG"
image = Image.open(image_path)
image = transforms.Compose([
transforms.Resize(512),
transforms.ToTensor(),
])(image)
image = image.view(1, *image.shape)
image = util.crop_mult(image, mult=8, HWmax=[4032, 4032])
transforms.ToPILImage()(image[0]).show(command='fim')
data = util.get_colorization_data(
[image], opt, ab_thresh=0., p=0.125)
img = torch.cat((data["A"], data["B"]), dim=1)
hint = torch.cat((data["hint_B"], data["mask_B"]), dim=1)
# print(data["mask_B"], data["hint_B"])
# data["hint_B"] = torch.zeros_like(data["hint_B"])
# data["mask_B"] = torch.zeros_like(data["mask_B"])
# model = Colorization()
with torch.no_grad():
model = Colorization()
model.eval()
for param in model.parameters():
param.requires_grad = False
model.model.set_requires_grad(model.model.netG)
# model(data)
# transforms.ToPILImage()(image[0]).show(command='fim')
# to_visualize = ['gray', 'hint', 'hint_ab', 'fake_entr',
# 'real', 'fake_reg', 'real_ab', 'fake_ab_reg', ]
# visuals = util.get_subset_dict(
# model.model.get_current_visuals(), to_visualize)
# for key, value in visuals.items():
# print(key)
# transforms.ToPILImage()(value[0]).show(command='fim')
output = model(img, hint)
output = util.lab2rgb(output, opt=opt)
transforms.ToPILImage()(output[0]).show(command='fim')
traced_model = torch.jit.trace(
model, (img, hint), check_trace=False)
mlmodel = ct.convert(model=traced_model, inputs=[
ct.TensorType(name="image", shape=ct.Shape(
shape=(1, 3, ct.RangeDim(1, 4096), ct.RangeDim(1, 4096)))),
ct.TensorType(name="hint", shape=ct.Shape(
shape=(1, 3, ct.RangeDim(1, 4096), ct.RangeDim(1, 4096)))),
])
mlmodel.save("~/color.mlmodel")
| 29.222222 | 79 | 0.65253 | [
"MIT"
] | zengxinzhy/colorization-pytorch | imtest.py | 3,419 | Python |
from __future__ import division
from itertools import combinations_with_replacement
import numpy as np
import math
import sys
def shuffle_data(X, y, seed=None):
if seed:
np.random.seed(seed)
n_samples = X.shape[0]
idx = np.arange(n_samples)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
return X, y
# Divide dataset based on if sample value on feature index is larger than
# the given threshold
def divide_on_feature(X, feature_i, threshold):
split_func = None
if isinstance(threshold, int) or isinstance(threshold, float):
split_func = lambda sample: sample[feature_i] >= threshold
else:
split_func = lambda sample: sample[feature_i] == threshold
X_1 = np.array([sample for sample in X if split_func(sample)])
X_2 = np.array([sample for sample in X if not split_func(sample)])
return np.array([X_1, X_2])
def polynomial_features(X, degree):
n_samples, n_features = np.shape(X)
def index_combinations():
combs = [combinations_with_replacement(range(n_features), i) for i in range(0, degree + 1)]
flat_combs = [item for sublist in combs for item in sublist]
return flat_combs
combinations = index_combinations()
n_output_features = len(combinations)
X_new = np.empty((n_samples, n_output_features))
for i, index_combs in enumerate(combinations):
X_new[:, i] = np.prod(X[:, index_combs], axis=1)
return X_new
# Return random subsets (with replacements) of the data
def get_random_subsets(X, y, n_subsets, replacements=True):
n_samples = np.shape(X)[0]
# Concatenate x and y and do a random shuffle
X_y = np.concatenate((X, y.reshape((1, len(y))).T), axis=1)
np.random.shuffle(X_y)
subsets = []
# Uses 50% of training samples without replacements
subsample_size = n_samples // 2
if replacements:
subsample_size = n_samples # 100% with replacements
for _ in range(n_subsets):
idx = np.random.choice(
range(n_samples),
size=np.shape(range(subsample_size)),
replace=replacements)
X = X_y[idx][:, :-1]
y = X_y[idx][:, -1]
subsets.append([X, y])
return subsets
# Normalize the dataset X
def normalize(X, axis=-1, order=2):
l2 = np.atleast_1d(np.linalg.norm(X, order, axis))
l2[l2 == 0] = 1
return X / np.expand_dims(l2, axis)
# Standardize the dataset X
def standardize(X):
X_std = X
mean = X.mean(axis=0)
std = X.std(axis=0)
for col in range(np.shape(X)[1]):
if std[col]:
X_std[:, col] = (X_std[:, col] - mean[col]) / std[col]
# X_std = (X - X.mean(axis=0)) / X.std(axis=0)
return X_std
# Split the data into train and test sets
def train_test_split(X, y, test_size=0.5, shuffle=True, seed=None):
if shuffle:
X, y = shuffle_data(X, y, seed)
# Split the training data from test data in the ratio specified in
# test_size
split_i = len(y) - int(len(y) // (1 / test_size))
x_train, x_test = X[:split_i], X[split_i:]
y_train, y_test = y[:split_i], y[split_i:]
return x_train, x_test, y_train, y_test
# Split the data into k sets of training / test data
def k_fold_cross_validation_sets(X, y, k, shuffle=True):
if shuffle:
X, y = shuffle_data(X, y)
n_samples = len(y)
left_overs = {}
n_left_overs = (n_samples % k)
if n_left_overs != 0:
left_overs["X"] = X[-n_left_overs:]
left_overs["y"] = y[-n_left_overs:]
X = X[:-n_left_overs]
y = y[:-n_left_overs]
X_split = np.split(X, k)
y_split = np.split(y, k)
sets = []
for i in range(k):
X_test, y_test = X_split[i], y_split[i]
X_train = np.concatenate(X_split[:i] + X_split[i + 1:], axis=0)
y_train = np.concatenate(y_split[:i] + y_split[i + 1:], axis=0)
sets.append([X_train, X_test, y_train, y_test])
# Add left over samples to last set as training samples
if n_left_overs != 0:
np.append(sets[-1][0], left_overs["X"], axis=0)
np.append(sets[-1][2], left_overs["y"], axis=0)
return np.array(sets)
# Making an array of nominal values into a binarized matrix
def categorical_to_binary(x):
n_col = np.amax(x) + 1
binarized = np.zeros((len(x), n_col))
for i in range(len(x)):
binarized[i, x[i]] = 1
return binarized
# Converting from binary vectors to nominal values
def binary_to_categorical(x):
categorical = []
for i in range(len(x)):
if not 1 in x[i]:
categorical.append(0)
else:
i_where_one = np.where(x[i] == 1)[0][0]
categorical.append(i_where_one)
return categorical
# Converts a vector into an diagonal matrix
def make_diagonal(x):
m = np.zeros((len(x), len(x)))
for i in range(len(m[0])):
m[i, i] = x[i]
return m
| 29.125 | 99 | 0.626405 | [
"MIT"
] | MachineLearningCommunity/ML-From-Scratch | mlfromscratch/utils/data_manipulation.py | 4,893 | Python |
'''
This file is a part of Test Mile Arjuna
Copyright 2018 Test Mile Software Testing Pvt Ltd
Website: www.TestMile.com
Email: support [at] testmile.com
Creator: Rahul Verma
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 arjuna.core.exceptions import *
class DataSourceFinished(StopIteration):
def __init__(self, msg=None):
super().__init__(msg is None and "Done" or msg)
class EmptyListDataRecordLookupException(Exception):
def __init__(self, index):
super().__init__("Invalid index [%s] used for list data record lookup. It is empty.".format(index))
class ListDataRecordLookupException(Exception):
def __init__(self, index, max_index):
super().__init__(
"Invalid index [%s] used for list data record lookup. Use indices between 0 and %d".format(index,
max_index))
class MapDataRecordLookupException(Exception):
def __init__(self, key):
super().__init__("Invalid Key/header [%s] used for map data record lookup.".format(key))
class DataSourceConstructionError(Exception):
def __init__(self, message, name, exc):
super().__init__(message)
self.name = name
self.exc = exc
def get_Exception(self):
return self.exc
def get_Name(self):
return self.name
class InvalidTestObjectException(Exception):
pass
class SessionNodesFinishedException(Exception):
def __init__(self):
super().__init__("Done")
class SubTestsFinished(Exception):
def __init__(self):
super().__init__("Done")
class TestGroupsFinishedException(Exception):
def __init__(self):
super().__init__("Done")
class PickerMisConfigurationException(Exception):
def __init__(self):
super().__init__("Picker is misconfigured.")
# Test Result Related
class StepResultEvent(ArjunaException):
def __init__(self, step):
super().__init__(step.assert_message)
self.step = step
class Pass(StepResultEvent):
def __init__(self, check):
super().__init__(check)
class Error(StepResultEvent):
def __init__(self, step):
super().__init__(step)
class Failure(StepResultEvent):
def __init__(self, step):
super().__init__(step)
class DependencyNotMet(Exception):
def __init__(self, iid):
self.iid = iid | 27.485714 | 114 | 0.688496 | [
"Apache-2.0"
] | test-mile/arjuna | arjuna/engine/unitee/exceptions.py | 2,886 | Python |
# 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.
"""General utilities."""
import numpy
from pytorch_lightning.utilities.apply_func import move_data_to_device # noqa: F401
from pytorch_lightning.utilities.distributed import AllGatherGrad # noqa: F401
from pytorch_lightning.utilities.enums import ( # noqa: F401
_AcceleratorType,
_StrategyType,
AMPType,
DistributedType,
GradClipAlgorithmType,
LightningEnum,
ModelSummaryMode,
)
from pytorch_lightning.utilities.grads import grad_norm # noqa: F401
from pytorch_lightning.utilities.imports import ( # noqa: F401
_APEX_AVAILABLE,
_BAGUA_AVAILABLE,
_DEEPSPEED_AVAILABLE,
_FAIRSCALE_AVAILABLE,
_FAIRSCALE_FULLY_SHARDED_AVAILABLE,
_FAIRSCALE_OSS_FP16_BROADCAST_AVAILABLE,
_GROUP_AVAILABLE,
_HIVEMIND_AVAILABLE,
_HOROVOD_AVAILABLE,
_HPU_AVAILABLE,
_HYDRA_AVAILABLE,
_HYDRA_EXPERIMENTAL_AVAILABLE,
_IPU_AVAILABLE,
_IS_INTERACTIVE,
_IS_WINDOWS,
_module_available,
_OMEGACONF_AVAILABLE,
_POPTORCH_AVAILABLE,
_RICH_AVAILABLE,
_TORCH_GREATER_EQUAL_1_9,
_TORCH_GREATER_EQUAL_1_10,
_TORCH_GREATER_EQUAL_1_11,
_TORCH_QUANTIZE_AVAILABLE,
_TORCHTEXT_AVAILABLE,
_TORCHVISION_AVAILABLE,
_TPU_AVAILABLE,
_XLA_AVAILABLE,
)
from pytorch_lightning.utilities.parameter_tying import find_shared_parameters, set_shared_parameters # noqa: F401
from pytorch_lightning.utilities.parsing import AttributeDict, flatten_dict, is_picklable # noqa: F401
from pytorch_lightning.utilities.rank_zero import ( # noqa: F401
rank_zero_deprecation,
rank_zero_info,
rank_zero_only,
rank_zero_warn,
)
FLOAT16_EPSILON = numpy.finfo(numpy.float16).eps
FLOAT32_EPSILON = numpy.finfo(numpy.float32).eps
FLOAT64_EPSILON = numpy.finfo(numpy.float64).eps
| 33.169014 | 115 | 0.780042 | [
"Apache-2.0"
] | jerome-habana/pytorch-lightning | pytorch_lightning/utilities/__init__.py | 2,355 | Python |
"""
WSGI config for poker project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'poker.settings')
application = get_wsgi_application()
| 22.764706 | 78 | 0.782946 | [
"MIT"
] | yuriymironov96/python-poker | poker/wsgi.py | 387 | Python |
#!/usr/bin/env python
# Import required modules
from __future__ import print_function
from future import standard_library
standard_library.install_aliases()
from builtins import str
import os
import argparse
import subprocess
import ICA_AROMA_functions as aromafunc
import shutil
import classification_plots
# Change to script directory
cwd = os.path.realpath(os.path.curdir)
scriptDir = os.path.dirname(os.path.abspath(__file__))
os.chdir(scriptDir)
#-------------------------------------------- PARSER --------------------------------------------#
parser = argparse.ArgumentParser(
description=
'Script to run ICA-AROMA v0.3 beta (\'ICA-based Automatic Removal Of Motion Artifacts\') on fMRI data. See the companion manual for further information.'
)
# Required options
reqoptions = parser.add_argument_group('Required arguments')
reqoptions.add_argument(
'-o', '-out', dest="outDir", required=True, help='Output directory name')
# Required options in non-Feat mode
nonfeatoptions = parser.add_argument_group('Required arguments - generic mode')
nonfeatoptions.add_argument(
'-i',
'-in',
dest="inFile",
required=False,
help='Input file name of fMRI data (.nii.gz)')
nonfeatoptions.add_argument(
'-mc',
dest="mc",
required=False,
help=
'File name of the motion parameters obtained after motion realingment (e.g., FSL mcflirt). Note that the order of parameters does not matter, should your file not originate from FSL mcflirt. (e.g., /home/user/PROJECT/SUBJECT.feat/mc/prefiltered_func_data_mcf.par'
)
nonfeatoptions.add_argument(
'-a',
'-affmat',
dest="affmat",
default="",
help=
'File name of the mat-file describing the affine registration (e.g., FSL FLIRT) of the functional data to structural space (.mat file). (e.g., /home/user/PROJECT/SUBJECT.feat/reg/example_func2highres.mat'
)
nonfeatoptions.add_argument(
'-w',
'-warp',
dest="warp",
default="",
help=
'File name of the warp-file describing the non-linear registration (e.g., FSL FNIRT) of the structural data to MNI152 space (.nii.gz). (e.g., /home/user/PROJECT/SUBJECT.feat/reg/highres2standard_warp.nii.gz'
)
nonfeatoptions.add_argument(
'-m',
'-mask',
dest="mask",
default="",
help=
'File name of the mask to be used for MELODIC (denoising will be performed on the original/non-masked input data)'
)
# Required options in Feat mode
featoptions = parser.add_argument_group('Required arguments - FEAT mode')
featoptions.add_argument(
'-f',
'-feat',
dest="inFeat",
required=False,
help=
'Feat directory name (Feat should have been run without temporal filtering and including registration to MNI152)'
)
# Optional options
optoptions = parser.add_argument_group('Optional arguments')
optoptions.add_argument('-tr', dest="TR", help='TR in seconds', type=float)
optoptions.add_argument(
'-den',
dest="denType",
default="nonaggr",
help=
'Type of denoising strategy: \'no\': only classification, no denoising; \'nonaggr\': non-aggresssive denoising (default); \'aggr\': aggressive denoising; \'both\': both aggressive and non-aggressive denoising (seperately)'
)
optoptions.add_argument(
'-md',
'-meldir',
dest="melDir",
default="",
help='MELODIC directory name, in case MELODIC has been run previously.')
optoptions.add_argument(
'-dim',
dest="dim",
default=0,
help=
'Dimensionality reduction into #num dimensions when running MELODIC (default: automatic estimation; i.e. -dim 0)',
type=int)
optoptions.add_argument(
'-ow',
'-overwrite',
dest="overwrite",
action='store_true',
help='Overwrite existing output',
default=False)
print(
'\n------------------------------- RUNNING ICA-AROMA ------------------------------- '
)
print(
'--------------- \'ICA-based Automatic Removal Of Motion Artifacts\' --------------- \n'
)
#--------------------------------------- PARSE ARGUMENTS ---------------------------------------#
args = parser.parse_args()
# Define variables based on the type of input (i.e. Feat directory or specific input arguments), and check whether the specified files exist.
cancel = False
if args.inFeat:
inFeat = args.inFeat
# Check whether the Feat directory exists
if not os.path.isdir(inFeat):
print('The specified Feat directory does not exist.')
print(
'\n----------------------------- ICA-AROMA IS CANCELED -----------------------------\n'
)
exit()
# Define the variables which should be located in the Feat directory
inFile = os.path.join(args.inFeat, 'filtered_func_data.nii.gz')
mc = os.path.join(args.inFeat, 'mc', 'prefiltered_func_data_mcf.par')
affmat = os.path.join(args.inFeat, 'reg', 'example_func2highres.mat')
warp = os.path.join(args.inFeat, 'reg', 'highres2standard_warp.nii.gz')
# Check whether these files actually exist
if not os.path.isfile(inFile):
print('Missing filtered_func_data.nii.gz in Feat directory.')
cancel = True
if not os.path.isfile(mc):
print('Missing mc/prefiltered_func_data_mcf.mat in Feat directory.')
cancel = True
if not os.path.isfile(affmat):
print('Missing reg/example_func2highres.mat in Feat directory.')
cancel = True
if not os.path.isfile(warp):
print('Missing reg/highres2standard_warp.nii.gz in Feat directory.')
cancel = True
# Check whether a melodic.ica directory exists
if os.path.isdir(os.path.join(args.inFeat, 'filtered_func_data.ica')):
melDir = os.path.join(args.inFeat, 'filtered_func_data.ica')
else:
melDir = args.melDir
else:
inFile = args.inFile
mc = args.mc
affmat = args.affmat
warp = args.warp
melDir = args.melDir
# Check whether the files exist
if not inFile:
print('No input file specified.')
else:
if not os.path.isfile(inFile):
print('The specified input file does not exist.')
cancel = True
if not mc:
print('No mc file specified.')
else:
if not os.path.isfile(mc):
print('The specified mc file does does not exist.')
cancel = True
if affmat:
if not os.path.isfile(affmat):
print('The specified affmat file does not exist.')
cancel = True
if warp:
if not os.path.isfile(warp):
print('The specified warp file does not exist.')
cancel = True
# Parse the arguments which do not depend on whether a Feat directory has been specified
outDir = args.outDir
dim = args.dim
denType = args.denType
# Check if the mask exists, when specified.
if args.mask:
if not os.path.isfile(args.mask):
print('The specified mask does not exist.')
cancel = True
# Check if the type of denoising is correctly specified, when specified
if not (denType == 'nonaggr') and not (denType == 'aggr') and not (
denType == 'both') and not (denType == 'no'):
print(
'Type of denoising was not correctly specified. Non-aggressive denoising will be run.'
)
denType = 'nonaggr'
# If the criteria for file/directory specifications have not been met. Cancel ICA-AROMA.
if cancel:
print(
'\n----------------------------- ICA-AROMA IS CANCELED -----------------------------\n'
)
exit()
#------------------------------------------- PREPARE -------------------------------------------#
# Define the FSL-bin directory
fslDir = os.path.join(os.environ["FSLDIR"], 'bin', '')
# Create output directory if needed
if os.path.isdir(outDir) and args.overwrite is False:
print(
'Output directory', outDir, """already exists.
AROMA will not continue.
Rerun with the -overwrite option to explicitly overwrite existing output."""
)
exit()
elif os.path.isdir(outDir) and args.overwrite is True:
print('Warning! Output directory', outDir,
'exists and will be overwritten.\n')
shutil.rmtree(outDir)
os.makedirs(outDir)
else:
os.makedirs(outDir)
# Get TR of the fMRI data, if not specified
if args.TR:
TR = args.TR
else:
cmd = ' '.join([
os.path.join(fslDir, 'fslinfo'), inFile,
'| grep pixdim4 | awk \'{print $2}\''
])
TR = float(subprocess.getoutput(cmd))
# Check TR
if TR == 1:
print('Warning! Please check whether the determined TR (of ' + str(TR) +
's) is correct!\n')
elif TR == 0:
print(
'TR is zero. ICA-AROMA requires a valid TR and will therefore exit. Please check the header, or define the TR as an additional argument.\n----------------------------- ICA-AROMA IS CANCELED -----------------------------\n'
)
exit()
# Define/create mask. Either by making a copy of the specified mask, or by creating a new one.
mask = os.path.join(outDir, 'mask.nii.gz')
if args.mask:
shutil.copyfile(args.mask, mask)
else:
# If a Feat directory is specified, and an example_func is present use example_func to create a mask
if args.inFeat and os.path.isfile(
os.path.join(inFeat, 'example_func.nii.gz')):
os.system(' '.join([
os.path.join(fslDir, 'bet'),
os.path.join(inFeat, 'example_func.nii.gz'),
os.path.join(outDir, 'bet'), '-f 0.3 -n -m -R'
]))
os.system(' '.join(
['mv', os.path.join(outDir, 'bet_mask.nii.gz'), mask]))
if os.path.isfile(os.path.join(outDir, 'bet.nii.gz')):
os.remove(os.path.join(outDir, 'bet.nii.gz'))
else:
if args.inFeat:
print(
' - No example_func was found in the Feat directory. A mask will be created including all voxels with varying intensity over time in the fMRI data. Please check!\n'
)
os.system(' '.join(
[os.path.join(fslDir, 'fslmaths'), inFile, '-Tstd -bin', mask]))
#---------------------------------------- Run ICA-AROMA ----------------------------------------#
print('Step 1) MELODIC')
aromafunc.runICA(fslDir, inFile, outDir, melDir, mask, dim, TR)
print('Step 2) Automatic classification of the components')
print(' - registering the spatial maps to MNI')
melIC = os.path.join(outDir, 'melodic_IC_thr.nii.gz')
melIC_MNI = os.path.join(outDir, 'melodic_IC_thr_MNI2mm.nii.gz')
aromafunc.register2MNI(fslDir, melIC, melIC_MNI, affmat, warp)
print(' - extracting the CSF & Edge fraction features')
edgeFract, csfFract = aromafunc.feature_spatial(fslDir, outDir, scriptDir,
melIC_MNI)
print(' - extracting the Maximum RP correlation feature')
melmix = os.path.join(outDir, 'melodic.ica', 'melodic_mix')
maxRPcorr = aromafunc.feature_time_series(melmix, mc)
print(' - extracting the High-frequency content feature')
melFTmix = os.path.join(outDir, 'melodic.ica', 'melodic_FTmix')
HFC = aromafunc.feature_frequency(melFTmix, TR)
print(' - classification')
motionICs = aromafunc.classification(outDir, maxRPcorr, edgeFract, HFC,
csfFract)
# classification_plots.classification_plot(os.path.join(outDir, 'classification_overview.txt'),
# outDir)
if (denType != 'no'):
print('Step 3) Data denoising')
aromafunc.denoising(fslDir, inFile, outDir, melmix, denType, motionICs)
# Remove thresholded melodic_IC file
os.remove(melIC)
# Revert to old directory
os.chdir(cwd)
print(
'\n----------------------------------- Finished -----------------------------------\n'
)
| 35.521472 | 267 | 0.629102 | [
"MIT"
] | spunt/bspm | thirdparty/ICA_AROMA_79x95x69/ICA_AROMA.py | 11,580 | Python |
#!/usr/bin/env python
from markovLatex.markov import TextGenerator
import argparse, sys
def main(args):
parser = argparse.ArgumentParser(description="Generate pseudorandom text from input text files.")
parser.add_argument('files', type=str, nargs='+', help='one or more input text files')
parser.add_argument('-p','--paragraphs', type=int, default=1, help='The number of paragraphs in the output text')
args = parser.parse_args(args)
corpus = []
for arg in args.files:
try:
corpus.append(open(arg, 'r'))
except Exception as e:
print(e)
if corpus:
try:
text_generator = TextGenerator(corpus)
for i in range(0, args.paragraphs):
print(text_generator.paragraph())
except Exception as e:
print(e)
return 1
return 0
if __name__ == '__main__':
sys.exit(main(sys.argv[1:]))
| 27.617647 | 117 | 0.614483 | [
"MIT"
] | SeanMcGrath/MarkovLatex | markovgen.py | 939 | Python |
# -*- coding: utf-8 -*-
# Copyright Hannah von Reth <[email protected]>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
# OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
# SUCH DAMAGE.
### fetch functions
from CraftCore import CraftCore
from CraftDebug import deprecated
import utils
import io
import os
import urllib
import subprocess
import sys
import re
def getFile(url, destdir, filename='', quiet=None) -> bool:
"""download file from 'url' into 'destdir'"""
if quiet is None:
quiet = CraftCore.settings.getboolean("ContinuousIntegration", "Enabled", False)
CraftCore.log.debug("getFile called. url: %s" % url)
if url == "":
CraftCore.log.error("fetch: no url given")
return False
pUrl = urllib.parse.urlparse(url)
if not filename:
filename = os.path.basename(pUrl.path)
utils.createDir(destdir)
if pUrl.scheme == "s3":
return s3File(url, destdir, filename)
elif pUrl.scheme == "minio":
return minioGet(pUrl.netloc + pUrl.path, destdir, filename)
# curl and wget basically only work when we have a cert store on windows
if not CraftCore.compiler.isWindows or os.path.exists(os.path.join(CraftCore.standardDirs.etcDir(), "cacert.pem")):
if not CraftCore.settings.getboolean("General", "NoWget"):
if CraftCore.cache.findApplication("wget"):
return wgetFile(url, destdir, filename, quiet)
if CraftCore.cache.findApplication("curl"):
return curlFile(url, destdir, filename, quiet)
if os.path.exists(os.path.join(destdir, filename)):
return True
powershell = CraftCore.cache.findApplication("powershell")
if powershell:
filename = os.path.join(destdir, filename)
return utils.system([powershell, "-NoProfile", "-ExecutionPolicy", "ByPass", "-Command",
f"[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; (new-object net.webclient).DownloadFile(\"{url}\", \"{filename}\")"])
else:
def dlProgress(count, blockSize, totalSize):
if totalSize != -1:
percent = int(count * blockSize * 100 / totalSize)
utils.printProgress(percent)
else:
sys.stdout.write(("\r%s bytes downloaded" % (count * blockSize)))
sys.stdout.flush()
try:
urllib.request.urlretrieve(url, filename=os.path.join(destdir, filename),
reporthook=dlProgress if CraftCore.debug.verbose() >= 0 else None)
except Exception as e:
CraftCore.log.warning(e)
return False
if CraftCore.debug.verbose() >= 0:
sys.stdout.write("\n")
sys.stdout.flush()
return True
def curlFile(url, destdir, filename, quiet):
"""download file with curl from 'url' into 'destdir', if filename is given to the file specified"""
curl = CraftCore.cache.findApplication("curl")
command = [curl, "-C", "-", "--retry", "10", "-L", "--ftp-ssl", "--fail"]
cert = os.path.join(CraftCore.standardDirs.etcDir(), "cacert.pem")
if os.path.exists(cert):
command += ["--cacert", cert]
# the default of 20 might not be enough for sourceforge ...
command += ["--max-redirs", "50"]
command += ["-o", os.path.join(destdir, filename)]
command += [url]
CraftCore.log.debug("curlfile called")
if CraftCore.debug.verbose() < 1:
if quiet:
with io.StringIO() as tmp:
ciMode = CraftCore.settings.getboolean("ContinuousIntegration", "Enabled", False)
if ciMode:
command += ["-v"]
if not utils.system(command, logCommand=ciMode, stdout=tmp, stderr=subprocess.STDOUT):
CraftCore.log.warning(tmp.getvalue())
return False
if ciMode:
loc = re.findall(r"Host: ([^\s]+)", tmp.getvalue())
if loc:
CraftCore.log.info(f"Downloaded from: {loc[-1]}")
return True
elif CraftCore.cache.checkCommandOutputFor(curl, "--progress-bar"):
command += ["--progress-bar"]
CraftCore.log.info(f"curl {url}")
return utils.system(command, displayProgress=True, logCommand=False, stderr=subprocess.STDOUT)
command += ["-v"]
return utils.system(command)
def wgetFile(url, destdir, filename, quiet):
"""download file with wget from 'url' into 'destdir', if filename is given to the file specified"""
wget = CraftCore.cache.findApplication("wget")
command = [wget, "-c", "-t", "10"]
cert = os.path.join(CraftCore.standardDirs.etcDir(), "cacert.pem")
if os.path.exists(cert):
command += ["--ca-certificate", cert]
# the default of 20 might not be enough for sourceforge ...
command += ["--max-redirect", "50"]
if CraftCore.settings.getboolean("General", "EMERGE_NO_PASSIVE_FTP", False):
command += ["--no-passive-ftp"]
if not filename:
command += ["-P", destdir]
else:
command += ["-O", os.path.join(destdir, filename)]
command += [url]
if CraftCore.debug.verbose() < 1:
if quiet:
with io.StringIO() as tmp:
ciMode = CraftCore.settings.getboolean("ContinuousIntegration", "Enabled", False)
if not utils.system(command, logCommand=ciMode, stdout=tmp, stderr=subprocess.STDOUT):
CraftCore.log.warning(tmp.getvalue())
return False
if ciMode:
loc = re.findall(r"Location: ([^\s]+)", tmp.getvalue())
if loc:
CraftCore.log.info(f"Downloaded from: {loc[-1]}")
return True
elif CraftCore.cache.checkCommandOutputFor(wget, "--show-progress"):
command += ["-q", "--show-progress"]
CraftCore.log.info(f"wget {url}")
return utils.system(command, displayProgress=True, logCommand=False, stderr=subprocess.STDOUT)
return utils.system(command)
def s3File(url : str, destdir : str, filename : str) ->bool:
aws = CraftCore.cache.findApplication("aws")
if not aws:
CraftCore.log.critical("aws not found, please install awscli. \"pip install awscli\" ")
return False
return utils.system([aws, "s3", "cp", url, os.path.join(destdir, filename)])
def minioGet(url : str, destdir : str, filename : str) ->bool:
minio = None
if CraftCore.compiler.isWindows:
minio = CraftCore.cache.findApplication("minio")
if not minio:
minio = CraftCore.cache.findApplication("mc")
if not minio:
CraftCore.log.critical("minio client not found, please install minio")
return False
return utils.system([minio, "cp", url, os.path.join(destdir, filename)])
| 43.448649 | 175 | 0.635357 | [
"BSD-2-Clause"
] | C-EO/craft | bin/Utils/GetFiles.py | 8,038 | Python |
#!/usr/bin/env python
import unittest
import Cheetah
import Cheetah.Parser
import Cheetah.Template
class Chep_2_Conditionalized_Import_Behavior(unittest.TestCase):
def test_ModuleLevelImport(self):
''' Verify module level (traditional) import behavior '''
pass
def test_InlineImport(self):
''' Verify (new) inline import behavior works '''
template = '''
#def funky($s)
#try
#import urllib
#except ImportError
#pass
#end try
#return urllib.quote($s)
#end def
'''
try:
template = Cheetah.Template.Template.compile(template)
except Cheetah.Parser.ParseError, ex:
self.fail('Failed to properly generate code %s' % ex)
template = template()
rc = tepmlate.funky('abc def')
assert rc == 'abc+def'
def test_LegacyMode(self):
''' Verify disabling of CHEP #2 works '''
pass
if __name__ == '__main__':
unittest.main()
| 26.95 | 66 | 0.565863 | [
"MIT"
] | analurandis/Tur | backend/venv/Lib/site-packages/Cheetah/Tests/Cheps.py | 1,078 | Python |
import requests
from requests.auth import HTTPBasicAuth
r = requests.get('http://localhost:5000', auth=HTTPBasicAuth('username', 'password'))
print(r.status_code) | 32.6 | 85 | 0.785276 | [
"Apache-2.0"
] | silianpan/seal-spider-demo | requests/demo27.py | 163 | Python |
#!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'zhekudblog.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| 28.636364 | 74 | 0.684127 | [
"MIT"
] | ZhekuD/ZhekuDBlog | src/zhekudblog/manage.py | 630 | Python |
import discord
from discord.ext import commands
from core import checks
from core.models import PermissionLevel
class Suggest(commands.Cog):
"""
Let's you send a suggestion to a designated channel.
"""
def __init__(self, bot):
self.bot = bot
self.coll = bot.plugin_db.get_partition(self)
@commands.command(aliases = ['ssc'])
@checks.has_permissions(PermissionLevel.ADMIN)
async def setsuggestchannel(self, ctx, channel: discord.TextChannel):
"""
Set the channel where suggestions go.
"""
await self.coll.find_one_and_update(
{"_id": "config"},
{"$set": {"suggestion-channel": {"channel": str(channel.id)}}},
upsert=True,
)
embed=discord.Embed(title=f'Set suggestion channel to {channel}.', color=0x4dff73)
embed.set_author(name="Success!")
embed.set_footer(text="Task succeeded successfully.")
await ctx.send(embed=embed)
@commands.command()
async def suggest(self, ctx, *, suggestion):
"""
Suggest something!
"""
async with ctx.channel.typing():
config = await self.coll.find_one({"_id": "config"})
if config is None:
embed=discord.Embed(title="Suggestion channel not set.", color=self.bot.error_colour)
embed.set_author(name="Error.")
embed.set_footer("Task failed successfully.")
await ctx.send(embed=embed)
else:
suggestion_channel = self.bot.get_channel(int(config["suggestion-channel"]["channel"]))
embed=discord.Embed(title=suggestion, color=self.bot.main_color)
embed.set_author(name=f"Suggestion by {ctx.author}:", icon_url=ctx.author.avatar_url)
await suggestion_channel.send(embed=embed)
suggestion_channel.send(embed).then(embedMessage = {
embedMessage.add_reaction('👍');
embedMessage.add_reaction('👎');
});
await ctx.message.add_reaction('\N{WHITE HEAVY CHECK MARK}')
def setup(bot):
bot.add_cog(Suggest(bot))
| 38.836364 | 103 | 0.617041 | [
"MIT"
] | AlertShamrock/modmail-plugins-2 | suggest/suggest.py | 2,142 | Python |
import numpy as np
import tempfile
import os
import pytest
import torch
from anndata import AnnData
from scvi.dataset import (
AnnDatasetFromAnnData,
CortexDataset,
SyntheticDataset,
GeneExpressionDataset,
Dataset10X,
)
from scvi.inference import (
JointSemiSupervisedTrainer,
AlternateSemiSupervisedTrainer,
ClassifierTrainer,
UnsupervisedTrainer,
AdapterTrainer,
TotalTrainer,
TotalPosterior,
)
from scvi.inference.posterior import unsupervised_clustering_accuracy
from scvi.inference.posterior_utils import load_posterior
from scvi.inference.annotation import compute_accuracy_rf, compute_accuracy_svc
from scvi.models import VAE, SCANVI, VAEC, LDVAE, TOTALVI, AutoZIVAE
from scvi.models.distributions import ZeroInflatedNegativeBinomial, NegativeBinomial
from scvi.models.classifier import Classifier
from scvi.models.log_likelihood import log_zinb_positive, log_nb_positive
from scvi import set_seed
set_seed(0)
use_cuda = True
def test_cortex(save_path):
cortex_dataset = CortexDataset(save_path=save_path)
vae = VAE(cortex_dataset.nb_genes, cortex_dataset.n_batches)
trainer_cortex_vae = UnsupervisedTrainer(
vae, cortex_dataset, train_size=0.5, use_cuda=use_cuda
)
trainer_cortex_vae.train(n_epochs=1)
trainer_cortex_vae.train_set.reconstruction_error()
trainer_cortex_vae.train_set.differential_expression_stats()
trainer_cortex_vae.train_set.generate_feature_correlation_matrix(
n_samples=2, correlation_type="pearson"
)
trainer_cortex_vae.train_set.generate_feature_correlation_matrix(
n_samples=2, correlation_type="spearman"
)
trainer_cortex_vae.train_set.imputation(n_samples=1)
trainer_cortex_vae.test_set.imputation(n_samples=5)
trainer_cortex_vae.corrupt_posteriors(corruption="binomial")
trainer_cortex_vae.corrupt_posteriors()
trainer_cortex_vae.train(n_epochs=1)
trainer_cortex_vae.uncorrupt_posteriors()
trainer_cortex_vae.train_set.imputation_benchmark(
n_samples=1, show_plot=False, title_plot="imputation", save_path=save_path
)
trainer_cortex_vae.train_set.generate_parameters()
n_cells, n_genes = (
len(trainer_cortex_vae.train_set.indices),
cortex_dataset.nb_genes,
)
n_samples = 3
(dropout, means, dispersions) = trainer_cortex_vae.train_set.generate_parameters()
assert dropout.shape == (n_cells, n_genes) and means.shape == (n_cells, n_genes)
assert dispersions.shape == (n_cells, n_genes)
(dropout, means, dispersions) = trainer_cortex_vae.train_set.generate_parameters(
n_samples=n_samples
)
assert dropout.shape == (n_samples, n_cells, n_genes)
assert means.shape == (n_samples, n_cells, n_genes)
(dropout, means, dispersions) = trainer_cortex_vae.train_set.generate_parameters(
n_samples=n_samples, give_mean=True
)
assert dropout.shape == (n_cells, n_genes) and means.shape == (n_cells, n_genes)
full = trainer_cortex_vae.create_posterior(
vae, cortex_dataset, indices=np.arange(len(cortex_dataset))
)
x_new, x_old = full.generate(n_samples=10)
assert x_new.shape == (cortex_dataset.nb_cells, cortex_dataset.nb_genes, 10)
assert x_old.shape == (cortex_dataset.nb_cells, cortex_dataset.nb_genes)
trainer_cortex_vae.train_set.imputation_benchmark(
n_samples=1, show_plot=False, title_plot="imputation", save_path=save_path
)
svaec = SCANVI(
cortex_dataset.nb_genes, cortex_dataset.n_batches, cortex_dataset.n_labels
)
trainer_cortex_svaec = JointSemiSupervisedTrainer(
svaec, cortex_dataset, n_labelled_samples_per_class=3, use_cuda=use_cuda
)
trainer_cortex_svaec.train(n_epochs=1)
trainer_cortex_svaec.labelled_set.accuracy()
trainer_cortex_svaec.full_dataset.reconstruction_error()
svaec = SCANVI(
cortex_dataset.nb_genes, cortex_dataset.n_batches, cortex_dataset.n_labels
)
trainer_cortex_svaec = AlternateSemiSupervisedTrainer(
svaec, cortex_dataset, n_labelled_samples_per_class=3, use_cuda=use_cuda
)
trainer_cortex_svaec.train(n_epochs=1, lr=1e-2)
trainer_cortex_svaec.unlabelled_set.accuracy()
data_train, labels_train = trainer_cortex_svaec.labelled_set.raw_data()
data_test, labels_test = trainer_cortex_svaec.unlabelled_set.raw_data()
compute_accuracy_svc(
data_train,
labels_train,
data_test,
labels_test,
param_grid=[{"C": [1], "kernel": ["linear"]}],
)
compute_accuracy_rf(
data_train,
labels_train,
data_test,
labels_test,
param_grid=[{"max_depth": [3], "n_estimators": [10]}],
)
cls = Classifier(cortex_dataset.nb_genes, n_labels=cortex_dataset.n_labels)
cls_trainer = ClassifierTrainer(cls, cortex_dataset)
cls_trainer.train(n_epochs=1)
cls_trainer.train_set.accuracy()
def test_synthetic_1():
synthetic_dataset = SyntheticDataset()
synthetic_dataset.cell_types = np.array(["A", "B", "C"])
svaec = SCANVI(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
)
trainer_synthetic_svaec = JointSemiSupervisedTrainer(
svaec, synthetic_dataset, use_cuda=use_cuda
)
trainer_synthetic_svaec.train(n_epochs=1)
trainer_synthetic_svaec.labelled_set.entropy_batch_mixing()
with tempfile.TemporaryDirectory() as temp_dir:
posterior_save_path = os.path.join(temp_dir, "posterior_data")
original_post = trainer_synthetic_svaec.labelled_set.sequential()
original_post.save_posterior(posterior_save_path)
new_svaec = SCANVI(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
)
new_post = load_posterior(posterior_save_path, model=new_svaec, use_cuda=False)
assert np.array_equal(new_post.indices, original_post.indices)
assert np.array_equal(new_post.gene_dataset.X, original_post.gene_dataset.X)
assert np.array_equal(
new_post.gene_dataset.labels, original_post.gene_dataset.labels
)
trainer_synthetic_svaec.full_dataset.knn_purity()
trainer_synthetic_svaec.labelled_set.show_t_sne(n_samples=5)
trainer_synthetic_svaec.unlabelled_set.show_t_sne(n_samples=5, color_by="labels")
trainer_synthetic_svaec.labelled_set.show_t_sne(
n_samples=5, color_by="batches and labels"
)
trainer_synthetic_svaec.labelled_set.clustering_scores()
trainer_synthetic_svaec.labelled_set.clustering_scores(prediction_algorithm="gmm")
trainer_synthetic_svaec.unlabelled_set.unsupervised_classification_accuracy()
trainer_synthetic_svaec.unlabelled_set.differential_expression_score(
synthetic_dataset.labels.ravel() == 1,
synthetic_dataset.labels.ravel() == 2,
n_samples=2,
M_permutation=10,
)
trainer_synthetic_svaec.unlabelled_set.one_vs_all_degenes(
n_samples=2, M_permutation=10
)
def test_synthetic_2():
synthetic_dataset = SyntheticDataset()
vaec = VAEC(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
)
trainer_synthetic_vaec = JointSemiSupervisedTrainer(
vaec,
synthetic_dataset,
use_cuda=use_cuda,
frequency=1,
early_stopping_kwargs={
"early_stopping_metric": "reconstruction_error",
"on": "labelled_set",
"save_best_state_metric": "reconstruction_error",
},
)
trainer_synthetic_vaec.train(n_epochs=2)
def base_benchmark(gene_dataset):
vae = VAE(gene_dataset.nb_genes, gene_dataset.n_batches, gene_dataset.n_labels)
trainer = UnsupervisedTrainer(vae, gene_dataset, train_size=0.5, use_cuda=use_cuda)
trainer.train(n_epochs=1)
return trainer
def ldvae_benchmark(dataset, n_epochs, use_cuda=True):
ldvae = LDVAE(
dataset.nb_genes, n_batch=dataset.n_batches, latent_distribution="normal"
)
trainer = UnsupervisedTrainer(ldvae, dataset, use_cuda=use_cuda)
trainer.train(n_epochs=n_epochs)
trainer.test_set.reconstruction_error()
trainer.test_set.marginal_ll()
ldvae = LDVAE(dataset.nb_genes, n_batch=dataset.n_batches, latent_distribution="ln")
trainer = UnsupervisedTrainer(ldvae, dataset, use_cuda=use_cuda)
trainer.train(n_epochs=n_epochs)
trainer.test_set.reconstruction_error()
ldvae.get_loadings()
return trainer
def totalvi_benchmark(dataset, n_epochs, use_cuda=True):
totalvae = TOTALVI(
dataset.nb_genes, len(dataset.protein_names), n_batch=dataset.n_batches
)
trainer = TotalTrainer(
totalvae, dataset, train_size=0.5, use_cuda=use_cuda, early_stopping_kwargs=None
)
trainer.train(n_epochs=n_epochs)
trainer.test_set.reconstruction_error()
trainer.test_set.marginal_ll()
trainer.test_set.get_protein_background_mean()
trainer.test_set.get_latent()
trainer.test_set.generate()
trainer.test_set.get_sample_dropout()
trainer.test_set.get_normalized_denoised_expression(transform_batch=0)
trainer.test_set.get_normalized_denoised_expression(transform_batch=0)
trainer.test_set.imputation()
trainer.test_set.get_protein_mean()
trainer.test_set.one_vs_all_degenes(n_samples=2, M_permutation=10)
trainer.test_set.generate_feature_correlation_matrix(n_samples=2)
trainer.test_set.generate_feature_correlation_matrix(n_samples=2, transform_batch=0)
return trainer
def test_synthetic_3():
gene_dataset = SyntheticDataset()
trainer = base_benchmark(gene_dataset)
adapter_trainer = AdapterTrainer(
trainer.model, gene_dataset, trainer.train_set, frequency=1
)
adapter_trainer.train(n_path=1, n_epochs=1)
def test_nb_not_zinb():
synthetic_dataset = SyntheticDataset()
svaec = SCANVI(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
labels_groups=[0, 0, 1],
reconstruction_loss="nb",
)
trainer_synthetic_svaec = JointSemiSupervisedTrainer(
svaec, synthetic_dataset, use_cuda=use_cuda
)
trainer_synthetic_svaec.train(n_epochs=1)
def test_poisson_not_zinb():
synthetic_dataset = SyntheticDataset()
svaec = SCANVI(
synthetic_dataset.nb_genes,
synthetic_dataset.n_batches,
synthetic_dataset.n_labels,
labels_groups=[0, 0, 1],
reconstruction_loss="poisson",
)
trainer_synthetic_svaec = JointSemiSupervisedTrainer(
svaec, synthetic_dataset, use_cuda=use_cuda
)
trainer_synthetic_svaec.train(n_epochs=1)
def test_classifier_accuracy(save_path):
cortex_dataset = CortexDataset(save_path=save_path)
cls = Classifier(cortex_dataset.nb_genes, n_labels=cortex_dataset.n_labels)
cls_trainer = ClassifierTrainer(
cls,
cortex_dataset,
metrics_to_monitor=["accuracy"],
frequency=1,
early_stopping_kwargs={
"early_stopping_metric": "accuracy",
"save_best_state_metric": "accuracy",
},
)
cls_trainer.train(n_epochs=2)
cls_trainer.train_set.accuracy()
def test_LDVAE(save_path):
synthetic_datset_one_batch = SyntheticDataset(n_batches=1)
ldvae_benchmark(synthetic_datset_one_batch, n_epochs=1, use_cuda=False)
synthetic_datset_two_batches = SyntheticDataset(n_batches=2)
ldvae_benchmark(synthetic_datset_two_batches, n_epochs=1, use_cuda=False)
def test_sampling_zl(save_path):
cortex_dataset = CortexDataset(save_path=save_path)
cortex_vae = VAE(cortex_dataset.nb_genes, cortex_dataset.n_batches)
trainer_cortex_vae = UnsupervisedTrainer(
cortex_vae, cortex_dataset, train_size=0.5, use_cuda=use_cuda
)
trainer_cortex_vae.train(n_epochs=2)
cortex_cls = Classifier((cortex_vae.n_latent + 1), n_labels=cortex_dataset.n_labels)
trainer_cortex_cls = ClassifierTrainer(
cortex_cls, cortex_dataset, sampling_model=cortex_vae, sampling_zl=True
)
trainer_cortex_cls.train(n_epochs=2)
trainer_cortex_cls.test_set.accuracy()
def test_annealing_procedures(save_path):
cortex_dataset = CortexDataset(save_path=save_path)
cortex_vae = VAE(cortex_dataset.nb_genes, cortex_dataset.n_batches)
trainer_cortex_vae = UnsupervisedTrainer(
cortex_vae,
cortex_dataset,
train_size=0.5,
use_cuda=use_cuda,
n_epochs_kl_warmup=1,
)
trainer_cortex_vae.train(n_epochs=2)
assert trainer_cortex_vae.kl_weight >= 0.99, "Annealing should be over"
trainer_cortex_vae = UnsupervisedTrainer(
cortex_vae,
cortex_dataset,
train_size=0.5,
use_cuda=use_cuda,
n_epochs_kl_warmup=5,
)
trainer_cortex_vae.train(n_epochs=2)
assert trainer_cortex_vae.kl_weight <= 0.99, "Annealing should be proceeding"
# iter
trainer_cortex_vae = UnsupervisedTrainer(
cortex_vae,
cortex_dataset,
train_size=0.5,
use_cuda=use_cuda,
n_iter_kl_warmup=1,
n_epochs_kl_warmup=None,
)
trainer_cortex_vae.train(n_epochs=2)
assert trainer_cortex_vae.kl_weight >= 0.99, "Annealing should be over"
def test_differential_expression(save_path):
dataset = CortexDataset(save_path=save_path)
n_cells = len(dataset)
all_indices = np.arange(n_cells)
vae = VAE(dataset.nb_genes, dataset.n_batches)
trainer = UnsupervisedTrainer(vae, dataset, train_size=0.5, use_cuda=use_cuda)
trainer.train(n_epochs=2)
post = trainer.create_posterior(vae, dataset, shuffle=False, indices=all_indices)
with tempfile.TemporaryDirectory() as temp_dir:
posterior_save_path = os.path.join(temp_dir, "posterior_data")
post = post.sequential(batch_size=3)
post.save_posterior(posterior_save_path)
new_vae = VAE(dataset.nb_genes, dataset.n_batches)
new_post = load_posterior(posterior_save_path, model=new_vae, use_cuda=False)
assert new_post.data_loader.batch_size == 3
assert np.array_equal(new_post.indices, post.indices)
assert np.array_equal(new_post.gene_dataset.X, post.gene_dataset.X)
# Sample scale example
px_scales = post.scale_sampler(
n_samples_per_cell=4, n_samples=None, selection=all_indices
)["scale"]
assert (
px_scales.shape[1] == dataset.nb_genes
), "posterior scales should have shape (n_samples, n_genes)"
# Differential expression different models
idx_1 = [1, 2, 3]
idx_2 = [4, 5, 6, 7]
de_dataframe = post.differential_expression_score(
idx1=idx_1,
idx2=idx_2,
n_samples=10,
mode="vanilla",
use_permutation=True,
M_permutation=100,
)
de_dataframe = post.differential_expression_score(
idx1=idx_1,
idx2=idx_2,
n_samples=10,
mode="change",
use_permutation=True,
M_permutation=100,
cred_interval_lvls=[0.5, 0.95],
)
print(de_dataframe.keys())
assert (
de_dataframe["lfc_confidence_interval_0.5_min"]
<= de_dataframe["lfc_confidence_interval_0.5_max"]
).all()
assert (
de_dataframe["lfc_confidence_interval_0.95_min"]
<= de_dataframe["lfc_confidence_interval_0.95_max"]
).all()
# DE estimation example
de_probabilities = de_dataframe.loc[:, "proba_de"]
assert ((0.0 <= de_probabilities) & (de_probabilities <= 1.0)).all()
# Test totalVI DE
sp = os.path.join(save_path, "10X")
dataset = Dataset10X(dataset_name="pbmc_10k_protein_v3", save_path=sp)
n_cells = len(dataset)
all_indices = np.arange(n_cells)
vae = TOTALVI(
dataset.nb_genes, len(dataset.protein_names), n_batch=dataset.n_batches
)
trainer = TotalTrainer(
vae, dataset, train_size=0.5, use_cuda=use_cuda, early_stopping_kwargs=None
)
trainer.train(n_epochs=2)
post = trainer.create_posterior(
vae, dataset, shuffle=False, indices=all_indices, type_class=TotalPosterior
)
# Differential expression different models
idx_1 = [1, 2, 3]
idx_2 = [4, 5, 6, 7]
de_dataframe = post.differential_expression_score(
idx1=idx_1,
idx2=idx_2,
n_samples=10,
mode="vanilla",
use_permutation=True,
M_permutation=100,
)
de_dataframe = post.differential_expression_score(
idx1=idx_1,
idx2=idx_2,
n_samples=10,
mode="change",
use_permutation=True,
M_permutation=100,
)
def test_totalvi(save_path):
synthetic_dataset_one_batch = SyntheticDataset(n_batches=1)
totalvi_benchmark(synthetic_dataset_one_batch, n_epochs=1, use_cuda=use_cuda)
synthetic_dataset_two_batches = SyntheticDataset(n_batches=2)
totalvi_benchmark(synthetic_dataset_two_batches, n_epochs=1, use_cuda=use_cuda)
# adversarial testing
dataset = synthetic_dataset_two_batches
totalvae = TOTALVI(
dataset.nb_genes, len(dataset.protein_names), n_batch=dataset.n_batches
)
trainer = TotalTrainer(
totalvae,
dataset,
train_size=0.5,
use_cuda=use_cuda,
early_stopping_kwargs=None,
use_adversarial_loss=True,
)
trainer.train(n_epochs=1)
with tempfile.TemporaryDirectory() as temp_dir:
posterior_save_path = os.path.join(temp_dir, "posterior_data")
original_post = trainer.create_posterior(
totalvae,
dataset,
indices=np.arange(len(dataset)),
type_class=TotalPosterior,
)
original_post.save_posterior(posterior_save_path)
new_totalvae = TOTALVI(
dataset.nb_genes, len(dataset.protein_names), n_batch=dataset.n_batches
)
new_post = load_posterior(
posterior_save_path, model=new_totalvae, use_cuda=False
)
assert new_post.posterior_type == "TotalPosterior"
assert np.array_equal(
new_post.gene_dataset.protein_expression, dataset.protein_expression
)
def test_autozi(save_path):
data = SyntheticDataset(n_batches=1)
for disp_zi in ["gene", "gene-label"]:
autozivae = AutoZIVAE(
n_input=data.nb_genes,
dispersion=disp_zi,
zero_inflation=disp_zi,
n_labels=data.n_labels,
)
trainer_autozivae = UnsupervisedTrainer(
model=autozivae, gene_dataset=data, train_size=0.5
)
trainer_autozivae.train(n_epochs=2, lr=1e-2)
trainer_autozivae.test_set.elbo()
trainer_autozivae.test_set.reconstruction_error()
trainer_autozivae.test_set.marginal_ll()
def test_multibatches_features():
data = [
np.random.randint(1, 5, size=(20, 10)),
np.random.randint(1, 10, size=(20, 10)),
np.random.randint(1, 10, size=(20, 10)),
np.random.randint(1, 10, size=(30, 10)),
]
dataset = GeneExpressionDataset()
dataset.populate_from_per_batch_list(data)
vae = VAE(dataset.nb_genes, dataset.n_batches)
trainer = UnsupervisedTrainer(vae, dataset, train_size=0.5, use_cuda=use_cuda)
trainer.train(n_epochs=2)
trainer.test_set.imputation(n_samples=2, transform_batch=0)
trainer.train_set.imputation(n_samples=2, transform_batch=[0, 1, 2])
def test_deprecated_munkres():
y = np.array([0, 1, 0, 1, 0, 1, 1, 1])
y_pred = np.array([0, 0, 0, 0, 1, 1, 1, 1])
reward, assignment = unsupervised_clustering_accuracy(y, y_pred)
assert reward == 0.625
assert (assignment == np.array([[0, 0], [1, 1]])).all()
y = np.array([1, 1, 2, 2, 0, 0, 3, 3])
y_pred = np.array([1, 1, 2, 2, 3, 3, 0, 0])
reward, assignment = unsupervised_clustering_accuracy(y, y_pred)
assert reward == 1.0
assert (assignment == np.array([[0, 3], [1, 1], [2, 2], [3, 0]])).all()
def test_zinb_distribution():
theta = 100.0 + torch.rand(size=(2,))
mu = 15.0 * torch.ones_like(theta)
pi = torch.randn_like(theta)
x = torch.randint_like(mu, high=20)
log_p_ref = log_zinb_positive(x, mu, theta, pi)
dist = ZeroInflatedNegativeBinomial(mu=mu, theta=theta, zi_logits=pi)
log_p_zinb = dist.log_prob(x)
assert (log_p_ref - log_p_zinb).abs().max().item() <= 1e-8
torch.manual_seed(0)
s1 = dist.sample((100,))
assert s1.shape == (100, 2)
s2 = dist.sample(sample_shape=(4, 3))
assert s2.shape == (4, 3, 2)
log_p_ref = log_nb_positive(x, mu, theta)
dist = NegativeBinomial(mu=mu, theta=theta)
log_p_nb = dist.log_prob(x)
assert (log_p_ref - log_p_nb).abs().max().item() <= 1e-8
s1 = dist.sample((1000,))
assert s1.shape == (1000, 2)
assert (s1.mean(0) - mu).abs().mean() <= 1e0
assert (s1.std(0) - (mu + mu * mu / theta) ** 0.5).abs().mean() <= 1e0
size = (50, 3)
theta = 100.0 + torch.rand(size=size)
mu = 15.0 * torch.ones_like(theta)
pi = torch.randn_like(theta)
x = torch.randint_like(mu, high=20)
dist1 = ZeroInflatedNegativeBinomial(mu=mu, theta=theta, zi_logits=pi)
dist2 = NegativeBinomial(mu=mu, theta=theta)
assert dist1.log_prob(x).shape == size
assert dist2.log_prob(x).shape == size
with pytest.raises(ValueError):
ZeroInflatedNegativeBinomial(mu=-mu, theta=theta, zi_logits=pi)
with pytest.warns(UserWarning):
dist1.log_prob(-x) # ensures neg values raise warning
with pytest.warns(UserWarning):
dist2.log_prob(0.5 * x) # ensures float values raise warning
def test_anndata_loader():
x = np.random.randint(low=0, high=100, size=(15, 4))
batch_ids = np.random.randint(low=0, high=2, size=(15,))
n_batches = 2
adata = AnnData(X=x, obs=dict(batch=batch_ids))
_ = AnnDatasetFromAnnData(adata, batch_label="batch")
dataset = AnnDatasetFromAnnData(adata, batch_label="batch")
assert (
dataset.n_batches == n_batches
), "AnnDatasetFromAnnData should not modify the anndata object"
| 35.495974 | 88 | 0.70857 | [
"MIT"
] | shaoxin0801/scVI | tests/test_scvi.py | 22,043 | Python |
# -*- coding: utf-8 -*-
# Define here the models for your spider middleware
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/spider-middleware.html
from scrapy import signals
class JianshuSpiderSpiderMiddleware(object):
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the spider middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_spider_input(self, response, spider):
# Called for each response that goes through the spider
# middleware and into the spider.
# Should return None or raise an exception.
return None
def process_spider_output(self, response, result, spider):
# Called with the results returned from the Spider, after
# it has processed the response.
# Must return an iterable of Request, dict or Item objects.
for i in result:
yield i
def process_spider_exception(self, response, exception, spider):
# Called when a spider or process_spider_input() method
# (from other spider middleware) raises an exception.
# Should return either None or an iterable of Response, dict
# or Item objects.
pass
def process_start_requests(self, start_requests, spider):
# Called with the start requests of the spider, and works
# similarly to the process_spider_output() method, except
# that it doesn’t have a response associated.
# Must return only requests (not items).
for r in start_requests:
yield r
def spider_opened(self, spider):
spider.logger.info('Spider opened: %s' % spider.name)
class JianshuSpiderDownloaderMiddleware(object):
# Not all methods need to be defined. If a method is not defined,
# scrapy acts as if the downloader middleware does not modify the
# passed objects.
@classmethod
def from_crawler(cls, crawler):
# This method is used by Scrapy to create your spiders.
s = cls()
crawler.signals.connect(s.spider_opened, signal=signals.spider_opened)
return s
def process_request(self, request, spider):
# Called for each request that goes through the downloader
# middleware.
# Must either:
# - return None: continue processing this request
# - or return a Response object
# - or return a Request object
# - or raise IgnoreRequest: process_exception() methods of
# installed downloader middleware will be called
return None
def process_response(self, request, response, spider):
# Called with the response returned from the downloader.
# Must either;
# - return a Response object
# - return a Request object
# - or raise IgnoreRequest
return response
def process_exception(self, request, exception, spider):
# Called when a download handler or a process_request()
# (from other downloader middleware) raises an exception.
# Must either:
# - return None: continue processing this exception
# - return a Response object: stops process_exception() chain
# - return a Request object: stops process_exception() chain
pass
def spider_opened(self, spider):
spider.logger.info('Spider opened: %s' % spider.name)
| 34.701923 | 78 | 0.667221 | [
"Apache-2.0"
] | 2904574716qq/spider_python | scrapy/jianshu_spider/jianshu_spider/middlewares.py | 3,611 | Python |
#!/usr/bin/env python
# Copyright 2016 Medical Research Council Harwell.
#
# 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.
#
#
# @author Neil Horner <[email protected]>
from setuptools.command import easy_install
dependencies = ["pyqtgraph", "appdirs", "SimpleITK", "numpy"]
for dep in dependencies:
try:
mod = __import__(dep) # try to import module
print("{0} already installed.".format(dep))
except ImportError:
# If it fails, try to easy install it
easy_install.main(["--user", dep])
| 29.617647 | 74 | 0.735849 | [
"Apache-2.0"
] | Dorky-Lever/vpv | setup.py | 1,007 | Python |
# This file is part of Scapy
# See http://www.secdev.org/projects/scapy for more information
# Copyright (C) Philippe Biondi <[email protected]>
# This program is published under a GPLv2 license
#############################################################################
# #
# hsrp.py --- HSRP protocol support for Scapy #
# #
# Copyright (C) 2010 Mathieu RENARD mathieu.renard(at)gmail.com #
# #
# This program is free software; you can redistribute it and/or modify it #
# under the terms of the GNU General Public License version 2 as #
# published by the Free Software Foundation; version 2. #
# #
# This program is distributed in the hope that it will be useful, but #
# WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU #
# General Public License for more details. #
# #
#############################################################################
# HSRP Version 1
# Ref. RFC 2281
# HSRP Version 2
# Ref. http://www.smartnetworks.jp/2006/02/hsrp_8_hsrp_version_2.html
##
# $Log: hsrp.py,v $
# Revision 0.2 2011/05/01 15:23:34 mrenard
# Cleanup code
"""
HSRP (Hot Standby Router Protocol): proprietary redundancy protocol for Cisco routers. # noqa: E501
"""
from scapy.fields import ByteEnumField, ByteField, IPField, SourceIPField, \
StrFixedLenField, XIntField, XShortField
from scapy.packet import Packet, bind_layers, bind_bottom_up
from scapy.layers.inet import DestIPField, UDP
from scapy.layers.inet6 import DestIP6Field
class HSRP(Packet):
name = "HSRP"
fields_desc = [
ByteField("version", 0),
ByteEnumField("opcode", 0, {0: "Hello", 1: "Coup", 2: "Resign", 3: "Advertise"}), # noqa: E501
ByteEnumField("state", 16, {0: "Initial", 1: "Learn", 2: "Listen", 4: "Speak", 8: "Standby", 16: "Active"}), # noqa: E501
ByteField("hellotime", 3),
ByteField("holdtime", 10),
ByteField("priority", 120),
ByteField("group", 1),
ByteField("reserved", 0),
StrFixedLenField("auth", b"cisco" + b"\00" * 3, 8),
IPField("virtualIP", "192.168.1.1")]
def guess_payload_class(self, payload):
if self.underlayer.len > 28:
return HSRPmd5
else:
return Packet.guess_payload_class(self, payload)
class HSRPmd5(Packet):
name = "HSRP MD5 Authentication"
fields_desc = [
ByteEnumField("type", 4, {4: "MD5 authentication"}),
ByteField("len", None),
ByteEnumField("algo", 0, {1: "MD5"}),
ByteField("padding", 0x00),
XShortField("flags", 0x00),
SourceIPField("sourceip", None),
XIntField("keyid", 0x00),
StrFixedLenField("authdigest", b"\00" * 16, 16)]
def post_build(self, p, pay):
if self.len is None and pay:
tmp_len = len(pay)
p = p[:1] + hex(tmp_len)[30:] + p[30:]
return p
bind_bottom_up(UDP, HSRP, dport=1985)
bind_bottom_up(UDP, HSRP, sport=1985)
bind_bottom_up(UDP, HSRP, dport=2029)
bind_bottom_up(UDP, HSRP, sport=2029)
bind_layers(UDP, HSRP, dport=1985, sport=1985)
bind_layers(UDP, HSRP, dport=2029, sport=2029)
DestIPField.bind_addr(UDP, "224.0.0.2", dport=1985)
DestIP6Field.bind_addr(UDP, "ff02::66", dport=2029)
| 41.844444 | 130 | 0.545672 | [
"MIT"
] | 4shadoww/hakkuframework | lib/scapy/layers/hsrp.py | 3,766 | Python |
# model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe'),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='GARPNHead',
in_channels=256,
feat_channels=256,
octave_base_scale=8,
scales_per_octave=3,
octave_ratios=[0.5, 1.0, 2.0],
anchor_strides=[4, 8, 16, 32, 64],
anchor_base_sizes=None,
anchoring_means=[.0, .0, .0, .0],
anchoring_stds=[0.07, 0.07, 0.14, 0.14],
target_means=(.0, .0, .0, .0),
target_stds=[0.07, 0.07, 0.11, 0.11],
loc_filter_thr=0.01,
loss_loc=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=81,
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1],
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))
# model training and testing settings
train_cfg = dict(
rpn=dict(
ga_assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
ga_sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
center_ratio=0.2,
ignore_ratio=0.5,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=300,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False))
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=300,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=1e-3, nms=dict(type='nms', iou_thr=0.5), max_per_img=100))
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/ga_faster_rcnn_r50_caffe_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
| 30.665 | 76 | 0.581608 | [
"Apache-2.0"
] | 1243France/SCB_Loss | configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x.py | 6,133 | Python |
# -*- coding: utf-8 -*-
###
# (C) Copyright (2012-2017) Hewlett Packard Enterprise Development LP
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
###
from pprint import pprint
from config_loader import try_load_from_file
from hpOneView.exceptions import HPOneViewException
from hpOneView.oneview_client import OneViewClient
config = {
"ip": "<oneview_ip>",
"credentials": {
"userName": "<oneview_administrator_name>",
"password": "<oneview_administrator_password>",
}
}
# Try load config from a file (if there is a config file)
config = try_load_from_file(config)
oneview_client = OneViewClient(config)
# To run this sample you must define a server hardware type uri and an enclosure group uri
enclosure_group_uri = None
server_hardware_type_uri = None
# To run the example 'get a specific storage system' you must define a storage system ID
storage_system_id = None
# To run the example 'get port model associated with a server hardware' you must define a server hardware uri
server_hardware_uri = None
try:
# Create a server profile template to associate with the server profile
basic_server_template = oneview_client.server_profile_templates.create(dict(
name="ProfileTemplate101",
serverHardwareTypeUri=server_hardware_type_uri,
enclosureGroupUri=enclosure_group_uri
))
server_template_uri = basic_server_template['uri']
# Create a server profile
print("\nCreate a basic connection-less assigned server profile")
basic_profile_options = dict(
name="Profile101",
serverProfileTemplateUri=server_template_uri,
serverHardwareTypeUri=server_hardware_type_uri,
enclosureGroupUri=enclosure_group_uri
)
basic_profile = oneview_client.server_profiles.create(basic_profile_options)
profile_uri = basic_profile["uri"]
pprint(basic_profile)
except HPOneViewException as e:
print(e.msg)
# Update bootMode from recently created profile
print("\nUpdate bootMode from recently created profile")
profile_to_update = basic_profile.copy()
profile_to_update["bootMode"] = dict(manageMode=True, mode="BIOS")
profile_updated = oneview_client.server_profiles.update(resource=profile_to_update, id_or_uri=profile_to_update["uri"])
pprint(profile_updated)
# Patch
print("\nUpdate the profile configuration from server profile template")
profile_updated = oneview_client.server_profiles.patch(id_or_uri=profile_uri, operation="replace",
path="/templateCompliance", value="Compliant")
pprint(profile_updated)
# Get all
print("\nGet list of all server profiles")
all_profiles = oneview_client.server_profiles.get_all()
for profile in all_profiles:
print(' %s' % profile['name'])
# Get by property
print("\nGet a list of server profiles that matches the specified macType")
profile_mac_type = all_profiles[1]["macType"]
profiles = oneview_client.server_profiles.get_by('macType', profile_mac_type)
for profile in profiles:
print(' %s' % profile['name'])
# Get by name
print("\nGet a server profile by name")
profile = oneview_client.server_profiles.get_by_name("Profile101")
pprint(profile)
# Get by uri
print("\nGet a server profile by uri")
profile = oneview_client.server_profiles.get(profile_uri)
pprint(profile)
if oneview_client.api_version <= 500:
# Retrieve ServerProfile schema
# This method available only for API version <= 500
print("\nRetrieve the generated ServerProfile schema")
schema = oneview_client.server_profiles.get_schema()
pprint(schema)
try:
# Server profile compliance preview
print("\nGets the preview of manual and automatic updates required to make the server profile consistent "
"with its template.")
schema = oneview_client.server_profiles.get_compliance_preview(profile_uri)
pprint(schema)
except HPOneViewException as e:
print(e.msg)
# Get profile ports
print("\nRetrieve the port model associated with a server hardware type and enclosure group")
profile_ports = oneview_client.server_profiles.get_profile_ports(enclosureGroupUri=enclosure_group_uri,
serverHardwareTypeUri=server_hardware_type_uri)
pprint(profile_ports)
try:
# Get profile ports
if server_hardware_uri:
print("\nRetrieve the port model associated with a server hardware")
profile_ports = oneview_client.server_profiles.get_profile_ports(serverHardwareUri=server_hardware_uri)
pprint(profile_ports)
except HPOneViewException as e:
print(e.msg)
try:
# Retrieve the error or status messages associated with the specified profile
print("\nList profile status messages associated with a profile")
messages = oneview_client.server_profiles.get_messages(profile_uri)
pprint(messages)
except HPOneViewException as e:
print(e.msg)
try:
# Transform an server profile
print("\nTransform an existing profile by supplying a new server hardware type and/or enclosure group.")
server_transformed = oneview_client.server_profiles.get_transformation(
basic_profile['uri'], enclosureGroupUri=enclosure_group_uri, serverHardwareTypeUri=server_hardware_type_uri)
print("Transformation complete. Updating server profile with the new configuration.")
profile_updated = oneview_client.server_profiles.update(server_transformed, server_transformed["uri"])
pprint(profile_updated)
except HPOneViewException as e:
print(e.msg)
try:
# Get the list of networks and network sets that are available to a server profile along with their respective ports
print("\nList all Ethernet networks associated with a server hardware type and enclosure group")
available_networks = oneview_client.server_profiles.get_available_networks(
enclosureGroupUri=enclosure_group_uri, serverHardwareTypeUri=server_hardware_type_uri, view='Ethernet')
pprint(available_networks)
except HPOneViewException as e:
print(e.msg)
try:
# Get the all Ethernet networks associated with a server hardware type, enclosure group and scopeuris
if oneview_client.api_version >= 600:
enclosure_group_uri = "/rest/enclosure-groups/8cf8fd62-ad9f-4946-abf7-6dac9cb59253"
server_hardware_type_uri = "/rest/server-hardware-types/B342B5D4-387D-4DEB-ADBB-9D7256DF2A47"
available_networks = oneview_client.server_profiles.get_available_networks(enclosureGroupUri=enclosure_group_uri,
serverHardwareTypeUri=server_hardware_type_uri, view='Ethernet',
scope_uris="\"'/rest/scopes/3bb0c754-fd38-45af-be8a-4d4419de06e9'\"")
if len(available_networks) > 0:
pprint(available_networks)
else:
print("No Server Profiles Group found.")
except HPOneViewException as e:
print(e.msg)
try:
# Get the list of available servers
print("\nList all available servers associated with a server hardware type and enclosure group")
available_servers = oneview_client.server_profiles.get_available_servers(
enclosureGroupUri=enclosure_group_uri, serverHardwareTypeUri=server_hardware_type_uri)
pprint(available_servers)
except HPOneViewException as e:
print(e.msg)
try:
# List available storage systems
print("\nList available storage systems associated with the given enclosure group URI and server hardware type URI")
available_storage_systems = oneview_client.server_profiles.get_available_storage_systems(
count=25, start=0, enclosureGroupUri=enclosure_group_uri, serverHardwareTypeUri=server_hardware_type_uri)
pprint(available_storage_systems)
except HPOneViewException as e:
print(e.msg)
try:
# Get a specific storage system
if storage_system_id:
print("\nRetrieve a specific storage system associated with the given enclosure group URI, a server hardware"
" type URI and a storage system ID")
available_storage_system = oneview_client.server_profiles.get_available_storage_system(
storageSystemId=storage_system_id, enclosureGroupUri=enclosure_group_uri,
serverHardwareTypeUri=server_hardware_type_uri)
pprint(available_storage_system)
except HPOneViewException as e:
print(e.msg)
try:
# List available targets
print("\nList all available servers and bays for a given enclosure group.")
available_targets = oneview_client.server_profiles.get_available_targets(enclosureGroupUri=enclosure_group_uri)
pprint(available_targets)
except HPOneViewException as e:
print(e.msg)
# Generate a new Server Profile Template based on an existing Server Profile
new_spt = oneview_client.server_profiles.get_new_profile_template(basic_profile['uri'])
print('\nNew SPT generated:')
pprint(new_spt)
new_spt['name'] = 'spt_generated_from_sp'
new_spt = oneview_client.server_profile_templates.create(new_spt)
print('\nNew SPT created successfully.')
oneview_client.server_profile_templates.delete(new_spt)
print('\nDropped recently created SPT.')
# Delete the created server profile
print("\nDelete the created server profile")
oneview_client.server_profiles.delete(basic_profile)
print("The server profile was successfully deleted.")
# Delete the created server profile template
oneview_client.server_profile_templates.delete(basic_server_template)
# Delete all server profile (filtering)
print("\nRemove all profiles that match the name 'Profile fake'")
# Create a new profile to delete
oneview_client.server_profiles.create(dict(
name="Profile fake",
serverHardwareTypeUri=server_hardware_type_uri,
enclosureGroupUri=enclosure_group_uri
))
oneview_client.server_profiles.delete_all(filter="name='Profile fake'")
print("The server profiles were successfully deleted.")
| 42.325581 | 152 | 0.758608 | [
"MIT"
] | HewlettPackard/python-hpOneView | examples/server_profiles.py | 10,920 | Python |
import sqlite3
import pandas as pd
import numpy as np
import csv
import gzip
from collections import defaultdict
if __name__ == '__main__':
conn = sqlite3.connect('data/instacart.db')
c = conn.cursor()
# Get the orders properly sorted, so we can directly
# group by user_id, order_id and then compute the weights.
q = """
SELECT user_id, order_id, days_since_prior_order
FROM orders
ORDER BY order_number
"""
orders = pd.read_sql(q, conn)
# First day is 0
orders.ix[orders.days_since_prior_order == '', 'days_since_prior_order'] = 0
# Cumsum to obtain total days since *first* order
orders_g = orders.groupby(['user_id'])['days_since_prior_order'].cumsum()
orders['cumulative_days'] = orders_g.astype(int)
# But I need to subtract cumulative_days from the actual day of the
# order we want to compute... which will be the maximum
max_cum_days = orders.groupby(['user_id'])['cumulative_days'].max()
max_cum_days = max_cum_days.reset_index()
max_cum_days.columns = ['user_id', 'max_order_day']
orders = pd.merge(orders, max_cum_days, on = "user_id", how = 'left')
# Compute weights
orders['w_periodic'] = (np.cos(2 * (orders['max_order_day'] - orders['cumulative_days']) / 365.0 * 3.14) + 1) / 2
orders['w_decay'] = 1.0 / ((365 - orders['cumulative_days']) / 365.0 + 1.0)
# Remove unwanted columns (for DB storage, let's try not do duplicate)
res = orders
res = res.drop(['days_since_prior_order', 'cumulative_days', 'max_order_day'],
axis = 1)
# Insert weights into the DB
res.to_sql('order_weights', conn, if_exists = 'replace')
c.execute("CREATE INDEX IF NOT EXISTS idx_tmp1 ON order_weights(user_id)")
c.execute("CREATE INDEX IF NOT EXISTS idx_tmp2 ON order_weights(order_id)")
| 37.68 | 118 | 0.662951 | [
"MIT"
] | Keesiu/meta-kaggle | data/external/repositories_2to3/164369/kaggle-public-master/instacart/compute_weights_2.py | 1,884 | Python |
from rolepermissions.roles import AbstractUserRole
class Admin(AbstractUserRole):
available_permissions = {
'deactivate_user': True,
'activate_user': True,
'change_user_permissions': True,
'create_client_record': True,
'delete_client_recods': True,
'update_client_recods': True,
}
class Manager(AbstractUserRole):
available_permissions = {
'create_client_record': True,
'delete_client_recods': True,
'update_client_recods': True,
}
| 24.952381 | 50 | 0.666031 | [
"MIT"
] | lunyamwis/appraisal-system | app/api/roles/roles.py | 524 | Python |
import cv2
import numpy as np
import math
from vcam import vcam,meshGen
def nothing(x):
pass
WINDOW_NAME = "output"
cv2.namedWindow(WINDOW_NAME,cv2.WINDOW_NORMAL)
cv2.resizeWindow(WINDOW_NAME,700,700)
# Creating the tracker bar for all the features
cv2.createTrackbar("X",WINDOW_NAME,500,1000,nothing)
cv2.createTrackbar("Y",WINDOW_NAME,500,1000,nothing)
cv2.createTrackbar("Z",WINDOW_NAME,0,1000,nothing)
cv2.createTrackbar("alpha",WINDOW_NAME,180,360,nothing)
cv2.createTrackbar("beta",WINDOW_NAME,180,360,nothing)
cv2.createTrackbar("gama",WINDOW_NAME,180,360,nothing)
cv2.createTrackbar("K1",WINDOW_NAME,0,100000,nothing)
cv2.createTrackbar("K2",WINDOW_NAME,0,100000,nothing)
cv2.createTrackbar("P1",WINDOW_NAME,0,100000,nothing)
cv2.createTrackbar("P2",WINDOW_NAME,0,100000,nothing)
cv2.createTrackbar("focus",WINDOW_NAME,600,1000,nothing)
cv2.createTrackbar("Sx",WINDOW_NAME,100,1000,nothing)
cv2.createTrackbar("Sy",WINDOW_NAME,100,1000,nothing)
# cap = cv2.VideoCapture(0)
# ret,img = cap.read()
img = cv2.imread("chess.png")
H,W = img.shape[:2]
c1 = vcam(H=H,W=W)
plane = meshGen(H,W)
plane.Z = plane.X*0 + 1
pts3d = plane.getPlane()
while True:
# ret, img = cap.read()
img = cv2.imread("chess.png")
X = -cv2.getTrackbarPos("X",WINDOW_NAME) + 500
Y = -cv2.getTrackbarPos("Y",WINDOW_NAME) + 500
Z = -cv2.getTrackbarPos("Z",WINDOW_NAME)
alpha = cv2.getTrackbarPos("alpha",WINDOW_NAME) - 180
beta = cv2.getTrackbarPos("beta",WINDOW_NAME) - 180
gamma = -cv2.getTrackbarPos("gama",WINDOW_NAME) - 180
c1.focus = cv2.getTrackbarPos("focus",WINDOW_NAME) - 500
c1.sx = (cv2.getTrackbarPos("Sx",WINDOW_NAME)+1)/100
c1.sy = (cv2.getTrackbarPos("Sy",WINDOW_NAME)+1)/100
k1 = cv2.getTrackbarPos("K1",WINDOW_NAME)/100000
k2 = cv2.getTrackbarPos("K2",WINDOW_NAME)/100000
p1 = cv2.getTrackbarPos("P1",WINDOW_NAME)/100000
p2 = cv2.getTrackbarPos("P2",WINDOW_NAME)/100000
c1.KpCoeff[0] = k1
c1.KpCoeff[1] = k2
c1.KpCoeff[2] = p1
c1.KpCoeff[3] = p2
c1.set_tvec(X,Y,Z)
c1.set_rvec(alpha,beta,gamma)
pts2d = c1.project(pts3d)
map_x,map_y = c1.getMaps(pts2d)
output = cv2.remap(img,map_x,map_y,interpolation=cv2.INTER_LINEAR)
M = c1.RT
print("\n\n############## Camera Matrix ##################")
print(M)
cv2.imshow("output",output)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
| 30.826667 | 67 | 0.728374 | [
"MIT"
] | kaustubh-sadekar/VirtualCam | GUI.py | 2,312 | Python |
from minpiler.std import M
i = 0
M.label .loop
M.print(i)
i += 1
if i < 10:
M.jump .loop
| 10.444444 | 26 | 0.606383 | [
"MIT"
] | 3e45/minpiler | samples/std/jump.py | 94 | Python |
from django.core.management.base import NoArgsCommand
from ella.core.management import regenerate_listing_handlers
class Command(NoArgsCommand):
def handle_noargs(self, **options):
regenerate_listing_handlers()
| 28.125 | 60 | 0.804444 | [
"BSD-3-Clause"
] | chr1043086360/ella | ella/core/management/commands/regenerate_listing_handlers.py | 225 | Python |
import _plotly_utils.basevalidators
class ExponentformatValidator(
_plotly_utils.basevalidators.EnumeratedValidator
):
def __init__(
self,
plotly_name='exponentformat',
parent_name='scatterpolar.marker.colorbar',
**kwargs
):
super(ExponentformatValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type='colorbars',
role='style',
values=['none', 'e', 'E', 'power', 'SI', 'B'],
**kwargs
)
| 25.090909 | 58 | 0.592391 | [
"MIT"
] | Elpiro/plotly.py | plotly/validators/scatterpolar/marker/colorbar/_exponentformat.py | 552 | Python |
#!/usr/bin/python
'''
Gapfilling function that utilizes pFBA and flux sampling to find most
parsimonious additional reactions to achieve minimum flux through the objective
Author: Matthew Jenior
'''
import pandas
import math
import copy
import time
import random
# Using Cobrapy 0.13.0
import cobra
import cobra.test
from cobra.flux_analysis.sampling import OptGPSampler
from cobra.manipulation.delete import *
from cobra.flux_analysis.parsimonious import add_pfba
from cobra.medium import find_boundary_types
from cobra.util import solver as sutil
# pFBA gapfiller
def pfba_gapfill(model, reaction_bag, obj=None, obj_lb=10., obj_constraint=False,
iters=1, tasks=None, task_lb=0.05,
add_exchanges=True, extracellular='e', cores=4):
'''
Function that utilizes iterations of pFBA solution with a universal reaction bag
in order to gapfill a model.
Parameters
----------
model : cobra.Model
Model to be gapfilled
reaction_bag : cobra.Model
Reaction bag reference to use during gapfilling
obj : string
Reaction ID for objective function in model to be gapfilled.
obj_lb : float
Lower bound for objective function
obj_constraint : bool
Sets objective as contstraint which must be maximized
tasks : list or None
List of reactions IDs (strings) of metabolic tasks
to set a minimum lower bound for
task_lb : float
Lower bound for any metabolic tasks
iters : int
Number of gapfilling rounds. Unique reactions from each round are
saved and the union is added simulatneously to the model
add_exchanges : bool
Identifies extracellular metabolites added during gapfilling that
are not associated with exchange reactions and creates them
extracellular : string
Label for extracellular compartment of model
cores : int
Number of processors to utilize during flux sampling
'''
start_time = time.time()
# Save some basic network info for downstream membership testing
orig_rxn_ids = set([str(x.id) for x in model.reactions])
orig_cpd_ids = set([str(y.id) for y in model.metabolites])
univ_rxn_ids = set([str(z.id) for z in reaction_bag.reactions])
# Find overlap in model and reaction bag
overlap_rxn_ids = univ_rxn_ids.intersection(orig_rxn_ids)
# Get model objective reaction ID
if obj == None:
obj = get_objective(model)
else:
obj = obj
# Modify universal reaction bag
new_rxn_ids = set()
print('Creating universal model...')
with reaction_bag as universal:
# Remove overlapping reactions from universal bag, and reset objective if needed
for rxn in overlap_rxn_ids:
universal.reactions.get_by_id(rxn).remove_from_model()
# Set objective in universal if told by user
# Made constraint as fraction of minimum in next step
if obj_constraint:
universal.add_reactions([model.reactions.get_by_id(obj)])
universal.objective = obj
orig_rxn_ids.remove(obj)
orig_rxns = []
for rxn in orig_rxn_ids:
orig_rxns.append(copy.deepcopy(model.reactions.get_by_id(rxn)))
else:
orig_rxns = list(copy.deepcopy(model.reactions))
# Add pFBA to universal model and add model reactions
add_pfba(universal)
#universal = copy.deepcopy(universal) # reset solver
universal.add_reactions(orig_rxns)
# If previous objective not set as constraint, set minimum lower bound
if not obj_constraint:
universal.reactions.get_by_id(obj).lower_bound = obj_lb
# Set metabolic tasks that must carry flux in gapfilled solution
if tasks != None:
for task in tasks:
try:
universal.reactions.get_by_id(task).lower_bound = task_lb
except:
print(task + 'not found in model. Ignoring.')
continue
# Run FBA and save solution
print('Optimizing model with combined reactions...')
solution = universal.optimize()
if iters > 1:
print('Generating flux sampling object...')
sutil.fix_objective_as_constraint(universal, fraction=0.99)
optgp_object = OptGPSampler(universal, processes=cores)
# Assess the sampled flux distributions
print('Sampling ' + str(iters) + ' flux distributions...')
flux_samples = optgp_object.sample(iters)
rxns = list(flux_samples.columns)
for distribution in flux_samples.iterrows():
for flux in range(0, len(list(distribution[1]))):
if abs(list(distribution[1])[flux]) > 1e-6:
new_rxn_ids |= set([rxns[flux]]).difference(orig_rxn_ids)
else:
rxns = list(solution.fluxes.index)
fluxes = list(solution.fluxes)
for flux in range(0, len(fluxes)):
if abs(fluxes[flux]) > 1e-6:
new_rxn_ids |= set([rxns[flux]])
# Screen new reaction IDs
if obj in new_rxn_ids: new_rxn_ids.remove(obj)
for rxn in orig_rxn_ids:
try:
new_rxn_ids.remove(rxn)
except:
continue
# Get reactions and metabolites to be added to the model
print('Gapfilling model...')
new_rxns = copy.deepcopy([reaction_bag.reactions.get_by_id(rxn) for rxn in new_rxn_ids])
new_cpd_ids = set()
for rxn in new_rxns: new_cpd_ids |= set([str(x.id) for x in list(rxn.metabolites)])
new_cpd_ids = new_cpd_ids.difference(orig_cpd_ids)
new_cpds = copy.deepcopy([reaction_bag.metabolites.get_by_id(cpd) for cpd in new_cpd_ids])
# Copy model and gapfill
new_model = copy.deepcopy(model)
new_model.add_metabolites(new_cpds)
new_model.add_reactions(new_rxns)
# Identify extracellular metabolites with no exchanges
if add_exchanges == True:
new_exchanges = extend_exchanges(new_model, new_cpd_ids, extracellular)
if len(new_exchanges) > 0: new_rxn_ids |= new_exchanges
duration = int(round(time.time() - start_time))
print('Took ' + str(duration) + ' seconds to gapfill ' + str(len(new_rxn_ids)) + \
' reactions and ' + str(len(new_cpd_ids)) + ' metabolites.')
new_obj_val = new_model.slim_optimize()
if new_obj_val > 1e-6:
print('Gapfilled model objective now carries flux (' + str(new_obj_val) + ').')
else:
print('Gapfilled model objective still does not carry flux.')
return new_model
# Adds missing exchanges for extracellulart metbaolites
def extend_exchanges(model, cpd_ids, ex):
model_exchanges = set(find_boundary_types(model, 'exchange', external_compartment=ex))
new_ex_ids = set()
for cpd in cpd_ids:
cpd = model.metabolites.get_by_id(cpd)
if str(cpd.compartment) != ex:
continue
else:
if bool(set(cpd.reactions) & model_exchanges) == False:
try:
new_id = 'EX_' + cpd.id
model.add_boundary(cpd, type='exchange', reaction_id=new_id, lb=-1000.0, ub=1000.0)
new_ex_ids |= set([new_id])
except ValueError:
pass
return new_ex_ids
# Returns the reaction ID of the objective reaction
def get_objective(model):
if len(list(model.objective.variables)) == 0:
raise IndexError('Model has no objective set.')
expression = str(model.objective.expression).split()
if 'reverse' in expression[0]:
obj_id = expression[2].split('*')[-1]
else:
obj_id = expression[0].split('*')[-1]
return obj_id
| 37.40566 | 103 | 0.640858 | [
"MIT"
] | csbl/CSBL-code-repo | pfba_gapfiller.py | 7,930 | Python |
import torch
import time
from audio_zen.acoustics.feature import mag_phase
from audio_zen.acoustics.mask import decompress_cIRM
from audio_zen.inferencer.base_inferencer import BaseInferencer
# for log
from utils.logger import log
print=log
def cumulative_norm(input):
eps = 1e-10
device = input.device
data_type = input.dtype
n_dim = input.ndim
assert n_dim in (3, 4)
if n_dim == 3:
n_channels = 1
batch_size, n_freqs, n_frames = input.size()
else:
batch_size, n_channels, n_freqs, n_frames = input.size()
input = input.reshape(batch_size * n_channels, n_freqs, n_frames)
step_sum = torch.sum(input, dim=1) # [B, T]
step_pow_sum = torch.sum(torch.square(input), dim=1)
cumulative_sum = torch.cumsum(step_sum, dim=-1) # [B, T]
cumulative_pow_sum = torch.cumsum(step_pow_sum, dim=-1) # [B, T]
entry_count = torch.arange(n_freqs, n_freqs * n_frames + 1, n_freqs, dtype=data_type, device=device)
entry_count = entry_count.reshape(1, n_frames) # [1, T]
entry_count = entry_count.expand_as(cumulative_sum) # [1, T] => [B, T]
cum_mean = cumulative_sum / entry_count # B, T
cum_var = (cumulative_pow_sum - 2 * cum_mean * cumulative_sum) / entry_count + cum_mean.pow(2) # B, T
cum_std = (cum_var + eps).sqrt() # B, T
cum_mean = cum_mean.reshape(batch_size * n_channels, 1, n_frames)
cum_std = cum_std.reshape(batch_size * n_channels, 1, n_frames)
x = (input - cum_mean) / cum_std
if n_dim == 4:
x = x.reshape(batch_size, n_channels, n_freqs, n_frames)
return x
class Inferencer(BaseInferencer):
def __init__(self, config, checkpoint_path, output_dir):
super().__init__(config, checkpoint_path, output_dir)
@torch.no_grad()
def mag(self, noisy, inference_args):
noisy_complex = self.torch_stft(noisy)
noisy_mag, noisy_phase = mag_phase(noisy_complex) # [B, F, T] => [B, 1, F, T]
enhanced_mag = self.model(noisy_mag.unsqueeze(1)).squeeze(1)
enhanced = self.torch_istft((enhanced_mag, noisy_phase), length=noisy.size(-1), use_mag_phase=True)
enhanced = enhanced.detach().squeeze(0).cpu().numpy()
return enhanced
@torch.no_grad()
def scaled_mask(self, noisy, inference_args):
noisy_complex = self.torch_stft(noisy)
noisy_mag, noisy_phase = mag_phase(noisy_complex)
# [B, F, T] => [B, 1, F, T] => model => [B, 2, F, T] => [B, F, T, 2]
noisy_mag = noisy_mag.unsqueeze(1)
scaled_mask = self.model(noisy_mag)
scaled_mask = scaled_mask.permute(0, 2, 3, 1)
enhanced_complex = noisy_complex * scaled_mask
enhanced = self.torch_istft(enhanced_complex, length=noisy.size(-1), use_mag_phase=False)
enhanced = enhanced.detach().squeeze(0).cpu().numpy()
return enhanced
@torch.no_grad()
def sub_band_crm_mask(self, noisy, inference_args):
pad_mode = inference_args["pad_mode"]
n_neighbor = inference_args["n_neighbor"]
noisy = noisy.cpu().numpy().reshape(-1)
noisy_D = self.librosa_stft(noisy)
noisy_real = torch.tensor(noisy_D.real, device=self.device)
noisy_imag = torch.tensor(noisy_D.imag, device=self.device)
noisy_mag = torch.sqrt(torch.square(noisy_real) + torch.square(noisy_imag)) # [F, T]
n_freqs, n_frames = noisy_mag.size()
noisy_mag = noisy_mag.reshape(1, 1, n_freqs, n_frames)
noisy_mag_padded = self._unfold(noisy_mag, pad_mode, n_neighbor) # [B, N, C, F_s, T] <=> [1, 257, 1, 31, T]
noisy_mag_padded = noisy_mag_padded.squeeze(0).squeeze(1) # [257, 31, 200] <=> [B, F_s, T]
pred_crm = self.model(noisy_mag_padded).detach() # [B, 2, T] <=> [F, 2, T]
pred_crm = pred_crm.permute(0, 2, 1).contiguous() # [B, T, 2]
lim = 9.99
pred_crm = lim * (pred_crm >= lim) - lim * (pred_crm <= -lim) + pred_crm * (torch.abs(pred_crm) < lim)
pred_crm = -10 * torch.log((10 - pred_crm) / (10 + pred_crm))
enhanced_real = pred_crm[:, :, 0] * noisy_real - pred_crm[:, :, 1] * noisy_imag
enhanced_imag = pred_crm[:, :, 1] * noisy_real + pred_crm[:, :, 0] * noisy_imag
enhanced_real = enhanced_real.cpu().numpy()
enhanced_imag = enhanced_imag.cpu().numpy()
enhanced = self.librosa_istft(enhanced_real + 1j * enhanced_imag, length=len(noisy))
return enhanced
@torch.no_grad()
def full_band_crm_mask(self, noisy, inference_args):
noisy_complex = self.torch_stft(noisy)
noisy_mag, _ = mag_phase(noisy_complex)
noisy_mag = noisy_mag.unsqueeze(1)
t1 = time.time()
pred_crm = self.model(noisy_mag)
t2 = time.time()
pred_crm = pred_crm.permute(0, 2, 3, 1)
pred_crm = decompress_cIRM(pred_crm)
enhanced_real = pred_crm[..., 0] * noisy_complex.real - pred_crm[..., 1] * noisy_complex.imag
enhanced_imag = pred_crm[..., 1] * noisy_complex.real + pred_crm[..., 0] * noisy_complex.imag
enhanced_complex = torch.stack((enhanced_real, enhanced_imag), dim=-1)
enhanced = self.torch_istft(enhanced_complex, length=noisy.size(-1))
enhanced = enhanced.detach().squeeze(0).cpu().numpy()
#
rtf = (t2 - t1) / (len(enhanced) * 1.0 / self.acoustic_config["sr"])
print(f"model rtf: {rtf}")
return enhanced
@torch.no_grad()
def overlapped_chunk(self, noisy, inference_args):
sr = self.acoustic_config["sr"]
noisy = noisy.squeeze(0)
num_mics = 8
chunk_length = sr * inference_args["chunk_length"]
chunk_hop_length = chunk_length // 2
num_chunks = int(noisy.shape[-1] / chunk_hop_length) + 1
win = torch.hann_window(chunk_length, device=noisy.device)
prev = None
enhanced = None
# 模拟语音的静音段，防止一上来就给语音，处理的不好
for chunk_idx in range(num_chunks):
if chunk_idx == 0:
pad = torch.zeros((num_mics, 256), device=noisy.device)
chunk_start_position = chunk_idx * chunk_hop_length
chunk_end_position = chunk_start_position + chunk_length
# concat([(8, 256), (..., ... + chunk_length)])
noisy_chunk = torch.cat((pad, noisy[:, chunk_start_position:chunk_end_position]), dim=1)
enhanced_chunk = self.model(noisy_chunk.unsqueeze(0))
enhanced_chunk = torch.squeeze(enhanced_chunk)
enhanced_chunk = enhanced_chunk[256:]
# Save the prior half chunk,
cur = enhanced_chunk[:chunk_length // 2]
# only for the 1st chunk,no overlap for the very 1st chunk prior half
prev = enhanced_chunk[chunk_length // 2:] * win[chunk_length // 2:]
else:
# use the previous noisy data as the pad
pad = noisy[:, (chunk_idx * chunk_hop_length - 256):(chunk_idx * chunk_hop_length)]
chunk_start_position = chunk_idx * chunk_hop_length
chunk_end_position = chunk_start_position + chunk_length
noisy_chunk = torch.cat((pad, noisy[:8, chunk_start_position:chunk_end_position]), dim=1)
enhanced_chunk = self.model(noisy_chunk.unsqueeze(0))
enhanced_chunk = torch.squeeze(enhanced_chunk)
enhanced_chunk = enhanced_chunk[256:]
# 使用这个窗函数来对拼接的位置进行平滑？
enhanced_chunk = enhanced_chunk * win[:len(enhanced_chunk)]
tmp = enhanced_chunk[:chunk_length // 2]
cur = tmp[:min(len(tmp), len(prev))] + prev[:min(len(tmp), len(prev))]
prev = enhanced_chunk[chunk_length // 2:]
if enhanced is None:
enhanced = cur
else:
enhanced = torch.cat((enhanced, cur), dim=0)
enhanced = enhanced[:noisy.shape[1]]
return enhanced.detach().squeeze(0).cpu().numpy()
@torch.no_grad()
def time_domain(self, noisy, inference_args):
noisy = noisy.to(self.device)
enhanced = self.model(noisy)
return enhanced.detach().squeeze().cpu().numpy()
if __name__ == '__main__':
a = torch.rand(10, 2, 161, 200)
print(cumulative_norm(a).shape)
| 39.364929 | 116 | 0.624007 | [
"Apache-2.0"
] | hit-thusz-RookieCJ/FullSubNet-plus | speech_enhance/fullsubnet/inferencer/inferencer.py | 8,392 | Python |
# test_codecs.py from CPython 2.7, modified for Jython
from test import test_support
import unittest
import codecs
import locale
import sys, StringIO
if not test_support.is_jython:
import _testcapi
class Queue(object):
"""
queue: write bytes at one end, read bytes from the other end
"""
def __init__(self):
self._buffer = ""
def write(self, chars):
self._buffer += chars
def read(self, size=-1):
if size<0:
s = self._buffer
self._buffer = ""
return s
else:
s = self._buffer[:size]
self._buffer = self._buffer[size:]
return s
class ReadTest(unittest.TestCase):
def check_partial(self, input, partialresults):
# get a StreamReader for the encoding and feed the bytestring version
# of input to the reader byte by byte. Read everything available from
# the StreamReader and check that the results equal the appropriate
# entries from partialresults.
q = Queue()
r = codecs.getreader(self.encoding)(q)
result = u""
for (c, partialresult) in zip(input.encode(self.encoding), partialresults):
q.write(c)
result += r.read()
self.assertEqual(result, partialresult)
# check that there's nothing left in the buffers
self.assertEqual(r.read(), u"")
self.assertEqual(r.bytebuffer, "")
self.assertEqual(r.charbuffer, u"")
# do the check again, this time using a incremental decoder
d = codecs.getincrementaldecoder(self.encoding)()
result = u""
for (c, partialresult) in zip(input.encode(self.encoding), partialresults):
result += d.decode(c)
self.assertEqual(result, partialresult)
# check that there's nothing left in the buffers
self.assertEqual(d.decode("", True), u"")
self.assertEqual(d.buffer, "")
# Check whether the reset method works properly
d.reset()
result = u""
for (c, partialresult) in zip(input.encode(self.encoding), partialresults):
result += d.decode(c)
self.assertEqual(result, partialresult)
# check that there's nothing left in the buffers
self.assertEqual(d.decode("", True), u"")
self.assertEqual(d.buffer, "")
# check iterdecode()
encoded = input.encode(self.encoding)
self.assertEqual(
input,
u"".join(codecs.iterdecode(encoded, self.encoding))
)
def test_readline(self):
def getreader(input):
stream = StringIO.StringIO(input.encode(self.encoding))
return codecs.getreader(self.encoding)(stream)
def readalllines(input, keepends=True, size=None):
reader = getreader(input)
lines = []
while True:
line = reader.readline(size=size, keepends=keepends)
if not line:
break
lines.append(line)
return "|".join(lines)
s = u"foo\nbar\r\nbaz\rspam\u2028eggs"
sexpected = u"foo\n|bar\r\n|baz\r|spam\u2028|eggs"
sexpectednoends = u"foo|bar|baz|spam|eggs"
self.assertEqual(readalllines(s, True), sexpected)
self.assertEqual(readalllines(s, False), sexpectednoends)
self.assertEqual(readalllines(s, True, 10), sexpected)
self.assertEqual(readalllines(s, False, 10), sexpectednoends)
# Test long lines (multiple calls to read() in readline())
vw = []
vwo = []
for (i, lineend) in enumerate(u"\n \r\n \r \u2028".split()):
vw.append((i*200)*u"\3042" + lineend)
vwo.append((i*200)*u"\3042")
self.assertEqual(readalllines("".join(vw), True), "".join(vw))
self.assertEqual(readalllines("".join(vw), False),"".join(vwo))
# Test lines where the first read might end with \r, so the
# reader has to look ahead whether this is a lone \r or a \r\n
for size in xrange(80):
for lineend in u"\n \r\n \r \u2028".split():
s = 10*(size*u"a" + lineend + u"xxx\n")
reader = getreader(s)
for i in xrange(10):
self.assertEqual(
reader.readline(keepends=True),
size*u"a" + lineend,
)
reader = getreader(s)
for i in xrange(10):
self.assertEqual(
reader.readline(keepends=False),
size*u"a",
)
def test_bug1175396(self):
s = [
'<%!--===================================================\r\n',
' BLOG index page: show recent articles,\r\n',
' today\'s articles, or articles of a specific date.\r\n',
'========================================================--%>\r\n',
'<%@inputencoding="ISO-8859-1"%>\r\n',
'<%@pagetemplate=TEMPLATE.y%>\r\n',
'<%@import=import frog.util, frog%>\r\n',
'<%@import=import frog.objects%>\r\n',
'<%@import=from frog.storageerrors import StorageError%>\r\n',
'<%\r\n',
'\r\n',
'import logging\r\n',
'log=logging.getLogger("Snakelets.logger")\r\n',
'\r\n',
'\r\n',
'user=self.SessionCtx.user\r\n',
'storageEngine=self.SessionCtx.storageEngine\r\n',
'\r\n',
'\r\n',
'def readArticlesFromDate(date, count=None):\r\n',
' entryids=storageEngine.listBlogEntries(date)\r\n',
' entryids.reverse() # descending\r\n',
' if count:\r\n',
' entryids=entryids[:count]\r\n',
' try:\r\n',
' return [ frog.objects.BlogEntry.load(storageEngine, date, Id) for Id in entryids ]\r\n',
' except StorageError,x:\r\n',
' log.error("Error loading articles: "+str(x))\r\n',
' self.abort("cannot load articles")\r\n',
'\r\n',
'showdate=None\r\n',
'\r\n',
'arg=self.Request.getArg()\r\n',
'if arg=="today":\r\n',
' #-------------------- TODAY\'S ARTICLES\r\n',
' self.write("<h2>Today\'s articles</h2>")\r\n',
' showdate = frog.util.isodatestr() \r\n',
' entries = readArticlesFromDate(showdate)\r\n',
'elif arg=="active":\r\n',
' #-------------------- ACTIVE ARTICLES redirect\r\n',
' self.Yredirect("active.y")\r\n',
'elif arg=="login":\r\n',
' #-------------------- LOGIN PAGE redirect\r\n',
' self.Yredirect("login.y")\r\n',
'elif arg=="date":\r\n',
' #-------------------- ARTICLES OF A SPECIFIC DATE\r\n',
' showdate = self.Request.getParameter("date")\r\n',
' self.write("<h2>Articles written on %s</h2>"% frog.util.mediumdatestr(showdate))\r\n',
' entries = readArticlesFromDate(showdate)\r\n',
'else:\r\n',
' #-------------------- RECENT ARTICLES\r\n',
' self.write("<h2>Recent articles</h2>")\r\n',
' dates=storageEngine.listBlogEntryDates()\r\n',
' if dates:\r\n',
' entries=[]\r\n',
' SHOWAMOUNT=10\r\n',
' for showdate in dates:\r\n',
' entries.extend( readArticlesFromDate(showdate, SHOWAMOUNT-len(entries)) )\r\n',
' if len(entries)>=SHOWAMOUNT:\r\n',
' break\r\n',
' \r\n',
]
stream = StringIO.StringIO("".join(s).encode(self.encoding))
reader = codecs.getreader(self.encoding)(stream)
for (i, line) in enumerate(reader):
self.assertEqual(line, s[i])
def test_readlinequeue(self):
q = Queue()
writer = codecs.getwriter(self.encoding)(q)
reader = codecs.getreader(self.encoding)(q)
# No lineends
writer.write(u"foo\r")
self.assertEqual(reader.readline(keepends=False), u"foo")
writer.write(u"\nbar\r")
self.assertEqual(reader.readline(keepends=False), u"")
self.assertEqual(reader.readline(keepends=False), u"bar")
writer.write(u"baz")
self.assertEqual(reader.readline(keepends=False), u"baz")
self.assertEqual(reader.readline(keepends=False), u"")
# Lineends
writer.write(u"foo\r")
self.assertEqual(reader.readline(keepends=True), u"foo\r")
writer.write(u"\nbar\r")
self.assertEqual(reader.readline(keepends=True), u"\n")
self.assertEqual(reader.readline(keepends=True), u"bar\r")
writer.write(u"baz")
self.assertEqual(reader.readline(keepends=True), u"baz")
self.assertEqual(reader.readline(keepends=True), u"")
writer.write(u"foo\r\n")
self.assertEqual(reader.readline(keepends=True), u"foo\r\n")
def test_bug1098990_a(self):
s1 = u"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy\r\n"
s2 = u"offending line: ladfj askldfj klasdj fskla dfzaskdj fasklfj laskd fjasklfzzzzaa%whereisthis!!!\r\n"
s3 = u"next line.\r\n"
s = (s1+s2+s3).encode(self.encoding)
stream = StringIO.StringIO(s)
reader = codecs.getreader(self.encoding)(stream)
self.assertEqual(reader.readline(), s1)
self.assertEqual(reader.readline(), s2)
self.assertEqual(reader.readline(), s3)
self.assertEqual(reader.readline(), u"")
def test_bug1098990_b(self):
s1 = u"aaaaaaaaaaaaaaaaaaaaaaaa\r\n"
s2 = u"bbbbbbbbbbbbbbbbbbbbbbbb\r\n"
s3 = u"stillokay:bbbbxx\r\n"
s4 = u"broken!!!!badbad\r\n"
s5 = u"againokay.\r\n"
s = (s1+s2+s3+s4+s5).encode(self.encoding)
stream = StringIO.StringIO(s)
reader = codecs.getreader(self.encoding)(stream)
self.assertEqual(reader.readline(), s1)
self.assertEqual(reader.readline(), s2)
self.assertEqual(reader.readline(), s3)
self.assertEqual(reader.readline(), s4)
self.assertEqual(reader.readline(), s5)
self.assertEqual(reader.readline(), u"")
class UTF32Test(ReadTest):
encoding = "utf-32"
spamle = ('\xff\xfe\x00\x00'
's\x00\x00\x00p\x00\x00\x00a\x00\x00\x00m\x00\x00\x00'
's\x00\x00\x00p\x00\x00\x00a\x00\x00\x00m\x00\x00\x00')
spambe = ('\x00\x00\xfe\xff'
'\x00\x00\x00s\x00\x00\x00p\x00\x00\x00a\x00\x00\x00m'
'\x00\x00\x00s\x00\x00\x00p\x00\x00\x00a\x00\x00\x00m')
def test_only_one_bom(self):
_,_,reader,writer = codecs.lookup(self.encoding)
# encode some stream
s = StringIO.StringIO()
f = writer(s)
f.write(u"spam")
f.write(u"spam")
d = s.getvalue()
# check whether there is exactly one BOM in it
self.assertTrue(d == self.spamle or d == self.spambe)
# try to read it back
s = StringIO.StringIO(d)
f = reader(s)
self.assertEqual(f.read(), u"spamspam")
def test_badbom(self):
s = StringIO.StringIO(4*"\xff")
f = codecs.getreader(self.encoding)(s)
self.assertRaises(UnicodeError, f.read)
s = StringIO.StringIO(8*"\xff")
f = codecs.getreader(self.encoding)(s)
self.assertRaises(UnicodeError, f.read)
def test_partial(self):
self.check_partial(
u"\x00\xff\u0100\uffff",
[
u"", # first byte of BOM read
u"", # second byte of BOM read
u"", # third byte of BOM read
u"", # fourth byte of BOM read => byteorder known
u"",
u"",
u"",
u"\x00",
u"\x00",
u"\x00",
u"\x00",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100\uffff",
]
)
def test_handlers(self):
self.assertEqual((u'\ufffd', 1),
codecs.utf_32_decode('\x01', 'replace', True))
self.assertEqual((u'', 1),
codecs.utf_32_decode('\x01', 'ignore', True))
def test_errors(self):
self.assertRaises(UnicodeDecodeError, codecs.utf_32_decode,
"\xff", "strict", True)
def test_issue8941(self):
# Issue #8941: insufficient result allocation when decoding into
# surrogate pairs on UCS-2 builds.
encoded_le = '\xff\xfe\x00\x00' + '\x00\x00\x01\x00' * 1024
self.assertEqual(u'\U00010000' * 1024,
codecs.utf_32_decode(encoded_le)[0])
encoded_be = '\x00\x00\xfe\xff' + '\x00\x01\x00\x00' * 1024
self.assertEqual(u'\U00010000' * 1024,
codecs.utf_32_decode(encoded_be)[0])
class UTF32LETest(ReadTest):
encoding = "utf-32-le"
def test_partial(self):
self.check_partial(
u"\x00\xff\u0100\uffff",
[
u"",
u"",
u"",
u"\x00",
u"\x00",
u"\x00",
u"\x00",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100\uffff",
]
)
def test_simple(self):
self.assertEqual(u"\U00010203".encode(self.encoding), "\x03\x02\x01\x00")
def test_errors(self):
self.assertRaises(UnicodeDecodeError, codecs.utf_32_le_decode,
"\xff", "strict", True)
def test_issue8941(self):
# Issue #8941: insufficient result allocation when decoding into
# surrogate pairs on UCS-2 builds.
encoded = '\x00\x00\x01\x00' * 1024
self.assertEqual(u'\U00010000' * 1024,
codecs.utf_32_le_decode(encoded)[0])
class UTF32BETest(ReadTest):
encoding = "utf-32-be"
def test_partial(self):
self.check_partial(
u"\x00\xff\u0100\uffff",
[
u"",
u"",
u"",
u"\x00",
u"\x00",
u"\x00",
u"\x00",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100\uffff",
]
)
def test_simple(self):
self.assertEqual(u"\U00010203".encode(self.encoding), "\x00\x01\x02\x03")
def test_errors(self):
self.assertRaises(UnicodeDecodeError, codecs.utf_32_be_decode,
"\xff", "strict", True)
def test_issue8941(self):
# Issue #8941: insufficient result allocation when decoding into
# surrogate pairs on UCS-2 builds.
encoded = '\x00\x01\x00\x00' * 1024
self.assertEqual(u'\U00010000' * 1024,
codecs.utf_32_be_decode(encoded)[0])
class UTF16Test(ReadTest):
encoding = "utf-16"
spamle = '\xff\xfes\x00p\x00a\x00m\x00s\x00p\x00a\x00m\x00'
spambe = '\xfe\xff\x00s\x00p\x00a\x00m\x00s\x00p\x00a\x00m'
def test_only_one_bom(self):
_,_,reader,writer = codecs.lookup(self.encoding)
# encode some stream
s = StringIO.StringIO()
f = writer(s)
f.write(u"spam")
f.write(u"spam")
d = s.getvalue()
# check whether there is exactly one BOM in it
self.assertTrue(d == self.spamle or d == self.spambe)
# try to read it back
s = StringIO.StringIO(d)
f = reader(s)
self.assertEqual(f.read(), u"spamspam")
def test_badbom(self):
s = StringIO.StringIO("\xff\xff")
f = codecs.getreader(self.encoding)(s)
self.assertRaises(UnicodeError, f.read)
s = StringIO.StringIO("\xff\xff\xff\xff")
f = codecs.getreader(self.encoding)(s)
self.assertRaises(UnicodeError, f.read)
def test_partial(self):
self.check_partial(
u"\x00\xff\u0100\uffff",
[
u"", # first byte of BOM read
u"", # second byte of BOM read => byteorder known
u"",
u"\x00",
u"\x00",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100\uffff",
]
)
def test_handlers(self):
self.assertEqual((u'\ufffd', 1),
codecs.utf_16_decode('\x01', 'replace', True))
self.assertEqual((u'', 1),
codecs.utf_16_decode('\x01', 'ignore', True))
def test_errors(self):
self.assertRaises(UnicodeDecodeError, codecs.utf_16_decode, "\xff", "strict", True)
def test_bug691291(self):
# Files are always opened in binary mode, even if no binary mode was
# specified. This means that no automatic conversion of '\n' is done
# on reading and writing.
s1 = u'Hello\r\nworld\r\n'
s = s1.encode(self.encoding)
self.addCleanup(test_support.unlink, test_support.TESTFN)
with open(test_support.TESTFN, 'wb') as fp:
fp.write(s)
with codecs.open(test_support.TESTFN, 'U', encoding=self.encoding) as reader:
self.assertEqual(reader.read(), s1)
class UTF16LETest(ReadTest):
encoding = "utf-16-le"
def test_partial(self):
self.check_partial(
u"\x00\xff\u0100\uffff",
[
u"",
u"\x00",
u"\x00",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100\uffff",
]
)
def test_errors(self):
self.assertRaises(UnicodeDecodeError, codecs.utf_16_le_decode, "\xff", "strict", True)
class UTF16BETest(ReadTest):
encoding = "utf-16-be"
def test_partial(self):
self.check_partial(
u"\x00\xff\u0100\uffff",
[
u"",
u"\x00",
u"\x00",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff\u0100",
u"\x00\xff\u0100",
u"\x00\xff\u0100\uffff",
]
)
def test_errors(self):
self.assertRaises(UnicodeDecodeError, codecs.utf_16_be_decode, "\xff", "strict", True)
class UTF8Test(ReadTest):
encoding = "utf-8"
def test_partial(self):
self.check_partial(
u"\x00\xff\u07ff\u0800\uffff",
[
u"\x00",
u"\x00",
u"\x00\xff",
u"\x00\xff",
u"\x00\xff\u07ff",
u"\x00\xff\u07ff",
u"\x00\xff\u07ff",
u"\x00\xff\u07ff\u0800",
u"\x00\xff\u07ff\u0800",
u"\x00\xff\u07ff\u0800",
u"\x00\xff\u07ff\u0800\uffff",
]
)
class UTF7Test(ReadTest):
encoding = "utf-7"
def test_partial(self):
self.check_partial(
u"a+-b",
[
u"a",
u"a",
u"a+",
u"a+-",
u"a+-b",
]
)
# Jython extra (test supplementary characters)
@unittest.skipIf(not test_support.is_jython, "Jython supports surrogate pairs")
def test_partial_supp(self):
# Check the encoding is what we think it is
ustr = u"x\U00023456.\u0177\U00023456\u017az"
bstr = b'x+2E3cVg.+AXfYTdxWAXo-z'
self.assertEqual(ustr.encode(self.encoding), bstr)
self.check_partial(
ustr,
[
u"x",
u"x", # '+' added: begins Base64
u"x",
u"x",
u"x",
u"x",
u"x",
u"x",
u"x\U00023456.", # '.' added: ends Base64
u"x\U00023456.", # '+' added: begins Base64
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.",
u"x\U00023456.\u0177\U00023456\u017a", # '-' added: ends Base64
u"x\U00023456.\u0177\U00023456\u017az",
]
)
class UTF16ExTest(unittest.TestCase):
def test_errors(self):
self.assertRaises(UnicodeDecodeError, codecs.utf_16_ex_decode, "\xff", "strict", 0, True)
def test_bad_args(self):
self.assertRaises(TypeError, codecs.utf_16_ex_decode)
@unittest.skipIf(test_support.is_jython, "Jython has no _codecs.readbuffer_encode method")
class ReadBufferTest(unittest.TestCase):
def test_array(self):
import array
self.assertEqual(
codecs.readbuffer_encode(array.array("c", "spam")),
("spam", 4)
)
def test_empty(self):
self.assertEqual(codecs.readbuffer_encode(""), ("", 0))
def test_bad_args(self):
self.assertRaises(TypeError, codecs.readbuffer_encode)
self.assertRaises(TypeError, codecs.readbuffer_encode, 42)
@unittest.skipIf(test_support.is_jython, "Jython has no _codecs.charbuffer_encode method")
class CharBufferTest(unittest.TestCase):
def test_string(self):
self.assertEqual(codecs.charbuffer_encode("spam"), ("spam", 4))
def test_empty(self):
self.assertEqual(codecs.charbuffer_encode(""), ("", 0))
def test_bad_args(self):
self.assertRaises(TypeError, codecs.charbuffer_encode)
self.assertRaises(TypeError, codecs.charbuffer_encode, 42)
class UTF8SigTest(ReadTest):
encoding = "utf-8-sig"
def test_partial(self):
self.check_partial(
u"\ufeff\x00\xff\u07ff\u0800\uffff",
[
u"",
u"",
u"", # First BOM has been read and skipped
u"",
u"",
u"\ufeff", # Second BOM has been read and emitted
u"\ufeff\x00", # "\x00" read and emitted
u"\ufeff\x00", # First byte of encoded u"\xff" read
u"\ufeff\x00\xff", # Second byte of encoded u"\xff" read
u"\ufeff\x00\xff", # First byte of encoded u"\u07ff" read
u"\ufeff\x00\xff\u07ff", # Second byte of encoded u"\u07ff" read
u"\ufeff\x00\xff\u07ff",
u"\ufeff\x00\xff\u07ff",
u"\ufeff\x00\xff\u07ff\u0800",
u"\ufeff\x00\xff\u07ff\u0800",
u"\ufeff\x00\xff\u07ff\u0800",
u"\ufeff\x00\xff\u07ff\u0800\uffff",
]
)
def test_bug1601501(self):
# SF bug #1601501: check that the codec works with a buffer
unicode("\xef\xbb\xbf", "utf-8-sig")
def test_bom(self):
d = codecs.getincrementaldecoder("utf-8-sig")()
s = u"spam"
self.assertEqual(d.decode(s.encode("utf-8-sig")), s)
def test_stream_bom(self):
unistring = u"ABC\u00A1\u2200XYZ"
bytestring = codecs.BOM_UTF8 + "ABC\xC2\xA1\xE2\x88\x80XYZ"
reader = codecs.getreader("utf-8-sig")
for sizehint in [None] + range(1, 11) + \
[64, 128, 256, 512, 1024]:
istream = reader(StringIO.StringIO(bytestring))
ostream = StringIO.StringIO()
while 1:
if sizehint is not None:
data = istream.read(sizehint)
else:
data = istream.read()
if not data:
break
ostream.write(data)
got = ostream.getvalue()
self.assertEqual(got, unistring)
def test_stream_bare(self):
unistring = u"ABC\u00A1\u2200XYZ"
bytestring = "ABC\xC2\xA1\xE2\x88\x80XYZ"
reader = codecs.getreader("utf-8-sig")
for sizehint in [None] + range(1, 11) + \
[64, 128, 256, 512, 1024]:
istream = reader(StringIO.StringIO(bytestring))
ostream = StringIO.StringIO()
while 1:
if sizehint is not None:
data = istream.read(sizehint)
else:
data = istream.read()
if not data:
break
ostream.write(data)
got = ostream.getvalue()
self.assertEqual(got, unistring)
class EscapeDecodeTest(unittest.TestCase):
def test_empty(self):
self.assertEqual(codecs.escape_decode(""), ("", 0))
class RecodingTest(unittest.TestCase):
def test_recoding(self):
f = StringIO.StringIO()
f2 = codecs.EncodedFile(f, "unicode_internal", "utf-8")
# f2.write(u"a")
# Must be bytes in Jython (and probably should have been in CPython)
f2.write(b"\x00\x00\x00\x61")
f2.close()
# Python used to crash on this at exit because of a refcount
# bug in _codecsmodule.c
# From RFC 3492
punycode_testcases = [
# A Arabic (Egyptian):
(u"\u0644\u064A\u0647\u0645\u0627\u0628\u062A\u0643\u0644"
u"\u0645\u0648\u0634\u0639\u0631\u0628\u064A\u061F",
"egbpdaj6bu4bxfgehfvwxn"),
# B Chinese (simplified):
(u"\u4ED6\u4EEC\u4E3A\u4EC0\u4E48\u4E0D\u8BF4\u4E2D\u6587",
"ihqwcrb4cv8a8dqg056pqjye"),
# C Chinese (traditional):
(u"\u4ED6\u5011\u7232\u4EC0\u9EBD\u4E0D\u8AAA\u4E2D\u6587",
"ihqwctvzc91f659drss3x8bo0yb"),
# D Czech: Pro<ccaron>prost<ecaron>nemluv<iacute><ccaron>esky
(u"\u0050\u0072\u006F\u010D\u0070\u0072\u006F\u0073\u0074"
u"\u011B\u006E\u0065\u006D\u006C\u0075\u0076\u00ED\u010D"
u"\u0065\u0073\u006B\u0079",
"Proprostnemluvesky-uyb24dma41a"),
# E Hebrew:
(u"\u05DC\u05DE\u05D4\u05D4\u05DD\u05E4\u05E9\u05D5\u05D8"
u"\u05DC\u05D0\u05DE\u05D3\u05D1\u05E8\u05D9\u05DD\u05E2"
u"\u05D1\u05E8\u05D9\u05EA",
"4dbcagdahymbxekheh6e0a7fei0b"),
# F Hindi (Devanagari):
(u"\u092F\u0939\u0932\u094B\u0917\u0939\u093F\u0928\u094D"
u"\u0926\u0940\u0915\u094D\u092F\u094B\u0902\u0928\u0939"
u"\u0940\u0902\u092C\u094B\u0932\u0938\u0915\u0924\u0947"
u"\u0939\u0948\u0902",
"i1baa7eci9glrd9b2ae1bj0hfcgg6iyaf8o0a1dig0cd"),
#(G) Japanese (kanji and hiragana):
(u"\u306A\u305C\u307F\u3093\u306A\u65E5\u672C\u8A9E\u3092"
u"\u8A71\u3057\u3066\u304F\u308C\u306A\u3044\u306E\u304B",
"n8jok5ay5dzabd5bym9f0cm5685rrjetr6pdxa"),
# (H) Korean (Hangul syllables):
(u"\uC138\uACC4\uC758\uBAA8\uB4E0\uC0AC\uB78C\uB4E4\uC774"
u"\uD55C\uAD6D\uC5B4\uB97C\uC774\uD574\uD55C\uB2E4\uBA74"
u"\uC5BC\uB9C8\uB098\uC88B\uC744\uAE4C",
"989aomsvi5e83db1d2a355cv1e0vak1dwrv93d5xbh15a0dt30a5j"
"psd879ccm6fea98c"),
# (I) Russian (Cyrillic):
(u"\u043F\u043E\u0447\u0435\u043C\u0443\u0436\u0435\u043E"
u"\u043D\u0438\u043D\u0435\u0433\u043E\u0432\u043E\u0440"
u"\u044F\u0442\u043F\u043E\u0440\u0443\u0441\u0441\u043A"
u"\u0438",
"b1abfaaepdrnnbgefbaDotcwatmq2g4l"),
# (J) Spanish: Porqu<eacute>nopuedensimplementehablarenEspa<ntilde>ol
(u"\u0050\u006F\u0072\u0071\u0075\u00E9\u006E\u006F\u0070"
u"\u0075\u0065\u0064\u0065\u006E\u0073\u0069\u006D\u0070"
u"\u006C\u0065\u006D\u0065\u006E\u0074\u0065\u0068\u0061"
u"\u0062\u006C\u0061\u0072\u0065\u006E\u0045\u0073\u0070"
u"\u0061\u00F1\u006F\u006C",
"PorqunopuedensimplementehablarenEspaol-fmd56a"),
# (K) Vietnamese:
# T<adotbelow>isaoh<odotbelow>kh<ocirc>ngth<ecirchookabove>ch\
# <ihookabove>n<oacute>iti<ecircacute>ngVi<ecircdotbelow>t
(u"\u0054\u1EA1\u0069\u0073\u0061\u006F\u0068\u1ECD\u006B"
u"\u0068\u00F4\u006E\u0067\u0074\u0068\u1EC3\u0063\u0068"
u"\u1EC9\u006E\u00F3\u0069\u0074\u0069\u1EBF\u006E\u0067"
u"\u0056\u0069\u1EC7\u0074",
"TisaohkhngthchnitingVit-kjcr8268qyxafd2f1b9g"),
#(L) 3<nen>B<gumi><kinpachi><sensei>
(u"\u0033\u5E74\u0042\u7D44\u91D1\u516B\u5148\u751F",
"3B-ww4c5e180e575a65lsy2b"),
# (M) <amuro><namie>-with-SUPER-MONKEYS
(u"\u5B89\u5BA4\u5948\u7F8E\u6075\u002D\u0077\u0069\u0074"
u"\u0068\u002D\u0053\u0055\u0050\u0045\u0052\u002D\u004D"
u"\u004F\u004E\u004B\u0045\u0059\u0053",
"-with-SUPER-MONKEYS-pc58ag80a8qai00g7n9n"),
# (N) Hello-Another-Way-<sorezore><no><basho>
(u"\u0048\u0065\u006C\u006C\u006F\u002D\u0041\u006E\u006F"
u"\u0074\u0068\u0065\u0072\u002D\u0057\u0061\u0079\u002D"
u"\u305D\u308C\u305E\u308C\u306E\u5834\u6240",
"Hello-Another-Way--fc4qua05auwb3674vfr0b"),
# (O) <hitotsu><yane><no><shita>2
(u"\u3072\u3068\u3064\u5C4B\u6839\u306E\u4E0B\u0032",
"2-u9tlzr9756bt3uc0v"),
# (P) Maji<de>Koi<suru>5<byou><mae>
(u"\u004D\u0061\u006A\u0069\u3067\u004B\u006F\u0069\u3059"
u"\u308B\u0035\u79D2\u524D",
"MajiKoi5-783gue6qz075azm5e"),
# (Q) <pafii>de<runba>
(u"\u30D1\u30D5\u30A3\u30FC\u0064\u0065\u30EB\u30F3\u30D0",
"de-jg4avhby1noc0d"),
# (R) <sono><supiido><de>
(u"\u305D\u306E\u30B9\u30D4\u30FC\u30C9\u3067",
"d9juau41awczczp"),
# (S) -> $1.00 <-
(u"\u002D\u003E\u0020\u0024\u0031\u002E\u0030\u0030\u0020"
u"\u003C\u002D",
"-> $1.00 <--")
]
for i in punycode_testcases:
if len(i)!=2:
print repr(i)
class PunycodeTest(unittest.TestCase):
def test_encode(self):
for uni, puny in punycode_testcases:
# Need to convert both strings to lower case, since
# some of the extended encodings use upper case, but our
# code produces only lower case. Converting just puny to
# lower is also insufficient, since some of the input characters
# are upper case.
self.assertEqual(uni.encode("punycode").lower(), puny.lower())
def test_decode(self):
for uni, puny in punycode_testcases:
self.assertEqual(uni, puny.decode("punycode"))
class UnicodeInternalTest(unittest.TestCase):
def test_bug1251300(self):
# Decoding with unicode_internal used to not correctly handle "code
# points" above 0x10ffff on UCS-4 builds.
if sys.maxunicode > 0xffff:
ok = [
("\x00\x10\xff\xff", u"\U0010ffff"),
("\x00\x00\x01\x01", u"\U00000101"),
("", u""),
]
not_ok = [
"\x7f\xff\xff\xff",
"\x80\x00\x00\x00",
"\x81\x00\x00\x00",
"\x00",
"\x00\x00\x00\x00\x00",
]
for internal, uni in ok:
if sys.byteorder == "little":
internal = "".join(reversed(internal))
self.assertEqual(uni, internal.decode("unicode_internal"))
for internal in not_ok:
if sys.byteorder == "little":
internal = "".join(reversed(internal))
self.assertRaises(UnicodeDecodeError, internal.decode,
"unicode_internal")
def test_decode_error_attributes(self):
if sys.maxunicode > 0xffff:
try:
"\x00\x00\x00\x00\x00\x11\x11\x00".decode("unicode_internal")
except UnicodeDecodeError, ex:
if test_support.is_jython:
# Jython delegates internally to utf-32be and it shows here
self.assertEqual("utf-32", ex.encoding)
else:
self.assertEqual("unicode_internal", ex.encoding)
self.assertEqual("\x00\x00\x00\x00\x00\x11\x11\x00", ex.object)
self.assertEqual(4, ex.start)
self.assertEqual(8, ex.end)
else:
self.fail("UnicodeDecodeError not raised")
def test_decode_callback(self):
if sys.maxunicode > 0xffff:
codecs.register_error("UnicodeInternalTest", codecs.ignore_errors)
decoder = codecs.getdecoder("unicode_internal")
ab = u"ab".encode("unicode_internal")
ignored = decoder("%s\x22\x22\x22\x22%s" % (ab[:4], ab[4:]),
"UnicodeInternalTest")
self.assertEqual((u"ab", 12), ignored)
def test_encode_length(self):
# Issue 3739
encoder = codecs.getencoder("unicode_internal")
self.assertEqual(encoder(u"a")[1], 1)
self.assertEqual(encoder(u"\xe9\u0142")[1], 2)
encoder = codecs.getencoder("string-escape")
self.assertEqual(encoder(r'\x00')[1], 4)
# From http://www.gnu.org/software/libidn/draft-josefsson-idn-test-vectors.html
nameprep_tests = [
# 3.1 Map to nothing.
('foo\xc2\xad\xcd\x8f\xe1\xa0\x86\xe1\xa0\x8bbar'
'\xe2\x80\x8b\xe2\x81\xa0baz\xef\xb8\x80\xef\xb8\x88\xef'
'\xb8\x8f\xef\xbb\xbf',
'foobarbaz'),
# 3.2 Case folding ASCII U+0043 U+0041 U+0046 U+0045.
('CAFE',
'cafe'),
# 3.3 Case folding 8bit U+00DF (german sharp s).
# The original test case is bogus; it says \xc3\xdf
('\xc3\x9f',
'ss'),
# 3.4 Case folding U+0130 (turkish capital I with dot).
('\xc4\xb0',
'i\xcc\x87'),
# 3.5 Case folding multibyte U+0143 U+037A.
('\xc5\x83\xcd\xba',
'\xc5\x84 \xce\xb9'),
# 3.6 Case folding U+2121 U+33C6 U+1D7BB.
# XXX: skip this as it fails in UCS-2 mode
#('\xe2\x84\xa1\xe3\x8f\x86\xf0\x9d\x9e\xbb',
# 'telc\xe2\x88\x95kg\xcf\x83'),
(None, None),
# 3.7 Normalization of U+006a U+030c U+00A0 U+00AA.
('j\xcc\x8c\xc2\xa0\xc2\xaa',
'\xc7\xb0 a'),
# 3.8 Case folding U+1FB7 and normalization.
('\xe1\xbe\xb7',
'\xe1\xbe\xb6\xce\xb9'),
# 3.9 Self-reverting case folding U+01F0 and normalization.
# The original test case is bogus, it says `\xc7\xf0'
('\xc7\xb0',
'\xc7\xb0'),
# 3.10 Self-reverting case folding U+0390 and normalization.
('\xce\x90',
'\xce\x90'),
# 3.11 Self-reverting case folding U+03B0 and normalization.
('\xce\xb0',
'\xce\xb0'),
# 3.12 Self-reverting case folding U+1E96 and normalization.
('\xe1\xba\x96',
'\xe1\xba\x96'),
# 3.13 Self-reverting case folding U+1F56 and normalization.
('\xe1\xbd\x96',
'\xe1\xbd\x96'),
# 3.14 ASCII space character U+0020.
(' ',
' '),
# 3.15 Non-ASCII 8bit space character U+00A0.
('\xc2\xa0',
' '),
# 3.16 Non-ASCII multibyte space character U+1680.
('\xe1\x9a\x80',
None),
# 3.17 Non-ASCII multibyte space character U+2000.
('\xe2\x80\x80',
' '),
# 3.18 Zero Width Space U+200b.
('\xe2\x80\x8b',
''),
# 3.19 Non-ASCII multibyte space character U+3000.
('\xe3\x80\x80',
' '),
# 3.20 ASCII control characters U+0010 U+007F.
('\x10\x7f',
'\x10\x7f'),
# 3.21 Non-ASCII 8bit control character U+0085.
('\xc2\x85',
None),
# 3.22 Non-ASCII multibyte control character U+180E.
('\xe1\xa0\x8e',
None),
# 3.23 Zero Width No-Break Space U+FEFF.
('\xef\xbb\xbf',
''),
# 3.24 Non-ASCII control character U+1D175.
('\xf0\x9d\x85\xb5',
None),
# 3.25 Plane 0 private use character U+F123.
('\xef\x84\xa3',
None),
# 3.26 Plane 15 private use character U+F1234.
('\xf3\xb1\x88\xb4',
None),
# 3.27 Plane 16 private use character U+10F234.
('\xf4\x8f\x88\xb4',
None),
# 3.28 Non-character code point U+8FFFE.
('\xf2\x8f\xbf\xbe',
None),
# 3.29 Non-character code point U+10FFFF.
('\xf4\x8f\xbf\xbf',
None),
# 3.30 Surrogate code U+DF42.
('\xed\xbd\x82',
None),
# 3.31 Non-plain text character U+FFFD.
('\xef\xbf\xbd',
None),
# 3.32 Ideographic description character U+2FF5.
('\xe2\xbf\xb5',
None),
# 3.33 Display property character U+0341.
('\xcd\x81',
'\xcc\x81'),
# 3.34 Left-to-right mark U+200E.
('\xe2\x80\x8e',
None),
# 3.35 Deprecated U+202A.
('\xe2\x80\xaa',
None),
# 3.36 Language tagging character U+E0001.
('\xf3\xa0\x80\x81',
None),
# 3.37 Language tagging character U+E0042.
('\xf3\xa0\x81\x82',
None),
# 3.38 Bidi: RandALCat character U+05BE and LCat characters.
('foo\xd6\xbebar',
None),
# 3.39 Bidi: RandALCat character U+FD50 and LCat characters.
('foo\xef\xb5\x90bar',
None),
# 3.40 Bidi: RandALCat character U+FB38 and LCat characters.
('foo\xef\xb9\xb6bar',
'foo \xd9\x8ebar'),
# 3.41 Bidi: RandALCat without trailing RandALCat U+0627 U+0031.
('\xd8\xa71',
None),
# 3.42 Bidi: RandALCat character U+0627 U+0031 U+0628.
('\xd8\xa71\xd8\xa8',
'\xd8\xa71\xd8\xa8'),
# 3.43 Unassigned code point U+E0002.
# Skip this test as we allow unassigned
#('\xf3\xa0\x80\x82',
# None),
(None, None),
# 3.44 Larger test (shrinking).
# Original test case reads \xc3\xdf
('X\xc2\xad\xc3\x9f\xc4\xb0\xe2\x84\xa1j\xcc\x8c\xc2\xa0\xc2'
'\xaa\xce\xb0\xe2\x80\x80',
'xssi\xcc\x87tel\xc7\xb0 a\xce\xb0 '),
# 3.45 Larger test (expanding).
# Original test case reads \xc3\x9f
('X\xc3\x9f\xe3\x8c\x96\xc4\xb0\xe2\x84\xa1\xe2\x92\x9f\xe3\x8c'
'\x80',
'xss\xe3\x82\xad\xe3\x83\xad\xe3\x83\xa1\xe3\x83\xbc\xe3'
'\x83\x88\xe3\x83\xabi\xcc\x87tel\x28d\x29\xe3\x82'
'\xa2\xe3\x83\x91\xe3\x83\xbc\xe3\x83\x88')
]
@unittest.skipIf(test_support.is_jython, "FIXME: incomplete unicodedata module")
class NameprepTest(unittest.TestCase):
def test_nameprep(self):
from encodings.idna import nameprep
for pos, (orig, prepped) in enumerate(nameprep_tests):
if orig is None:
# Skipped
continue
# The Unicode strings are given in UTF-8
orig = unicode(orig, "utf-8")
if prepped is None:
# Input contains prohibited characters
self.assertRaises(UnicodeError, nameprep, orig)
else:
prepped = unicode(prepped, "utf-8")
try:
self.assertEqual(nameprep(orig), prepped)
except Exception,e:
raise test_support.TestFailed("Test 3.%d: %s" % (pos+1, str(e)))
@unittest.skipIf(test_support.is_jython, "FIXME: Jython issue 2000 missing support for IDNA")
class IDNACodecTest(unittest.TestCase):
def test_builtin_decode(self):
self.assertEqual(unicode("python.org", "idna"), u"python.org")
self.assertEqual(unicode("python.org.", "idna"), u"python.org.")
self.assertEqual(unicode("xn--pythn-mua.org", "idna"), u"pyth\xf6n.org")
self.assertEqual(unicode("xn--pythn-mua.org.", "idna"), u"pyth\xf6n.org.")
def test_builtin_encode(self):
self.assertEqual(u"python.org".encode("idna"), "python.org")
self.assertEqual("python.org.".encode("idna"), "python.org.")
self.assertEqual(u"pyth\xf6n.org".encode("idna"), "xn--pythn-mua.org")
self.assertEqual(u"pyth\xf6n.org.".encode("idna"), "xn--pythn-mua.org.")
def test_stream(self):
import StringIO
r = codecs.getreader("idna")(StringIO.StringIO("abc"))
r.read(3)
self.assertEqual(r.read(), u"")
def test_incremental_decode(self):
self.assertEqual(
"".join(codecs.iterdecode("python.org", "idna")),
u"python.org"
)
self.assertEqual(
"".join(codecs.iterdecode("python.org.", "idna")),
u"python.org."
)
self.assertEqual(
"".join(codecs.iterdecode("xn--pythn-mua.org.", "idna")),
u"pyth\xf6n.org."
)
self.assertEqual(
"".join(codecs.iterdecode("xn--pythn-mua.org.", "idna")),
u"pyth\xf6n.org."
)
decoder = codecs.getincrementaldecoder("idna")()
self.assertEqual(decoder.decode("xn--xam", ), u"")
self.assertEqual(decoder.decode("ple-9ta.o", ), u"\xe4xample.")
self.assertEqual(decoder.decode(u"rg"), u"")
self.assertEqual(decoder.decode(u"", True), u"org")
decoder.reset()
self.assertEqual(decoder.decode("xn--xam", ), u"")
self.assertEqual(decoder.decode("ple-9ta.o", ), u"\xe4xample.")
self.assertEqual(decoder.decode("rg."), u"org.")
self.assertEqual(decoder.decode("", True), u"")
def test_incremental_encode(self):
self.assertEqual(
"".join(codecs.iterencode(u"python.org", "idna")),
"python.org"
)
self.assertEqual(
"".join(codecs.iterencode(u"python.org.", "idna")),
"python.org."
)
self.assertEqual(
"".join(codecs.iterencode(u"pyth\xf6n.org.", "idna")),
"xn--pythn-mua.org."
)
self.assertEqual(
"".join(codecs.iterencode(u"pyth\xf6n.org.", "idna")),
"xn--pythn-mua.org."
)
encoder = codecs.getincrementalencoder("idna")()
self.assertEqual(encoder.encode(u"\xe4x"), "")
self.assertEqual(encoder.encode(u"ample.org"), "xn--xample-9ta.")
self.assertEqual(encoder.encode(u"", True), "org")
encoder.reset()
self.assertEqual(encoder.encode(u"\xe4x"), "")
self.assertEqual(encoder.encode(u"ample.org."), "xn--xample-9ta.org.")
self.assertEqual(encoder.encode(u"", True), "")
class CodecsModuleTest(unittest.TestCase):
def test_decode(self):
self.assertEqual(codecs.decode('\xe4\xf6\xfc', 'latin-1'),
u'\xe4\xf6\xfc')
self.assertRaises(TypeError, codecs.decode)
self.assertEqual(codecs.decode('abc'), u'abc')
self.assertRaises(UnicodeDecodeError, codecs.decode, '\xff', 'ascii')
def test_encode(self):
self.assertEqual(codecs.encode(u'\xe4\xf6\xfc', 'latin-1'),
'\xe4\xf6\xfc')
self.assertRaises(TypeError, codecs.encode)
self.assertRaises(LookupError, codecs.encode, u"foo", "__spam__")
self.assertEqual(codecs.encode(u'abc'), 'abc')
self.assertRaises(UnicodeEncodeError, codecs.encode, u'\xffff', 'ascii')
def test_register(self):
self.assertRaises(TypeError, codecs.register)
self.assertRaises(TypeError, codecs.register, 42)
def test_lookup(self):
self.assertRaises(TypeError, codecs.lookup)
self.assertRaises(LookupError, codecs.lookup, "__spam__")
self.assertRaises(LookupError, codecs.lookup, " ")
def test_getencoder(self):
self.assertRaises(TypeError, codecs.getencoder)
self.assertRaises(LookupError, codecs.getencoder, "__spam__")
def test_getdecoder(self):
self.assertRaises(TypeError, codecs.getdecoder)
self.assertRaises(LookupError, codecs.getdecoder, "__spam__")
def test_getreader(self):
self.assertRaises(TypeError, codecs.getreader)
self.assertRaises(LookupError, codecs.getreader, "__spam__")
def test_getwriter(self):
self.assertRaises(TypeError, codecs.getwriter)
self.assertRaises(LookupError, codecs.getwriter, "__spam__")
def test_lookup_issue1813(self):
# Issue #1813: under Turkish locales, lookup of some codecs failed
# because 'I' is lowercased as a dotless "i"
oldlocale = locale.getlocale(locale.LC_CTYPE)
self.addCleanup(locale.setlocale, locale.LC_CTYPE, oldlocale)
try:
locale.setlocale(locale.LC_CTYPE, 'tr_TR')
except locale.Error:
# Unsupported locale on this system
self.skipTest('test needs Turkish locale')
c = codecs.lookup('ASCII')
self.assertEqual(c.name, 'ascii')
class StreamReaderTest(unittest.TestCase):
def setUp(self):
self.reader = codecs.getreader('utf-8')
self.stream = StringIO.StringIO('\xed\x95\x9c\n\xea\xb8\x80')
def test_readlines(self):
f = self.reader(self.stream)
self.assertEqual(f.readlines(), [u'\ud55c\n', u'\uae00'])
class EncodedFileTest(unittest.TestCase):
def test_basic(self):
f = StringIO.StringIO('\xed\x95\x9c\n\xea\xb8\x80')
ef = codecs.EncodedFile(f, 'utf-16-le', 'utf-8')
self.assertEqual(ef.read(), '\\\xd5\n\x00\x00\xae')
f = StringIO.StringIO()
ef = codecs.EncodedFile(f, 'utf-8', 'latin1')
ef.write('\xc3\xbc')
self.assertEqual(f.getvalue(), '\xfc')
class Str2StrTest(unittest.TestCase):
def test_read(self):
sin = "\x80".encode("base64_codec")
reader = codecs.getreader("base64_codec")(StringIO.StringIO(sin))
sout = reader.read()
self.assertEqual(sout, "\x80")
self.assertIsInstance(sout, str)
def test_readline(self):
sin = "\x80".encode("base64_codec")
reader = codecs.getreader("base64_codec")(StringIO.StringIO(sin))
sout = reader.readline()
self.assertEqual(sout, "\x80")
self.assertIsInstance(sout, str)
all_unicode_encodings = [
"ascii",
"base64_codec",
# FIXME: Jython issue 1066: "big5",
# FIXME: Jython issue 1066: "big5hkscs",
"charmap",
"cp037",
"cp1006",
"cp1026",
"cp1140",
"cp1250",
"cp1251",
"cp1252",
"cp1253",
"cp1254",
"cp1255",
"cp1256",
"cp1257",
"cp1258",
"cp424",
"cp437",
"cp500",
"cp720",
"cp737",
"cp775",
"cp850",
"cp852",
"cp855",
"cp856",
"cp857",
"cp858",
"cp860",
"cp861",
"cp862",
"cp863",
"cp864",
"cp865",
"cp866",
"cp869",
"cp874",
"cp875",
# FIXME: Jython issue 1066: "cp932",
# FIXME: Jython issue 1066: "cp949",
# FIXME: Jython issue 1066: "cp950",
# FIXME: Jython issue 1066: "euc_jis_2004",
# FIXME: Jython issue 1066: 'euc_jisx0213',
# FIXME: Jython issue 1066: 'euc_jp',
# FIXME: Jython issue 1066: 'euc_kr',
# FIXME: Jython issue 1066: 'gb18030',
# FIXME: Jython issue 1066: 'gb2312',
# FIXME: Jython issue 1066: 'gbk',
"hex_codec",
"hp_roman8",
# FIXME: Jython issue 1066: 'hz',
# FIXME: Jython issue 1066: "idna",
# FIXME: Jython issue 1066: 'iso2022_jp',
# FIXME: Jython issue 1066: 'iso2022_jp_1',
# FIXME: Jython issue 1066: 'iso2022_jp_2',
# FIXME: Jython issue 1066: 'iso2022_jp_2004',
# FIXME: Jython issue 1066: 'iso2022_jp_3',
# FIXME: Jython issue 1066: 'iso2022_jp_ext',
# FIXME: Jython issue 1066: 'iso2022_kr',
"iso8859_1",
"iso8859_10",
"iso8859_11",
"iso8859_13",
"iso8859_14",
"iso8859_15",
"iso8859_16",
"iso8859_2",
"iso8859_3",
"iso8859_4",
"iso8859_5",
"iso8859_6",
"iso8859_7",
"iso8859_8",
"iso8859_9",
# FIXME: Jython issue 1066: 'johab',
"koi8_r",
"koi8_u",
"latin_1",
"mac_cyrillic",
"mac_greek",
"mac_iceland",
"mac_latin2",
"mac_roman",
"mac_turkish",
"palmos",
"ptcp154",
"punycode",
"raw_unicode_escape",
"rot_13",
# FIXME: Jython issue 1066: 'shift_jis',
# FIXME: Jython issue 1066: 'shift_jis_2004',
# FIXME: Jython issue 1066: 'shift_jisx0213',
"tis_620",
"unicode_escape",
"unicode_internal",
"utf_16",
"utf_16_be",
"utf_16_le",
"utf_7",
"utf_8",
]
if hasattr(codecs, "mbcs_encode"):
all_unicode_encodings.append("mbcs")
# The following encodings work only with str, not unicode
all_string_encodings = [
"quopri_codec",
"string_escape",
"uu_codec",
]
# The following encoding is not tested, because it's not supposed
# to work:
# "undefined"
# The following encodings don't work in stateful mode
broken_unicode_with_streams = [
"base64_codec",
"hex_codec",
"punycode",
"unicode_internal"
]
broken_incremental_coders = broken_unicode_with_streams[:]
# The following encodings only support "strict" mode
only_strict_mode = [
"idna",
"zlib_codec",
"bz2_codec",
]
try:
import bz2
except ImportError:
pass
else:
all_unicode_encodings.append("bz2_codec")
broken_unicode_with_streams.append("bz2_codec")
try:
import zlib
except ImportError:
pass
else:
all_unicode_encodings.append("zlib_codec")
broken_unicode_with_streams.append("zlib_codec")
class BasicUnicodeTest(unittest.TestCase):
@unittest.skipIf(test_support.is_jython, "_testcapi module not present in Jython")
def test_basics(self):
s = u"abc123" # all codecs should be able to encode these
for encoding in all_unicode_encodings:
name = codecs.lookup(encoding).name
if encoding.endswith("_codec"):
name += "_codec"
elif encoding == "latin_1":
name = "latin_1"
self.assertEqual(encoding.replace("_", "-"), name.replace("_", "-"))
(bytes, size) = codecs.getencoder(encoding)(s)
self.assertEqual(size, len(s), "%r != %r (encoding=%r)" % (size, len(s), encoding))
(chars, size) = codecs.getdecoder(encoding)(bytes)
self.assertEqual(chars, s, "%r != %r (encoding=%r)" % (chars, s, encoding))
if encoding not in broken_unicode_with_streams:
# check stream reader/writer
q = Queue()
writer = codecs.getwriter(encoding)(q)
encodedresult = ""
for c in s:
writer.write(c)
encodedresult += q.read()
q = Queue()
reader = codecs.getreader(encoding)(q)
decodedresult = u""
for c in encodedresult:
q.write(c)
decodedresult += reader.read()
self.assertEqual(decodedresult, s, "%r != %r (encoding=%r)" % (decodedresult, s, encoding))
if encoding not in broken_incremental_coders:
# check incremental decoder/encoder (fetched via the Python
# and C API) and iterencode()/iterdecode()
try:
encoder = codecs.getincrementalencoder(encoding)()
cencoder = _testcapi.codec_incrementalencoder(encoding)
except LookupError: # no IncrementalEncoder
pass
else:
# check incremental decoder/encoder
encodedresult = ""
for c in s:
encodedresult += encoder.encode(c)
encodedresult += encoder.encode(u"", True)
decoder = codecs.getincrementaldecoder(encoding)()
decodedresult = u""
for c in encodedresult:
decodedresult += decoder.decode(c)
decodedresult += decoder.decode("", True)
self.assertEqual(decodedresult, s, "%r != %r (encoding=%r)" % (decodedresult, s, encoding))
# check C API
encodedresult = ""
for c in s:
encodedresult += cencoder.encode(c)
encodedresult += cencoder.encode(u"", True)
cdecoder = _testcapi.codec_incrementaldecoder(encoding)
decodedresult = u""
for c in encodedresult:
decodedresult += cdecoder.decode(c)
decodedresult += cdecoder.decode("", True)
self.assertEqual(decodedresult, s, "%r != %r (encoding=%r)" % (decodedresult, s, encoding))
# check iterencode()/iterdecode()
result = u"".join(codecs.iterdecode(codecs.iterencode(s, encoding), encoding))
self.assertEqual(result, s, "%r != %r (encoding=%r)" % (result, s, encoding))
# check iterencode()/iterdecode() with empty string
result = u"".join(codecs.iterdecode(codecs.iterencode(u"", encoding), encoding))
self.assertEqual(result, u"")
if encoding not in only_strict_mode:
# check incremental decoder/encoder with errors argument
try:
encoder = codecs.getincrementalencoder(encoding)("ignore")
cencoder = _testcapi.codec_incrementalencoder(encoding, "ignore")
except LookupError: # no IncrementalEncoder
pass
else:
encodedresult = "".join(encoder.encode(c) for c in s)
decoder = codecs.getincrementaldecoder(encoding)("ignore")
decodedresult = u"".join(decoder.decode(c) for c in encodedresult)
self.assertEqual(decodedresult, s, "%r != %r (encoding=%r)" % (decodedresult, s, encoding))
encodedresult = "".join(cencoder.encode(c) for c in s)
cdecoder = _testcapi.codec_incrementaldecoder(encoding, "ignore")
decodedresult = u"".join(cdecoder.decode(c) for c in encodedresult)
self.assertEqual(decodedresult, s, "%r != %r (encoding=%r)" % (decodedresult, s, encoding))
def test_seek(self):
# all codecs should be able to encode these
s = u"%s\n%s\n" % (100*u"abc123", 100*u"def456")
for encoding in all_unicode_encodings:
if encoding == "idna": # FIXME: See SF bug #1163178
continue
if encoding in broken_unicode_with_streams:
continue
reader = codecs.getreader(encoding)(StringIO.StringIO(s.encode(encoding)))
for t in xrange(5):
# Test that calling seek resets the internal codec state and buffers
reader.seek(0, 0)
line = reader.readline()
self.assertEqual(s[:len(line)], line)
def test_bad_decode_args(self):
for encoding in all_unicode_encodings:
decoder = codecs.getdecoder(encoding)
self.assertRaises(TypeError, decoder)
if encoding not in ("idna", "punycode"):
self.assertRaises(TypeError, decoder, 42)
def test_bad_encode_args(self):
for encoding in all_unicode_encodings:
encoder = codecs.getencoder(encoding)
self.assertRaises(TypeError, encoder)
def test_encoding_map_type_initialized(self):
from encodings import cp1140
# This used to crash, we are only verifying there's no crash.
table_type = type(cp1140.encoding_table)
self.assertEqual(table_type, table_type)
class BasicStrTest(unittest.TestCase):
def test_basics(self):
s = "abc123"
for encoding in all_string_encodings:
(bytes, size) = codecs.getencoder(encoding)(s)
self.assertEqual(size, len(s))
(chars, size) = codecs.getdecoder(encoding)(bytes)
self.assertEqual(chars, s, "%r != %r (encoding=%r)" % (chars, s, encoding))
class CharmapTest(unittest.TestCase):
def test_decode_with_string_map(self):
self.assertEqual(
codecs.charmap_decode("\x00\x01\x02", "strict", u"abc"),
(u"abc", 3)
)
self.assertEqual(
codecs.charmap_decode("\x00\x01\x02", "replace", u"ab"),
(u"ab\ufffd", 3)
)
self.assertEqual(
codecs.charmap_decode("\x00\x01\x02", "replace", u"ab\ufffe"),
(u"ab\ufffd", 3)
)
self.assertEqual(
codecs.charmap_decode("\x00\x01\x02", "ignore", u"ab"),
(u"ab", 3)
)
self.assertEqual(
codecs.charmap_decode("\x00\x01\x02", "ignore", u"ab\ufffe"),
(u"ab", 3)
)
allbytes = "".join(chr(i) for i in xrange(256))
self.assertEqual(
codecs.charmap_decode(allbytes, "ignore", u""),
(u"", len(allbytes))
)
class WithStmtTest(unittest.TestCase):
def test_encodedfile(self):
f = StringIO.StringIO("\xc3\xbc")
with codecs.EncodedFile(f, "latin-1", "utf-8") as ef:
self.assertEqual(ef.read(), "\xfc")
def test_streamreaderwriter(self):
f = StringIO.StringIO("\xc3\xbc")
info = codecs.lookup("utf-8")
with codecs.StreamReaderWriter(f, info.streamreader,
info.streamwriter, 'strict') as srw:
self.assertEqual(srw.read(), u"\xfc")
class BomTest(unittest.TestCase):
def test_seek0(self):
data = u"1234567890"
tests = ("utf-16",
"utf-16-le",
"utf-16-be",
"utf-32",
"utf-32-le",
"utf-32-be",
)
self.addCleanup(test_support.unlink, test_support.TESTFN)
for encoding in tests:
# Check if the BOM is written only once
with codecs.open(test_support.TESTFN, 'w+', encoding=encoding) as f:
f.write(data)
f.write(data)
f.seek(0)
self.assertEqual(f.read(), data * 2)
f.seek(0)
self.assertEqual(f.read(), data * 2)
# Check that the BOM is written after a seek(0)
with codecs.open(test_support.TESTFN, 'w+', encoding=encoding) as f:
f.write(data[0])
self.assertNotEqual(f.tell(), 0)
f.seek(0)
f.write(data)
f.seek(0)
self.assertEqual(f.read(), data)
# (StreamWriter) Check that the BOM is written after a seek(0)
with codecs.open(test_support.TESTFN, 'w+', encoding=encoding) as f:
f.writer.write(data[0])
self.assertNotEqual(f.writer.tell(), 0)
f.writer.seek(0)
f.writer.write(data)
f.seek(0)
self.assertEqual(f.read(), data)
# Check that the BOM is not written after a seek() at a position
# different than the start
with codecs.open(test_support.TESTFN, 'w+', encoding=encoding) as f:
f.write(data)
f.seek(f.tell())
f.write(data)
f.seek(0)
self.assertEqual(f.read(), data * 2)
# (StreamWriter) Check that the BOM is not written after a seek()
# at a position different than the start
with codecs.open(test_support.TESTFN, 'w+', encoding=encoding) as f:
f.writer.write(data)
f.writer.seek(f.writer.tell())
f.writer.write(data)
f.seek(0)
self.assertEqual(f.read(), data * 2)
def test_main():
test_support.run_unittest(
UTF32Test,
UTF32LETest,
UTF32BETest,
UTF16Test,
UTF16LETest,
UTF16BETest,
UTF8Test,
UTF8SigTest,
UTF7Test,
UTF16ExTest,
ReadBufferTest,
CharBufferTest,
EscapeDecodeTest,
RecodingTest,
PunycodeTest,
UnicodeInternalTest,
NameprepTest,
IDNACodecTest,
CodecsModuleTest,
StreamReaderTest,
EncodedFileTest,
Str2StrTest,
BasicUnicodeTest,
BasicStrTest,
CharmapTest,
WithStmtTest,
BomTest,
)
if __name__ == "__main__":
test_main()
| 35.96849 | 115 | 0.557084 | [
"EPL-1.0"
] | Gumtree/gumtree | framework/extensions/org.python.jython/Lib/test/test_codecs.py | 60,499 | Python |
import sys
import pyodbc
from django.db import connections
from request.utils import retrieve_server_name_tcp_port
def validate_database_name(server_name,database_name):
try:
server_name = retrieve_server_name_tcp_port(server_name)
print(server_name)
cnxn = pyodbc.connect(r"Driver={ODBC Driver 17 for SQL Server};Server="+str(server_name)+";Database=master;Trusted_Connection=yes;APP='sql-dashboard check database existance '")
custom_cursor = cnxn.cursor()
custom_cursor.execute("SELECT name AS database_name FROM sys.databases WHERE name = '{0}'".format(database_name))
database_name_exists = custom_cursor.fetchall()
if(database_name_exists):
data = {
'is_taken':True
}
if data['is_taken']:
data['error_message'] = "Database " + database_name + " already exists on " + server_name
cnxn.close()
return data
else:
data = {
'is_taken':False
}
return data
except:
error = sys.exc_info()
print(error)
| 37.548387 | 186 | 0.611684 | [
"MIT"
] | marcosfreccia/SQLDashboard | request/create_database.py | 1,164 | Python |
# Copyright 2018 The TensorFlow Authors. 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.
# ==============================================================================
"""Tests for Adam."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.keras import optimizers
from tensorflow.python.keras.optimizer_v2 import adam
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
def adam_update_numpy(param,
g_t,
t,
m,
v,
lr=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-7):
lr_t = lr * np.sqrt(1 - beta2**(t + 1)) / (1 - beta1**(t + 1))
m_t = beta1 * m + (1 - beta1) * g_t
v_t = beta2 * v + (1 - beta2) * g_t * g_t
param_t = param - lr_t * m_t / (np.sqrt(v_t) + epsilon)
return param_t, m_t, v_t
def adam_update_numpy_amsgrad(param,
g_t,
t,
m,
v,
vhat,
lr=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-7):
lr_t = lr * np.sqrt(1 - beta2**(t + 1)) / (1 - beta1**(t + 1))
m_t = beta1 * m + (1 - beta1) * g_t
v_t = beta2 * v + (1 - beta2) * g_t * g_t
vhat_t = np.maximum(vhat, v_t)
param_t = param - lr_t * m_t / (np.sqrt(vhat_t) + epsilon)
return param_t, m_t, v_t, vhat_t
def adam_sparse_update_numpy_amsgrad(param,
indices,
g_t,
t,
m,
v,
vhat,
lr=0.001,
beta1=0.9,
beta2=0.999,
epsilon=1e-7):
m_t, v_t, vhat_t, param_t = (np.copy(m), np.copy(v), np.copy(vhat),
np.copy(param))
lr_t = lr * np.sqrt(1 - beta2**(t + 1)) / (1 - beta1**(t + 1))
m_t_slice = beta1 * m[indices] + (1 - beta1) * g_t
v_t_slice = beta2 * v[indices] + (1 - beta2) * g_t * g_t
m_t[indices] = m_t_slice
v_t[indices] = v_t_slice
v_hat_t = np.maximum(vhat_t, v_t)
v_hat_t_slice = v_hat_t[indices]
param_t_slice = param[indices] - (
lr_t * (m_t_slice / (np.sqrt(v_hat_t_slice) + epsilon)))
param_t[indices] = param_t_slice
return param_t, m_t, v_t, vhat_t
def get_beta_accumulators(opt, dtype):
local_step = math_ops.cast(opt.iterations + 1, dtype)
beta_1_t = math_ops.cast(opt._get_hyper("beta_1"), dtype)
beta_1_power = math_ops.pow(beta_1_t, local_step)
beta_2_t = math_ops.cast(opt._get_hyper("beta_2"), dtype)
beta_2_power = math_ops.pow(beta_2_t, local_step)
return (beta_1_power, beta_2_power)
class AdamOptimizerTest(test.TestCase):
@test_util.run_deprecated_v1
def testSparse(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.0, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.0, 0.01], dtype=dtype.as_numpy_dtype)
var0 = resource_variable_ops.ResourceVariable(var0_np)
var1 = resource_variable_ops.ResourceVariable(var1_np)
grads0_np_indices = np.array([0, 2], dtype=np.int32)
grads0 = ops.IndexedSlices(
constant_op.constant(grads0_np[grads0_np_indices]),
constant_op.constant(grads0_np_indices), constant_op.constant([3]))
grads1_np_indices = np.array([0, 2], dtype=np.int32)
grads1 = ops.IndexedSlices(
constant_op.constant(grads1_np[grads1_np_indices]),
constant_op.constant(grads1_np_indices), constant_op.constant([3]))
opt = adam.Adam()
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 3.0, 4.0], self.evaluate(var1))
beta_1_power, beta_2_power = get_beta_accumulators(opt, dtype)
# Run 3 steps of Adam
for t in range(3):
self.assertAllCloseAccordingToType(0.9**(t + 1),
self.evaluate(beta_1_power))
self.assertAllCloseAccordingToType(0.999**(t + 1),
self.evaluate(beta_2_power))
update.run()
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
@test_util.run_deprecated_v1
def testSparseDevicePlacement(self):
for index_dtype in [dtypes.int32, dtypes.int64]:
with self.cached_session(force_gpu=test.is_gpu_available()):
# If a GPU is available, tests that all optimizer ops can be placed on
# it (i.e. they have GPU kernels).
var = variables.Variable([[1.0], [2.0]])
indices = constant_op.constant([0, 1], dtype=index_dtype)
g_sum = lambda: math_ops.reduce_sum(array_ops.gather(var, indices)) # pylint: disable=cell-var-from-loop
optimizer = adam.Adam(3.0)
minimize_op = optimizer.minimize(g_sum, var_list=[var])
variables.global_variables_initializer().run()
minimize_op.run()
@test_util.run_deprecated_v1
def testSparseRepeatedIndices(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
repeated_index_update_var = variables.Variable(
[[1.0], [2.0]], dtype=dtype)
aggregated_update_var = variables.Variable(
[[1.0], [2.0]], dtype=dtype)
grad_repeated_index = ops.IndexedSlices(
constant_op.constant(
[0.1, 0.1], shape=[2, 1], dtype=dtype),
constant_op.constant([1, 1]),
constant_op.constant([2, 1]))
grad_aggregated = ops.IndexedSlices(
constant_op.constant(
[0.2], shape=[1, 1], dtype=dtype),
constant_op.constant([1]),
constant_op.constant([2, 1]))
repeated_update = adam.Adam().apply_gradients(
[(grad_repeated_index, repeated_index_update_var)])
aggregated_update = adam.Adam().apply_gradients(
[(grad_aggregated, aggregated_update_var)])
variables.global_variables_initializer().run()
self.assertAllClose(aggregated_update_var.eval(),
self.evaluate(repeated_index_update_var))
for _ in range(3):
repeated_update.run()
aggregated_update.run()
self.assertAllClose(aggregated_update_var.eval(),
self.evaluate(repeated_index_update_var))
def doTestBasic(self, use_callable_params=False):
for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
with self.session(graph=ops.Graph()):
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = resource_variable_ops.ResourceVariable(
var0_np, name="var0_%d" % i)
var1 = resource_variable_ops.ResourceVariable(
var1_np, name="var1_%d" % i)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
learning_rate = lambda: 0.001
beta1 = lambda: 0.9
beta2 = lambda: 0.999
epsilon = lambda: 1e-8
if not use_callable_params:
learning_rate = learning_rate()
beta1 = beta1()
beta2 = beta2()
epsilon = epsilon()
opt = adam.Adam(learning_rate=learning_rate)
if not context.executing_eagerly():
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
# Run 3 steps of Adam
for t in range(3):
beta_1_power, beta_2_power = get_beta_accumulators(opt, dtype)
self.assertAllCloseAccordingToType(0.9**(t + 1),
self.evaluate(beta_1_power))
self.assertAllCloseAccordingToType(0.999**(t + 1),
self.evaluate(beta_2_power))
if not context.executing_eagerly():
self.evaluate(update)
else:
opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
@test_util.run_in_graph_and_eager_modes(reset_test=True)
def testResourceBasic(self):
self.doTestBasic()
def testBasicCallableParams(self):
with context.eager_mode():
self.doTestBasic(use_callable_params=True)
@test_util.run_in_graph_and_eager_modes(reset_test=True)
def testBasicWithAmsgrad(self):
for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
with self.session(graph=ops.Graph()):
# Initialize variables for numpy implementation.
m0, v0, v0hat, m1, v1, v1hat = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = resource_variable_ops.ResourceVariable(
var0_np, name="var0_%d" % i)
var1 = resource_variable_ops.ResourceVariable(
var1_np, name="var1_%d" % i)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
opt = adam.Adam(amsgrad=True)
if not context.executing_eagerly():
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
# Run 3 steps of Adam
for t in range(3):
beta_1_power, beta_2_power = get_beta_accumulators(opt, dtype)
self.assertAllCloseAccordingToType(0.9**(t + 1),
self.evaluate(beta_1_power))
self.assertAllCloseAccordingToType(0.999**(t + 1),
self.evaluate(beta_2_power))
if not context.executing_eagerly():
self.evaluate(update)
else:
opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
var0_np, m0, v0, v0hat = adam_update_numpy_amsgrad(
var0_np, grads0_np, t, m0, v0, v0hat)
var1_np, m1, v1, v1hat = adam_update_numpy_amsgrad(
var1_np, grads1_np, t, m1, v1, v1hat)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
@test_util.run_in_graph_and_eager_modes
def testSparseWithAmsgrad(self):
# dtypes.half does not work on gpu + eager.
for dtype in [dtypes.float32, dtypes.float64]:
with self.cached_session():
m0 = np.array([[0.0], [0.0]])
v0 = np.array([[0.0], [0.0]])
v0hat = np.array([[0.0], [0.0]])
indices_np = np.array([1])
indices = constant_op.constant(indices_np, dtype=dtypes.int32)
var0_np = np.array([[1.0], [2.0]], dtype=dtype.as_numpy_dtype)
repeated_index_update_var = variables.Variable(var0_np, dtype=dtype)
aggregated_update_var = variables.Variable(var0_np, dtype=dtype)
grads0_np = np.array([[0.2]], dtype=dtype.as_numpy_dtype)
grad_repeated_index = ops.IndexedSlices(
constant_op.constant([0.1, 0.1], shape=[2, 1], dtype=dtype),
constant_op.constant([1, 1]), constant_op.constant([2, 1]))
grad_aggregated = ops.IndexedSlices(grads0_np, indices,
constant_op.constant([2, 1]))
opt_repeated = adam.Adam(amsgrad=True)
opt_aggregated = adam.Adam(amsgrad=True)
if not context.executing_eagerly():
repeated_update = opt_repeated.apply_gradients(
[(grad_repeated_index, repeated_index_update_var)])
aggregated_update = opt_aggregated.apply_gradients(
[(grad_aggregated, aggregated_update_var)])
self.evaluate(variables.global_variables_initializer())
self.assertAllClose(
self.evaluate(aggregated_update_var),
self.evaluate(repeated_index_update_var))
for t in range(3):
if not context.executing_eagerly():
self.evaluate(repeated_update)
self.evaluate(aggregated_update)
else:
opt_repeated.apply_gradients(
[(grad_repeated_index, repeated_index_update_var)])
opt_aggregated.apply_gradients(
[(grad_aggregated, aggregated_update_var)])
var0_np, m0, v0, v0hat = adam_sparse_update_numpy_amsgrad(
var0_np, indices_np, grads0_np, t, m0, v0, v0hat)
# Validate updated params
self.assertAllCloseAccordingToType(
var0_np, self.evaluate(aggregated_update_var))
self.assertAllCloseAccordingToType(
self.evaluate(aggregated_update_var),
self.evaluate(repeated_index_update_var))
@test_util.run_deprecated_v1
def testBasicWithLearningRateDecay(self):
for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
with self.session(graph=ops.Graph()):
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = resource_variable_ops.ResourceVariable(
var0_np, name="var0_%d" % i)
var1 = resource_variable_ops.ResourceVariable(
var1_np, name="var1_%d" % i)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
learning_rate = 0.001
beta_1 = 0.9
beta_2 = 0.999
epsilon = 1e-7
decay = 0.5
opt = adam.Adam(
learning_rate=learning_rate,
beta_1=beta_1,
beta_2=beta_2,
epsilon=epsilon,
decay=decay)
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
self.evaluate(variables.global_variables_initializer())
# Run 3 steps of Adam
for t in range(3):
self.evaluate(update)
lr_np = learning_rate / (1 + decay * t)
var0_np, m0, v0 = adam_update_numpy(
var0_np, grads0_np, t, m0, v0, lr=lr_np)
var1_np, m1, v1 = adam_update_numpy(
var1_np, grads1_np, t, m1, v1, lr=lr_np)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
@test_util.run_deprecated_v1
def testTensorLearningRate(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = variables.Variable(var0_np)
var1 = variables.Variable(var1_np)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
opt = adam.Adam(constant_op.constant(0.001))
update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
beta_1_power, beta_2_power = get_beta_accumulators(opt, dtype)
# Run 3 steps of Adam
for t in range(3):
self.assertAllCloseAccordingToType(0.9**(t + 1),
self.evaluate(beta_1_power))
self.assertAllCloseAccordingToType(0.999**(t + 1),
self.evaluate(beta_2_power))
update.run()
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
@test_util.run_deprecated_v1
def testSharing(self):
for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
with self.cached_session():
# Initialize variables for numpy implementation.
m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype)
var0 = variables.Variable(var0_np)
var1 = variables.Variable(var1_np)
grads0 = constant_op.constant(grads0_np)
grads1 = constant_op.constant(grads1_np)
opt = adam.Adam()
update1 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
update2 = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
variables.global_variables_initializer().run()
beta_1_power, beta_2_power = get_beta_accumulators(opt, dtype)
# Fetch params to validate initial values
self.assertAllClose([1.0, 2.0], self.evaluate(var0))
self.assertAllClose([3.0, 4.0], self.evaluate(var1))
# Run 3 steps of intertwined Adam1 and Adam2.
for t in range(3):
self.assertAllCloseAccordingToType(0.9**(t + 1),
self.evaluate(beta_1_power))
self.assertAllCloseAccordingToType(0.999**(t + 1),
self.evaluate(beta_2_power))
if t % 2 == 0:
update1.run()
else:
update2.run()
var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)
# Validate updated params
self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
def testSlotsUniqueEager(self):
with context.eager_mode():
v1 = resource_variable_ops.ResourceVariable(1.)
v2 = resource_variable_ops.ResourceVariable(1.)
opt = adam.Adam(1.)
opt.minimize(lambda: v1 + v2, var_list=[v1, v2])
# There should be iteration, and two unique slot variables for v1 and v2.
self.assertEqual(5, len(set(opt.variables())))
self.assertEqual(
self.evaluate(opt.variables()[0]), self.evaluate(opt.iterations))
def testSetWeightsFromV1AdamWithoutMinimize(self):
keras_v1_adam = optimizers.Adam()
keras_v2_adam = adam.Adam()
keras_v2_adam.set_weights(keras_v1_adam.get_weights())
keras_v1_iteration = keras_v1_adam.iterations
keras_v2_iteration = keras_v2_adam.iterations
self.evaluate(variables.global_variables_initializer())
self.assertEqual(
self.evaluate(keras_v1_iteration), self.evaluate(keras_v2_iteration))
def testConstructAdamWithLR(self):
opt = adam.Adam(lr=1.0)
opt_2 = adam.Adam(learning_rate=0.1, lr=1.0)
opt_3 = adam.Adam(learning_rate=0.1)
self.assertIsInstance(opt.lr, variables.Variable)
self.assertIsInstance(opt_2.lr, variables.Variable)
self.assertIsInstance(opt_3.lr, variables.Variable)
self.evaluate(variables.global_variables_initializer())
self.assertAllClose(self.evaluate(opt.lr), (1.0))
self.assertAllClose(self.evaluate(opt_2.lr), (1.0))
self.assertAllClose(self.evaluate(opt_3.lr), (0.1))
def testConstructAdamWithEpsilonValues(self):
opt = adam.Adam(epsilon=None)
config = opt.get_config()
self.assertEqual(config["epsilon"], 1e-7)
opt = adam.Adam(epsilon=1e-8)
config = opt.get_config()
self.assertEqual(config["epsilon"], 1e-8)
if __name__ == "__main__":
test.main()
| 43.27307 | 113 | 0.618766 | [
"Apache-2.0"
] | 5seunghoon/tensorflow | tensorflow/python/keras/optimizer_v2/adam_test.py | 22,978 | Python |
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union
import warnings
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
from ... import models as _models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class ContainerGroupsOperations:
"""ContainerGroupsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.mgmt.containerinstance.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
def list(
self,
**kwargs: Any
) -> AsyncIterable["_models.ContainerGroupListResult"]:
"""Get a list of container groups in the specified subscription.
Get a list of container groups in the specified subscription. This operation returns properties
of each container group including containers, image registry credentials, restart policy, IP
address type, OS type, state, and volumes.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either ContainerGroupListResult or the result of cls(response)
:rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.containerinstance.models.ContainerGroupListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ContainerGroupListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
accept = "application/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('ContainerGroupListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list.metadata = {'url': '/subscriptions/{subscriptionId}/providers/Microsoft.ContainerInstance/containerGroups'} # type: ignore
def list_by_resource_group(
self,
resource_group_name: str,
**kwargs: Any
) -> AsyncIterable["_models.ContainerGroupListResult"]:
"""Get a list of container groups in the specified subscription and resource group.
Get a list of container groups in a specified subscription and resource group. This operation
returns properties of each container group including containers, image registry credentials,
restart policy, IP address type, OS type, state, and volumes.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either ContainerGroupListResult or the result of cls(response)
:rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.containerinstance.models.ContainerGroupListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ContainerGroupListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
accept = "application/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list_by_resource_group.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('ContainerGroupListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list_by_resource_group.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups'} # type: ignore
async def get(
self,
resource_group_name: str,
container_group_name: str,
**kwargs: Any
) -> "_models.ContainerGroup":
"""Get the properties of the specified container group.
Gets the properties of the specified container group in the specified subscription and resource
group. The operation returns the properties of each container group including containers, image
registry credentials, restart policy, IP address type, OS type, state, and volumes.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param container_group_name: The name of the container group.
:type container_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ContainerGroup, or the result of cls(response)
:rtype: ~azure.mgmt.containerinstance.models.ContainerGroup
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ContainerGroup"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
accept = "application/json"
# Construct URL
url = self.get.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('ContainerGroup', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}'} # type: ignore
async def _create_or_update_initial(
self,
resource_group_name: str,
container_group_name: str,
container_group: "_models.ContainerGroup",
**kwargs: Any
) -> "_models.ContainerGroup":
cls = kwargs.pop('cls', None) # type: ClsType["_models.ContainerGroup"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self._create_or_update_initial.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(container_group, 'ContainerGroup')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if response.status_code == 200:
deserialized = self._deserialize('ContainerGroup', pipeline_response)
if response.status_code == 201:
deserialized = self._deserialize('ContainerGroup', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}'} # type: ignore
async def begin_create_or_update(
self,
resource_group_name: str,
container_group_name: str,
container_group: "_models.ContainerGroup",
**kwargs: Any
) -> AsyncLROPoller["_models.ContainerGroup"]:
"""Create or update container groups.
Create or update container groups with specified configurations.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param container_group_name: The name of the container group.
:type container_group_name: str
:param container_group: The properties of the container group to be created or updated.
:type container_group: ~azure.mgmt.containerinstance.models.ContainerGroup
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling.
Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either ContainerGroup or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.containerinstance.models.ContainerGroup]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["_models.ContainerGroup"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._create_or_update_initial(
resource_group_name=resource_group_name,
container_group_name=container_group_name,
container_group=container_group,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('ContainerGroup', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}'} # type: ignore
async def update(
self,
resource_group_name: str,
container_group_name: str,
resource: "_models.Resource",
**kwargs: Any
) -> "_models.ContainerGroup":
"""Update container groups.
Updates container group tags with specified values.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param container_group_name: The name of the container group.
:type container_group_name: str
:param resource: The container group resource with just the tags to be updated.
:type resource: ~azure.mgmt.containerinstance.models.Resource
:keyword callable cls: A custom type or function that will be passed the direct response
:return: ContainerGroup, or the result of cls(response)
:rtype: ~azure.mgmt.containerinstance.models.ContainerGroup
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ContainerGroup"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self.update.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(resource, 'Resource')
body_content_kwargs['content'] = body_content
request = self._client.patch(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize('ContainerGroup', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}'} # type: ignore
async def _delete_initial(
self,
resource_group_name: str,
container_group_name: str,
**kwargs: Any
) -> Optional["_models.ContainerGroup"]:
cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.ContainerGroup"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
accept = "application/json"
# Construct URL
url = self._delete_initial.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.delete(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('ContainerGroup', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}'} # type: ignore
async def begin_delete(
self,
resource_group_name: str,
container_group_name: str,
**kwargs: Any
) -> AsyncLROPoller["_models.ContainerGroup"]:
"""Delete the specified container group.
Delete the specified container group in the specified subscription and resource group. The
operation does not delete other resources provided by the user, such as volumes.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param container_group_name: The name of the container group.
:type container_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling.
Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either ContainerGroup or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.containerinstance.models.ContainerGroup]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["_models.ContainerGroup"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._delete_initial(
resource_group_name=resource_group_name,
container_group_name=container_group_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('ContainerGroup', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}'} # type: ignore
async def _restart_initial(
self,
resource_group_name: str,
container_group_name: str,
**kwargs: Any
) -> None:
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
accept = "application/json"
# Construct URL
url = self._restart_initial.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.post(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_restart_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}/restart'} # type: ignore
async def begin_restart(
self,
resource_group_name: str,
container_group_name: str,
**kwargs: Any
) -> AsyncLROPoller[None]:
"""Restarts all containers in a container group.
Restarts all containers in a container group in place. If container image has updates, new
image will be downloaded.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param container_group_name: The name of the container group.
:type container_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling.
Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType[None]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._restart_initial(
resource_group_name=resource_group_name,
container_group_name=container_group_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_restart.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}/restart'} # type: ignore
async def stop(
self,
resource_group_name: str,
container_group_name: str,
**kwargs: Any
) -> None:
"""Stops all containers in a container group.
Stops all containers in a container group. Compute resources will be deallocated and billing
will stop.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param container_group_name: The name of the container group.
:type container_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
accept = "application/json"
# Construct URL
url = self.stop.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.post(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
stop.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}/stop'} # type: ignore
async def _start_initial(
self,
resource_group_name: str,
container_group_name: str,
**kwargs: Any
) -> None:
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-03-01"
accept = "application/json"
# Construct URL
url = self._start_initial.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.post(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_start_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}/start'} # type: ignore
async def begin_start(
self,
resource_group_name: str,
container_group_name: str,
**kwargs: Any
) -> AsyncLROPoller[None]:
"""Starts all containers in a container group.
Starts all containers in a container group. Compute resources will be allocated and billing
will start.
:param resource_group_name: The name of the resource group.
:type resource_group_name: str
:param container_group_name: The name of the container group.
:type container_group_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be AsyncARMPolling.
Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType[None]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._start_initial(
resource_group_name=resource_group_name,
container_group_name=container_group_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'containerGroupName': self._serialize.url("container_group_name", container_group_name, 'str'),
}
if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_start.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ContainerInstance/containerGroups/{containerGroupName}/start'} # type: ignore
| 50.033019 | 209 | 0.674484 | [
"MIT"
] | Codejune/azure-sdk-for-python | sdk/containerinstance/azure-mgmt-containerinstance/azure/mgmt/containerinstance/aio/operations/_container_groups_operations.py | 42,428 | Python |
from workerInfra.enum import SchedulerTriggerEnum, EnvEnum
from workerInfra.domain import LoggerInterface
from workerInfra.models import SchedulerJobModel
from workerService.envReaderService import EnvReaderService
from workerService.scheduler import SchedulerService
from workerService.logger import BasicLoggerService
from workerService.outputs import DiscordOutputService
from workerService.sources import (
WorkerService,
RedditWorkerService,
YoutubeWorkerService,
TwitterWorkerService,
TwitchWorkerService,
PokemonGoWorkerService,
FFXIVWorkerService
)
from workerService.sources.rssWorkerService import RssWorkerService
class ApiEventsService():
_env: EnvReaderService
_logger: LoggerInterface
_scheduler: SchedulerService
_isDebug: bool
def __init__(self) -> None:
self._env = EnvReaderService()
self._logger = BasicLoggerService()
self._scheduler = SchedulerService()
self._isDebug = self._env.getValue(EnvEnum.ISDEBUG)
pass
def startup(self) -> None:
self._scheduler.addJob(self.enableSourceReddit())
self._scheduler.addJob(self.enableSourceYoutube())
self._scheduler.addJob(self.enableSourceTwitter())
self._scheduler.addJob(self.enableSourceTwitch())
self._scheduler.addJob(self.enableSourcePokemonGo())
self._scheduler.addJob(self.enableSourceFFXIV())
self._scheduler.addJob(self.enableSourceRss())
self._scheduler.addJob(self.enableOutputDiscord())
self._scheduler.start()
def enableSourceRss(self) -> SchedulerJobModel:
s = SchedulerJobModel(functionName=WorkerService(RssWorkerService()).init, trigger=SchedulerTriggerEnum.INTERVAL, minutes=30)
if self._isDebug is True:
s.trigger = SchedulerTriggerEnum.NONE
if self._env.getValue(EnvEnum.RSSENABLED) is False:
s.enabled = False
return s
def enableSourceReddit(self) -> SchedulerJobModel:
s = SchedulerJobModel(functionName=WorkerService(RedditWorkerService()).init, trigger=SchedulerTriggerEnum.INTERVAL, minutes=25)
if self._isDebug is True:
s.trigger = SchedulerTriggerEnum.NONE
if self._env.getValue(EnvEnum.REDDITENABLED) is False:
s.enabled = False
return s
def enableSourceYoutube(self) -> SchedulerJobModel:
s = SchedulerJobModel(functionName=WorkerService(YoutubeWorkerService()).init, trigger=SchedulerTriggerEnum.INTERVAL, minutes=35)
if self._isDebug is True:
s.trigger = SchedulerTriggerEnum.NONE
if self._env.getValue(EnvEnum.YOUTUBEENABLED) is False:
s.enabled = False
return s
def enableSourceTwitter(self) -> SchedulerJobModel:
s = SchedulerJobModel(functionName=WorkerService(TwitterWorkerService()).init, trigger=SchedulerTriggerEnum.INTERVAL, minutes=37)
if self._isDebug is True:
s.trigger = SchedulerTriggerEnum.NONE
if self._env.getValue(EnvEnum.TWITCHEANBLED) is False:
s.enabled = False
return s
def enableSourceTwitch(self) -> SchedulerJobModel:
s = SchedulerJobModel(functionName=WorkerService(TwitchWorkerService()).init, trigger=SchedulerTriggerEnum.INTERVAL, minutes=30)
if self._isDebug is True:
s.trigger = SchedulerTriggerEnum.NONE
if self._env.getValue(EnvEnum.TWITCHEANBLED) is False:
s.enabled = False
return s
def enableSourcePokemonGo(self) -> SchedulerJobModel:
s = SchedulerJobModel(functionName=WorkerService(PokemonGoWorkerService()).init, trigger=SchedulerTriggerEnum.INTERVAL, minutes=35)
if self._isDebug is True:
s.trigger = SchedulerTriggerEnum.NONE
if self._env.getValue(EnvEnum.POKEMONGOENABLED) is False:
s.enabled = False
return s
def enableSourceFFXIV(self) -> SchedulerJobModel:
s = SchedulerJobModel(functionName=WorkerService(FFXIVWorkerService()).init, trigger=SchedulerTriggerEnum.INTERVAL, minutes=45)
if self._isDebug is True:
s.trigger = SchedulerTriggerEnum.NONE
if self._env.getValue(EnvEnum.FFXIVENABLED) is False:
s.enabled = False
return s
def enableOutputDiscord(self) -> SchedulerJobModel:
s = SchedulerJobModel(functionName=DiscordOutputService().init, trigger=SchedulerTriggerEnum.INTERVAL, minutes=3)
if self._isDebug is True:
s.minutes = 1
return s
def shutdown(self) -> None:
pass
| 41.527273 | 139 | 0.71563 | [
"MIT"
] | jtom38/newsbot.worker | workerApi/apiEventsService.py | 4,568 | Python |
class Debt:
def __init__(self, id_nbr, principal, rate, minimum, frequency=12):
self.id = id_nbr
self.principal = principal
self.rate = rate
self.minimum = minimum
self.frequency = frequency
def calculate_compound(self, periods):
new_principal = self.principal * (1 + self.rate / self.frequency) ** periods
return new_principal
def list_compounds(self, periods):
balance_list = []
for period in range(periods + 1):
balance_list.append(self.calculate_compound(period))
return balance_list
def calculate_next_principal(self, current_principal, current_payment = 0):
next_principal = current_principal * (1 + self.rate / self.frequency) - current_payment
return next_principal
def list_minimum_payments(self, current_principal, periods, minimum):
balance_list = [float(current_principal)]
next_period_principal = current_principal
for period in range(periods):
next_period_principal = self.calculate_next_principal(current_principal, minimum)
if next_period_principal < 0.0:
balance_list.append(0.0)
break
balance_list.append(next_period_principal)
current_principal = next_period_principal
return balance_list | 37.611111 | 95 | 0.66839 | [
"MIT"
] | Luigi-PastorePica/FreeD | src/freed/debt_class.py | 1,354 | Python |
from typing import Union
import Geometry
from Geometry import Line
from Geometry import Point
import cmath
class Circle:
def __init__(self, center: Union[Point, tuple, list], radius: float):
if isinstance(center, tuple) or isinstance(center, list):
assert len(center) == 2, "Center must be a 2-tuple or list"
center = Point(center[0], center[1])
self.center = center
self.radius = radius
def area(self) -> float:
return cmath.pi * self.radius ** 2
def circumference(self) -> float:
return 2 * cmath.pi * self.radius
def tangent(self, p: Point) -> Line:
# try:
# m = Geometry.slope(self.center, p)
# except ZeroDivisionError:
# return Line(0, 1, -p.y)
# if m == 0:
# return Line(1, 0, -p.x)
# m = -1/m
# return Geometry.slope_point_line(m, p)
x, y = p.x, p.y
c_x , c_y = -self.center.x, -self.center.y
return Line(x + c_x, y + c_y, x*c_x + y*c_y + c_x**2 + c_y**2 - self.radius**2)
def normal(self, p: Point) -> Line:
return Line.construct(p, self.center)
def power(self, p: Point) -> float:
return Geometry.distance(self.center, p) ** 2 - self.radius ** 2
def is_tangent(self, l: Line) -> bool:
return l.distance(self.center) == self.radius
def is_normal(self, l: Line) -> bool:
return l(self.center) == 0
def equation(self) -> str:
(x, y) = self.center
return f"x^2 + 2*{-x}*x + 2*{-y}*y + y^2 + {x**2 + y**2 - self.radius**2} = 0"
def parametric_equation(self, theta_resolution: float = 0.01, semi=False):
i = 0
if semi:
k = cmath.pi
else:
k = 2 * cmath.pi
while i < k:
yield self.center.x + self.radius * cmath.cos(i), self.center.y + self.radius * cmath.sin(i)
i += theta_resolution
def sector_length(self, theta: float) -> float:
"""Returns the length of a sector of the circle which subtended angle theta(radians) at center."""
return self.radius * theta
def sector_area(self, theta: float) -> float:
"""Returns the area of a sector of the circle which subtended angle theta(radians) at center."""
return self.radius ** 2 * theta / 2
def intersetion(self, other) -> Union[Point, None]:
if isinstance(other, Circle):
c1 = self.center
c2 = other.center
m = Geometry.slope(c1, c2)
theta = cmath.atan(m)
d = Geometry.distance(c1, c2)
if d == self.radius + other.radius:
"""Two circles are touching each other"""
x = c1.x + self.radius * cmath.cos(theta)
y = c1.y + self.radius * cmath.sin(theta)
return Point(x, y)
elif d < self.radius + other.radius:
"""Two circles intersect"""
r1 = self.radius
r2 = other.radius
theta = cmath.asin(r2 / d)
x = c1.x + r1 * cmath.cos(theta)
y = c1.y + r1 * cmath.sin(theta)
p1 = Point(x, y)
l = Line.construct(c1, c2)
p2 = l.image(p1)
return (p1, p2)
else:
return None
else:
raise ValueError("Can only intersect with another circle")
def __repr__(self):
return 'Circle(center={0}, radius={1})'.format(self.center, self.radius)
def __eq__(self, other):
if isinstance(other, Circle):
return self.center == other.center and self.radius == other.radius
else:
return False
def __ne__(self, other):
return not self == other
def __hash__(self):
return hash((self.center, self.radius))
def __str__(self):
return 'Circle(center={0}, radius={1})'.format(self.center, self.radius)
def construct(p0: Point, p1: Point, p2: Point) -> Circle:
try:
assert not Geometry.colinear(p0, p1, p2)
except AssertionError:
raise AssertionError("Circle can not be constructed from three points that are colinear")
l1 = Geometry.perpendicular_bisector(p0, p1)
l2 = Geometry.perpendicular_bisector(p1, p2)
center = l1.intersection(l2)
radius = Geometry.distance(center, p0)
return Circle(center, radius)
| 31.28169 | 106 | 0.55493 | [
"MIT"
] | Lakshmikanth2001/ElectricPy | Geometry/circle.py | 4,442 | Python |
from collections import OrderedDict
from PyQt5 import QtCore
from PyQt5 import QtWidgets
from easygraphics.dialog._indexed_order_list import IndexedOrderedDict
__all__ = ['MultipleFieldsDialog']
class MultipleFieldsDialog(QtWidgets.QDialog):
"""Dialog with multiple fields stored in a dict, with the label
being the key and the entry being the corresponding value"""
def __init__(self, labels=None, title="Demo", masks=None):
super(MultipleFieldsDialog, self).__init__(None,
QtCore.Qt.WindowSystemMenuHint |
QtCore.Qt.WindowTitleHint)
self.enters = IndexedOrderedDict()
self.setWindowTitle(title)
# set up a special case for quick demo
if labels is None:
labels = ["Regular field", "Masked field"]
masks = [False, True]
self.setWindowTitle("MultipleFieldsDialog demo")
if masks is not None:
assert len(masks) == len(labels)
layout = QtWidgets.QGridLayout()
layout.setColumnStretch(1, 1)
layout.setColumnMinimumWidth(1, 250)
self._labels_ = []
self.fields = []
for index, choice in enumerate(labels):
self._labels_.append(QtWidgets.QLabel())
self._labels_[index].setText(choice)
self.fields.append(QtWidgets.QLineEdit())
self.fields[index].setText('')
self.enters[choice] = ''
if masks is not None and masks[index]:
self.fields[index].setEchoMode(QtWidgets.QLineEdit.Password)
layout.addWidget(self._labels_[index], index, 0)
layout.addWidget(self.fields[index], index, 1)
button_box = QtWidgets.QDialogButtonBox()
confirm_button = button_box.addButton(QtWidgets.QDialogButtonBox.Ok)
layout.addWidget(button_box, index + 1, 1)
confirm_button.clicked.connect(self.confirm)
self.setLayout(layout)
self.setWindowTitle(title)
self.show()
self.raise_()
def confirm(self):
"""Selection completed, set the value and close"""
o_dict = self.enters
for index, item in enumerate(self._labels_):
o_dict[item.text()] = self.fields[index].text()
self.close()
if __name__ == '__main__':
app = QtWidgets.QApplication([])
dialog = MultipleFieldsDialog()
dialog.exec_()
print(dialog.get_ordered_dict())
| 34.736111 | 83 | 0.62375 | [
"BSD-3-Clause"
] | royqh1979/PyEasyGraphics | easygraphics/dialog/multifields.py | 2,501 | Python |
from musicautobot.numpy_encode import *
from musicautobot.config import *
from musicautobot.music_transformer import *
from musicautobot.utils.midifile import *
from musicautobot.utils.file_processing import process_all
from musicautobot.numpy_encode import *
from musicautobot.config import *
from musicautobot.music_transformer import *
from musicautobot.utils.midifile import *
from musicautobot.utils.file_processing import process_all
import random
def create_databunch(files, data_save_name, path):
save_file = path/data_save_name
if save_file.exists():
data = load_data(path, data_save_name)
else:
save_file.parent.mkdir(exist_ok=True, parents=True)
vocab = MusicVocab.create()
processors = [OpenNPFileProcessor(), MusicItemProcessor()]
data = MusicDataBunch.from_files(files, path, processors=processors, encode_position=True)
data.save(data_save_name)
return data
def timeout_func(data, seconds):
print("Timeout:", seconds)
def process_metadata(midi_file):
# Get outfile and check if it exists
out_file = numpy_path/midi_file.relative_to(midi_path).with_suffix('.npy')
out_file.parent.mkdir(parents=True, exist_ok=True)
if out_file.exists(): return
npenc = transform_midi(midi_file)
if npenc is not None: np.save(out_file, npenc)
def transform_midi(midi_file):
input_path = midi_file
# Part 1: Filter out midi tracks (drums, repetitive instruments, etc.)
try:
# if duet_only and num_piano_tracks(input_path) not in [1, 2]: return None
input_file = compress_midi_file(input_path, min_variation=min_variation, cutoff=cutoff) # remove non note tracks and standardize instruments
if input_file is None: return None
except Exception as e:
if 'badly form' in str(e): return None # ignore badly formatted midi errors
if 'out of range' in str(e): return None # ignore badly formatted midi errors
print('Error parsing midi', input_path, e)
return None
# Part 2. Compress rests and long notes
stream = file2stream(input_file) # 1.
try:
chordarr = stream2chordarr(stream) # 2. max_dur = quarter_len * sample_freq (4). 128 = 8 bars
except Exception as e:
print('Could not encode to chordarr:', input_path, e)
print(traceback.format_exc())
return None
# Part 3. Compress song rests - Don't want songs with really long pauses
# (this happens because we filter out midi tracks).
chord_trim = trim_chordarr_rests(chordarr)
chord_short = shorten_chordarr_rests(chord_trim)
delta_trim = chord_trim.shape[0] - chord_short.shape[0]
# if delta_trim > 500:
# print(f'Removed {delta_trim} rests from {input_path}. Skipping song')
# return None
chordarr = chord_short
# Part 3. Chord array to numpy
npenc = chordarr2npenc(chordarr)
if not is_valid_npenc(npenc, input_path=input_path):
return None
return npenc
# Location of your midi files
midi_path = Path('data/midi/TPD')
# Location of preprocessed numpy files
numpy_path = Path('data/numpy/preprocessed data')
# Location of models and cached dataset
data_path = Path('data/cached')
data_save_name = 'TPD_musicitem_data_save.pkl'
# num_tracks = [1, 2] # number of tracks to support
cutoff = 5 # max instruments
min_variation = 3 # minimum number of different midi notes played
# max_dur = 128
midi_files = get_files(midi_path, '.mid', recurse=True)
print('Loading model...')
batch_size = 1
encode_position = True
dl_tfms = [batch_position_tfm] if encode_position else []
data = load_data(data_path, data_save_name, bs=batch_size, encode_position=encode_position, dl_tfms=dl_tfms)
config = default_config()
config['encode_position'] = encode_position
learn = music_model_learner(data, config=config.copy())
learn.fit_one_cycle(4)
learn.save('TPD_model')
| 36.055046 | 148 | 0.724173 | [
"MIT"
] | adam1214/musicautobot | transformer code/train.py | 3,930 | Python |
import logging
from typing import Dict, List, Tuple
import aiosqlite
from btcgreen.server.address_manager import (
BUCKET_SIZE,
NEW_BUCKET_COUNT,
NEW_BUCKETS_PER_ADDRESS,
AddressManager,
ExtendedPeerInfo,
)
log = logging.getLogger(__name__)
class AddressManagerStore:
"""
Metadata table:
- private key
- new table count
- tried table count
Nodes table:
* Maps entries from new/tried table to unique node ids.
- node_id
- IP, port, together with the IP, port of the source peer.
New table:
* Stores node_id, bucket for each occurrence in the new table of an entry.
* Once we know the buckets, we can also deduce the bucket positions.
Every other information, such as tried_matrix, map_addr, map_info, random_pos,
be deduced and it is not explicitly stored, instead it is recalculated.
"""
db: aiosqlite.Connection
@classmethod
async def create(cls, connection) -> "AddressManagerStore":
self = cls()
self.db = connection
await self.db.commit()
await self.db.execute("CREATE TABLE IF NOT EXISTS peer_metadata(key text,value text)")
await self.db.commit()
await self.db.execute("CREATE TABLE IF NOT EXISTS peer_nodes(node_id int,value text)")
await self.db.commit()
await self.db.execute("CREATE TABLE IF NOT EXISTS peer_new_table(node_id int,bucket int)")
await self.db.commit()
return self
async def clear(self) -> None:
cursor = await self.db.execute("DELETE from peer_metadata")
await cursor.close()
cursor = await self.db.execute("DELETE from peer_nodes")
await cursor.close()
cursor = await self.db.execute("DELETE from peer_new_table")
await cursor.close()
await self.db.commit()
async def get_metadata(self) -> Dict[str, str]:
cursor = await self.db.execute("SELECT key, value from peer_metadata")
metadata = await cursor.fetchall()
await cursor.close()
return {key: value for key, value in metadata}
async def is_empty(self) -> bool:
metadata = await self.get_metadata()
if "key" not in metadata:
return True
if int(metadata.get("new_count", 0)) > 0:
return False
if int(metadata.get("tried_count", 0)) > 0:
return False
return True
async def get_nodes(self) -> List[Tuple[int, ExtendedPeerInfo]]:
cursor = await self.db.execute("SELECT node_id, value from peer_nodes")
nodes_id = await cursor.fetchall()
await cursor.close()
return [(node_id, ExtendedPeerInfo.from_string(info_str)) for node_id, info_str in nodes_id]
async def get_new_table(self) -> List[Tuple[int, int]]:
cursor = await self.db.execute("SELECT node_id, bucket from peer_new_table")
entries = await cursor.fetchall()
await cursor.close()
return [(node_id, bucket) for node_id, bucket in entries]
async def set_metadata(self, metadata) -> None:
for key, value in metadata:
cursor = await self.db.execute(
"INSERT OR REPLACE INTO peer_metadata VALUES(?, ?)",
(key, value),
)
await cursor.close()
await self.db.commit()
async def set_nodes(self, node_list) -> None:
for node_id, peer_info in node_list:
cursor = await self.db.execute(
"INSERT OR REPLACE INTO peer_nodes VALUES(?, ?)",
(node_id, peer_info.to_string()),
)
await cursor.close()
await self.db.commit()
async def set_new_table(self, entries) -> None:
for node_id, bucket in entries:
cursor = await self.db.execute(
"INSERT OR REPLACE INTO peer_new_table VALUES(?, ?)",
(node_id, bucket),
)
await cursor.close()
await self.db.commit()
async def serialize(self, address_manager: AddressManager):
metadata = []
nodes = []
new_table_entries = []
metadata.append(("key", str(address_manager.key)))
unique_ids = {}
count_ids = 0
for node_id, info in address_manager.map_info.items():
unique_ids[node_id] = count_ids
if info.ref_count > 0:
assert count_ids != address_manager.new_count
nodes.append((count_ids, info))
count_ids += 1
metadata.append(("new_count", str(count_ids)))
tried_ids = 0
for node_id, info in address_manager.map_info.items():
if info.is_tried:
assert info is not None
assert tried_ids != address_manager.tried_count
nodes.append((count_ids, info))
count_ids += 1
tried_ids += 1
metadata.append(("tried_count", str(tried_ids)))
for bucket in range(NEW_BUCKET_COUNT):
for i in range(BUCKET_SIZE):
if address_manager.new_matrix[bucket][i] != -1:
index = unique_ids[address_manager.new_matrix[bucket][i]]
new_table_entries.append((index, bucket))
await self.clear()
await self.set_metadata(metadata)
await self.set_nodes(nodes)
await self.set_new_table(new_table_entries)
async def deserialize(self) -> AddressManager:
address_manager = AddressManager()
metadata = await self.get_metadata()
nodes = await self.get_nodes()
new_table_entries = await self.get_new_table()
address_manager.clear()
address_manager.key = int(metadata["key"])
address_manager.new_count = int(metadata["new_count"])
# address_manager.tried_count = int(metadata["tried_count"])
address_manager.tried_count = 0
new_table_nodes = [(node_id, info) for node_id, info in nodes if node_id < address_manager.new_count]
for n, info in new_table_nodes:
address_manager.map_addr[info.peer_info.host] = n
address_manager.map_info[n] = info
info.random_pos = len(address_manager.random_pos)
address_manager.random_pos.append(n)
address_manager.id_count = len(new_table_nodes)
tried_table_nodes = [(node_id, info) for node_id, info in nodes if node_id >= address_manager.new_count]
# lost_count = 0
for node_id, info in tried_table_nodes:
tried_bucket = info.get_tried_bucket(address_manager.key)
tried_bucket_pos = info.get_bucket_position(address_manager.key, False, tried_bucket)
if address_manager.tried_matrix[tried_bucket][tried_bucket_pos] == -1:
info.random_pos = len(address_manager.random_pos)
info.is_tried = True
id_count = address_manager.id_count
address_manager.random_pos.append(id_count)
address_manager.map_info[id_count] = info
address_manager.map_addr[info.peer_info.host] = id_count
address_manager.tried_matrix[tried_bucket][tried_bucket_pos] = id_count
address_manager.id_count += 1
address_manager.tried_count += 1
# else:
# lost_count += 1
# address_manager.tried_count -= lost_count
for node_id, bucket in new_table_entries:
if node_id >= 0 and node_id < address_manager.new_count:
info = address_manager.map_info[node_id]
bucket_pos = info.get_bucket_position(address_manager.key, True, bucket)
if address_manager.new_matrix[bucket][bucket_pos] == -1 and info.ref_count < NEW_BUCKETS_PER_ADDRESS:
info.ref_count += 1
address_manager.new_matrix[bucket][bucket_pos] = node_id
for node_id, info in list(address_manager.map_info.items()):
if not info.is_tried and info.ref_count == 0:
address_manager.delete_new_entry_(node_id)
address_manager.load_used_table_positions()
return address_manager
| 39.941176 | 117 | 0.627516 | [
"Apache-2.0"
] | BTCgreen-Network/btcgreen-blockchain | btcgreen/server/address_manager_store.py | 8,148 | Python |
# Generated by Django 3.0.5 on 2020-05-13 09:46
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('project_core', '0118_calls_need_to_be_part_of_a_funding_instrument'),
('grant_management', '0041_allows_media_to_not_be_related_blog_post'),
]
operations = [
migrations.CreateModel(
name='ProjectSocialNetwork',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_on', models.DateTimeField(auto_now_add=True, help_text='Date and time at which the entry was created')),
('modified_on', models.DateTimeField(auto_now=True, help_text='Date and time at which the entry was modified', null=True)),
('url', models.URLField(blank=True, help_text='Address of social media entry (e.g. https://twitter.com/SwissPolar)', null=True)),
('project', models.ForeignKey(help_text='Choose related project', on_delete=django.db.models.deletion.PROTECT, to='project_core.Project')),
],
options={
'abstract': False,
},
),
migrations.RenameField(
model_name='dataset',
old_name='date_published',
new_name='published_date',
),
migrations.RemoveField(
model_name='publication',
name='date_time_published',
),
migrations.AddField(
model_name='publication',
name='published_date',
field=models.DateField(blank=True, help_text='Date of the publication', null=True),
),
migrations.AlterField(
model_name='dataset',
name='doi',
field=models.CharField(blank=True, help_text='DOI reference for entry', max_length=100, null=True),
),
migrations.AlterField(
model_name='dataset',
name='title',
field=models.CharField(help_text='Dataset title', max_length=1000),
),
migrations.AlterField(
model_name='publication',
name='doi',
field=models.CharField(blank=True, help_text='DOI reference for entry', max_length=100, null=True),
),
migrations.AlterField(
model_name='publication',
name='reference',
field=models.CharField(blank=True, help_text='Journal reference for entry', max_length=1000, null=True),
),
migrations.AlterField(
model_name='publication',
name='title',
field=models.CharField(help_text='Publication title', max_length=1000),
),
migrations.AlterField(
model_name='socialnetwork',
name='name',
field=models.CharField(help_text='Please enter social network title (e.g. Twitter, Facebook, Instagram, Blog)', max_length=100),
),
migrations.DeleteModel(
name='ProjectSocialMedia',
),
migrations.AddField(
model_name='projectsocialnetwork',
name='social_network',
field=models.ForeignKey(help_text='Choose the related social network', on_delete=django.db.models.deletion.PROTECT, to='grant_management.SocialNetwork'),
),
]
| 41.814815 | 165 | 0.61116 | [
"MIT"
] | Swiss-Polar-Institute/project-application | ProjectApplication/grant_management/migrations/0042_improves_project_data_publications_social_media_types.py | 3,387 | Python |
import contextlib
import os.path
import subprocess
import pytest
from pre_commit import parse_shebang
from pre_commit.util import CalledProcessError
from pre_commit.util import cmd_output
from pre_commit.util import cmd_output_b
from testing.auto_namedtuple import auto_namedtuple
TESTING_DIR = os.path.abspath(os.path.dirname(__file__))
def docker_is_running() -> bool: # pragma: win32 no cover
try:
cmd_output_b('docker', 'ps')
except CalledProcessError: # pragma: no cover
return False
else:
return True
def get_resource_path(path):
return os.path.join(TESTING_DIR, 'resources', path)
def cmd_output_mocked_pre_commit_home(
*args, tempdir_factory, pre_commit_home=None, env=None, **kwargs,
):
if pre_commit_home is None:
pre_commit_home = tempdir_factory.get()
env = env if env is not None else os.environ
kwargs.setdefault('stderr', subprocess.STDOUT)
# Don't want to write to the home directory
env = dict(env, PRE_COMMIT_HOME=pre_commit_home)
ret, out, _ = cmd_output(*args, env=env, **kwargs)
return ret, out.replace('\r\n', '\n'), None
skipif_cant_run_coursier = pytest.mark.skipif(
os.name == 'nt' or parse_shebang.find_executable('cs') is None,
reason="coursier isn't installed or can't be found",
)
skipif_cant_run_docker = pytest.mark.skipif(
os.name == 'nt' or not docker_is_running(),
reason="Docker isn't running or can't be accessed",
)
skipif_cant_run_swift = pytest.mark.skipif(
parse_shebang.find_executable('swift') is None,
reason="swift isn't installed or can't be found",
)
xfailif_windows = pytest.mark.xfail(os.name == 'nt', reason='windows')
def run_opts(
all_files=False,
files=(),
color=False,
verbose=False,
hook=None,
remote_branch='',
local_branch='',
from_ref='',
to_ref='',
remote_name='',
remote_url='',
hook_stage='commit',
show_diff_on_failure=False,
commit_msg_filename='',
checkout_type='',
is_squash_merge='',
rewrite_command='',
):
# These are mutually exclusive
assert not (all_files and files)
return auto_namedtuple(
all_files=all_files,
files=files,
color=color,
verbose=verbose,
hook=hook,
remote_branch=remote_branch,
local_branch=local_branch,
from_ref=from_ref,
to_ref=to_ref,
remote_name=remote_name,
remote_url=remote_url,
hook_stage=hook_stage,
show_diff_on_failure=show_diff_on_failure,
commit_msg_filename=commit_msg_filename,
checkout_type=checkout_type,
is_squash_merge=is_squash_merge,
rewrite_command=rewrite_command,
)
@contextlib.contextmanager
def cwd(path):
original_cwd = os.getcwd()
os.chdir(path)
try:
yield
finally:
os.chdir(original_cwd)
def git_commit(*args, fn=cmd_output, msg='commit!', all_files=True, **kwargs):
kwargs.setdefault('stderr', subprocess.STDOUT)
cmd = ('git', 'commit', '--allow-empty', '--no-gpg-sign', *args)
if all_files: # allow skipping `-a` with `all_files=False`
cmd += ('-a',)
if msg is not None: # allow skipping `-m` with `msg=None`
cmd += ('-m', msg)
ret, out, _ = fn(*cmd, **kwargs)
return ret, out.replace('\r\n', '\n')
| 28.4 | 78 | 0.659624 | [
"MIT"
] | DjKuj/reformat | testing/util.py | 3,408 | Python |
"""Database exceptions."""
class BaseError(Exception):
"""The base exception."""
class NotFoundError(BaseError):
"""When an item was not found in the database."""
| 17.5 | 53 | 0.674286 | [
"Apache-2.0"
] | jdkandersson/OpenAlchemyPackage | database/open_alchemy/package_database/exceptions.py | 175 | Python |
from spytest import st
def init(dut):
st.create_init_config_db(dut)
def extend(dut):
st.log("Extend base config if needed", dut=dut)
st.config(dut, "config feature state nat enabled")
st.config(dut, "config feature state sflow enabled")
| 23.272727 | 56 | 0.710938 | [
"Apache-2.0"
] | akokhan/sonic-mgmt | spytest/apis/common/base_config.py | 256 | Python |
import sys
import os
import re
import importlib
import warnings
is_pypy = '__pypy__' in sys.builtin_module_names
warnings.filterwarnings('ignore',
r'.+ distutils\b.+ deprecated',
DeprecationWarning)
def warn_distutils_present():
if 'distutils' not in sys.modules:
return
if is_pypy and sys.version_info < (3, 7):
# PyPy for 3.6 unconditionally imports distutils, so bypass the warning
# https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
return
warnings.warn(
"Distutils was imported before Setuptools, but importing Setuptools "
"also replaces the `distutils` module in `sys.modules`. This may lead "
"to undesirable behaviors or errors. To avoid these issues, avoid "
"using distutils directly, ensure that setuptools is installed in the "
"traditional way (e.g. not an editable install), and/or make sure "
"that setuptools is always imported before distutils.")
def clear_distutils():
if 'distutils' not in sys.modules:
return
warnings.warn("Setuptools is replacing distutils.")
mods = [name for name in sys.modules if re.match(r'distutils\b', name)]
for name in mods:
del sys.modules[name]
def enabled():
"""
Allow selection of distutils by environment variable.
"""
which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'stdlib')
return which == 'local'
def ensure_local_distutils():
clear_distutils()
distutils = importlib.import_module('setuptools._distutils')
distutils.__name__ = 'distutils'
sys.modules['distutils'] = distutils
# sanity check that submodules load as expected
core = importlib.import_module('distutils.core')
assert '_distutils' in core.__file__, core.__file__
def do_override():
"""
Ensure that the local copy of distutils is preferred over stdlib.
See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
for more motivation.
"""
if enabled():
warn_distutils_present()
ensure_local_distutils()
class DistutilsMetaFinder:
def find_spec(self, fullname, path, target=None):
if path is not None:
return
method_name = 'spec_for_{fullname}'.format(**locals())
method = getattr(self, method_name, lambda: None)
return method()
def spec_for_distutils(self):
import importlib.abc
import importlib.util
class DistutilsLoader(importlib.abc.Loader):
def create_module(self, spec):
return importlib.import_module('setuptools._distutils')
def exec_module(self, module):
pass
return importlib.util.spec_from_loader('distutils', DistutilsLoader())
def spec_for_pip(self):
"""
Ensure stdlib distutils when running under pip.
See pypa/pip#8761 for rationale.
"""
if self.pip_imported_during_build():
return
clear_distutils()
self.spec_for_distutils = lambda: None
@staticmethod
def pip_imported_during_build():
"""
Detect if pip is being imported in a build script. Ref #2355.
"""
import traceback
return any(
frame.f_globals['__file__'].endswith('setup.py')
for frame, line in traceback.walk_stack(None)
)
DISTUTILS_FINDER = DistutilsMetaFinder()
def add_shim():
sys.meta_path.insert(0, DISTUTILS_FINDER)
def remove_shim():
try:
sys.meta_path.remove(DISTUTILS_FINDER)
except ValueError:
pass
| 29.581395 | 120 | 0.635482 | [
"MIT"
] | JE-Chen/je_old_repo | DatabaseControlWrapper_JE/venv/Lib/site-packages/_distutils_hack/__init__.py | 3,816 | Python |
## Start of header boilerplate #################################################
from aocbase import readInput
import re
import collections
def lineParse(s, f, fp):
m = fp.match(s)
if m==None:
raise s
return tuple(map(f, m.groups()))
def fileParse(inp):
return list(inp.splitlines())
## End of header boilerplate ###################################################
def mapMaze(s):
mz = dict()
for y,line in enumerate(s):
for x, c in enumerate(line):
if c != ' ':
mz[x,y] = c
return mz
def findTeleporters(mz):
loc = dict()
for (x, y), value in mz.items():
if isinstance(value, str) and value.isupper():
for dx, dy in ((0,1),(1,0),(-1,0),(0,-1)):
nxtChar = mz.get((x+dx, y+dy), '')
if isinstance(nxtChar, tuple): continue
prevChar = mz.get((x-dx, y-dy), '')
if (nxtChar.isupper() and prevChar == '.'):
name = (mz[min(x, x+dx),min(y, y+dy)] +
mz[max(x+dx, x), max(y+dy, y)])
if name not in loc:
loc[name] = list()
loc[name].append(((x, y), (x-dx, y-dy)))
for l in loc.values():
if len(l) != 2: continue
mz[l[0][0]] = l[1][1]
mz[l[1][0]] = l[0][1]
return loc['AA'][0][0], loc['ZZ'][0][0]
def findTeleportersD(mz):
start, stop = findTeleporters(mz)
minx = min((c[0] for c in mz.keys()))
maxx = max((c[0] for c in mz.keys()))
miny = min((c[1] for c in mz.keys()))
maxy = max((c[1] for c in mz.keys()))
portalLocations = [key for key in mz.keys() if isinstance(mz[key], tuple)]
for x, y in portalLocations:
mz[x, y] = mz[x, y] + (abs(x - minx) < 2 or abs(x - maxx) < 2 or abs(y - miny) < 2 or abs(y - maxy) < 2,)
return start, stop
def colorMap(mz, start, stop):
d = collections.deque()
d.append(start)
v = dict()
v[start] = 0
while len(d)>0:
cur = d.popleft()
x, y = cur
for dx, dy in ((0,1),(1,0),(-1,0),(0,-1)):
nx, ny = x+dx, y+dy
if (nx, ny) == stop:
return v[x,y] -1
if (nx, ny) not in mz:
continue
if isinstance(mz[nx, ny], tuple):
nx, ny = mz[nx, ny]
if mz[nx, ny] != '.':
continue
if (nx, ny) in v and v[x, y] + 1 >= v[nx, ny]:
continue
v[nx, ny] = v[x, y]+1
d.append((nx, ny))
def colorMapD(mz, start, stop):
maxLevel = len([t for t in mz.values() if isinstance(t, tuple)])//2
d = collections.deque()
d.append(start+(0, ))
v = dict()
v[start+(0, )] = 0
while len(d)>0:
cur = d.popleft()
x, y, lvl = cur
for dx, dy in ((0,1),(1,0),(-1,0),(0,-1)):
nx, ny, nlvl = x+dx, y+dy, lvl
if (nx, ny) == stop and lvl == 0:
return v[x,y,lvl] - 1
if (nx, ny) not in mz:
continue
if isinstance(mz[nx, ny], tuple):
nx, ny, outer = mz[nx, ny]
if outer:
if lvl == 0:
continue
nlvl -= 1
else:
if lvl == maxLevel:
continue
nlvl += 1
if mz[nx, ny] != '.':
continue
if (nx, ny, nlvl) in v and v[x, y, lvl] + 1 >= v[nx, ny, nlvl]:
continue
v[nx, ny, nlvl] = v[x, y, lvl]+1
d.append((nx, ny, nlvl))
def part1(pinp):
mz = mapMaze(pinp)
start, stop = findTeleporters(mz)
return colorMap(mz, start, stop)
def part2(pinp):
mz = mapMaze(pinp)
start, stop = findTeleportersD(mz)
return colorMapD(mz, start, stop)
## Start of footer boilerplate #################################################
if __name__ == "__main__":
inp = readInput()
## Update for input specifics ##############################################
parseInp = fileParse(inp)
print("Input is '" + str(parseInp[:10])[:100] +
('...' if len(parseInp)>10 or len(str(parseInp[:10]))>100 else '') + "'")
print("Solution to part 1: {}".format(part1(parseInp)))
print("Solution to part 2: {}".format(part2(parseInp)))
## End of footer boilerplate ###################################################
| 33.074074 | 113 | 0.439418 | [
"Apache-2.0"
] | Dyr-El/advent_of_code_2019 | Dyr-El-python/day20.py | 4,465 | Python |
import inspect
import warnings
from abc import ABCMeta, abstractmethod
from mmcv_custom.fileio.zipreader import ZipReader
class BaseStorageBackend(metaclass=ABCMeta):
"""Abstract class of storage backends.
All backends need to implement two apis: `get()` and `get_text()`.
`get()` reads the file as a byte stream and `get_text()` reads the file
as texts.
"""
@abstractmethod
def get(self, filepath):
pass
@abstractmethod
def get_text(self, filepath):
pass
class CephBackend(BaseStorageBackend):
"""Ceph storage backend.
Args:
path_mapping (dict|None): path mapping dict from local path to Petrel
path. When `path_mapping={'src': 'dst'}`, `src` in `filepath` will
be replaced by `dst`. Default: None.
"""
def __init__(self, path_mapping=None):
try:
import ceph
warnings.warn('Ceph is deprecate in favor of Petrel.')
except ImportError:
raise ImportError('Please install ceph to enable CephBackend.')
self._client = ceph.S3Client()
assert isinstance(path_mapping, dict) or path_mapping is None
self.path_mapping = path_mapping
def get(self, filepath):
filepath = str(filepath)
if self.path_mapping is not None:
for k, v in self.path_mapping.items():
filepath = filepath.replace(k, v)
value = self._client.Get(filepath)
value_buf = memoryview(value)
return value_buf
def get_text(self, filepath):
raise NotImplementedError
class PetrelBackend(BaseStorageBackend):
"""Petrel storage backend (for internal use).
Args:
path_mapping (dict|None): path mapping dict from local path to Petrel
path. When `path_mapping={'src': 'dst'}`, `src` in `filepath` will
be replaced by `dst`. Default: None.
"""
def __init__(self, path_mapping=None):
try:
from petrel_client import client
except ImportError:
raise ImportError('Please install petrel_client to enable '
'PetrelBackend.')
self._client = client.Client()
assert isinstance(path_mapping, dict) or path_mapping is None
self.path_mapping = path_mapping
def get(self, filepath):
filepath = str(filepath)
if self.path_mapping is not None:
for k, v in self.path_mapping.items():
filepath = filepath.replace(k, v)
value = self._client.Get(filepath)
value_buf = memoryview(value)
return value_buf
def get_text(self, filepath):
raise NotImplementedError
class MemcachedBackend(BaseStorageBackend):
"""Memcached storage backend.
Attributes:
server_list_cfg (str): Config file for memcached server list.
client_cfg (str): Config file for memcached client.
sys_path (str | None): Additional path to be appended to `sys.path`.
Default: None.
"""
def __init__(self, server_list_cfg, client_cfg, sys_path=None):
if sys_path is not None:
import sys
sys.path.append(sys_path)
try:
import mc
except ImportError:
raise ImportError(
'Please install memcached to enable MemcachedBackend.')
self.server_list_cfg = server_list_cfg
self.client_cfg = client_cfg
self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg,
self.client_cfg)
# mc.pyvector servers as a point which points to a memory cache
self._mc_buffer = mc.pyvector()
def get(self, filepath):
filepath = str(filepath)
import mc
self._client.Get(filepath, self._mc_buffer)
value_buf = mc.ConvertBuffer(self._mc_buffer)
return value_buf
def get_text(self, filepath):
raise NotImplementedError
class LmdbBackend(BaseStorageBackend):
"""Lmdb storage backend.
Args:
db_path (str): Lmdb database path.
readonly (bool, optional): Lmdb environment parameter. If True,
disallow any write operations. Default: True.
lock (bool, optional): Lmdb environment parameter. If False, when
concurrent access occurs, do not lock the database. Default: False.
readahead (bool, optional): Lmdb environment parameter. If False,
disable the OS filesystem readahead mechanism, which may improve
random read performance when a database is larger than RAM.
Default: False.
Attributes:
db_path (str): Lmdb database path.
"""
def __init__(self,
db_path,
readonly=True,
lock=False,
readahead=False,
**kwargs):
try:
import lmdb
except ImportError:
raise ImportError('Please install lmdb to enable LmdbBackend.')
self.db_path = str(db_path)
self._client = lmdb.open(
self.db_path,
readonly=readonly,
lock=lock,
readahead=readahead,
**kwargs)
def get(self, filepath):
"""Get values according to the filepath.
Args:
filepath (str | obj:`Path`): Here, filepath is the lmdb key.
"""
filepath = str(filepath)
with self._client.begin(write=False) as txn:
value_buf = txn.get(filepath.encode('ascii'))
return value_buf
def get_text(self, filepath):
raise NotImplementedError
def is_zip_path(path):
return '.zip@' in path
class HardDiskBackend(BaseStorageBackend):
"""Raw hard disks storage backend."""
def get(self, filepath):
filepath = str(filepath)
if is_zip_path(filepath):
value_buf = ZipReader.read(filepath)
else:
with open(filepath, 'rb') as f:
value_buf = f.read()
return value_buf
def get_text(self, filepath):
filepath = str(filepath)
with open(filepath, 'r') as f:
value_buf = f.read()
return value_buf
class FileClient(object):
"""A general file client to access files in different backend.
The client loads a file or text in a specified backend from its path
and return it as a binary file. it can also register other backend
accessor with a given name and backend class.
Attributes:
backend (str): The storage backend type. Options are "disk", "ceph",
"memcached" and "lmdb".
client (:obj:`BaseStorageBackend`): The backend object.
"""
_backends = {
'disk': HardDiskBackend,
'ceph': CephBackend,
'memcached': MemcachedBackend,
'lmdb': LmdbBackend,
'petrel': PetrelBackend,
}
def __init__(self, backend='disk', **kwargs):
if backend not in self._backends:
raise ValueError(
f'Backend {backend} is not supported. Currently supported ones'
f' are {list(self._backends.keys())}')
self.backend = backend
self.client = self._backends[backend](**kwargs)
@classmethod
def register_backend(cls, name, backend):
if not inspect.isclass(backend):
raise TypeError(
f'backend should be a class but got {type(backend)}')
if not issubclass(backend, BaseStorageBackend):
raise TypeError(
f'backend {backend} is not a subclass of BaseStorageBackend')
cls._backends[name] = backend
def get(self, filepath):
return self.client.get(filepath)
def get_text(self, filepath):
return self.client.get_text(filepath)
| 31.582996 | 79 | 0.613511 | [
"Apache-2.0"
] | MendelXu/mmdetection-1 | mmcv_custom/fileio/file_client.py | 7,801 | Python |
r"""
This app is used to invert the styleGAN series synthesis network. We find
the matching latent vector w for given images so that we can manipulate
images in the latent feature space.
Ref: https://github.com/rosinality/stylegan2-pytorch/blob/master/projector.py # noqa
"""
import argparse
import os
import sys
import mmcv
import numpy as np
import torch
import torch.nn.functional as F
from mmcv import Config
from mmcv.runner import load_checkpoint
from PIL import Image
from torch import optim
from torchvision import transforms
from torchvision.utils import save_image
from tqdm import tqdm
# yapf: disable
sys.path.append(os.path.abspath(os.path.join(__file__, '../..'))) # isort:skip # noqa
from mmgen.apis import set_random_seed # isort:skip # noqa
from mmgen.models import build_model # isort:skip # noqa
from mmgen.models.architectures.lpips import PerceptualLoss # isort:skip # noqa
# yapf: enable
def parse_args():
parser = argparse.ArgumentParser(
description='Image projector to the StyleGAN-based generator latent \
spaces')
parser.add_argument('config', help='evaluation config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument(
'files',
metavar='FILES',
nargs='+',
help='path to image files to be projected')
parser.add_argument(
'--results-path', type=str, help='path to store projection results.')
parser.add_argument(
'--use-cpu',
action='store_true',
help='whether to use cpu device for sampling')
parser.add_argument('--seed', type=int, default=2021, help='random seed')
parser.add_argument(
'--deterministic',
action='store_true',
help='whether to set deterministic options for CUDNN backend.')
parser.add_argument(
'--sample-model',
type=str,
default='ema',
help='use which mode (ema/orig) in sampling.')
parser.add_argument(
'--lr-rampup',
type=float,
default=0.05,
help='proportion of the learning rate warmup iters in the total iters')
parser.add_argument(
'--lr-rampdown',
type=float,
default=0.25,
help='proportion of the learning rate decay iters in the total iters')
parser.add_argument(
'--lr', type=float, default=0.1, help='maximum learning rate')
parser.add_argument(
'--noise',
type=float,
default=0.05,
help='strength of the noise level')
parser.add_argument(
'--noise-ramp',
type=float,
default=0.75,
help='proportion of the noise level decay iters in the total iters',
)
parser.add_argument(
'--total-iters', type=int, default=1000, help='optimize iterations')
parser.add_argument(
'--noise-regularize',
type=float,
default=1e5,
help='weight of the noise regularization',
)
parser.add_argument(
'--mse', type=float, default=0, help='weight of the mse loss')
parser.add_argument(
'--n-mean-latent',
type=int,
default=10000,
help='sampling times to obtain the mean latent')
parser.add_argument(
'--w-plus',
action='store_true',
help='allow to use distinct latent codes to each layers',
)
args = parser.parse_args()
return args
def noise_regularize(noises):
loss = 0
for noise in noises:
size = noise.shape[2]
while True:
loss = (
loss +
(noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2) +
(noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2))
if size <= 8:
break
noise = noise.reshape([-1, 1, size // 2, 2, size // 2, 2])
noise = noise.mean([3, 5])
size //= 2
return loss
def noise_normalize_(noises):
for noise in noises:
mean = noise.mean()
std = noise.std()
noise.data.add_(-mean).div_(std)
def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05):
lr_ramp = min(1, (1 - t) / rampdown)
lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
lr_ramp = lr_ramp * min(1, t / rampup)
return initial_lr * lr_ramp
def latent_noise(latent, strength):
noise = torch.randn_like(latent) * strength
return latent + noise
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# set random seeds
if args.seed is not None:
print('set random seed to', args.seed)
set_random_seed(args.seed, deterministic=args.deterministic)
# build the model and load checkpoint
model = build_model(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
_ = load_checkpoint(model, args.checkpoint, map_location='cpu')
# sanity check for models without ema
if not model.use_ema:
args.sample_model = 'orig'
if args.sample_model == 'ema':
generator = model.generator_ema
else:
generator = model.generator
mmcv.print_log(f'Sampling model: {args.sample_model}', 'mmgen')
generator.eval()
device = 'cpu'
if not args.use_cpu:
generator = generator.cuda()
device = 'cuda'
img_size = min(generator.out_size, 256)
transform = transforms.Compose([
transforms.Resize(img_size),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])
# read images
imgs = []
for imgfile in args.files:
img = Image.open(imgfile).convert('RGB')
img = transform(img)
img = img[[2, 1, 0], ...]
imgs.append(img)
imgs = torch.stack(imgs, 0).to(device)
# get mean and standard deviation of style latents
with torch.no_grad():
noise_sample = torch.randn(
args.n_mean_latent, generator.style_channels, device=device)
latent_out = generator.style_mapping(noise_sample)
latent_mean = latent_out.mean(0)
latent_std = ((latent_out - latent_mean).pow(2).sum() /
args.n_mean_latent)**0.5
latent_in = latent_mean.detach().clone().unsqueeze(0).repeat(
imgs.shape[0], 1)
if args.w_plus:
latent_in = latent_in.unsqueeze(1).repeat(1, generator.num_latents, 1)
latent_in.requires_grad = True
# define lpips loss
percept = PerceptualLoss(use_gpu=device.startswith('cuda'))
# initialize layer noises
noises_single = generator.make_injected_noise()
noises = []
for noise in noises_single:
noises.append(noise.repeat(imgs.shape[0], 1, 1, 1).normal_())
for noise in noises:
noise.requires_grad = True
optimizer = optim.Adam([latent_in] + noises, lr=args.lr)
pbar = tqdm(range(args.total_iters))
# run optimization
for i in pbar:
t = i / args.total_iters
lr = get_lr(t, args.lr, args.lr_rampdown, args.lr_rampup)
optimizer.param_groups[0]['lr'] = lr
noise_strength = latent_std * args.noise * max(
0, 1 - t / args.noise_ramp)**2
latent_n = latent_noise(latent_in, noise_strength.item())
img_gen = generator([latent_n],
input_is_latent=True,
injected_noise=noises)
batch, channel, height, width = img_gen.shape
if height > 256:
factor = height // 256
img_gen = img_gen.reshape(batch, channel, height // factor, factor,
width // factor, factor)
img_gen = img_gen.mean([3, 5])
p_loss = percept(img_gen, imgs).sum()
n_loss = noise_regularize(noises)
mse_loss = F.mse_loss(img_gen, imgs)
loss = p_loss + args.noise_regularize * n_loss + args.mse * mse_loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
noise_normalize_(noises)
pbar.set_description(
f' perceptual: {p_loss.item():.4f}, noise regularize:'
f'{n_loss.item():.4f}, mse: {mse_loss.item():.4f}, lr: {lr:.4f}')
results = generator([latent_in.detach().clone()],
input_is_latent=True,
injected_noise=noises)
# rescale value range to [0, 1]
results = ((results + 1) / 2)
results = results[:, [2, 1, 0], ...]
results = results.clamp_(0, 1)
mmcv.mkdir_or_exist(args.results_path)
# save projection results
result_file = {}
for i, input_name in enumerate(args.files):
noise_single = []
for noise in noises:
noise_single.append(noise[i:i + 1])
result_file[input_name] = {
'img': img_gen[i],
'latent': latent_in[i],
'injected_noise': noise_single,
}
img_name = os.path.splitext(
os.path.basename(input_name))[0] + '-project.png'
save_image(results[i], os.path.join(args.results_path, img_name))
torch.save(result_file, os.path.join(args.results_path,
'project_result.pt'))
if __name__ == '__main__':
main()
| 32.313589 | 88 | 0.610524 | [
"Apache-2.0"
] | DequanWang/actnn-mmgen | apps/stylegan_projector.py | 9,274 | Python |
def main(x):
matrix = []
exit_path = []
for i in range(0, x):
j = list(input())
if 'e' in j:
y = j.index("e")
exit_path.append(i)
exit_path.append(y)
j[y] = "-"
matrix.append(j)
row, col = 0, 0
matrix[row][col] = "S"
path = []
searching_path(matrix, path, exit_path, row, col)
def searching_path(m, path, exit_path, i, j):
r, c = len(m), len(m[0])
exit_row, exit_col = exit_path
# If destination is reached print
if i == exit_row and j == exit_col:
print("".join(e for e in path[1:]) + m[i][j])
m[exit_row][exit_col] = "-"
return
# explore
path.append(m[i][j])
# move down
if 0 <= i + 1 <= r - 1 and 0 <= j <= c - 1 and m[i + 1][j] == "-":
m[i + 1][j] = "D"
searching_path(m, path, exit_path, i + 1, j)
# move right
if 0 <= i <= r - 1 and 0 <= j + 1 <= c - 1 and m[i][j + 1] == '-':
m[i][j + 1] = 'R'
searching_path(m, path, exit_path, i, j + 1)
# move left
if 0 <= i <= r - 1 and 0 <= j - 1 <= c - 1 and m[i][j - 1] == '-':
m[i][j - 1] = "L"
searching_path(m, path, exit_path, i, j - 1)
# move up
if 0 <= i - 1 <= r - 1 and 0 <= j <= c - 1 and m[i - 1][j] == '-':
m[i - 1][j] = "U"
searching_path(m, path, exit_path, i - 1, j)
# if none of the above is explorable or invalid index backtrack
path.pop()
main(3)
| 25.877193 | 70 | 0.460339 | [
"MIT"
] | borislavstoychev/Algorithms | Recursion/labyrinth.py | 1,475 | Python |
from rabbitmq_utils import read_messages
def solve_arithmetic_phrase(channel, method, properties, body):
with open('output.txt', 'a') as file:
try:
body_str = eval(body)
result = eval(body_str)
file.write(f'{body_str} = {result}\n')
return result
except Exception as e:
print(f'Error while calculating "{body_str}": {str(e)}')
read_messages(solve_arithmetic_phrase)
| 28.125 | 68 | 0.624444 | [
"MIT"
] | ShaharGotshtat/parse-and-calculate-with-rabbitmq | calculator/calculator.py | 450 | Python |
from scrapy.utils.project import get_project_settings
from sqlalchemy import Column
from sqlalchemy import Integer
from sqlalchemy import String
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
def db_connect():
"""
Performs database connection using database settings from settings.py.
Returns sqlalchemy engine instance
"""
return create_engine(get_project_settings().get("CONNECTION_STRING"))
def create_table(engine):
""" create tables"""
Base.metadata.create_all(engine)
class Parliament(Base):
"""Sqlalchemy deals model"""
__tablename__ = "parliament"
id = Column(Integer, primary_key=True, autoincrement=True)
name = Column("name", String)
date_born = Column("date_born", String)
place_born = Column("place_born", String, nullable=True)
profession = Column("profession", String, nullable=True)
lang = Column("lang", String, nullable=True)
party = Column("party", String, nullable=True)
email = Column("email", String, nullable=True)
url = Column("url", String, nullable=True)
education = Column("education", String, nullable=True)
pp = Column("pp", String)
dob = Column("dob", String)
| 30.609756 | 74 | 0.723506 | [
"MIT"
] | Georgitanev/python38_proj_adata | src/parliamentbg/parliamentbg/models.py | 1,255 | Python |
import usocket as socket
import ustruct as struct
from ubinascii import hexlify
class MQTTException(Exception):
pass
class MQTTClient:
def __init__(
self,
client_id,
server,
port=0,
user=None,
password=None,
keepalive=0,
ssl=False,
ssl_params={},
):
if port == 0:
port = 8883 if ssl else 1883
self.client_id = client_id
self.sock = None
self.server = server
self.port = port
self.ssl = ssl
self.ssl_params = ssl_params
self.pid = 0
self.cb = None
self.user = user
self.pswd = password
self.keepalive = keepalive
self.lw_topic = None
self.lw_msg = None
self.lw_qos = 0
self.lw_retain = False
def _send_str(self, s):
self.sock.write(struct.pack("!H", len(s)))
self.sock.write(s)
def _recv_len(self):
n = 0
sh = 0
while 1:
b = self.sock.read(1)[0]
n |= (b & 0x7F) << sh
if not b & 0x80:
return n
sh += 7
def set_callback(self, f):
self.cb = f
def set_last_will(self, topic, msg, retain=False, qos=0):
assert 0 <= qos <= 2
assert topic
self.lw_topic = topic
self.lw_msg = msg
self.lw_qos = qos
self.lw_retain = retain
def connect(self, clean_session=True):
self.sock = socket.socket()
addr = socket.getaddrinfo(self.server, self.port)[0][-1]
self.sock.connect(addr)
if self.ssl:
import ussl
self.sock = ussl.wrap_socket(self.sock, **self.ssl_params)
premsg = bytearray(b"\x10\0\0\0\0\0")
msg = bytearray(b"\x04MQTT\x04\x02\0\0")
sz = 10 + 2 + len(self.client_id)
msg[6] = clean_session << 1
if self.user is not None:
sz += 2 + len(self.user) + 2 + len(self.pswd)
msg[6] |= 0xC0
if self.keepalive:
assert self.keepalive < 65536
msg[7] |= self.keepalive >> 8
msg[8] |= self.keepalive & 0x00FF
if self.lw_topic:
sz += 2 + len(self.lw_topic) + 2 + len(self.lw_msg)
msg[6] |= 0x4 | (self.lw_qos & 0x1) << 3 | (self.lw_qos & 0x2) << 3
msg[6] |= self.lw_retain << 5
i = 1
while sz > 0x7F:
premsg[i] = (sz & 0x7F) | 0x80
sz >>= 7
i += 1
premsg[i] = sz
self.sock.write(premsg, i + 2)
self.sock.write(msg)
# print(hex(len(msg)), hexlify(msg, ":"))
self._send_str(self.client_id)
if self.lw_topic:
self._send_str(self.lw_topic)
self._send_str(self.lw_msg)
if self.user is not None:
self._send_str(self.user)
self._send_str(self.pswd)
resp = self.sock.read(4)
assert resp[0] == 0x20 and resp[1] == 0x02
if resp[3] != 0:
raise MQTTException(resp[3])
return resp[2] & 1
def disconnect(self):
self.sock.write(b"\xe0\0")
self.sock.close()
def ping(self):
self.sock.write(b"\xc0\0")
def publish(self, topic, msg, retain=False, qos=0):
pkt = bytearray(b"\x30\0\0\0")
pkt[0] |= qos << 1 | retain
sz = 2 + len(topic) + len(msg)
if qos > 0:
sz += 2
assert sz < 2097152
i = 1
while sz > 0x7F:
pkt[i] = (sz & 0x7F) | 0x80
sz >>= 7
i += 1
pkt[i] = sz
# print(hex(len(pkt)), hexlify(pkt, ":"))
self.sock.write(pkt, i + 1)
self._send_str(topic)
if qos > 0:
self.pid += 1
pid = self.pid
struct.pack_into("!H", pkt, 0, pid)
self.sock.write(pkt, 2)
self.sock.write(msg)
if qos == 1:
while 1:
op = self.wait_msg()
if op == 0x40:
sz = self.sock.read(1)
assert sz == b"\x02"
rcv_pid = self.sock.read(2)
rcv_pid = rcv_pid[0] << 8 | rcv_pid[1]
if pid == rcv_pid:
return
elif qos == 2:
assert 0
def subscribe(self, topic, qos=0):
assert self.cb is not None, "Subscribe callback is not set"
pkt = bytearray(b"\x82\0\0\0")
self.pid += 1
struct.pack_into("!BH", pkt, 1, 2 + 2 + len(topic) + 1, self.pid)
# print(hex(len(pkt)), hexlify(pkt, ":"))
self.sock.write(pkt)
self._send_str(topic)
self.sock.write(qos.to_bytes(1, "little"))
while 1:
op = self.wait_msg()
if op == 0x90:
resp = self.sock.read(4)
# print(resp)
assert resp[1] == pkt[2] and resp[2] == pkt[3]
if resp[3] == 0x80:
raise MQTTException(resp[3])
return
# Wait for a single incoming MQTT message and process it.
# Subscribed messages are delivered to a callback previously
# set by .set_callback() method. Other (internal) MQTT
# messages processed internally.
def wait_msg(self):
res = self.sock.read(1)
self.sock.setblocking(True)
if res is None:
return None
if res == b"":
raise OSError(-1)
if res == b"\xd0": # PINGRESP
sz = self.sock.read(1)[0]
assert sz == 0
return None
op = res[0]
if op & 0xF0 != 0x30:
return op
sz = self._recv_len()
topic_len = self.sock.read(2)
topic_len = (topic_len[0] << 8) | topic_len[1]
topic = self.sock.read(topic_len)
sz -= topic_len + 2
if op & 6:
pid = self.sock.read(2)
pid = pid[0] << 8 | pid[1]
sz -= 2
msg = self.sock.read(sz)
self.cb(topic, msg)
if op & 6 == 2:
pkt = bytearray(b"\x40\x02\0\0")
struct.pack_into("!H", pkt, 2, pid)
self.sock.write(pkt)
elif op & 6 == 4:
assert 0
# Checks whether a pending message from server is available.
# If not, returns immediately with None. Otherwise, does
# the same processing as wait_msg.
def check_msg(self):
self.sock.setblocking(False)
return self.wait_msg()
| 29.99537 | 79 | 0.491897 | [
"Apache-2.0"
] | 299Soul/AliOS-Things | components/py_engine/micropython-lib/micropython/umqtt.simple/umqtt/simple.py | 6,479 | Python |
from .player import Player
class Cell:
EMPTY = ' '
def __init__(self, value = EMPTY):
self.value = value
def __eq__(self, other):
return other is not None and self.value == other.value
def __str__(self) -> str:
return str(self.value)
def assign(self, player: Player):
self.value = player.symbol | 21.9375 | 62 | 0.615385 | [
"MIT"
] | michelAlexis/tik-tak-toe | tiktaktoe/models/cell.py | 351 | Python |
from bcipy.feedback.visual.visual_feedback import VisualFeedback
from psychopy import core
from bcipy.helpers.load import load_json_parameters
from bcipy.display.display_main import init_display_window
# Load a parameters file
parameters = load_json_parameters(
'bcipy/parameters/parameters.json', value_cast=True)
display = init_display_window(parameters)
clock = core.Clock()
# Start Visual Feedback
visual_feedback = VisualFeedback(
display=display, parameters=parameters, clock=clock)
stimulus = 'A'
assertion = 'B'
message = 'Incorrect:'
visual_feedback.message_color = 'red'
timing = visual_feedback.administer(
stimulus, compare_assertion=assertion, message=message)
print(timing)
print(visual_feedback._type())
display.close()
| 31.208333 | 64 | 0.809079 | [
"MIT"
] | CAMBI-tech/BciPy | bcipy/feedback/demo/demo_visual_feedback.py | 749 | Python |
class RevitLinkOperations(object,IDisposable):
"""
This class is used to extend the IExternalResourceServer interface with methods to support operations
specifically related to Revit links.
"""
def Dispose(self):
""" Dispose(self: RevitLinkOperations) """
pass
def ReleaseUnmanagedResources(self,*args):
""" ReleaseUnmanagedResources(self: RevitLinkOperations,disposing: bool) """
pass
def SetGetLocalPathForOpenCallback(self,makeLocalCopyForOpen):
"""
SetGetLocalPathForOpenCallback(self: RevitLinkOperations,makeLocalCopyForOpen: IGetLocalPathForOpenCallback)
Sets the IGetLocalPathForOpenCallback that will support the "Open (and Unload)"
command for Revit links
obtained from an IExternalResourceServer.
makeLocalCopyForOpen: The IGetLocalPathForOpenCallback that will support the "Open (and Unload)"
command.
"""
pass
def SetOnLocalLinkSharedCoordinatesSavedCallback(self,onLocalLinkSharedCoordinatesSaved):
"""
SetOnLocalLinkSharedCoordinatesSavedCallback(self: RevitLinkOperations,onLocalLinkSharedCoordinatesSaved: IOnLocalLinkSharedCoordinatesSavedCallback)
Sets the callback that will be called when the Revit user saves new shared
coordinate
settings to a linked document obtained from an
IExternalResourceServer.
onLocalLinkSharedCoordinatesSaved: An IOnLocalLinkSharedCoordinatesSavedCallback object that can respond when the
user
saves new shared coordinates to a Revit link document obtained from
IExternalResourceServer.
"""
pass
def __enter__(self,*args):
""" __enter__(self: IDisposable) -> object """
pass
def __exit__(self,*args):
""" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) """
pass
def __init__(self,*args):
""" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """
pass
def __repr__(self,*args):
""" __repr__(self: object) -> str """
pass
IsValidObject=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""Specifies whether the .NET object represents a valid Revit entity.
Get: IsValidObject(self: RevitLinkOperations) -> bool
"""
| 29.641975 | 215 | 0.735943 | [
"MIT"
] | BCSharp/ironpython-stubs | release/stubs.min/Autodesk/Revit/DB/__init___parts/RevitLinkOperations.py | 2,401 | Python |
##########################################################################
#
# Copyright (c) 2013, Image Engine Design Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above
# copyright notice, this list of conditions and the following
# disclaimer.
#
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with
# the distribution.
#
# * Neither the name of John Haddon nor the names of
# any other contributors to this software may be used to endorse or
# promote products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
##########################################################################
import Gaffer
import GafferUI
import GafferScene
Gaffer.Metadata.registerNode(
GafferScene.SceneSwitch,
"description",
"""
Chooses between multiple input scene, passing through the
chosen input to the output.
""",
plugs = {
"index" : [
"description",
"""
The index of the input which is passed through. A value
of 0 chooses the first input, 1 the second and so on. Values
larger than the number of available inputs wrap back around to
the beginning.
"""
]
}
)
GafferUI.PlugValueWidget.registerCreator( GafferScene.SceneSwitch, "in[0-9]*", None )
| 33.704225 | 85 | 0.687004 | [
"BSD-3-Clause"
] | goddardl/gaffer | python/GafferSceneUI/SceneSwitchUI.py | 2,393 | Python |
import numpy as np
mdf = np.loadtxt('raleighDemandClassesFractions.csv', delimiter=',')
mucwp = np.loadtxt('raleighUserClassesWaterPrices.csv', delimiter=',')
mstp = np.loadtxt('raleighSewageTreatmentPrice.csv', delimiter=',')
print mdf.shape
print mucwp.shape
print mstp.shape
ave_prices_per_tier = mdf * mucwp
print ave_prices_per_tier.shape
monthly_prices = np.sum(ave_prices_per_tier.T * mstp, axis=0)
print monthly_prices
| 25.411765 | 70 | 0.787037 | [
"Apache-2.0"
] | SUEZNorthAmerica/WaterPaths | TestFiles/calculate_water_prices.py | 432 | Python |
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