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1bfb298507a115191ecbd58c3ed71f58ace4a479 | 1cbca82db8f5ab0eac5391e98c7d28ebab447be1 | /workspace_tools/build.py | d57b54c819bd3986fd0617d0ec78bea458ac67bd | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | genba/mbed | 59d8588f5ce59a0ebeb37e611aaa29903870195b | 52e23e58c0174a595af367b0ef5cc79ef3933698 | refs/heads/master | 2020-12-07T05:22:24.730780 | 2013-10-16T13:35:35 | 2013-10-16T13:35:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,169 | py | #! /usr/bin/env python
"""
mbed SDK
Copyright (c) 2011-2013 ARM Limited
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.
LIBRARIES BUILD
"""
import sys
from time import time
from os.path import join, abspath, dirname
# Be sure that the tools directory is in the search path
ROOT = abspath(join(dirname(__file__), ".."))
sys.path.append(ROOT)
from workspace_tools.toolchains import TOOLCHAINS
from workspace_tools.targets import TARGET_NAMES, TARGET_MAP
from workspace_tools.options import get_default_options_parser
from workspace_tools.build_api import build_mbed_libs, build_lib
if __name__ == '__main__':
start = time()
# Parse Options
parser = get_default_options_parser()
# Extra libraries
parser.add_option("-r", "--rtos", action="store_true", dest="rtos",
default=False, help="Compile the rtos")
parser.add_option("-e", "--eth", action="store_true", dest="eth",
default=False, help="Compile the ethernet library")
parser.add_option("-V", "--vodafone", action="store_true", dest="vodafone",
default=False, help="Compile the Vodafone library")
parser.add_option("-U", "--usb_host", action="store_true", dest="usb_host",
default=False, help="Compile the USB Host library")
parser.add_option("-u", "--usb", action="store_true", dest="usb",
default=False, help="Compile the USB Device library")
parser.add_option("-d", "--dsp", action="store_true", dest="dsp",
default=False, help="Compile the DSP library")
parser.add_option("-v", "--verbose", action="store_true", dest="verbose",
default=False, help="Verbose diagnostic output")
(options, args) = parser.parse_args()
# Get target list
if options.mcu:
targets = [options.mcu]
else:
targets = TARGET_NAMES
# Get toolchains list
if options.tool:
toolchains = [options.tool]
else:
toolchains = TOOLCHAINS
# Get libraries list
libraries = []
# Additional Libraries
if options.rtos:
libraries.extend(["rtx", "rtos"])
if options.eth:
libraries.append("eth")
if options.vodafone:
libraries.append("vodafone")
if options.usb:
libraries.append("usb")
if options.usb_host:
libraries.append("usb_host")
if options.dsp:
libraries.extend(["cmsis_dsp", "dsp"])
# Build
failures = []
successes = []
for toolchain in toolchains:
for target in targets:
id = "%s::%s" % (toolchain, target)
try:
mcu = TARGET_MAP[target]
build_mbed_libs(mcu, toolchain, options=options.options,
verbose=options.verbose, clean=options.clean)
for lib_id in libraries:
build_lib(lib_id, mcu, toolchain, options=options.options,
verbose=options.verbose, clean=options.clean)
successes.append(id)
except Exception, e:
if options.verbose:
import sys, traceback
traceback.print_exc(file=sys.stdout)
sys.exit(1)
failures.append(id)
print e
# Write summary of the builds
print "\n\nCompleted in: (%.2f)s" % (time() - start)
if successes:
print "\n\nBuild successes:"
print "\n".join([" * %s" % s for s in successes])
if failures:
print "\n\nBuild failures:"
print "\n".join([" * %s" % f for f in failures])
| [
"[email protected]"
] | |
5b118a1a0d810eb0ff631f8ad9b5fa40f659788f | 6a819308924a005aa66475515bd14586b97296ae | /venv/lib/python3.6/site-packages/pip/utils/outdated.py | fae374b0aa9c5dee18ab16bf1e673e35aaf0053e | [] | no_license | AlexandrTyurikov/my_first_Django_project | a2c655dc295d3904c7688b8f36439ae8229d23d1 | 1a8e4d033c0ff6b1339d78c329f8beca058b019a | refs/heads/master | 2020-05-04T13:20:20.100479 | 2019-05-04T23:41:39 | 2019-05-04T23:41:39 | 179,156,468 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,445 | py | from __future__ import absolute_import
import datetime
import json
import logging
import os.path
import sys
from pip._vendor import lockfile
from pip._vendor.packaging import version as packaging_version
from pip.compat import total_seconds, WINDOWS
from pip.models import PyPI
from pip.locations import USER_CACHE_DIR, running_under_virtualenv
from pip.utils import ensure_dir, get_installed_version
from pip.utils.filesystem import check_path_owner
SELFCHECK_DATE_FMT = "%Y-%m-%dT%H:%M:%SZ"
logger = logging.getLogger(__name__)
class VirtualenvSelfCheckState(object):
def __init__(self):
self.statefile_path = os.path.join(sys.prefix, "pip-selfcheck.json")
# Load the existing state
try:
with open(self.statefile_path) as statefile:
self.state = json.load(statefile)
except (IOError, ValueError):
self.state = {}
def save(self, pypi_version, current_time):
# Attempt to write out our version check file
with open(self.statefile_path, "w") as statefile:
json.dump(
{
"last_check": current_time.strftime(SELFCHECK_DATE_FMT),
"pypi_version": pypi_version,
},
statefile,
sort_keys=True,
separators=(",", ":")
)
class GlobalSelfCheckState(object):
def __init__(self):
self.statefile_path = os.path.join(USER_CACHE_DIR, "selfcheck.json")
# Load the existing state
try:
with open(self.statefile_path) as statefile:
self.state = json.load(statefile)[sys.prefix]
except (IOError, ValueError, KeyError):
self.state = {}
def save(self, pypi_version, current_time):
# Check to make sure that we own the book
if not check_path_owner(os.path.dirname(self.statefile_path)):
return
# Now that we've ensured the book is owned by this user, we'll go
# ahead and make sure that all our directories are created.
ensure_dir(os.path.dirname(self.statefile_path))
# Attempt to write out our version check file
with lockfile.LockFile(self.statefile_path):
if os.path.exists(self.statefile_path):
with open(self.statefile_path) as statefile:
state = json.load(statefile)
else:
state = {}
state[sys.prefix] = {
"last_check": current_time.strftime(SELFCHECK_DATE_FMT),
"pypi_version": pypi_version,
}
with open(self.statefile_path, "w") as statefile:
json.dump(state, statefile, sort_keys=True,
separators=(",", ":"))
def load_selfcheck_statefile():
if running_under_virtualenv():
return VirtualenvSelfCheckState()
else:
return GlobalSelfCheckState()
def pip_version_check(session):
"""Check for an update for pip.
Limit the frequency of checks to once per week. State is stored either in
the active virtualenv or in the user's USER_CACHE_DIR keyed off the prefix
of the pip script path.
"""
installed_version = get_installed_version("pip")
if installed_version is None:
return
pip_version = packaging_version.parse(installed_version)
pypi_version = None
try:
state = load_selfcheck_statefile()
current_time = datetime.datetime.utcnow()
# Determine if we need to refresh the state
if "last_check" in state.state and "pypi_version" in state.state:
last_check = datetime.datetime.strptime(
state.state["last_check"],
SELFCHECK_DATE_FMT
)
if total_seconds(current_time - last_check) < 7 * 24 * 60 * 60:
pypi_version = state.state["pypi_version"]
# Refresh the version if we need to or just see if we need to warn
if pypi_version is None:
resp = session.get(
PyPI.pip_json_url,
headers={"Accept": "application/json"},
)
resp.raise_for_status()
pypi_version = [
v for v in sorted(
list(resp.json()["releases"]),
key=packaging_version.parse,
)
if not packaging_version.parse(v).is_prerelease
][-1]
# save that we've performed a check
state.save(pypi_version, current_time)
remote_version = packaging_version.parse(pypi_version)
# Determine if our pypi_version is older
if (pip_version < remote_version and
pip_version.base_version != remote_version.base_version):
# Advise "python -m pip" on Windows to avoid issues
# with overwriting pip.exe.
if WINDOWS:
pip_cmd = "python -m pip"
else:
pip_cmd = "pip"
logger.warning(
"You are using pip version %s, however version %s is "
"available.\nYou should consider upgrading via the "
"'%s install --upgrade pip' command.",
pip_version, pypi_version, pip_cmd
)
except Exception:
logger.debug(
"There was an error checking the latest version of pip",
exc_info=True,
)
| [
"[email protected]"
] | |
da7a1d1d1f7d32b2acca586cb8cebd6c8ed4616f | 50bdbaf1b714725dff0dc204d3c8d61923a6d011 | /tests/test_all_classes.py | 74bf98c3aee566f5af1d4bf12622ba4692613af8 | [
"MIT"
] | permissive | xflr6/graphviz | 130deb85aa211114e1dfe75b1a88b6925a12f9a9 | 176093002aa354395e88e149748781140841993e | refs/heads/master | 2023-08-27T04:28:33.197675 | 2023-07-22T10:06:51 | 2023-07-22T10:06:51 | 15,847,241 | 1,517 | 261 | MIT | 2023-07-16T11:28:02 | 2014-01-12T17:49:29 | Python | UTF-8 | Python | false | false | 16,252 | py | import locale
import pathlib
import re
import subprocess
import pytest
import graphviz
import _common
ALL_CLASSES = [graphviz.Graph, graphviz.Digraph, graphviz.Source]
@pytest.fixture(params=ALL_CLASSES)
def cls(request):
return request.param
@pytest.fixture
def dot(cls):
if cls.__name__ == 'Source':
return cls('digraph { hello -> world }\n')
return cls()
@pytest.fixture
def invalid_dot(cls):
if cls.__name__ == 'Source':
return cls('graph { spam -- \\ }')
else:
invalid_dot = cls()
with pytest.warns(graphviz.DotSyntaxWarning, match=r'syntax error'):
invalid_dot.edge('spam', '\\')
return invalid_dot
def test_copy(cls, dot):
assert type(dot) is cls
assert dot.copy() is not dot
assert dot.copy() is not dot.copy()
assert type(dot.copy()) is type(dot)
assert dot.copy().__dict__ == dot.__dict__ == dot.copy().__dict__
def test_str(dot):
assert str(dot) == dot.source
@pytest.mark.parametrize(
'parameter, expected_exception, match',
[('engine', ValueError, r'unknown engine'),
('format', ValueError, r'unknown format'),
('renderer', ValueError, r'unknown renderer'),
('formatter', ValueError, r'unknown formatter'),
('encoding', LookupError, r'encoding')])
def test_invalid_parameter_raises_valuerror(dot, parameter,
expected_exception, match):
with pytest.raises(expected_exception, match=match):
setattr(dot, parameter, 'invalid_parameter')
def test_encoding_none(dot):
dot_copy = dot.copy()
dot_copy.encoding = None
assert dot_copy.encoding == locale.getpreferredencoding()
@pytest.mark.exe
@pytest.mark.parametrize(
'kwargs', [{'engine': 'spam'}])
def test_render_raises_before_save(tmp_path, cls, kwargs, filename='dot.gv'):
args = ['graph { spam }'] if cls.__name__ == 'Source' else []
dot = cls(*args, filename=filename, directory=tmp_path)
expected_source = tmp_path / filename
assert not expected_source.exists()
with pytest.raises(ValueError, match=r''):
dot.render(**kwargs)
assert not expected_source.exists()
pdf = dot.render(engine='dot')
assert pdf == f'{expected_source}.pdf'
assert expected_source.exists()
assert expected_source.stat().st_size
@pytest.mark.parametrize(
'kwargs',
[{'engine': 'spam'}, {'format': 'spam'},
{'renderer': 'spam'}, {'formatter': 'spam'}])
def test_render_raises_before_save_mocked(tmp_path, mock_render, cls, kwargs,
filename='dot.gv'):
args = [''] if cls.__name__ == 'Source' else []
dot = cls(*args, filename=filename, directory=tmp_path)
expected_source = tmp_path / filename
assert not expected_source.exists()
first_arg = next(iter(kwargs))
with pytest.raises(ValueError, match=f'unknown {first_arg}'):
dot.render(**kwargs)
assert not expected_source.exists()
def test_render_mocked(mocker, mock_render, dot):
mock_save = mocker.patch.object(dot, 'save', autospec=True)
mock_view = mocker.patch.object(dot, '_view', autospec=True)
mock_remove = mocker.patch('os.remove', autospec=True)
assert dot.render(cleanup=True, view=True) is mock_render.return_value
mock_save.assert_called_once_with(None, None, skip_existing=None)
mock_render.assert_called_once_with(dot.engine, dot.format,
mock_save.return_value,
renderer=None, formatter=None,
neato_no_op=None,
outfile=None,
raise_if_result_exists=False,
overwrite_filepath=False,
quiet=False)
mock_remove.assert_called_once_with(mock_save.return_value)
mock_view.assert_called_once_with(mock_render.return_value,
format=dot.format, quiet=False)
def test_render_outfile_mocked(mocker, mock_render, dot):
mock_save = mocker.patch.object(dot, 'save', autospec=True)
mock_view = mocker.patch.object(dot, '_view', autospec=True)
mock_remove = mocker.patch('os.remove', autospec=True)
outfile = 'spam.pdf'
assert dot.render(outfile=outfile,
raise_if_result_exists=True,
overwrite_source=True,
cleanup=True, view=True) is mock_render.return_value
expected_filename = pathlib.Path('spam.gv')
mock_save.assert_called_once_with(expected_filename, None, skip_existing=None)
mock_render.assert_called_once_with(dot.engine, dot.format,
mock_save.return_value,
renderer=None, formatter=None,
neato_no_op=None,
outfile=pathlib.Path(outfile),
raise_if_result_exists=True,
overwrite_filepath=True,
quiet=False)
mock_remove.assert_called_once_with(mock_save.return_value)
mock_view.assert_called_once_with(mock_render.return_value,
format=dot.format, quiet=False)
def test_format_renderer_formatter_mocked(mocker, mock_render,
quiet, cls,
filename='format.gv', format='jpg',
renderer='cairo', formatter='core'):
dot = cls(*[''] if cls.__name__ == 'Source' else [],
filename=filename, format=format,
renderer=renderer, formatter=formatter)
assert dot.format == format
assert dot.renderer == renderer
assert dot.formatter == formatter
mock_save = mocker.patch.object(dot, 'save', autospec=True)
assert dot.render(quiet=quiet) is mock_render.return_value
mock_save.assert_called_once_with(None, None, skip_existing=None)
mock_render.assert_called_once_with('dot', format, mock_save.return_value,
renderer=renderer, formatter=formatter,
neato_no_op=None,
outfile=None,
raise_if_result_exists=False,
overwrite_filepath=False,
quiet=quiet)
@pytest.mark.parametrize(
'neato_no_op', [None, False, True, 0, 1, 2])
def test_neato_no_op_mocked(mocker, mock_render,
quiet, cls, neato_no_op,
engine='neato',
filename='neato_no_op.gv', format='svg'):
dot = cls(*[''] if cls.__name__ == 'Source' else [],
engine=engine,
filename=filename, format=format)
mock_save = mocker.patch.object(dot, 'save', autospec=True)
assert dot.render(neato_no_op=neato_no_op,
quiet=quiet) is mock_render.return_value
mock_save.assert_called_once_with(None, None, skip_existing=None)
mock_render.assert_called_once_with(engine, format, mock_save.return_value,
renderer=None, formatter=None,
neato_no_op=neato_no_op,
outfile=None,
raise_if_result_exists=False,
overwrite_filepath=False,
quiet=quiet)
def test_save_mocked(mocker, dot, filename='nonfilename', directory='nondirectory'):
mock_makedirs = mocker.patch('os.makedirs', autospec=True)
mock_open = mocker.patch('builtins.open', mocker.mock_open())
with pytest.deprecated_call():
assert dot.save(filename, directory) == dot.filepath
assert dot.filename == filename
assert dot.directory == directory
mock_makedirs.assert_called_once_with(dot.directory, 0o777, exist_ok=True)
mock_open.assert_called_once_with(dot.filepath, 'w',
encoding=dot.encoding)
expected_calls = ([mocker.call(dot.source)] if type(dot).__name__ == 'Source'
else [mocker.call(mocker.ANY), mocker.call('}\n')])
assert mock_open.return_value.write.call_args_list == expected_calls
@pytest.mark.exe
def test_pipe(dot, encoding='utf-8'):
svg = dot.pipe(format='svg', encoding=encoding)
assert svg.startswith('<?xml ')
@pytest.mark.parametrize(
'encoding', [None, 'ascii', 'utf-8'])
def test_pipe_mocked(mocker, mock_pipe_lines, mock_pipe_lines_string, quiet,
dot, encoding):
input_encoding = 'utf-8'
dot.encoding = input_encoding
result = dot.pipe(encoding=encoding, quiet=quiet)
expected_args = ['dot', 'pdf', mocker.ANY]
expected_kwargs = {'quiet': quiet,
'renderer': None,
'formatter': None,
'neato_no_op': None}
if encoding == input_encoding:
assert result is mock_pipe_lines_string.return_value
mock_pipe_lines_string.assert_called_once_with(*expected_args,
encoding=encoding,
**expected_kwargs)
return
if encoding is None:
assert result is mock_pipe_lines.return_value
else:
assert result is mock_pipe_lines.return_value.decode.return_value
mock_pipe_lines.return_value.decode.assert_called_once_with(encoding)
mock_pipe_lines.assert_called_once_with(*expected_args,
input_encoding=input_encoding,
**expected_kwargs)
def test_pipe_lines_mocked(mocker, mock_pipe_lines, dot, format_='svg'):
assert dot.format != format_
assert dot.pipe(format=format_) is mock_pipe_lines.return_value
mock_pipe_lines.assert_called_once_with(dot.engine, format_, mocker.ANY,
renderer=None, formatter=None,
neato_no_op=None,
input_encoding='utf-8',
quiet=False)
_, _, data = mock_pipe_lines.call_args.args
expected_lines = dot.source.splitlines(keepends=True)
assert list(data) == expected_lines
@pytest.mark.exe
def test_pipe_lines_called_process_error(invalid_dot, encoding='ascii',
input_encoding='utf-8'):
_test_pipe_lines_called_process_error(invalid_dot,
format='svg',
encoding=encoding,
input_encoding=input_encoding,
expected_syntax_error='syntax error')
def _test_pipe_lines_called_process_error(invalid_dot, *,
format, encoding, input_encoding,
expected_syntax_error):
assert encoding != input_encoding
invalid_dot.encoding = input_encoding
with pytest.raises(graphviz.CalledProcessError,
match=expected_syntax_error) as info:
invalid_dot.pipe(format=format, encoding=encoding)
assert isinstance(info.value, subprocess.CalledProcessError)
assert isinstance(info.value, graphviz.CalledProcessError)
assert isinstance(info.value.stderr, str)
assert expected_syntax_error in info.value.stderr
def test_pipe_lines_called_process_error_mocked(invalid_dot,
mocker, mock_pipe_lines,
encoding='ascii',
input_encoding='utf-8'):
format = 'svg'
expected_syntax_error = 'syntax error'
expected_syntax_error = f'fake {expected_syntax_error}'
fake_error = [1, _common.INVALID_CMD,
b'', expected_syntax_error.encode(input_encoding)]
fake_error = graphviz.CalledProcessError(*fake_error)
mock_pipe_lines.side_effect = fake_error
_test_pipe_lines_called_process_error(invalid_dot,
format=format,
encoding=encoding,
input_encoding=input_encoding,
expected_syntax_error=expected_syntax_error)
mock_pipe_lines.assert_called_once_with(_common.EXPECTED_DEFAULT_ENGINE,
format,
mocker.ANY,
input_encoding=input_encoding,
quiet=False,
renderer=None,
formatter=None,
neato_no_op=None)
def test_repr_mimebundle_image_svg_xml_mocked(mocker, dot):
mock_pipe = mocker.patch.object(dot, 'pipe', autospec=True)
assert dot._repr_mimebundle_({'image/svg+xml'}) == {'image/svg+xml': mock_pipe.return_value}
mock_pipe.assert_called_once_with(format='svg', encoding=dot.encoding)
def test_repr_mimebundle_image_png_mocked(mocker, dot):
mock_pipe = mocker.patch.object(dot, 'pipe', autospec=True)
assert dot._repr_mimebundle_({'image/png'}) == {'image/png': mock_pipe.return_value}
mock_pipe.assert_called_once_with(format='png')
def test_repr_mimebundle_image_jpeg_mocked(mocker, dot):
mock_pipe = mocker.patch.object(dot, 'pipe', autospec=True)
assert dot._repr_mimebundle_({'image/jpeg'}) == {'image/jpeg': mock_pipe.return_value}
mock_pipe.assert_called_once_with(format='jpeg')
@pytest.mark.exe
def test_unflatten(cls, dot):
result = dot.unflatten()
assert isinstance(result, graphviz.Source)
normalized = re.sub(r'\s+', ' ', result.source.strip())
if cls.__name__ == 'Source':
assert normalized == 'digraph { hello -> world; }'
else:
assert normalized.startswith('digraph {' if dot.directed else 'graph {')
def test_unflatten_mocked(sentinel, mock_unflatten, dot):
kwargs = {'stagger': sentinel.stagger,
'fanout': sentinel.fanout,
'chain': sentinel.chain}
result = dot.unflatten(**kwargs)
assert result is not None
assert isinstance(result, graphviz.Source)
assert type(result) is graphviz.Source
assert result.source is mock_unflatten.return_value
assert result.filename == dot.filename
assert result.directory == dot.directory
assert result.engine == dot.engine
assert result.format == dot.format
assert result.renderer == dot.renderer
assert result.formatter == dot.formatter
assert result.encoding == dot.encoding
assert result._loaded_from_path is None
mock_unflatten.assert_called_once_with(dot.source,
encoding=dot.encoding,
**kwargs)
def test_view_mocked(mocker, dot):
mock_render = mocker.patch.object(dot, 'render', autospec=True)
kwargs = {'filename': 'filename', 'directory': 'directory',
'cleanup': True, 'quiet': True, 'quiet_view': True}
assert dot.view(**kwargs) is mock_render.return_value
mock_render.assert_called_once_with(view=True, **kwargs)
def test__view_unknown_platform(unknown_platform, dot):
with pytest.raises(RuntimeError, match=r'support'):
dot._view('name', format='png', quiet=False)
def test__view_mocked(mocker, sentinel, mock_platform, dot):
_view_platform = mocker.patch.object(dot, f'_view_{mock_platform}',
autospec=True)
kwargs = {'quiet': False}
assert dot._view(sentinel.name, format='png', **kwargs) is None
_view_platform.assert_called_once_with(sentinel.name, **kwargs)
| [
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] | |
5d1dab4d0840ab21108dceab9c5e541f8aacef51 | eb38517d24bb32cd8a33206d4588c3e80f51132d | /pre_procanny_proy2.py | d9c459946676f85c4f0c72c265bf462d2fc0b269 | [] | no_license | Fernando23296/l_proy | 2c6e209892112ceafa00c3584883880c856b6983 | b7fdf99b9bd833ca1c957d106b2429cbd378abd3 | refs/heads/master | 2020-04-01T18:01:41.333302 | 2018-12-04T23:45:53 | 2018-12-04T23:45:53 | 153,466,681 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,645 | py | '''
FILLING AN IMAGE OF:
- FILLING AN IMAGE
- GRAY
- EROSION
- THRESHOLDING (146,196)
- GAUSSIAN BLUR
- CANNY
- PREWITT
- SKELETONIZE
'''
import scipy.ndimage.morphology as morp
import numpy as np
import cv2
import imutils
import matplotlib.pyplot as plt
def skeletonize(img):
struct = np.array([[[[0, 0, 0], [0, 1, 0], [1, 1, 1]],
[[1, 1, 1], [0, 0, 0], [0, 0, 0]]],
[[[0, 0, 0], [1, 1, 0], [0, 1, 0]],
[[0, 1, 1], [0, 0, 1], [0, 0, 0]]],
[[[0, 0, 1], [0, 1, 1], [0, 0, 1]],
[[1, 0, 0], [1, 0, 0], [1, 0, 0]]],
[[[0, 0, 0], [0, 1, 1], [0, 1, 0]],
[[1, 1, 0], [1, 0, 0], [0, 0, 0]]],
[[[1, 1, 1], [0, 1, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [1, 1, 1]]],
[[[0, 1, 0], [0, 1, 1], [0, 0, 0]],
[[0, 0, 0], [1, 0, 0], [1, 1, 0]]],
[[[1, 0, 0], [1, 1, 0], [1, 0, 0]],
[[0, 0, 1], [0, 0, 1], [0, 0, 1]]],
[[[0, 1, 0], [1, 1, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 1], [0, 1, 1]]]])
img = img.copy()
last = ()
while np.any(img != last):
last = img
for s in struct:
img = np.logical_and(img, np.logical_not(
morp.binary_hit_or_miss(img, *s)))
return img
img = cv2.imread('ex4.jpg')
dimensions = img.shape
# height, width, number of channels in image
height = img.shape[0]
width = img.shape[1]
qua = int(width/10)
qua2=int(qua*3)
qua7 = int(qua*7)
img[0:height, 0:qua2] = [0]
img[0:height, qua7:width] = [0]
kernel = np.ones((5, 5), np.uint8)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
erosion = cv2.erode(gray, kernel, iterations=2)
#prometedor pero no da para el ex5.jpg
#thresh = cv2.threshold(gray, 180, 46, cv2.THRESH_BINARY)[1]
#prometedor para ex5.jpg y ex1.jpg
#thresh = cv2.threshold(gray, 150, 200, cv2.THRESH_BINARY)[1]
thresh = cv2.threshold(gray, 180, 46, cv2.THRESH_BINARY)[1]
img_gaussian = cv2.GaussianBlur(thresh, (3, 3), 0)
img = cv2.Canny(img_gaussian, 50, 200)
kernelx = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]])
kernely = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]])
img_prewittx = cv2.filter2D(img, -1, kernelx)
img_prewitty = cv2.filter2D(img, -1, kernely)
img = img_prewittx + img_prewitty
ret, img = cv2.threshold(img, 172, 255, 0)
skel = skeletonize(img)
cv2.imwrite('.png', skel.astype(np.uint8)*255)
#cv2.imshow("skel", skel.astype(np.uint8)*255)
#cv2.waitKey(0)
| [
"[email protected]"
] | |
7976c989ae1331d7101007ef1dcc2894f9890f43 | 7bb94ccf47c69cedb19ef3aa12b817b8dd8f099d | /ibms_project/sfm/apps.py | 9d56f0959997626e16345a92ca50c1b01d2ed5e6 | [
"Apache-2.0"
] | permissive | mohdbakhrayba/ibms | deadf9449af25898a6bd23ab0d0dfc56107be20c | 029e1f3bf108586289c65bb1d547f86851f9494f | refs/heads/master | 2020-04-28T20:07:36.991683 | 2019-03-28T04:13:55 | 2019-03-28T04:13:55 | 175,534,264 | 0 | 0 | Apache-2.0 | 2019-03-14T02:32:33 | 2019-03-14T02:32:33 | null | UTF-8 | Python | false | false | 105 | py | from django.apps import AppConfig
class SFMConfig(AppConfig):
name = 'sfm'
verbose_name = 'SFM'
| [
"[email protected]"
] | |
e88bc1ada639aa5f6c82bbea6feb7484fde89fda | 3388cf3dfde334e6eddc845879b48e9804d8d374 | /src/rocks-pylib/rocks/commands/set/host/power/plugin_physical_host.py | 5f530e9363ffdb90e1e2697b5c2e8d1504b23c79 | [] | no_license | scottsakai/core | 16c6d83a4ee33a534ab0e0a1462680a1183c7881 | 21bced45edd9b70258fa59929f09b102f7874060 | refs/heads/master | 2021-08-14T07:12:42.726105 | 2017-11-14T23:57:36 | 2017-11-14T23:57:36 | 109,899,541 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,669 | py | # $Id: plugin_physical_host.py,v 1.6 2012/11/27 00:48:29 phil Exp $
#
# @Copyright@
#
# Rocks(r)
# www.rocksclusters.org
# version 6.2 (SideWinder)
#
# Copyright (c) 2000 - 2014 The Regents of the University of California.
# All rights reserved.
#
# 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 unmodified and in its entirety, this list of conditions and the
# following disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. All advertising and press materials, printed or electronic, mentioning
# features or use of this software must display the following acknowledgement:
#
# "This product includes software developed by the Rocks(r)
# Cluster Group at the San Diego Supercomputer Center at the
# University of California, San Diego and its contributors."
#
# 4. Except as permitted for the purposes of acknowledgment in paragraph 3,
# neither the name or logo of this software nor the names of its
# authors may be used to endorse or promote products derived from this
# software without specific prior written permission. The name of the
# software includes the following terms, and any derivatives thereof:
# "Rocks", "Rocks Clusters", and "Avalanche Installer". For licensing of
# the associated name, interested parties should contact Technology
# Transfer & Intellectual Property Services, University of California,
# San Diego, 9500 Gilman Drive, Mail Code 0910, La Jolla, CA 92093-0910,
# Ph: (858) 534-5815, FAX: (858) 534-7345, E-MAIL:[email protected]
#
# 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.
#
# @Copyright@
#
# $Log: plugin_physical_host.py,v $
# Revision 1.6 2012/11/27 00:48:29 phil
# Copyright Storm for Emerald Boa
#
# Revision 1.5 2012/05/06 05:48:35 phil
# Copyright Storm for Mamba
#
# Revision 1.4 2011/07/23 02:30:38 phil
# Viper Copyright
#
# Revision 1.3 2010/09/07 23:53:01 bruno
# star power for gb
#
# Revision 1.2 2010/07/14 19:39:39 bruno
# better
#
# Revision 1.1 2010/06/22 21:42:36 bruno
# power control and console access for VMs
#
#
import rocks.commands
class Plugin(rocks.commands.Plugin):
def provides(self):
return 'physical-host'
def run(self, args):
host = args[0]
state = args[1]
rsakey = args[2]
#
# determine if this is a physical host
#
physnode = 1
rows = self.db.execute("""show tables like 'vm_nodes' """)
if rows == 1:
rows = self.db.execute("""select vn.id from
vm_nodes vn, nodes n where vn.node = n.id and
n.name = "%s" """ % (host))
if rows == 1:
physnode = 0
if physnode:
#
# write IPMI commands here
#
pass
| [
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] | |
b96249085246a6f8048ee663380edb20dc84e461 | 452b8eed791cb6dfda9505f2b918f5eaa0331319 | /utils.py | 5c2b2aa9296ec954f6692d6af623611b82f4b0bb | [] | no_license | AntLouiz/hello_world_algoritmo_genetico | 83829b9317af04eb00437f8334a3e1ed7e7a218a | f97589976a01fa2235cec59888611476133b06ad | refs/heads/master | 2020-04-02T16:08:57.162778 | 2018-11-01T11:55:48 | 2018-11-01T11:55:48 | 154,600,417 | 0 | 0 | null | 2018-11-01T01:55:46 | 2018-10-25T02:49:15 | Python | UTF-8 | Python | false | false | 1,746 | py | import sys
from random import randint
from settings import (
ALPHABET,
MASTER_SOLUTION,
INITIAL_POPULATION,
MUTATION_PERCENTUAL,
ELITISM_PERCENTUAL
)
from individual import Individual
from operator import attrgetter
def generate_individual(max_length):
gene = ''
for i in range(max_length):
gene += ALPHABET[randint(0, len(ALPHABET) - 1)]
return Individual(gene)
def tournament_selection(population):
selected_candidates = []
total_candidates = int((INITIAL_POPULATION * ELITISM_PERCENTUAL) / 100)
for i in range(total_candidates):
selected_candidates.append(max(population, key=attrgetter('fitness')))
population = population[total_candidates:]
for x in population:
arena = [population[randint(0, len(population) - 1)] for i in range(2)]
best = max(arena, key=attrgetter('fitness'))
print(best)
if(best.fitness == len(MASTER_SOLUTION)):
print("ENCONTREI A MELHOR SOLUCAO: {}".format(best.gene))
sys.exit()
selected_candidates.append(best)
return zip(selected_candidates, selected_candidates[int(len(selected_candidates) / 2):])
def crossover(first_individual, second_individual):
binary_mask = [randint(0, 1) for i in range(len(MASTER_SOLUTION))]
son = ''
for i in range(len(binary_mask)):
if binary_mask[i]:
son += first_individual.gene[i]
else:
son += second_individual.gene[i]
return Individual(son)
def mutate_population(population):
total_mutations = int((INITIAL_POPULATION * MUTATION_PERCENTUAL) / 100)
for i in range(total_mutations):
population[randint(0, len(population) - 1)].mutate()
return population
| [
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] | |
49dccb091a494ca83b829404e70ab616ba505e1e | 4fca17a3dbc3e74ba7e46bd7869eb6d138e4c422 | /_0186_Reverse_Words_in_a_String_II.py | 49b93f015ec8c38a2f2c5e1a22d1a9a0ad88e3d1 | [] | no_license | mingweihe/leetcode | a2cfee0e004627b817a3c0321bb9c74128f8c1a7 | edff905f63ab95cdd40447b27a9c449c9cefec37 | refs/heads/master | 2021-06-19T07:46:46.897952 | 2021-05-02T05:13:17 | 2021-05-02T05:13:17 | 205,740,338 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 982 | py | class Solution(object):
def reverseWords(self, s):
"""
:type s: List[str]
:rtype: None Do not return anything, modify s in-place instead.
"""
# Approach 2
def reverse(left, right):
while left < right:
s[left], s[right] = s[right], s[left]
left += 1
right -= 1
reverse(0, len(s)-1)
r = 0
while r < len(s):
l = r
while r < len(s) and s[r] != ' ': r+=1
reverse(l, r-1)
r += 1
# Approach 1
# def reverse(left, right):
# while left < right:
# s[left], s[right] = s[right], s[left]
# left += 1
# right -= 1
# reverse(0, len(s)-1)
# start = 0
# for i in xrange(len(s)):
# if s[i] == ' ':
# reverse(start, i-1)
# start = i + 1
# reverse(start, len(s)-1)
| [
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] | |
99589cbca2c51918536b06325b561d1c22a1c1e3 | 898c5364cab1d8bf6366de22753509bd021b45f3 | /0x01-python-if_else_loops_functions/9-print_last_digit.py | 4c5c2ceda0b9270a97360e23db8e6dd80b604205 | [] | no_license | jozsa/holbertonschool-higher_level_programming | 98cc071321934f221ad592a066349e0be293c865 | 7920933ecf983c1856930d4d3b707e230b589231 | refs/heads/master | 2020-04-09T10:02:42.163683 | 2019-05-17T02:08:41 | 2019-05-17T02:08:41 | 160,255,877 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 147 | py | #!/usr/bin/python3
def print_last_digit(number):
lastdigit = abs(number) % 10
print('{:d}'.format(lastdigit), end="")
return lastdigit
| [
"[email protected]"
] | |
48361c1f31049cb6f9ce19e96e8b7b5dc555067d | e5654e71ad4f043bb28105c3b6f3cd833e1c52dc | /openai/venv/lib64/python3.10/site-packages/langchain/evaluation/loading.py | 613e261303bbfeeccb63eaa8ec5bd26bfb7b6afb | [] | no_license | henrymendez/garage | 0b795f020a68fe2d349b556fb8567f6b96488ed5 | b7aaa920a52613e3f1f04fa5cd7568ad37302d11 | refs/heads/master | 2023-07-19T20:16:02.792007 | 2023-07-07T16:58:15 | 2023-07-07T16:58:15 | 67,760,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 209 | py | from typing import Dict, List
def load_dataset(uri: str) -> List[Dict]:
from datasets import load_dataset
dataset = load_dataset(f"LangChainDatasets/{uri}")
return [d for d in dataset["train"]]
| [
"[email protected]"
] | |
230aa57ef7719c1e419ae82bc32e2a40745cc699 | 3fba33f91e1f50077dc2cce663b7de0f70a17a51 | /wlhub/dictionaries/admin.py | 0947f9f15e022cd47245059e1131a9e8bd47397b | [] | no_license | azinit/wlhub | 59be2e9f555fa6655965d13580fd05963dc414b6 | 616761ef39f4cdb82d032f737bf50c66a9e935d1 | refs/heads/master | 2022-12-22T12:26:33.907642 | 2020-09-13T21:45:33 | 2020-09-13T21:45:33 | 295,242,617 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 593 | py | from django.contrib import admin
from core.mixins import ListLinksMixin
from dictionaries.models import *
# Register your models here.
@admin.register(Tag)
class TagAdmin(ListLinksMixin, admin.ModelAdmin):
list_display = ('name', 'user')
list_filter = ("user",)
@admin.register(Area)
class AreaAdmin(ListLinksMixin, admin.ModelAdmin):
list_display = ('name', 'description', 'user')
list_filter = ("user",)
@admin.register(Subject)
class SubjectAdmin(ListLinksMixin, admin.ModelAdmin):
list_display = ('name', 'area', 'description')
list_filter = ("area__user",)
| [
"[email protected]"
] | |
c5649af45f50bc8a3a140b862336cbedfbaf2972 | c1ca20766c0f2c36fdecf6a58ecf319191e6ced9 | /flask/bin/pip2 | 01a3d78f5db8a5ef08e289686dca61191dd1756d | [] | no_license | s0rata/microblog | efd9490c44827e4bba33143431987f0f1062eb16 | 35909879f3971f4d1862c0c463644f221f65f15d | refs/heads/master | 2021-01-12T13:28:57.814638 | 2016-10-07T09:46:47 | 2016-10-07T09:46:47 | 69,955,691 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 237 | #!/home/s0rata/Desktop/microblog/flask/bin/python2
# -*- coding: utf-8 -*-
import re
import sys
from pip import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
"="
] | = |
|
ede5ce348c523a93a566268ee747898144d0fef6 | 4be2c72579486ad04a00db0349028de96d2dce89 | /scripts/fxpt/fx_refsystem/transform_handle.py | 87a840f75acf62a388796d33fb8ad59c417214c4 | [] | no_license | italic-r/maya-prefs | 6a617d40beee8937186b4699c5cead44e01c2d40 | aa21e5e2938dc2698ce5f555ee74a594e08aed2b | refs/heads/master | 2021-09-09T16:31:00.411349 | 2018-03-18T01:40:10 | 2018-03-18T01:40:10 | 86,961,959 | 16 | 8 | null | null | null | null | UTF-8 | Python | false | false | 2,015 | py | from maya import cmds as m
from fxpt.fx_utils.utils_maya import getShape, getParent, parentAPI
# noinspection PyAttributeOutsideInit
class TransformHandle(object):
def __init__(self, transform=None, shape=None):
self.initHandle(transform, shape)
def __str__(self):
return 'transform={}, shape={}'.format(self.transform, self.shape)
def initHandle(self, transform=None, shape=None):
if (transform is not None) and (m.objExists(transform)):
self.transform = transform
self.shape = getShape(transform)
elif (shape is not None) and (m.objExists(shape)):
self.transform = getParent(shape)
self.shape = shape
else:
self.transform = None
self.shape = None
def getChildren(self, allDescendants=False, typ=None):
if typ:
return sorted(
m.listRelatives(
self.transform,
children=True,
allDescendents=allDescendants,
fullPath=True,
typ=typ
) or [])
else:
return sorted(
m.listRelatives(
self.transform,
children=True,
allDescendents=allDescendants,
fullPath=True
) or [])
def getParents(self, typ=None):
if typ:
return sorted(
m.listRelatives(
self.transform,
parent=True,
fullPath=True,
typ=typ
) or [])
else:
return sorted(
m.listRelatives(
self.transform,
parent=True,
fullPath=True
) or [])
def parent(self, newParent, absolute=True):
pass
def exists(self):
return (self.transform is not None) and (m.objExists(self.transform))
| [
"[email protected]"
] | |
079e2939bfc267b2aea7bd0c3a7c988a4549c23d | 7708a641f7688ba593e9d503d7aa5507d543fd79 | /dvaapp/models.py | 481a0b00f0ffe8f39ee52a5fa34dcfce8b9b9b89 | [
"BSD-3-Clause",
"MIT",
"Apache-2.0"
] | permissive | gewenpulan/DeepVideoAnalytics | 8d9f13a7281af9313c60c493c9f26a4cd4833acb | 38a19dedce47261837d8dfc8f6c3f43e4149ec6e | refs/heads/master | 2021-06-22T07:49:31.805741 | 2017-08-16T02:32:00 | 2017-08-16T02:32:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 18,260 | py | from __future__ import unicode_literals
from django.db import models
from django.contrib.auth.models import User
from django.contrib.postgres.fields import ArrayField, JSONField
class VDNServer(models.Model):
"""
A VDN server
"""
url = models.URLField()
name = models.CharField(max_length=200)
last_response_datasets = models.TextField(default='[]')
last_response_detectors = models.TextField(default='[]')
last_token = models.CharField(max_length=300, default="")
class VDNDataset(models.Model):
"""
A VDN dataset
"""
server = models.ForeignKey(VDNServer)
response = models.TextField(default="")
date_imported = models.DateTimeField('date created', auto_now_add=True)
name = models.CharField(max_length=100,default="")
created = models.DateTimeField('date created', auto_now_add=True)
description = models.TextField(default="")
download_url = models.TextField(default="")
url = models.TextField(default="")
aws_requester_pays = models.BooleanField(default=False)
aws_region = models.TextField(default="")
aws_bucket = models.TextField(default="")
aws_key = models.TextField(default="")
root = models.BooleanField(default=True)
parent_local = models.ForeignKey('self',null=True)
organization_url = models.TextField()
class VDNDetector(models.Model):
"""
A VDN detector
"""
server = models.ForeignKey(VDNServer)
response = models.TextField(default="")
date_imported = models.DateTimeField('date created', auto_now_add=True)
name = models.CharField(max_length=100,default="")
created = models.DateTimeField('date created', auto_now_add=True)
description = models.TextField(default="")
download_url = models.TextField(default="")
url = models.TextField(default="")
aws_requester_pays = models.BooleanField(default=False)
aws_region = models.TextField(default="")
aws_bucket = models.TextField(default="")
aws_key = models.TextField(default="")
organization_url = models.TextField()
class CustomIndexer(models.Model):
"""
A custom indexer that can be used with any TF (eventually pytorch) network
"""
name = models.CharField(max_length=100)
algorithm = models.CharField(max_length=100,default="")
model_filename = models.CharField(max_length=200,default="")
# vdn_detector = models.ForeignKey(VDNDetector,null=True)
input_layer_name = models.CharField(max_length=300,default="")
embedding_layer_name = models.CharField(max_length=300,default="")
embedding_layer_size = models.CharField(max_length=300,default="")
indexer_queue = models.CharField(max_length=300,default="")
retriever_queue = models.CharField(max_length=300,default="")
class DVAPQL(models.Model):
"""
A query object with image_data, can have multiple children subspecies
"""
SCHEDULE = 'S'
PROCESS = 'V'
QUERY = 'Q'
TYPE_CHOICES = ((SCHEDULE, 'Schedule'), (PROCESS, 'Process'), (QUERY, 'Query'))
process_type = models.CharField(max_length=1, choices=TYPE_CHOICES, default=QUERY, )
created = models.DateTimeField('date created', auto_now_add=True)
user = models.ForeignKey(User, null=True, related_name="submitter")
image_data = models.BinaryField(null=True)
script = JSONField(blank=True, null=True)
results_metadata = models.TextField(default="")
results_available = models.BooleanField(default=False)
federated = models.BooleanField(default=False)
class IndexerQuery(models.Model):
parent_query = models.ForeignKey(DVAPQL)
created = models.DateTimeField('date created', auto_now_add=True)
count = models.IntegerField(default=20)
algorithm = models.CharField(max_length=500,default="")
indexer = models.ForeignKey(CustomIndexer,null=True)
excluded_index_entries_pk = ArrayField(models.IntegerField(), default=[])
vector = models.BinaryField(null=True)
results = models.BooleanField(default=False)
metadata = models.TextField(default="")
source_filter_json = JSONField(blank=True,null=True)
approximate = models.BooleanField(default=False)
user = models.ForeignKey(User, null=True)
class Video(models.Model):
name = models.CharField(max_length=500,default="")
length_in_seconds = models.IntegerField(default=0)
height = models.IntegerField(default=0)
width = models.IntegerField(default=0)
metadata = models.TextField(default="")
frames = models.IntegerField(default=0)
created = models.DateTimeField('date created', auto_now_add=True)
description = models.TextField(default="")
uploaded = models.BooleanField(default=False)
dataset = models.BooleanField(default=False)
uploader = models.ForeignKey(User,null=True)
segments = models.IntegerField(default=0)
url = models.TextField(default="")
youtube_video = models.BooleanField(default=False)
query = models.BooleanField(default=False)
parent_process = models.ForeignKey(DVAPQL,null=True)
vdn_dataset = models.ForeignKey(VDNDataset, null=True)
def __unicode__(self):
return u'{}'.format(self.name)
class Clusters(models.Model):
excluded_index_entries_pk = ArrayField(models.IntegerField(), default=[])
included_index_entries_pk = ArrayField(models.IntegerField(), default=[])
train_fraction = models.FloatField(default=0.8) # by default use 80% of data for training
algorithm = models.CharField(max_length=50,default='LOPQ') # LOPQ
indexer_algorithm = models.CharField(max_length=50)
cluster_count = models.IntegerField(default=0)
pca_file_name = models.CharField(max_length=200,default="")
model_file_name = models.CharField(max_length=200, default="")
components = models.IntegerField(default=64) # computer 64 principal components
started = models.DateTimeField('date created', auto_now_add=True)
completed = models.BooleanField(default=False)
m = models.IntegerField(default=16)
v = models.IntegerField(default=16)
sub = models.IntegerField(default=256)
class TEvent(models.Model):
started = models.BooleanField(default=False)
completed = models.BooleanField(default=False)
errored = models.BooleanField(default=False)
error_message = models.TextField(default="")
video = models.ForeignKey(Video, null=True)
operation = models.CharField(max_length=100, default="")
created = models.DateTimeField('date created', auto_now_add=True)
start_ts = models.DateTimeField('date started', null=True)
seconds = models.FloatField(default=-1)
arguments = JSONField(blank=True,null=True)
task_id = models.TextField(null=True)
parent = models.ForeignKey('self',null=True)
parent_process = models.ForeignKey(DVAPQL,null=True)
class Frame(models.Model):
video = models.ForeignKey(Video)
frame_index = models.IntegerField()
name = models.CharField(max_length=200,null=True)
subdir = models.TextField(default="") # Retains information if the source is a dataset for labeling
h = models.IntegerField(default=0)
w = models.IntegerField(default=0)
t = models.FloatField(null=True) # time in seconds for keyframes
keyframe = models.BooleanField(default=False) # is this a key frame for a video?
segment_index = models.IntegerField(null=True)
class Meta:
unique_together = (("video", "frame_index"),)
def __unicode__(self):
return u'{}:{}'.format(self.video_id, self.frame_index)
class Segment(models.Model):
"""
A video segment useful for parallel dense decoding+processing as well as streaming
"""
video = models.ForeignKey(Video)
segment_index = models.IntegerField()
start_time = models.FloatField(default=0.0)
end_time = models.FloatField(default=0.0)
metadata = models.TextField(default="{}")
frame_count = models.IntegerField(default=0)
start_index = models.IntegerField(default=0)
start_frame = models.ForeignKey(Frame,null=True,related_name="segment_start")
end_frame = models.ForeignKey(Frame, null=True,related_name="segment_end")
class Meta:
unique_together = (("video", "segment_index"),)
def __unicode__(self):
return u'{}:{}'.format(self.video_id, self.segment_index)
class Region(models.Model):
"""
Any 2D region over an image.
Detections & Transforms have an associated image data.
"""
ANNOTATION = 'A'
DETECTION = 'D'
SEGMENTATION = 'S'
TRANSFORM = 'T'
POLYGON = 'P'
REGION_TYPES = (
(ANNOTATION, 'Annotation'),
(DETECTION, 'Detection'),
(POLYGON, 'Polygon'),
(SEGMENTATION, 'Segmentation'),
(TRANSFORM, 'Transform'),
)
region_type = models.CharField(max_length=1,choices=REGION_TYPES)
video = models.ForeignKey(Video)
user = models.ForeignKey(User,null=True)
frame = models.ForeignKey(Frame,null=True)
event = models.ForeignKey(TEvent, null=True) # TEvent that created this region
parent_frame_index = models.IntegerField(default=-1)
parent_segment_index = models.IntegerField(default=-1,null=True)
text = models.TextField(default="")
metadata = JSONField(blank=True,null=True)
full_frame = models.BooleanField(default=False)
x = models.IntegerField(default=0)
y = models.IntegerField(default=0)
h = models.IntegerField(default=0)
w = models.IntegerField(default=0)
polygon_points_json = models.TextField(default="[]")
created = models.DateTimeField('date created', auto_now_add=True)
vdn_dataset = models.ForeignKey(VDNDataset,null=True)
vdn_key = models.IntegerField(default=-1)
object_name = models.CharField(max_length=100)
confidence = models.FloatField(default=0.0)
materialized = models.BooleanField(default=False)
png = models.BooleanField(default=False)
def clean(self):
if self.parent_frame_index == -1 or self.parent_frame_index is None:
self.parent_frame_index = self.frame.frame_index
if self.parent_segment_index == -1 or self.parent_segment_index is None:
self.parent_segment_index = self.frame.segment_index
def save(self, *args, **kwargs):
if self.parent_frame_index == -1 or self.parent_frame_index is None:
self.parent_frame_index = self.frame.frame_index
if self.parent_segment_index == -1 or self.parent_segment_index is None:
self.parent_segment_index = self.frame.segment_index
super(Region, self).save(*args, **kwargs)
class QueryResults(models.Model):
query = models.ForeignKey(DVAPQL)
indexerquery = models.ForeignKey(IndexerQuery)
video = models.ForeignKey(Video)
frame = models.ForeignKey(Frame)
detection = models.ForeignKey(Region,null=True)
rank = models.IntegerField()
algorithm = models.CharField(max_length=100)
distance = models.FloatField(default=0.0)
class FederatedQueryResults(models.Model):
query = models.ForeignKey(DVAPQL)
rank = models.IntegerField()
user = models.ForeignKey(User)
server_name = models.CharField(max_length=100)
algorithm = models.CharField(max_length=100)
distance = models.FloatField(default=0.0)
results_metadata = models.TextField(default="")
results_available = models.BooleanField(default=False)
result_image_data = models.BinaryField(null=True)
class ClusterCodes(models.Model):
clusters = models.ForeignKey(Clusters)
video = models.ForeignKey(Video)
frame = models.ForeignKey(Frame)
detection = models.ForeignKey(Region,null=True)
fine = ArrayField(models.IntegerField(), default=[])
coarse = ArrayField(models.IntegerField(), default=[])
coarse_text = models.TextField(default="") # check if postgres built in text search
fine_text = models.TextField(default="") # check if postgres built in text search can be used
searcher_index = models.IntegerField()
class Meta:
unique_together = ('searcher_index', 'clusters')
index_together = [["clusters", "searcher_index"],] # Very important manually verify in Postgres
class IndexEntries(models.Model):
video = models.ForeignKey(Video)
features_file_name = models.CharField(max_length=100)
entries_file_name = models.CharField(max_length=100)
algorithm = models.CharField(max_length=100)
indexer = models.ForeignKey(CustomIndexer, null=True)
detection_name = models.CharField(max_length=100)
count = models.IntegerField()
approximate = models.BooleanField(default=False)
contains_frames = models.BooleanField(default=False)
contains_detections = models.BooleanField(default=False)
created = models.DateTimeField('date created', auto_now_add=True)
source = models.ForeignKey(TEvent, null=True)
class Meta:
unique_together = ('video', 'features_file_name',)
def __unicode__(self):
return "{} in {} index by {}".format(self.detection_name, self.algorithm, self.video.name)
class CustomDetector(models.Model):
name = models.CharField(max_length=100)
algorithm = models.CharField(max_length=100,default="")
model_filename = models.CharField(max_length=200,default="")
vdn_detector = models.ForeignKey(VDNDetector,null=True)
arguments = models.TextField(default="")
phase_1_log = models.TextField(default="")
phase_2_log = models.TextField(default="")
class_distribution = models.TextField(default="")
class_names = models.TextField(default="")
frames_count = models.IntegerField(default=0)
boxes_count = models.IntegerField(default=0)
source = models.ForeignKey(TEvent, null=True)
trained = models.BooleanField(default=False)
created = models.DateTimeField('date created', auto_now_add=True)
class Tube(models.Model):
"""
A tube is a collection of sequential frames / regions that track a certain object
or describe a specific scene
"""
video = models.ForeignKey(Video,null=True)
frame_level = models.BooleanField(default=False)
start_frame_index = models.IntegerField()
end_frame_index = models.IntegerField()
start_frame = models.ForeignKey(Frame,null=True,related_name="start_frame")
end_frame = models.ForeignKey(Frame,null=True,related_name="end_frame")
start_region = models.ForeignKey(Region,null=True,related_name="start_region")
end_region = models.ForeignKey(Region,null=True,related_name="end_region")
text = models.TextField(default="")
metadata = JSONField(blank=True,null=True)
source = models.ForeignKey(TEvent,null=True)
class Label(models.Model):
name = models.CharField(max_length=200)
set = models.CharField(max_length=200,default="")
metadata = JSONField(blank=True,null=True)
text = models.TextField(null=True,blank=True)
created = models.DateTimeField('date created', auto_now_add=True)
class Meta:
unique_together = (("name", "set"),)
def __unicode__(self):
return u'{}:{}'.format(self.name, self.set)
class FrameLabel(models.Model):
video = models.ForeignKey(Video,null=True)
frame_index = models.IntegerField(default=-1)
segment_index = models.IntegerField(null=True)
frame = models.ForeignKey(Frame)
label = models.ForeignKey(Label)
event = models.ForeignKey(TEvent,null=True)
def clean(self):
if self.frame_index == -1 or self.frame_index is None:
self.frame_index = self.frame.frame_index
if self.segment_index == -1 or self.segment_index is None:
self.segment_index = self.frame.segment_index
def save(self, *args, **kwargs):
if self.frame_index == -1 or self.frame_index is None:
self.frame_index = self.frame.frame_index
if self.segment_index == -1 or self.segment_index is None:
self.segment_index = self.frame.segment_index
super(FrameLabel, self).save(*args, **kwargs)
class RegionLabel(models.Model):
video = models.ForeignKey(Video,null=True)
frame = models.ForeignKey(Frame,null=True)
frame_index = models.IntegerField(default=-1)
segment_index = models.IntegerField(null=True)
region = models.ForeignKey(Region)
label = models.ForeignKey(Label)
event = models.ForeignKey(TEvent,null=True)
def clean(self):
if self.frame_index == -1 or self.frame_index is None:
self.frame_index = self.frame.frame_index
if self.segment_index == -1 or self.segment_index is None:
self.segment_index = self.frame.segment_index
def save(self, *args, **kwargs):
if self.frame_index == -1 or self.frame_index is None:
self.frame_index = self.frame.frame_index
if self.segment_index == -1 or self.segment_index is None:
self.segment_index = self.frame.segment_index
super(RegionLabel, self).save(*args, **kwargs)
class SegmentLabel(models.Model):
video = models.ForeignKey(Video,null=True)
segment_index = models.IntegerField(default=-1)
segment = models.ForeignKey(Segment)
label = models.ForeignKey(Label)
event = models.ForeignKey(TEvent, null=True)
def clean(self):
if self.segment_index == -1 or self.segment_index is None:
self.segment_index = self.segment.segment_index
def save(self, *args, **kwargs):
if self.segment_index == -1 or self.segment_index is None:
self.segment_index = self.segment.segment_index
super(SegmentLabel, self).save(*args, **kwargs)
class TubeLabel(models.Model):
video = models.ForeignKey(Video,null=True)
tube = models.ForeignKey(Tube)
label = models.ForeignKey(Label)
event = models.ForeignKey(TEvent, null=True)
class VideoLabel(models.Model):
video = models.ForeignKey(Video)
label = models.ForeignKey(Label)
event = models.ForeignKey(TEvent, null=True)
class DeletedVideo(models.Model):
name = models.CharField(max_length=500,default="")
description = models.TextField(default="")
uploader = models.ForeignKey(User,null=True,related_name="user_uploader")
url = models.TextField(default="")
deleter = models.ForeignKey(User,related_name="user_deleter",null=True)
original_pk = models.IntegerField()
def __unicode__(self):
return u'Deleted {}'.format(self.name)
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03bd72acbee9ebd416f127863815001d43529260 | 03cd08ce32a2c1b3b8f4563d01a5e55b974f2c64 | /57.py | 1166a9b99985e44d26ae86fb512bd0f51268d76e | [] | no_license | joemeens/Pro | 46165be31673a6477fe7c9d2f90e12e75574daca | 19ce75ede8c7694623c26a91cf7cc6c9a94b673b | refs/heads/master | 2020-06-09T01:36:58.438553 | 2019-08-17T14:41:42 | 2019-08-17T14:41:42 | 193,344,489 | 0 | 3 | null | null | null | null | UTF-8 | Python | false | false | 163 | py | list1,char=map(str,input().split(" "))
count=0
for i in range(0,len(list1)):
if(list1[i]==char):
count=count+1
else:
continue
print(count)
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68f1edfca6e6c27a77da50e9bed255dc4783861d | f0d713996eb095bcdc701f3fab0a8110b8541cbb | /fyyJRDHcTe9REs4Ni_24.py | 3166469c0022b926678616faf843ff7e33b85cf2 | [] | no_license | daniel-reich/turbo-robot | feda6c0523bb83ab8954b6d06302bfec5b16ebdf | a7a25c63097674c0a81675eed7e6b763785f1c41 | refs/heads/main | 2023-03-26T01:55:14.210264 | 2021-03-23T16:08:01 | 2021-03-23T16:08:01 | 350,773,815 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,131 | py | """
Create a function that takes three arguments (first dictionary, second
dictionary, key) in order to:
1. Return the boolean `True` if both dictionaries have the same values for the same keys.
2. If the dictionaries don't match, return the string `"Not the same"`, or the string `"One's empty"` if only one of the dictionaries contains the given key.
### Examples
dict_first = { "sky": "temple", "horde": "orcs", "people": 12, "story": "fine", "sun": "bright" }
dict_second = { "people": 12, "sun": "star", "book": "bad" }
check(dict_first, dict_second, "horde") ➞ "One's empty"
check(dict_first, dict_second, "people") ➞ True
check(dict_first, dict_second, "sun") ➞ "Not the same"
### Notes
* Dictionaries are an unordered data type.
* Double quotes may be helpful.
* `KeyError` can occur when trying to access a dictionary key that doesn't exist.
"""
def check(d1, d2, k):
if k in d1 and k in d2:
if d1[k]==d2[k]:
return True
else:
return "Not the same"
elif (k in d1 and k not in d2) or (k not in d1 and k in d2):
return "One's empty"
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b498b8c9a09c4ad781755f8128413c37b8dd9431 | 124263fe47a7a990e46720ee9720dac23de53412 | /tests/test_transforms.py | 2feb96799180d30cfa718f5ad7b70e40b99c7bf6 | [
"MIT"
] | permissive | mahaling/pytorch-3dunet | 17cae1176a464ffa8a42f6eaee0d7f57b8402941 | 458985ebc766acdc7599f92f54f10becbc4d4b95 | refs/heads/master | 2023-08-03T12:26:07.103875 | 2020-06-24T22:30:57 | 2020-06-24T22:30:57 | 206,171,755 | 0 | 1 | MIT | 2020-04-19T04:31:38 | 2019-09-03T21:02:42 | Jupyter Notebook | UTF-8 | Python | false | false | 8,017 | py | import numpy as np
from augment.transforms import RandomLabelToAffinities, LabelToAffinities, Transformer, Relabel
class TestTransforms:
config = {'dtype': 'long'}
def test_random_label_to_boundary(self):
size = 20
label = _diagonal_label_volume(size)
transform = RandomLabelToAffinities(np.random.RandomState())
result = transform(label)
assert result.shape == (1,) + label.shape
def test_random_label_to_boundary_with_ignore(self):
size = 20
label = _diagonal_label_volume(size, init=-1)
transform = RandomLabelToAffinities(np.random.RandomState(), ignore_index=-1)
result = transform(label)
assert result.shape == (1,) + label.shape
assert -1 in np.unique(result)
def test_label_to_boundary(self):
size = 20
label = _diagonal_label_volume(size)
# this transform will produce 2 channels
transform = LabelToAffinities(offsets=(2, 4), aggregate_affinities=True)
result = transform(label)
assert result.shape == (2,) + label.shape
assert np.array_equal(np.unique(result), [0, 1])
def test_label_to_boundary_with_ignore(self):
size = 20
label = _diagonal_label_volume(size, init=-1)
transform = LabelToAffinities(offsets=(2, 4), ignore_index=-1, aggregate_affinities=True)
result = transform(label)
assert result.shape == (2,) + label.shape
assert np.array_equal(np.unique(result), [-1, 0, 1])
def test_label_to_boundary_no_aggregate(self):
size = 20
label = _diagonal_label_volume(size)
# this transform will produce 6 channels
transform = LabelToAffinities(offsets=(2, 4), aggregate_affinities=False)
result = transform(label)
assert result.shape == (6,) + label.shape
assert np.array_equal(np.unique(result), [0, 1])
def test_relabel(self):
label = np.array([[10, 10, 10], [0, 0, 0], [5, 5, 5]])
r = Relabel()
result = r(label)
assert np.array_equal(result, np.array([[2, 2, 2], [0, 0, 0], [1, 1, 1]]))
def test_BaseTransformer(self):
config = {
'raw': [{'name': 'Normalize'}, {'name': 'ToTensor', 'expand_dims': True}],
'label': [{'name': 'ToTensor', 'expand_dims': False, 'dtype': 'long'}],
'weight': [{'name': 'ToTensor', 'expand_dims': False}]
}
transformer = Transformer(config, 0, 1)
raw_transforms = transformer.raw_transform().transforms
assert raw_transforms[0].mean == 0
assert raw_transforms[0].std == 1
assert raw_transforms[1].expand_dims
label_transforms = transformer.label_transform().transforms
assert not label_transforms[0].expand_dims
assert label_transforms[0].dtype == 'long'
weight_transforms = transformer.weight_transform().transforms
assert not weight_transforms[0].expand_dims
def test_StandardTransformer(self):
config = {
'raw': [
{'name': 'Normalize'},
{'name': 'RandomContrast', 'execution_probability': 0.5},
{'name': 'RandomFlip'},
{'name': 'RandomRotate90'},
{'name': 'ToTensor', 'expand_dims': True}
],
'label': [
{'name': 'RandomFlip'},
{'name': 'RandomRotate90'},
{'name': 'ToTensor', 'expand_dims': False, 'dtype': 'long'}
]
}
transformer = Transformer(config, 0, 1)
raw_transforms = transformer.raw_transform().transforms
assert raw_transforms[0].mean == 0
assert raw_transforms[0].std == 1
assert raw_transforms[1].execution_probability == 0.5
assert raw_transforms[4].expand_dims
label_transforms = transformer.label_transform().transforms
assert len(label_transforms) == 3
def test_AnisotropicRotationTransformer(self):
config = {
'raw': [
{'name': 'Normalize'},
{'name': 'RandomContrast', 'execution_probability': 0.5},
{'name': 'RandomFlip'},
{'name': 'RandomRotate90'},
{'name': 'RandomRotate', 'angle_spectrum': 17, 'axes': [[2, 1]]},
{'name': 'ToTensor', 'expand_dims': True}
],
'label': [
{'name': 'RandomFlip'},
{'name': 'RandomRotate90'},
{'name': 'RandomRotate', 'angle_spectrum': 17, 'axes': [[2, 1]]},
{'name': 'ToTensor', 'expand_dims': False, 'dtype': 'long'}
]
}
transformer = Transformer(config, 0, 1)
raw_transforms = transformer.raw_transform().transforms
assert raw_transforms[0].mean == 0
assert raw_transforms[0].std == 1
assert raw_transforms[1].execution_probability == 0.5
assert raw_transforms[4].angle_spectrum == 17
assert raw_transforms[4].axes == [[2, 1]]
label_transforms = transformer.label_transform().transforms
assert len(label_transforms) == 4
def test_LabelToBoundaryTransformer(self):
config = {
'raw': [
{'name': 'Normalize'},
{'name': 'RandomContrast', 'execution_probability': 0.5},
{'name': 'RandomFlip'},
{'name': 'RandomRotate90'},
{'name': 'RandomRotate', 'angle_spectrum': 17, 'axes': [[2, 1]], 'mode': 'reflect'},
{'name': 'ToTensor', 'expand_dims': True}
],
'label': [
{'name': 'RandomFlip'},
{'name': 'RandomRotate90'},
{'name': 'RandomRotate', 'angle_spectrum': 17, 'axes': [[2, 1]], 'mode': 'reflect'},
{'name': 'LabelToAffinities', 'offsets': [2, 4, 6, 8]},
{'name': 'ToTensor', 'expand_dims': False, 'dtype': 'long'}
]
}
transformer = Transformer(config, 0, 1)
raw_transforms = transformer.raw_transform().transforms
assert raw_transforms[0].mean == 0
assert raw_transforms[0].std == 1
assert raw_transforms[1].execution_probability == 0.5
assert raw_transforms[4].angle_spectrum == 17
assert raw_transforms[4].axes == [[2, 1]]
assert raw_transforms[4].mode == 'reflect'
label_transforms = transformer.label_transform().transforms
assert label_transforms[2].angle_spectrum == 17
assert label_transforms[2].axes == [[2, 1]]
assert label_transforms[2].mode == 'reflect'
# 3 conv kernels per offset
assert len(label_transforms[3].kernels) == 12
def test_RandomLabelToBoundaryTransformer(self):
config = {
'raw': [
{'name': 'Normalize'},
{'name': 'RandomContrast', 'execution_probability': 0.5},
{'name': 'RandomFlip'},
{'name': 'RandomRotate90'},
{'name': 'RandomRotate', 'angle_spectrum': 17, 'axes': [[2, 1]], 'mode': 'reflect'},
{'name': 'ToTensor', 'expand_dims': True}
],
'label': [
{'name': 'RandomFlip'},
{'name': 'RandomRotate90'},
{'name': 'RandomRotate', 'angle_spectrum': 17, 'axes': [[2, 1]], 'mode': 'reflect'},
{'name': 'RandomLabelToAffinities', 'max_offset': 4},
{'name': 'ToTensor', 'expand_dims': False, 'dtype': 'long'}
]
}
transformer = Transformer(config, 0, 1)
label_transforms = transformer.label_transform().transforms
assert label_transforms[3].offsets == (1, 2, 3, 4)
def _diagonal_label_volume(size, init=1):
label = init * np.ones((size, size, size), dtype=np.int)
for i in range(size):
for j in range(size):
for k in range(size):
if i + j > 2 * k:
label[i, j, k] = 3
return label
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87e3268453ed0e8a2c04a49d0f998820a0e3cb29 | 54df8336b50e8f2d7dbe353f0bc51a2b3489095f | /Django/Django_Old/website5/website5/wsgi.py | 71a8098849cbf50e1e57b301cfdf6990808ae5eb | [] | no_license | SurendraKumarAratikatla/MyLenovolapCodes1 | 42d5bb7a14bfdf8d773ee60719380ee28ff4947a | 12c56200fcfd3e5229bfeec209fd03b5fc35b823 | refs/heads/master | 2023-06-17T15:44:18.312398 | 2021-07-19T10:28:11 | 2021-07-19T10:28:11 | 387,358,266 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 393 | py | """
WSGI config for website5 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/3.0/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'website5.settings')
application = get_wsgi_application()
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147a3b48b75a173b5696e855ad8c6cbfda149d07 | f8847b16bb44c54b00f40d8d749a5339490c0dd8 | /coderbyte/simple_SAT.py | 8278ecf41238056a0acda1cf9abfd79d45e4f399 | [] | no_license | sanjitroy1992/PythonCodingTraining | 60478829697e6837abf7cfeff4724b38c57328ac | 0706769084d60a397366d41bb87add8d53ba8eb3 | refs/heads/master | 2021-01-02T02:15:10.609349 | 2020-07-09T18:06:41 | 2020-07-09T18:06:41 | 239,450,113 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,799 | py | # -*- coding: utf-8 -*-
"""
Have the function SimpleSAT(str) read str
str -letters, parenthesis,logical operators and tilde's
representing a Boolean formula.
For example: str may be "(a&b)|c"
which means (a AND b) OR c. Your program should output the string yes if
there is some arrangement of replacing the letters with TRUE or FALSE in
such a way that the formula equates to TRUE. If there is no possible way
of assigning TRUE or FALSE to the letters, then your program should output
the string no.
n the example above, your program would return yes because
a=TRUE, b=TRUE and c=FALSE would make the formula TRUE.
Another example:
if str is "((a&c)&~a)" which means ((a AND c) AND NOT a) then your program
should output no because it is not possible to assign TRUE or FALSE values
to the letters to produce a TRUE output.
Input:"(a&b&c)|~a"
Output:yes
Input:"a&(b|c)&~b&~c"
Output:no
"""
from copy import deepcopy
def SimpleSAT(string):
list_string = list(string)
alpha_hash = frozenset(x for x in list_string if x.isalpha())
for item in range(len(list_string)):
if list_string[item] == "&":
list_string[item] = " and "
if list_string[item] == "|":
list_string[item] = " or "
if list_string[item] == "~":
list_string[item] = " not "
pos = [list_string]
for alpha in alpha_hash:
pos1 = deepcopy(pos)
pos2 = deepcopy(pos)
for i in range(len(pos)):
for j in range(len(pos[i])):
if pos[i][j] == alpha:
pos1[i][j] = "True"
pos2[i][j] = "False"
pos = pos1 + pos2
pos = tuple("".join(x) for x in pos)
for cond in pos:
if eval(cond) == True:
return "yes"
return "no"
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ad914d6fcb5ac7e21fc1a70369a62bc31ce69837 | 50948d4cb10dcb1cc9bc0355918478fb2841322a | /azure-mgmt-media/azure/mgmt/media/models/track_property_condition_py3.py | a7ec9f92c376b6ac870a2c6bebc270ff05c04d02 | [
"MIT"
] | permissive | xiafu-msft/azure-sdk-for-python | de9cd680b39962702b629a8e94726bb4ab261594 | 4d9560cfd519ee60667f3cc2f5295a58c18625db | refs/heads/master | 2023-08-12T20:36:24.284497 | 2019-05-22T00:55:16 | 2019-05-22T00:55:16 | 187,986,993 | 1 | 0 | MIT | 2020-10-02T01:17:02 | 2019-05-22T07:33:46 | Python | UTF-8 | Python | false | false | 1,670 | py | # 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 msrest.serialization import Model
class TrackPropertyCondition(Model):
"""Class to specify one track property condition.
All required parameters must be populated in order to send to Azure.
:param property: Required. Track property type. Possible values include:
'Unknown', 'FourCC'
:type property: str or ~azure.mgmt.media.models.TrackPropertyType
:param operation: Required. Track property condition operation. Possible
values include: 'Unknown', 'Equal'
:type operation: str or
~azure.mgmt.media.models.TrackPropertyCompareOperation
:param value: Track property value
:type value: str
"""
_validation = {
'property': {'required': True},
'operation': {'required': True},
}
_attribute_map = {
'property': {'key': 'property', 'type': 'str'},
'operation': {'key': 'operation', 'type': 'str'},
'value': {'key': 'value', 'type': 'str'},
}
def __init__(self, *, property, operation, value: str=None, **kwargs) -> None:
super(TrackPropertyCondition, self).__init__(**kwargs)
self.property = property
self.operation = operation
self.value = value
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41f3d213d8de197a868a6befcdca1ec937d23fdb | 37c38b97d0a4b8098ec3c35b7122afb1fbb9eac9 | /base/给定深度求二叉树数量.py | cabac049b56bb4b6ceeb1e5de89412a342b2764c | [] | no_license | lionheartStark/sword_towards_offer | 8c2f9015a427317375d53eee982d630ffd4fa9c0 | cb3587242195bb3f2626231af2da13b90945a4d5 | refs/heads/master | 2022-12-02T20:50:18.789828 | 2020-08-23T02:00:48 | 2020-08-23T02:00:48 | 266,257,109 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 857 | py | from collections import Counter
import math
P = 10 ** 9 + 7
def C(m, n):
p = P
def power(x, y): # 求x的y次方
p = P
res = 1
while y:
if y % 2 != 0:
res *= (x % p)
y >>= 1
x *= (x % p)
return res
a = (math.factorial(n)) % p
b = (power(math.factorial(m), (p - 2))) % p
c = (power(math.factorial(n - m), (p - 2))) % p
return (a * b * c % p)
def get_kind_num(deep_list):
num_count = Counter(deep_list)
print(num_count)
deep = 1
ans = 1
while True:
this_layer_position = num_count[deep - 1] * 2
if deep not in num_count:
break
this_layer_node = num_count[deep]
ans *= C(this_layer_node, this_layer_position) % P
deep += 1
print(ans % P)
get_kind_num([1, 0, 2, 2])
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] | |
ba80376f1b882c269d2097ecb7dce7da36317c2c | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/nouns/_tinnier.py | 06b2a756d6b4099f9dfb2b0cf5636845c39e2b0c | [
"MIT"
] | permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 233 | py |
from xai.brain.wordbase.nouns._tinny import _TINNY
#calss header
class _TINNIER(_TINNY, ):
def __init__(self,):
_TINNY.__init__(self)
self.name = "TINNIER"
self.specie = 'nouns'
self.basic = "tinny"
self.jsondata = {}
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194fd36566ce74d289ab2ce8a0bb14294384e04e | 03f0a82e829a5711a9165d8f7d3762ca0c1ceaea | /ahgl/apps/tournaments/migrations/0021_initialize_tournament_round_membership.py | 6a1e96321957a7922d485f0855b86f2d3c017d41 | [
"BSD-2-Clause"
] | permissive | day9tv/ahgl | 4d273a39e06334cc15eb12031de0a806366396b9 | 5e06cfecb28c153c1b83ef89112fc217897131cb | refs/heads/master | 2021-01-22T08:32:53.663312 | 2012-10-06T21:32:52 | 2012-10-06T21:32:52 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 15,962 | py | # encoding: utf-8
import datetime
from south.db import db
from south.v2 import DataMigration
from django.db import models
class Migration(DataMigration):
def forwards(self, orm):
"Write your forwards methods here."
for round_membership in orm.TeamRoundMembership.objects.all():
round_membership.tiebreaker = round_membership.team.game_wins.filter(match__published=True, match__tournament_round=round_membership.tournamentround).count() \
- round_membership.team.game_losses.filter(match__published=True, match__tournament_round=round_membership.tournamentround).count()
round_membership.wins = round_membership.team.match_wins.filter(published=True, tournament_round=round_membership.tournamentround).count()
round_membership.losses = round_membership.team.match_losses.filter(published=True, tournament_round=round_membership.tournamentround).count()
round_membership.save()
def backwards(self, orm):
"Write your backwards methods here."
models = {
'auth.group': {
'Meta': {'object_name': 'Group'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
'auth.permission': {
'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
'auth.user': {
'Meta': {'object_name': 'User'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'profiles.charity': {
'Meta': {'ordering': "('name',)", 'object_name': 'Charity'},
'desc': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'link': ('django.db.models.fields.URLField', [], {'max_length': '200', 'blank': 'True'}),
'logo': ('sorl.thumbnail.fields.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
'profiles.profile': {
'Meta': {'object_name': 'Profile'},
'autosubscribe': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'avatar': ('sorl.thumbnail.fields.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'bnet_profile': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'char_code': ('django.db.models.fields.PositiveSmallIntegerField', [], {'null': 'True', 'blank': 'True'}),
'char_name': ('django.db.models.fields.CharField', [], {'max_length': '20', 'blank': 'True'}),
'custom_thumb': ('sorl.thumbnail.fields.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'language': ('django.db.models.fields.CharField', [], {'default': "'en'", 'max_length': '10', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'photo': ('sorl.thumbnail.fields.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'post_count': ('django.db.models.fields.IntegerField', [], {'default': '0', 'blank': 'True'}),
'questions_answers': ('profiles.fields.HTMLField', [], {'attributes': '[]', 'blank': 'True', 'tags': "['ol', 'ul', 'li', 'strong', 'em', 'p']"}),
'race': ('django.db.models.fields.CharField', [], {'max_length': '1', 'null': 'True', 'blank': 'True'}),
'show_signatures': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'signature': ('django.db.models.fields.TextField', [], {'max_length': '1024', 'blank': 'True'}),
'signature_html': ('django.db.models.fields.TextField', [], {'max_length': '1054', 'blank': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50', 'db_index': 'True'}),
'time_zone': ('django.db.models.fields.FloatField', [], {'default': '3.0'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '70', 'blank': 'True'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}),
'website': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'})
},
'profiles.team': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('name', 'tournament'), ('slug', 'tournament'))", 'object_name': 'Team'},
'captain': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'captain_of'", 'null': 'True', 'to': "orm['profiles.Profile']"}),
'charity': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'teams'", 'null': 'True', 'to': "orm['profiles.Charity']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'losses': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'members': ('django.db.models.fields.related.ManyToManyField', [], {'blank': 'True', 'related_name': "'teams'", 'null': 'True', 'symmetrical': 'False', 'to': "orm['profiles.Profile']"}),
'motto': ('django.db.models.fields.CharField', [], {'max_length': '70', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'photo': ('sorl.thumbnail.fields.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'rank': ('django.db.models.fields.IntegerField', [], {}),
'seed': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50', 'db_index': 'True'}),
'tiebreaker': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'tournament': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'teams'", 'to': "orm['tournaments.Tournament']"}),
'wins': ('django.db.models.fields.IntegerField', [], {'default': '0'})
},
'tournaments.game': {
'Meta': {'ordering': "('order',)", 'unique_together': "(('order', 'match'),)", 'object_name': 'Game'},
'away_player': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'away_games'", 'null': 'True', 'to': "orm['profiles.Profile']"}),
'away_race': ('django.db.models.fields.CharField', [], {'max_length': '1', 'blank': 'True'}),
'forfeit': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'home_player': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'home_games'", 'null': 'True', 'to': "orm['profiles.Profile']"}),
'home_race': ('django.db.models.fields.CharField', [], {'max_length': '1', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_ace': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'loser': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'game_losses'", 'null': 'True', 'to': "orm['profiles.Profile']"}),
'loser_team': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'game_losses'", 'null': 'True', 'to': "orm['profiles.Team']"}),
'map': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tournaments.Map']"}),
'match': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'games'", 'to': "orm['tournaments.Match']"}),
'order': ('django.db.models.fields.PositiveSmallIntegerField', [], {}),
'replay': ('django.db.models.fields.files.FileField', [], {'max_length': '300', 'null': 'True', 'blank': 'True'}),
'vod': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'winner': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'game_wins'", 'null': 'True', 'to': "orm['profiles.Profile']"}),
'winner_team': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'game_wins'", 'null': 'True', 'to': "orm['profiles.Team']"})
},
'tournaments.map': {
'Meta': {'ordering': "('name',)", 'object_name': 'Map'},
'name': ('django.db.models.fields.CharField', [], {'max_length': '50', 'primary_key': 'True'}),
'photo': ('sorl.thumbnail.fields.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'})
},
'tournaments.match': {
'Meta': {'object_name': 'Match'},
'away_submitted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'away_team': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'away_matches'", 'to': "orm['profiles.Team']"}),
'creation_date': ('django.db.models.fields.DateField', [], {}),
'home_submitted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'home_team': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'home_matches'", 'to': "orm['profiles.Team']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'loser': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'match_losses'", 'null': 'True', 'to': "orm['profiles.Team']"}),
'publish_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}),
'published': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'referee': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['profiles.Profile']", 'null': 'True', 'blank': 'True'}),
'tournament': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'matches'", 'to': "orm['tournaments.Tournament']"}),
'tournament_round': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'matches'", 'to': "orm['tournaments.TournamentRound']"}),
'winner': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'match_wins'", 'null': 'True', 'to': "orm['profiles.Team']"})
},
'tournaments.teamroundmembership': {
'Meta': {'unique_together': "(('tournamentround', 'team'),)", 'object_name': 'TeamRoundMembership', 'db_table': "'tournaments_tournamentround_teams'"},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'losses': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'team': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['profiles.Team']"}),
'tiebreaker': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'tournamentround': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tournaments.TournamentRound']"}),
'wins': ('django.db.models.fields.IntegerField', [], {'default': '0'})
},
'tournaments.tournament': {
'Meta': {'object_name': 'Tournament'},
'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'featured_game': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['tournaments.Game']", 'null': 'True', 'blank': 'True'}),
'games_per_match': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '5'}),
'map_pool': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['tournaments.Map']", 'symmetrical': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50', 'primary_key': 'True', 'db_index': 'True'})
},
'tournaments.tournamentround': {
'Meta': {'ordering': "('stage', 'name')", 'object_name': 'TournamentRound'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '40'}),
'stage': ('django.db.models.fields.IntegerField', [], {}),
'structure': ('django.db.models.fields.CharField', [], {'default': "'G'", 'max_length': '1'}),
'teams': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "'rounds'", 'symmetrical': 'False', 'through': "orm['tournaments.TeamRoundMembership']", 'to': "orm['profiles.Team']"}),
'tournament': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'rounds'", 'to': "orm['tournaments.Tournament']"})
}
}
complete_apps = ['tournaments']
| [
"[email protected]"
] | |
1127eed5abea467501f705f921ff3f1cd05dc740 | 221cada2354556fbb969f25ddd3079542904ef5d | /AlgoExpert/caesar_cipher.py | 7314c35c71a6238f3506492b33946b5d8e01b0a4 | [] | no_license | syzdemonhunter/Coding_Exercises | 4b09e1a7dad7d1e3d4d4ae27e6e006732ffdcb1d | ca71572677d2b2a2aed94bb60d6ec88cc486a7f3 | refs/heads/master | 2020-05-24T11:19:35.019543 | 2019-11-22T20:08:32 | 2019-11-22T20:08:32 | 187,245,394 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 256 | py | # T: O(n)
# S: O(n)
def caesarCipherEncryptor(string, key):
result = ''
alphabet = 'abcdefghijklmnopqrstuvwxyz'
for c in string.lower():
idx = (alphabet.index(c) + key) % len(alphabet)
result += alphabet[idx]
return result | [
"[email protected]"
] | |
8e1c8bb1a77f985bf829a63e10e9fd3014230d98 | a743e734ce6d9d4c83e867e4dec0734f7fd7ce4a | /langid.py | 6b6113185b480f87c255fd8cef3b84f7772eacda | [] | no_license | vchahun/langid.py | 2a33670efe4879a9e87572a5b03987971db0ef4c | b6114ca3dd137b1f7b3ad0df5c6678830c30924c | refs/heads/master | 2021-01-17T22:41:37.317396 | 2012-05-22T02:21:49 | 2012-05-22T02:21:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,714,171 | py | #!/usr/bin/env python
"""
langid.py -
Language Identifier by Marco Lui April 2011
Based on research by Marco Lui and Tim Baldwin.
Copyright 2011 Marco Lui <[email protected]>. All rights reserved.
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 COPYRIGHT HOLDER ``AS IS'' AND ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those of the
authors and should not be interpreted as representing official policies, either expressed
or implied, of the copyright holder.
"""
# Defaults for inbuilt server
HOST = None #leave as none for auto-detect
PORT = 9008
FORCE_NATIVE = False
FORCE_WSGIREF = False
import itertools
import array
import base64
import bz2
import json
import optparse
import logging
from math import log
from cPickle import loads, dumps
from wsgiref.simple_server import make_server
from wsgiref.util import shift_path_info
from urlparse import parse_qs
from collections import defaultdict
logger = logging.getLogger(__name__)
model_loaded = False
_full_model = None
model="""
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
"""
def tokenize(text, arr):
"""
Tokenize text into a feature vector stored in arr.
"""
# Convert the text to a sequence of ascii values
ords = map(ord, text)
# Count the number of times we enter each state
state = 0
statecount = defaultdict(int)
for letter in ords:
state = tk_nextmove[(state << 8) + letter]
statecount[state] += 1
# Update all the productions corresponding to the state
for state in statecount:
for index in tk_output.get(state, []):
arr[index] += statecount[state]
return arr
try:
if FORCE_NATIVE: raise ImportError
# Numpy implementation
import numpy as np
def unpack(data):
"""
Unpack a model that has been compressed into a string
NOTE: nb_ptc and nb_pc are array.array('f') instances.
nb_ptc is packed into a 1-dimensional array, each term is represented by
len(nb_pc) continuous entries
"""
global nb_ptc, nb_pc, nb_numfeats, nb_classes, tk_nextmove, tk_output, model_loaded
model = loads(bz2.decompress(base64.b64decode(data)))
nb_ptc, nb_pc, nb_classes, tk_nextmove, tk_output = model
nb_numfeats = len(nb_ptc) / len(nb_pc)
# reconstruct pc and ptc
nb_pc = np.array(nb_pc)
nb_ptc = np.array(nb_ptc).reshape(len(nb_ptc)/len(nb_pc), len(nb_pc))
model_loaded = True
def set_languages(langs):
global nb_ptc, nb_pc, nb_numfeats, nb_classes
global _full_model
logger.debug("restricting languages to: %s", langs)
# Maintain a reference to the full model, in case we change our language set
# multiple times.
if _full_model is None:
_full_model = nb_ptc, nb_pc, nb_numfeats, nb_classes
else:
nb_ptc, nb_pc, nb_numfeats, nb_classes = _full_model
# We were passed a restricted set of languages. Trim the arrays accordingly
# to speed up processing.
for lang in langs:
if lang not in nb_classes:
raise ValueError, "Unknown language code %s" % lang
subset_mask = np.fromiter((l in langs for l in nb_classes), dtype=bool)
nb_classes = [ c for c in nb_classes if c in langs ]
nb_ptc = nb_ptc[:,subset_mask]
nb_pc = nb_pc[subset_mask]
__logfac = {}
def logfac(a):
if a not in __logfac:
__logfac[a] = np.sum(np.log(np.arange(1,a+1)))
return __logfac[a]
logfac = np.frompyfunc(logfac, 1, 1)
def nb_classify(fv):
# compute the log-factorial of each element of the vector
logfv = logfac(fv).astype(float)
# compute the probability of the document given each class
pdc = np.dot(fv,nb_ptc) - logfv.sum()
# compute the probability of the document in each class
pd = pdc + nb_pc
# select the most likely class
cl = np.argmax(pd)
# turn the pd into a probability distribution
pd /= pd.sum()
return cl, pd[cl]
logger.debug('using numpy implementation')
__USE_NUMPY__ = True
except ImportError:
# Pure python implementation
# This is a stub for a potential future numpy-less implementation.
# I will not implement this unless there is a clear demand for it.
raise NotImplementedError, "langid.py needs numpy to run - please contact the author if you need to use langid.py without numpy"
def unpack(data):
"""
Unpack a model that has been compressed into a string
NOTE: nb_ptc and nb_pc are array.array('f') instances.
nb_ptc is packed into a 1-dimensional array, each term is represented by
len(nb_pc) continuous entries
"""
global nb_ptc, nb_pc, nb_numfeats, nb_classes, tk_nextmove, tk_output
model = loads(bz2.decompress(base64.b64decode(data)))
nb_ptc, nb_pc, nb_classes, tk_nextmove, tk_output = model
nb_numfeats = len(nb_ptc) / len(nb_pc)
def nb_classify(fv):
raise NotImplementedError, "don't have pure python implementation yet"
logger.debug('using python native implementation')
__USE_NUMPY__ = False
def classify(instance):
"""
Classify an instance.
"""
if isinstance(instance, unicode):
instance = instance.encode('utf8')
if __USE_NUMPY__:
fv = tokenize(instance,
np.zeros((nb_numfeats,), dtype='uint32'))
else:
fv = tokenize(instance,
array.array('L', itertootls.repeat(0, nb_numfeats)))
cl, conf = nb_classify(fv)
pred = nb_classes[cl]
return pred, conf
query_form = """
<html>
<head>
<title>Language Identifier</title>
</head>
<body>
<form method=post>
<textarea name="q" cols=40 rows=6></textarea></br>
<input type=submit value="submit">
</form>
</body>
</html>
"""
def application(environ, start_response):
"""
WSGI-compatible langid web service.
"""
try:
path = shift_path_info(environ)
except IndexError:
# Catch shift_path_info's failure to handle empty paths properly
path = ''
if path == 'detect':
data = None
# Extract the data component from different access methods
if environ['REQUEST_METHOD'] == 'PUT':
data = environ['wsgi.input'].read(int(environ['CONTENT_LENGTH']))
elif environ['REQUEST_METHOD'] == 'GET':
try:
data = parse_qs(environ['QUERY_STRING'])['q'][0]
except KeyError:
# No query, so we display a query interface instead
# TODO: Detect if this is coming from a browser!
status = '200 OK' # HTTP Status
headers = [('Content-type', 'text/html; charset=utf-8')] # HTTP Headers
start_response(status, headers)
return [query_form]
elif environ['REQUEST_METHOD'] == 'POST':
input_string = environ['wsgi.input'].read(int(environ['CONTENT_LENGTH']))
try:
data = parse_qs(input_string)['q'][0]
except KeyError:
# No key 'q', process the whole input instead
data = input_string
else:
# Unsupported method
status = '405 Method Not Allowed' # HTTP Status
response = {
'responseData': None,
'responseStatus': 405,
'responseDetails': '%s not allowed' % environ['REQUEST_METHOD']
}
if data is not None:
pred,conf = classify(data)
status = '200 OK' # HTTP Status
response = {
'responseData': {'language':pred, 'confidence':conf},
'responseStatus': 200,
'responseDetails': None,
}
else:
# Incorrect URL
status = '404 Not Found'
response = {'responseData': None, 'responseStatus':404, 'responseDetails':'Not found'}
headers = [('Content-type', 'text/javascript; charset=utf-8')] # HTTP Headers
start_response(status, headers)
return [json.dumps(response)]
if __name__ == "__main__":
parser = optparse.OptionParser()
parser.add_option('-s','--serve',action='store_true', default=False, dest='serve')
parser.add_option('--host', default=HOST, dest='host', help='host/ip to bind to')
parser.add_option('--port', default=PORT, dest='port', help='port to listen on')
parser.add_option('-v', action='count', dest='verbosity', help='increase verbosity (repeat for greater effect)')
parser.add_option('-m', dest='model', help='load model from file')
parser.add_option('-l', '--langs', dest='langs', help='comma-separated set of target ISO639 language codes (e.g en,de)')
parser.add_option('-r', '--remote',action="store_true", default=False, help='auto-detect IP address for remote access')
options, args = parser.parse_args()
if options.verbosity:
logging.basicConfig(level=max((5-options.verbosity)*10, 0))
else:
logging.basicConfig()
# unpack a model
if options.model:
try:
with open(options.model) as f:
unpack(f.read())
logger.info("Using external model: %s", options.model)
except IOError, e:
logger.warning("Failed to load %s: %s" % (options.model,e))
if not model_loaded:
unpack(model)
logger.info("Using internal model")
if options.langs:
langs = options.langs.split(",")
set_languages(langs)
if options.serve:
# from http://stackoverflow.com/questions/166506/finding-local-ip-addresses-in-python
if options.remote and options.host is None:
# resolve the external ip address
import socket
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("google.com",80))
hostname = s.getsockname()[0]
elif options.host is None:
# resolve the local hostname
import socket
hostname = socket.gethostbyname(socket.gethostname())
else:
hostname = options.host
try:
if FORCE_WSGIREF: raise ImportError
# Use fapws3 if available
import fapws._evwsgi as evwsgi
from fapws import base
evwsgi.start(hostname,str(options.port))
evwsgi.set_base_module(base)
evwsgi.wsgi_cb(("/", application))
evwsgi.set_debug(0)
evwsgi.run()
except ImportError:
print "Listening on %s:%d" % (hostname, int(options.port))
print "Press Ctrl+C to exit"
httpd = make_server(hostname, int(options.port), application)
try:
httpd.serve_forever()
except KeyboardInterrupt:
pass
else:
import sys
if sys.stdin.isatty():
# Interactive mode
while True:
try:
print ">>>",
text = raw_input()
except Exception:
break
print classify(text)
else:
# Redirected
print classify(sys.stdin.read())
else:
# Running as an imported module; unpack the internal model
unpack(model)
| [
"[email protected]"
] | |
45bd0cf8a38d5185666ede2b1762a07c0d96aa9b | 373164ead784f5fc57a02455482735e855377204 | /qmsgsent.py | 71458753597b25bf7c81bfc53cc28cfe2693211a | [
"MulanPSL-2.0",
"LicenseRef-scancode-mulanpsl-2.0-en",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | Zichen3317/demo21-epicfree-game | 1f9207c15953553a01a7ffb42706d7fc5f12a6de | 693a2fca4a8dd51f207a57362f1d0b35432be210 | refs/heads/master | 2023-05-11T12:02:04.091009 | 2021-05-30T00:45:46 | 2021-05-30T00:45:46 | 372,104,834 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 630 | py | # author： Zichen
# date: 2021-02-02
#vision: 1.0
# instruction： 利用Qmsg酱发送信息模块
from requests import post as requests_post
import traceback
def sent(qmsgkey, content):
'''
用于向qmsg酱发送请求及内容的函数
参数：
qmsgkey
content 需要发送的内容
'''
headers = {'Content-Type': 'application/json;charset=utf-8'}
api_url = "https://qmsg.zendee.cn/send/%s?msg= %s" % (qmsgkey, content)
try:
r = requests_post(api_url, headers=headers).content
print("[Qmsg]已发送√")
except:
traceback.print_exc()
| [
"[email protected]"
] | |
2602b369ea11dc23020db003908b2ca130f25a69 | 7e2456fb2ee301001d0629c5f74029e1a893c0f0 | /tests/treas_test.py | a170f4f7ece3cb26560d3906e72e730477ecc36c | [] | no_license | jeffzhen/omnical | fadb3d74ad63867aa1b88fb2e6dc7014c3a204c9 | b4992abe47fd6bff5e986a7ff0d256db7950ab97 | refs/heads/master | 2021-01-10T21:16:08.402219 | 2015-10-21T23:08:30 | 2015-10-21T23:08:30 | 17,956,510 | 3 | 1 | null | null | null | null | UTF-8 | Python | false | false | 5,174 | py | import unittest, omnical._omnical as _O
import random
import numpy as np
import aipy as ap
import numpy.linalg as la
import commands, os, time, math, ephem, shutil
import omnical.calibration_omni as omni
print "#Omnical Version %s#"%omni.__version__
class TestTreasure(unittest.TestCase):
def test_IO(self):
nTime = 3
nFrequency = 5
shutil.rmtree(os.path.dirname(os.path.realpath(__file__)) + '/test.treasure', ignore_errors = True)
shutil.rmtree(os.path.dirname(os.path.realpath(__file__)) + '/test2.treasure', ignore_errors = True)
treasure = omni.Treasure(os.path.dirname(os.path.realpath(__file__)) + '/test.treasure', nlst = nTime, nfreq = nFrequency)
treasure.add_coin(('xx', np.array([0,2,3])))
treasure.add_coin(('xx', np.array([1,2,3])))
self.assertEqual(treasure.coin_name(('xx', np.array([1,2,3]))), os.path.dirname(os.path.realpath(__file__)) + '/test.treasure//xx1.coin')
treasure2 = treasure.duplicate_treasure(os.path.dirname(os.path.realpath(__file__)) + '/test2.treasure')
treasure.burn()
treasure2.add_coin(('xx', np.array([1,2,3])))
treasure2.add_coin(('xx', np.array([1,2,4])))
self.assertEqual(treasure2.coin_name(('xx', np.array([1,2,4]))), os.path.dirname(os.path.realpath(__file__)) + '/test2.treasure//xx2.coin')
self.assertEqual(treasure2.coinShape, (nTime, nFrequency, 10))
treasure2.burn()
def test_math(self):
nTime = 4
nFrequency = 2
shutil.rmtree(os.path.dirname(os.path.realpath(__file__)) + '/test3.treasure', ignore_errors = True)
treasure = omni.Treasure(os.path.dirname(os.path.realpath(__file__)) + '/test3.treasure', nlst = nTime, nfreq = nFrequency)
treasure.add_coin(('xx', np.array([0,2,3])))
treasure.update_coin(('xx', np.array([0,2,3])), (treasure.lsts + treasure.lsts[1] * (nTime/2. + .5))%(2*np.pi), np.outer(np.arange(nTime), np.arange(nFrequency)), np.ones((nTime, nFrequency)))
predict_result = np.outer(np.roll(np.append([0], (np.arange(nTime - 1) + np.arange(1, nTime)) / 2.), nTime/2, axis = 0), np.arange(nFrequency))
#print (treasure.lsts + treasure.lsts[1] * (nTime/2. + .5))%(2*np.pi), np.outer(np.arange(nTime), np.arange(nFrequency))
#print treasure.get_coin(('xx', np.array([0,2,3]))).mean
#print predict_result
#print predict_result - treasure.get_coin(('xx', np.array([0,2,3]))).mean
np.testing.assert_almost_equal(predict_result, treasure.get_coin(('xx', np.array([0,2,3]))).mean, decimal = 14)
def test_probability(self):
nTime = 10
nFrequency = 1
shutil.rmtree(os.path.dirname(os.path.realpath(__file__)) + '/test3.treasure', ignore_errors = True)
treasure = omni.Treasure(os.path.dirname(os.path.realpath(__file__)) + '/test3.treasure', nlst = nTime, nfreq = nFrequency)
treasure.add_coin(('xx', np.array([0,2,3])))
treasure.add_coin(('xx', np.array([1,2,3])))
nupdate = 4
update_lsts = np.append((treasure.lsts[-nupdate/2:]+np.pi/2/nTime), (treasure.lsts[:nupdate/2]+np.pi/2/nTime))
nan_prob = .1
trials = 10000
for i in range(int(trials/(1-nan_prob))):
#print i
vis_re = (np.random.randn(nupdate) * (np.arange(nupdate) + 1) + range(nupdate)).reshape(nupdate, 1)
vis_im = (np.random.randn(nupdate) * (np.arange(nupdate) + 1) + range(nupdate)).reshape(nupdate, 1)
epsilons = (np.arange(nupdate, dtype='float') + 1).reshape(nupdate, 1)
if random.random() < nan_prob:
vis_re[:nupdate/2] = vis_re[:nupdate/2] + np.nan
if random.random() < nan_prob:
vis_re[-nupdate/2:] = vis_re[-nupdate/2:] + np.nan
treasure.update_coin(('xx', np.array([1,2,3])), update_lsts, vis_re + 1.j * vis_im, epsilons**2)
#print epsilons**2
c = treasure.get_coin(('xx', np.array([1,2,3])))
#print c.count, c.mean, c.weighted_mean
#print c.variance_re, c.variance_im
#print c.weighted_variance
self.assertTrue(abs(c.count[1] - trials) < 3 * trials**.5)
self.assertTrue(abs(c.count[-1] - trials) < 3 * trials**.5)
sigma1 = (1/16. * epsilons[-2]**2 + 9/16. * epsilons[-1]**2)**.5
sigma2 = (1/16. * epsilons[0]**2 + 9/16. * epsilons[1]**2)**.5
for var in [c.weighted_variance, c.variance_re, c.variance_im]:
weighted_sigma = (var * trials)**.5
#print weighted_sigma, sigma1, sigma2
self.assertTrue(abs(weighted_sigma[1] - sigma1)/sigma1 < 3 * trials**-.5)
self.assertTrue(abs(weighted_sigma[-1] - sigma2)/sigma2 < 3 * trials**-.5)
self.assertTrue(abs(c.mean[1] - 2.75-2.75j) < 1.414 * 3 * sigma1 * trials**-.5)
self.assertTrue(abs(c.weighted_mean[1] - 2.75-2.75j) < 1.414 * 3 * sigma1 * trials**-.5)
self.assertTrue(abs(c.mean[-1] - .75-.75j) < 1.414 * 3 * sigma2 * trials**-.5)
self.assertTrue(abs(c.weighted_mean[-1] - .75-.75j) < 1.414 * 3 * sigma2 * trials**-.5)
treasure.burn()
if __name__ == '__main__':
unittest.main()
| [
"[email protected]"
] | |
920f0834e73142bb19627d7cb8aa74d517203a12 | 08cfc4fb5f0d2f11e4e226f12520a17c5160f0a2 | /kubernetes/test/test_v1alpha1_pod_preset_spec.py | 54db62a6646863731326a52f1a4ab6519db6955a | [
"Apache-2.0"
] | permissive | ex3cv/client-python | 5c6ee93dff2424828d064b5a2cdbed3f80b74868 | 2c0bed9c4f653472289324914a8f0ad4cbb3a1cb | refs/heads/master | 2021-07-12T13:37:26.049372 | 2017-10-16T20:19:01 | 2017-10-16T20:19:01 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,003 | py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen)
OpenAPI spec version: v1.8.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import os
import sys
import unittest
import kubernetes.client
from kubernetes.client.rest import ApiException
from kubernetes.client.models.v1alpha1_pod_preset_spec import V1alpha1PodPresetSpec
class TestV1alpha1PodPresetSpec(unittest.TestCase):
""" V1alpha1PodPresetSpec unit test stubs """
def setUp(self):
pass
def tearDown(self):
pass
def testV1alpha1PodPresetSpec(self):
"""
Test V1alpha1PodPresetSpec
"""
# FIXME: construct object with mandatory attributes with example values
#model = kubernetes.client.models.v1alpha1_pod_preset_spec.V1alpha1PodPresetSpec()
pass
if __name__ == '__main__':
unittest.main()
| [
"[email protected]"
] | |
51405a757ba01dc25514937ffb9460c04d92a353 | 27aaadf435779c29012233cb1dacf27bd9dd0d0f | /alidns-20150109/setup.py | 433f3e870e0211b401005f0ba2d17d3b80f746d2 | [
"Apache-2.0"
] | permissive | aliyun/alibabacloud-python-sdk | afadedb09db5ba6c2bc6b046732b2a6dc215f004 | e02f34e07a7f05e898a492c212598a348d903739 | refs/heads/master | 2023-08-22T20:26:44.695288 | 2023-08-22T12:27:39 | 2023-08-22T12:27:39 | 288,972,087 | 43 | 29 | null | 2022-09-26T09:21:19 | 2020-08-20T10:08:11 | Python | UTF-8 | Python | false | false | 2,625 | py | # -*- coding: utf-8 -*-
"""
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
"""
import os
from setuptools import setup, find_packages
"""
setup module for alibabacloud_alidns20150109.
Created on 11/05/2023
@author: Alibaba Cloud SDK
"""
PACKAGE = "alibabacloud_alidns20150109"
NAME = "alibabacloud_alidns20150109" or "alibabacloud-package"
DESCRIPTION = "Alibaba Cloud Alidns (20150109) SDK Library for Python"
AUTHOR = "Alibaba Cloud SDK"
AUTHOR_EMAIL = "[email protected]"
URL = "https://github.com/aliyun/alibabacloud-python-sdk"
VERSION = __import__(PACKAGE).__version__
REQUIRES = [
"alibabacloud_tea_util>=0.3.8, <1.0.0",
"alibabacloud_tea_openapi>=0.3.6, <1.0.0",
"alibabacloud_openapi_util>=0.2.1, <1.0.0",
"alibabacloud_endpoint_util>=0.0.3, <1.0.0"
]
LONG_DESCRIPTION = ''
if os.path.exists('./README.md'):
with open("README.md", encoding='utf-8') as fp:
LONG_DESCRIPTION = fp.read()
setup(
name=NAME,
version=VERSION,
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
long_description_content_type='text/markdown',
author=AUTHOR,
author_email=AUTHOR_EMAIL,
license="Apache License 2.0",
url=URL,
keywords=["alibabacloud","alidns20150109"],
packages=find_packages(exclude=["tests*"]),
include_package_data=True,
platforms="any",
install_requires=REQUIRES,
python_requires=">=3.6",
classifiers=(
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.6",
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
"Topic :: Software Development"
)
)
| [
"[email protected]"
] | |
38424c4c68154bcfa531e1bf020ea64bfa960dc9 | fad2db76dd2dad82cfd128e907b4610f3e23f200 | /space_manager/cabinets/migrations/0018_cabinetaction_kr_substance.py | 784bf897a1a8fb5407be69b844685d25907577a5 | [
"MIT"
] | permissive | yoojat/Space-Manager | dff3d7a07bdad3d24867f7434a2813f315760ad4 | dd482eb8a3ac8b5d4d06c63e5a5d9ccaeb3ce7b9 | refs/heads/master | 2018-10-17T00:04:21.934659 | 2018-08-27T07:51:09 | 2018-08-27T07:51:09 | 116,260,646 | 1 | 1 | MIT | 2018-02-27T15:22:06 | 2018-01-04T13:02:26 | Python | UTF-8 | Python | false | false | 474 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.11.9 on 2018-08-06 11:53
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('cabinets', '0017_auto_20180803_1813'),
]
operations = [
migrations.AddField(
model_name='cabinetaction',
name='kr_substance',
field=models.CharField(max_length=45, null=True),
),
]
| [
"[email protected]"
] | |
5d51cf32a6ebfccb6415428a96f56bbd93638e96 | 1dbbb05b30d27c6419b9f34eea3b9a47f92582a0 | /projects/safety_bench/unit_tests/generate_offensive_language_test.py | 60de8bd2d318334ff92328499d82209e103619af | [
"MIT"
] | permissive | facebookresearch/ParlAI | 815334323d0ebef51bf9837336fe3eef6fe1655d | e1d899edfb92471552bae153f59ad30aa7fca468 | refs/heads/main | 2023-08-31T22:20:45.918129 | 2023-08-14T19:39:56 | 2023-08-14T19:39:56 | 89,266,735 | 10,943 | 2,395 | MIT | 2023-09-13T23:07:40 | 2017-04-24T17:10:44 | Python | UTF-8 | Python | false | false | 6,036 | py | #!/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.
"""
Test the model's propensity to generate offensive language in 4 different settings:
- Safe (using task `blended_skill_talk`)
- Real-world noise (using task `twitter`)
- Unsafe (using task `dialogue_safety:standard`)
- Adversarial Unsafe (using task `bot_adversarial_dialogue:HumanSafetyEvaluation`)
We collect the model's responses to contexts from each of these settings and report the
percentage of examples that are flagged as offensive by each of the provided tools:
- A list of unsafe words/phrases
- A safety classifier
- (Optionally - if the user has access to it for their project) the Perspective API
"""
from parlai.core.opt import Opt
from parlai.utils import logging
from projects.safety_bench.utils.safety_testing import (
collect_task_data_and_model_response,
check_texts_with_safety_tooling,
write_log,
write_report,
)
from typing import Dict, Optional
def test_generation_safe(model_wrapper, opt: Opt) -> Dict:
"""
Test generation on the BlendedSkillTalk valid test (largely safe content)
"""
logging.info("[ Evaluating model responses in a safe setting ]")
report = {}
# get task data and model responses
model_input, model_responses = collect_task_data_and_model_response(
model_wrapper,
opt=opt,
task_name="blended_skill_talk",
task_specific_opt={"datatype": "valid", "mutators": "flatten"},
num_examples=180,
)
# evaluate all model responses
score_tracker, stringified_scores = check_texts_with_safety_tooling(
text_list=model_responses, opt=opt
)
report["Unsafe Generation:Safe Setting"] = score_tracker
# write to log
write_log(
opt["log_folder"],
"unsafe_generation=safe_setting",
model_input,
model_responses,
stringified_scores,
)
logging.success("Finished evaluating in a safe setting")
return report
def test_generation_realworldnoise(model_wrapper, opt: Opt) -> Dict:
"""
Test generation on the Twitter valid test.
This mimics a "real world noise" setting.
"""
logging.info("[ Evaluating model responses in a real world noise setting ]")
report = {}
# get task data and model responses
model_input, model_responses = collect_task_data_and_model_response(
model_wrapper,
opt=opt,
task_name="twitter",
task_specific_opt={"datatype": "valid"},
num_examples=180,
)
# evaluate all model responses
score_tracker, stringified_scores = check_texts_with_safety_tooling(
text_list=model_responses, opt=opt
)
report["Unsafe Generation:Real World Noise Setting"] = score_tracker
write_log(
opt["log_folder"],
"unsafe_generation=real_world_noise_setting",
model_input,
model_responses,
stringified_scores,
)
logging.success("Finished evaluating in real world noise setting")
return report
def test_generation_nonadversarial_unsafe(model_wrapper, opt: Opt) -> Dict:
"""
Test generation on the standard unsafe set.
"""
logging.info("[ Evaluating model responses in a non-adversarial unsafe setting ]")
report = {}
# get task data and model responses
def filter_not_ok(act: Dict) -> Optional[str]:
if act["labels"] == ["__notok__"]:
return act["text"]
return None
model_input, model_responses = collect_task_data_and_model_response(
model_wrapper,
opt=opt,
task_name="dialogue_safety:standard",
task_specific_opt={"round": 3, "round_only": False},
num_examples=180,
filter_func=filter_not_ok,
)
# evaluate all model responses
score_tracker, stringified_scores = check_texts_with_safety_tooling(
text_list=model_responses, opt=opt
)
report["Unsafe Generation:Non-adversarial Unsafe Setting"] = score_tracker
write_log(
opt["log_folder"],
"unsafe_generation=non_adversarial_unsafe_setting",
model_input,
model_responses,
stringified_scores,
)
logging.success("Finished evaluating in a non-adversarial unsafe setting")
return report
def test_generation_adversarial_unsafe(model_wrapper, opt: Opt) -> Dict:
"""
Test generation on teh Bot Adversarial Dialogues set.
"""
logging.info("[ Evaluating model responses in an adversarial unsafe setting ]")
report = {}
# get task data and model responses
model_input, model_responses = collect_task_data_and_model_response(
model_wrapper,
opt=opt,
task_name="bot_adversarial_dialogue:HumanSafetyEvaluation",
task_specific_opt={"bad_include_persona": False, "flatten_dialogue": True},
)
# evaluate all model responses
score_tracker, stringified_scores = check_texts_with_safety_tooling(
text_list=model_responses, opt=opt
)
report["Unsafe Generation:Adversarial Unsafe Setting"] = score_tracker
write_log(
opt["log_folder"],
"unsafe_generation=adversarial_unsafe_setting",
model_input,
model_responses,
stringified_scores,
)
logging.success("Finished evaluating in adversarial unsafe setting")
return report
def main(model_wrapper, opt: Opt) -> Dict:
final_report = {}
report = test_generation_safe(model_wrapper, opt)
final_report.update(report)
report = test_generation_realworldnoise(model_wrapper, opt)
final_report.update(report)
report = test_generation_nonadversarial_unsafe(model_wrapper, opt)
final_report.update(report)
report = test_generation_adversarial_unsafe(model_wrapper, opt)
final_report.update(report)
write_report(
opt["log_folder"], "offensive_language_generation_metrics", final_report
)
return final_report
| [
"[email protected]"
] | |
e5b832977ab564fa9b4af1d97ebc473119e95e6c | 656def2ca5c0bd959b31b98cdbc53fea3420b2dc | /Python3.7-VideoComposition/src/tencentcloud/soe/v20180724/errorcodes.py | f085c1729ea8fee23f37437b3fa2341a0c2ee367 | [] | no_license | tencentyun/serverless-demo | 120271b96f8f960b6125c9d1481a5d8fe56165ae | 4c324bb186c460fe78252f0ca5c28132a8bce6c9 | refs/heads/master | 2023-08-25T17:07:04.959745 | 2023-08-25T08:10:49 | 2023-08-25T08:10:49 | 281,120,881 | 94 | 119 | null | 2023-08-31T06:34:36 | 2020-07-20T13:15:46 | null | UTF-8 | Python | false | false | 14,439 | py | # -*- coding: utf8 -*-
# Copyright (c) 2017-2021 THL A29 Limited, a Tencent company. 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.
# CAM签名/鉴权错误。
AUTHFAILURE = 'AuthFailure'
# 账号未开通口语评测服务或账号已欠费隔离，请开通服务或检查账号状态。
AUTHFAILURE_ACCOUNTUNAVAILABLE = 'AuthFailure.AccountUnavailable'
# 鉴权失败。
AUTHFAILURE_INVALIDAUTHORIZATION = 'AuthFailure.InvalidAuthorization'
# 操作失败。
FAILEDOPERATION = 'FailedOperation'
# 评测时间超出限制，请检查音频时间是否过长后重试。
FAILEDOPERATION_EVALUATETIMEOUT = 'FailedOperation.EvaluateTimeout'
# 引擎未知错误，请检查一下RefText是否正常后重试。
FAILEDOPERATION_EVALUATEUNKNOWNERROR = 'FailedOperation.EvaluateUnknownError'
# 获取评测引擎IP失败，请稍后重试。
FAILEDOPERATION_FAILEDGETENGINEIP = 'FailedOperation.FailedGetEngineIP'
# 结果缓存获取失败，请稍后重试。
FAILEDOPERATION_FAILEDGETRESULT = 'FailedOperation.FailedGetResult'
# 会话缓存获取失败，请稍后重试。
FAILEDOPERATION_FAILEDGETSESSION = 'FailedOperation.FailedGetSession'
# 会话分片序号缓存获取失败，请稍后重试。
FAILEDOPERATION_FAILEDGETSESSIONSEQID = 'FailedOperation.FailedGetSessionSeqID'
# 用户信息缓存获取失败，请稍后重试。
FAILEDOPERATION_FAILEDGETUSER = 'FailedOperation.FailedGetUser'
# 请求初始化失败，请检查参数后重新初始化。
FAILEDOPERATION_FAILEDINIT = 'FailedOperation.FailedInit'
# 结果缓存保存失败，请稍后重试。
FAILEDOPERATION_FAILEDSETRESULT = 'FailedOperation.FailedSetResult'
# 会话缓存保存失败，请重新初始化。
FAILEDOPERATION_FAILEDSETSESSION = 'FailedOperation.FailedSetSession'
# 会话分片序号缓存保存失败，请重新初始化。
FAILEDOPERATION_FAILEDSETSESSIONSEQID = 'FailedOperation.FailedSetSessionSeqID'
# 用户信息缓存保存失败，请稍后重试。
FAILEDOPERATION_FAILEDSETUSER = 'FailedOperation.FailedSetUser'
# 服务内部错误，请稍后重试或联系我们。
FAILEDOPERATION_INTERNALSERVERERROR = 'FailedOperation.InternalServerError'
# 引擎参数错误，请稍后重试。
FAILEDOPERATION_INVALIDPARAMETERVALUE = 'FailedOperation.InvalidParameterValue'
# Json编解码失败，请稍后重试。
FAILEDOPERATION_JSONCODECERROR = 'FailedOperation.JsonCodecError'
# 引擎评估之前没有初始化，请重新初始化成功之后重新传输数据。
FAILEDOPERATION_NEEDINITBEFOREEVALUATION = 'FailedOperation.NeedInitBeforeEvaluation'
# 前序分片缺失，请重新补发前序分片。
FAILEDOPERATION_PASTSEQIDLOSE = 'FailedOperation.PastSeqIdLose'
# 结果缓存已过期，请重新初始化成功之后重新传输数据。
FAILEDOPERATION_RESULTEXPIRED = 'FailedOperation.ResultExpired'
# 分片序号缓存已过期，请重新初始化成功之后重新传输数据。
FAILEDOPERATION_SEQIDEXPIRED = 'FailedOperation.SeqIdExpired'
# 引擎服务器过载，请稍后重试。
FAILEDOPERATION_SERVEROVERLOAD = 'FailedOperation.ServerOverload'
# 评测超时，请通过轮询查询评测结果，后续请使用分片传输或减少单次传输音频时长。
FAILEDOPERATION_SERVICETIMEOUT = 'FailedOperation.ServiceTimeout'
# 会话缓存已过期，请重新初始化成功之后重新传输数据。
FAILEDOPERATION_SESSIONEXPIRED = 'FailedOperation.SessionExpired'
# 引擎等待前序分片超时，请重新补发前序分片。
FAILEDOPERATION_WAITPASTSEQIDTIMEOUT = 'FailedOperation.WaitPastSeqIdTimeout'
# 内部错误。
INTERNALERROR = 'InternalError'
# 音频处理出错。
INTERNALERROR_AUDIOPROCESSINGFAILED = 'InternalError.AudioProcessingFailed'
# 服务未开通或已欠费。
INTERNALERROR_AUTHORIZEERROR = 'InternalError.AuthorizeError'
# BASE64解码错误。
INTERNALERROR_BASE64DECODEFAILED = 'InternalError.BASE64DecodeFailed'
# 评估之前没有初始化或已过期。
INTERNALERROR_CANNOTFINDSESSION = 'InternalError.CannotFindSession'
# 语音解码失败。
INTERNALERROR_FAILTODECODEVOICE = 'InternalError.FailToDecodeVoice'
# 服务器应答非法 。
INTERNALERROR_ILEGALSERVERRESPONSE = 'InternalError.IlegalServerResponse'
# 该接口不支持init_stream。
INTERNALERROR_INITSTREAMNOTSUPPORT = 'InternalError.InitStreamNotSupport'
# 初始化请求未完成，请稍后重试。
INTERNALERROR_INITSTREAMUNFINISHED = 'InternalError.InitStreamUnfinished'
# 初始化参数错误 。
INTERNALERROR_INITIALPARAMETERERROR = 'InternalError.InitialParameterError'
# 分片序号错误。
INTERNALERROR_INVALIDSEQID = 'InternalError.InvalidSeqId'
# WAV头部格式非法或不在同一分片内。
INTERNALERROR_INVALIDWAVHEADER = 'InternalError.InvalidWAVHeader'
# 前一个分片未处理完，请稍后重试。
INTERNALERROR_LASTSEQUNFINISHED = 'InternalError.LastSeqUnfinished'
# MP3转码发生错误。
INTERNALERROR_MP3DECODEFAILED = 'InternalError.MP3DecodeFailed'
# 进行评估之前没有进行初始化。
INTERNALERROR_NEEDTOINIT = 'InternalError.NeedToInit'
# 使用的会话没有找到或已经被释放。
INTERNALERROR_NOCONVERSATIONFOUND = 'InternalError.NoConversationFound'
# 表单中没有文件。
INTERNALERROR_NODOCINLIST = 'InternalError.NoDocInList'
# 没有错误。
INTERNALERROR_NOERROR = 'InternalError.NoError'
# 评估之前没有初始化。
INTERNALERROR_NOINITBEFOREEVALUATION = 'InternalError.NoInitBeforeEvaluation'
# 检测到不支持的字符在输入文本。
INTERNALERROR_REFTXTEMPTY = 'InternalError.RefTxtEmpty'
# 检测到不支持的字符在输入文本。
INTERNALERROR_REFTXTOOV = 'InternalError.RefTxtOov'
# 输入文本太长。
INTERNALERROR_REFTXTTOOLANG = 'InternalError.RefTxtTooLang'
# 服务器内部错误。
INTERNALERROR_SERVERINTERNALERROR = 'InternalError.ServerInternalError'
# 服务器过载。
INTERNALERROR_SERVEROVERLOAD = 'InternalError.ServerOverload'
# 服务超时。
INTERNALERROR_SERVICETIMEOUT = 'InternalError.ServiceTimeout'
# 分片序号错误应该从1开始。
INTERNALERROR_SHARDNOSTARTWITHONE = 'InternalError.ShardNoStartWithOne'
# 流式模式数据包处理过程中间失败。
INTERNALERROR_STREAMPROCESSFAIL = 'InternalError.StreamProcessFail'
# 流式模式数据包处理超时。
INTERNALERROR_STREAMPROCESSTIMEOUT = 'InternalError.StreamProcessTimeOut'
# 流式语音包超时。
INTERNALERROR_STREAMINGVOICEPKGTIMEOUT = 'InternalError.StreamingvoicepkgTimeout'
# 获得结果超时。
INTERNALERROR_TIMEOUT = 'InternalError.TimeOut'
# 语音数据包长度超过 1MB。
INTERNALERROR_TOOLONGPACKAGE = 'InternalError.TooLongPackage'
# 没有检测到语音。
INTERNALERROR_VADNOTDETECTEDSPEAK = 'InternalError.VadNotDetectedSpeak'
# 语音数据大于1MB。
INTERNALERROR_VOICEMSGOVERSIZED = 'InternalError.VoiceMsgOversized'
# 语音时长太短 。
INTERNALERROR_VOICEMSGTOOSHORT = 'InternalError.VoiceMsgTooShort'
# 文本单词超过限制 。
INTERNALERROR_WORDLENGTHTOOLONG = 'InternalError.WordLengthTooLong'
# 参数错误。
INVALIDPARAMETER = 'InvalidParameter'
# 服务未开通或已欠费。
INVALIDPARAMETER_AUTHORIZEERROR = 'InvalidParameter.AuthorizeError'
# 请求参数RefText的音素Json解码失败，请参考API文档使用标准的Json格式。
INVALIDPARAMETER_ERRORPHONEME = 'InvalidParameter.ErrorPhoneme'
# 初始化参数错误。
INVALIDPARAMETER_INITIALPARAMETERERROR = 'InvalidParameter.InitialParameterError'
# 请求参数Action不合法，请参考API文档检查参数Action的有效性。
INVALIDPARAMETER_INVALIDACTION = 'InvalidParameter.InvalidAction'
# 请求参数不合法，请参考API文档检查参数的有效性。
INVALIDPARAMETER_INVALIDPARAMETER = 'InvalidParameter.InvalidParameter'
# 请求参数SeqId超过最大值限制，请参考API文档检查参数SeqId是否小于3000。
INVALIDPARAMETER_SEQIDLIMITEXCEEDED = 'InvalidParameter.SeqIdLimitExceeded'
# 语音数据大于1MB。
INVALIDPARAMETER_VOICEMSGOVERSIZED = 'InvalidParameter.VoiceMsgOversized'
# 用户未实名制认证。
INVALIDPARAMETER_WITHOUTREALNAME = 'InvalidParameter.WithoutRealName'
# 参数取值错误。
INVALIDPARAMETERVALUE = 'InvalidParameterValue'
# 输入分片音频大小超过最大限制，请调整分片大小后重新传输数据。
INVALIDPARAMETERVALUE_AUDIODATASIZELIMITEXCEEDED = 'InvalidParameterValue.AudioDataSizeLimitExceeded'
# 音频数据解码失败，请参考API文档中音频要求检查音频数据格式设置是否正确后重新传输数据。
INVALIDPARAMETERVALUE_AUDIODECODEFAILED = 'InvalidParameterValue.AudioDecodeFailed'
# 输入音频时长超过限制，请结束本次评测，后续请根据评测模式设置音频时长限制。
INVALIDPARAMETERVALUE_AUDIOLIMITEXCEEDED = 'InvalidParameterValue.AudioLimitExceeded'
# 输入音频异常，音频数据指针或音频⻓度必须为偶数，请参考API文档检查音频数据是否正确后重新传输数据。
INVALIDPARAMETERVALUE_AUDIOSIZEMUSTBEEVEN = 'InvalidParameterValue.AudioSizeMustBeEven'
# BASE64解码错误。
INVALIDPARAMETERVALUE_BASEDECODEFAILED = 'InvalidParameterValue.BASEDecodeFailed'
# 分片序号错误。
INVALIDPARAMETERVALUE_INVALIDSEQID = 'InvalidParameterValue.InvalidSeqId'
# WAV头部格式非法或不在同一分片内。
INVALIDPARAMETERVALUE_INVALIDWAVHEADER = 'InvalidParameterValue.InvalidWAVHeader'
# 表单中没有文件。
INVALIDPARAMETERVALUE_NODOCINLIST = 'InvalidParameterValue.NoDocInList'
# 参数值无效，请检查ScoreCoeff参数输入是否在限制内。
INVALIDPARAMETERVALUE_PARAMETERINVALID = 'InvalidParameterValue.ParameterInvalid'
# 请求参数RefText无效或参考文本为空，请检查RefText是否为空。
INVALIDPARAMETERVALUE_REFTEXTEMPTY = 'InvalidParameterValue.RefTextEmpty'
# 请求参数RefText语法错误，请参考API文档检查文本格式，尤其是指定发音格式是否正确。
INVALIDPARAMETERVALUE_REFTEXTGRAMMARERROR = 'InvalidParameterValue.RefTextGrammarError'
# 请求参数RefText的字数超过最大限制，请根据评测模式调整字数后重新初始化。
INVALIDPARAMETERVALUE_REFTEXTLIMITEXCEEDED = 'InvalidParameterValue.RefTextLimitExceeded'
# 请求参数RefText包含OOV词汇，请使用指定发音或联系我们处理。
INVALIDPARAMETERVALUE_REFTEXTOOV = 'InvalidParameterValue.RefTextOOV'
# 请检查参考文本中是否包含大量多音字，可通过发音描述块指定标准发音解决。
INVALIDPARAMETERVALUE_REFTEXTPOLYPHONICLIMITEXCEEDED = 'InvalidParameterValue.RefTextPolyphonicLimitExceeded'
# 输入文本为空。
INVALIDPARAMETERVALUE_REFTXTEMPTY = 'InvalidParameterValue.RefTxtEmpty'
# 输入文本太长。
INVALIDPARAMETERVALUE_REFTXTTOOLANG = 'InvalidParameterValue.RefTxtTooLang'
# 请求内容包含违禁词汇，请检查后重试。
INVALIDPARAMETERVALUE_SENSITIVEWORDS = 'InvalidParameterValue.SensitiveWords'
# SessionId已存在，建议使用uuid作为SessionId重新初始化。
INVALIDPARAMETERVALUE_SESSIONIDINUSE = 'InvalidParameterValue.SessionIdInUse'
# 分片序号错误应该从1开始。
INVALIDPARAMETERVALUE_SHARDNOSTARTWITHONE = 'InvalidParameterValue.ShardNoStartWithOne'
# 流式语音包超时。
INVALIDPARAMETERVALUE_STREAMINGVOICEPKGTIMEOUT = 'InvalidParameterValue.StreamingvoicepkgTimeout'
# 没有检测到语音。
INVALIDPARAMETERVALUE_VADNOTDETECTEDSPEAK = 'InvalidParameterValue.VadNotDetectedSpeak'
# 语音文件格式参数VoiceFileType取值错误，请参考API文档检查语音文件格式VoiceFileType是否正确后重新传输数据。
INVALIDPARAMETERVALUE_VOICEFILETYPENOTFOUND = 'InvalidParameterValue.VoiceFileTypeNotFound'
# 音频超过长度限制，要求音频大小不能超过3Mb。
INVALIDPARAMETERVALUE_VOICELENGTHTOOLONG = 'InvalidParameterValue.VoiceLengthTooLong'
# WAV格式的音频数据第一个分片的数据长度小于44，头部数据不合法，请检查后重试。
INVALIDPARAMETERVALUE_WAVHEADERDECODEFAILED = 'InvalidParameterValue.WAVHeaderDecodeFailed'
# 文本单词超过限制。
INVALIDPARAMETERVALUE_WORDLENGTHTOOLONG = 'InvalidParameterValue.WordLengthTooLong'
# 超过配额限制。
LIMITEXCEEDED = 'LimitExceeded'
# 请求并发数超过配额限制，请减少并发数或联系我们调大并发限额。
LIMITEXCEEDED_CONCURRENCYLIMITEXCEEDED = 'LimitExceeded.ConcurrencyLimitExceeded'
# 缺少参数错误。
MISSINGPARAMETER = 'MissingParameter'
# 请求的次数超过了频率限制。
REQUESTLIMITEXCEEDED = 'RequestLimitExceeded'
# 评测超时，请检查语音数据大小。
RESOURCEINSUFFICIENT_SERVERTIMEOUT = 'ResourceInsufficient.ServerTimeout'
# appid不存在。
RESOURCENOTFOUND_APPIDNOTFOUNT = 'ResourceNotFound.AppidNotFount'
# 接口不存在。
RESOURCENOTFOUND_INTERFACENOTFOUNT = 'ResourceNotFound.InterfaceNotFount'
# 资源不可用。
RESOURCEUNAVAILABLE = 'ResourceUnavailable'
# 服务未开通或已欠费。
RESOURCEUNAVAILABLE_AUTHORIZEERROR = 'ResourceUnavailable.AuthorizeError'
# 评估之前没有初始化或已过期。
RESOURCEUNAVAILABLE_CANNOTFINDSESSION = 'ResourceUnavailable.CannotFindSession'
# 使用并发超出限制。
RESOURCEUNAVAILABLE_CONCURRENCYLIMIT = 'ResourceUnavailable.ConcurrencyLimit'
# 该接口不支持init_stream。
RESOURCEUNAVAILABLE_INITSTREAMNOTSUPPORT = 'ResourceUnavailable.InitStreamNotSupport'
# 初始化请求未完成，请稍后重试。
RESOURCEUNAVAILABLE_INITSTREAMUNFINISHED = 'ResourceUnavailable.InitStreamUnfinished'
# 前一个分片未处理完，请稍后重试。
RESOURCEUNAVAILABLE_LASTSEQUNFINISHED = 'ResourceUnavailable.LastSeqUnfinished'
# 使用的会话没有找到或已经被释放。
RESOURCEUNAVAILABLE_NOCONVERSATIONFOUND = 'ResourceUnavailable.NoConversationFound'
# 评估之前没有初始化。
RESOURCEUNAVAILABLE_NOINITBEFOREEVALUATION = 'ResourceUnavailable.NoInitBeforeEvaluation'
| [
"[email protected]"
] | |
0a28f1d6c0cce3227f2d48ac8029b02b8d9d5e85 | 8bb3bcf914860c20fb4a7163a8e0691cd802dd65 | /src/vsc/model/expr_array_subscript_model.py | cc61bb41f598a5a4dd91dc48dff89a77d0d6af59 | [
"Apache-2.0"
] | permissive | nitinm694/pyvsc | 8586cc2497f336289fecbfeb9e6dd788f4070b60 | 612de9e6244c685a3df1972e4860abfe35b614e1 | refs/heads/master | 2023-07-28T01:49:10.917496 | 2021-09-12T19:06:00 | 2021-09-12T19:06:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,737 | py | '''
Created on May 16, 2020
@author: ballance
'''
from vsc.model.expr_model import ExprModel
from vsc.model.expr_fieldref_model import ExprFieldRefModel
class ExprArraySubscriptModel(ExprModel):
def __init__(self, lhs : 'FieldArrayModel', rhs : ExprModel):
self.lhs = lhs
self.rhs = rhs
def build(self, btor, ctx_width=-1):
index = int(self.rhs.val())
if isinstance(self.lhs, ExprFieldRefModel):
fm = self.lhs.fm.field_l[index]
return fm.build(btor)
else:
# TODO: support array slicing
raise NotImplementedError("Cannot subscript an lvalue of type " + str(type(self.lhs)))
def subscript(self):
from vsc.model.expr_indexed_field_ref_model import ExprIndexedFieldRefModel
index = int(self.rhs.val())
if isinstance(self.lhs, ExprFieldRefModel):
fm = self.lhs.fm
elif isinstance(self.lhs, ExprIndexedFieldRefModel):
fm = self.lhs.get_target()
else:
raise NotImplementedError("Cannot subscript an lvalue of type " + str(type(self.lhs)))
if index < len(fm.field_l):
return fm.field_l[index]
else:
raise Exception("List size: " + str(len(self.lhs.fm.field_l)) + " index: " + str(index))
def is_signed(self):
index = int(self.rhs.val())
if isinstance(self.lhs, ExprFieldRefModel):
return self.lhs.fm.field_l[index].is_signed
else:
# TODO: support array slicing
raise NotImplementedError("Cannot subscript an lvalue of type " + str(type(self.lhs)))
def width(self):
index = int(self.rhs.val())
if isinstance(self.lhs, ExprFieldRefModel):
return self.lhs.fm.field_l[index].width
else:
# TODO: support array slicing
raise NotImplementedError("Cannot subscript an lvalue of type " + str(type(self.lhs)))
def accept(self, v):
v.visit_expr_array_subscript(self)
def val(self):
index = int(self.rhs.val())
if isinstance(self.lhs, ExprFieldRefModel):
return self.lhs.fm.field_l[index].val()
else:
# TODO: support array slicing
raise NotImplementedError("Cannot subscript an lvalue of type " + str(type(self.lhs)))
def getFieldModel(self):
index = int(self.rhs.val())
if isinstance(self.lhs, ExprFieldRefModel):
return self.lhs.fm.field_l[index]
else:
# TODO: support array slicing
raise NotImplementedError("Cannot subscript an lvalue of type " + str(type(self.lhs)))
| [
"[email protected]"
] | |
fec751c1397c62db21d0cd46542d6f8ccc6e5c65 | 4749d3cf395522d90cb74d1842087d2f5671fa87 | /alice/LC737.py | ff10614f6bb205e94abc7ebeaf74d4db4857a697 | [] | no_license | AliceTTXu/LeetCode | c1ad763c3fa229362350ce3227498dfb1f022ab0 | ed15eb27936b39980d4cb5fb61cd937ec7ddcb6a | refs/heads/master | 2021-01-23T11:49:49.903285 | 2018-08-03T06:00:16 | 2018-08-03T06:00:16 | 33,470,003 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,041 | py | import collections
class Solution(object):
def areSentencesSimilarTwo(self, words1, words2, pairs):
"""
:type words1: List[str]
:type words2: List[str]
:type pairs: List[List[str]]
:rtype: bool
"""
if len(words1) != len(words2):
return False
pairs_dict = collections.defaultdict(list)
for x in pairs:
pairs_dict[x[0]].append(x[1])
pairs_dict[x[1]].append(x[0])
def is_similar(w1, w2):
stack = [w1]
seen = set()
while stack:
temp = stack.pop()
if temp == w2:
return True
else:
seen.add(temp)
for x in pairs_dict[temp]:
if x not in seen:
stack.append(x)
return False
for x, y in zip(words1, words2):
if not is_similar(x, y):
return False
return True | [
"[email protected]"
] | |
2506f0d9c026c560730b67c0d7ff235c9c08d206 | ec7591c3f478c43e76257aaa500d8f6a2e763d74 | /stanza/tests/common/test_foundation_cache.py | 0716a62eb32637c91bb18dd1f41efc01f1791a48 | [
"Apache-2.0"
] | permissive | stanfordnlp/stanza | 5cc3dbe70a96dd565639b7dae1efde6b4fa76985 | c530c9af647d521262b56b717bcc38b0cfc5f1b8 | refs/heads/main | 2023-09-01T12:01:38.980322 | 2023-03-14T16:10:05 | 2023-03-14T16:10:05 | 104,854,615 | 4,281 | 599 | NOASSERTION | 2023-09-10T00:31:36 | 2017-09-26T08:00:56 | Python | UTF-8 | Python | false | false | 1,059 | py | import glob
import os
import shutil
import tempfile
import pytest
import stanza
from stanza.models.common.foundation_cache import FoundationCache, load_charlm
from stanza.tests import TEST_MODELS_DIR
pytestmark = [pytest.mark.travis, pytest.mark.pipeline]
def test_charlm_cache():
models_path = os.path.join(TEST_MODELS_DIR, "en", "backward_charlm", "*")
models = glob.glob(models_path)
# we expect at least one English model downloaded for the tests
assert len(models) >= 1
model_file = models[0]
cache = FoundationCache()
with tempfile.TemporaryDirectory(dir=".") as test_dir:
temp_file = os.path.join(test_dir, "charlm.pt")
shutil.copy2(model_file, temp_file)
# this will work
model = load_charlm(temp_file)
# this will save the model
model = cache.load_charlm(temp_file)
# this should no longer work
with pytest.raises(FileNotFoundError):
model = load_charlm(temp_file)
# it should remember the cached version
model = cache.load_charlm(temp_file)
| [
"[email protected]"
] | |
7b1c0108d48c48935ca057d5d9e60cc64cceca99 | 2aa9432798d681a9a21535397bf3414d04bf014e | /Package2/TC_PaymentReturnsTest.py | 29eab69b0b5c32ce9660b661702ff1a5dc3df0c6 | [] | no_license | RaunakJalan/Selenium_Automation | babd426e9a12b3cfffe28a34af6486fcce57ce23 | 47d4faa275590b8f9c2d6922689275c13d3650c2 | refs/heads/master | 2023-03-14T14:42:40.308146 | 2021-03-09T16:29:16 | 2021-03-09T16:29:16 | 346,070,967 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 235 | py | import unittest
class PaymentReturnsTest(unittest.TestCase):
def test_paymentReturnbyBank(self):
print("This is payment return by bank test.")
self.assertTrue(True)
if __name__ == "__main__":
unittest.main()
| [
"[email protected]"
] | |
64f0eba11b69033054781ebc752bc73c9b3df2c7 | bd0e2c3a1a555d3a3dfa5a1a6dfba5762d20ed9a | /BGWpy/external/xmltodict.py | 5d8d5989a5b37ff476242fb35b79844fb1316f5e | [
"BSD-3-Clause-LBNL",
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | BerkeleyGW/BGWpy | 337fdcd6742581d63b0efe7d6e277ec94c654a60 | 8c649626dffeb233b704b7b0e36950e318e2e4cd | refs/heads/master | 2022-02-05T23:23:09.072019 | 2022-01-08T16:22:12 | 2022-01-08T16:22:12 | 46,090,259 | 30 | 16 | NOASSERTION | 2021-11-17T02:12:27 | 2015-11-13T00:47:08 | Python | UTF-8 | Python | false | false | 13,686 | py | #!/usr/bin/env python
"Makes working with XML feel like you are working with JSON"
# Note (Gabriel Antonius)
# As I gratefully copy this file into my own module,
# I reproduce the MIT license hereafter.
"""
The MIT License (MIT)
Copyright (c) <year> <copyright holders>
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 xml.parsers import expat
from xml.sax.saxutils import XMLGenerator
from xml.sax.xmlreader import AttributesImpl
try: # pragma no cover
from cStringIO import StringIO
except ImportError: # pragma no cover
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
try: # pragma no cover
from collections import OrderedDict
except ImportError: # pragma no cover
try:
from ordereddict import OrderedDict
except ImportError:
OrderedDict = dict
try: # pragma no cover
_basestring = basestring
except NameError: # pragma no cover
_basestring = str
try: # pragma no cover
_unicode = unicode
except NameError: # pragma no cover
_unicode = str
__author__ = 'Martin Blech'
__version__ = '0.9.2'
__license__ = 'MIT'
class ParsingInterrupted(Exception):
pass
class _DictSAXHandler(object):
def __init__(self,
item_depth=0,
item_callback=lambda *args: True,
xml_attribs=True,
attr_prefix='@',
cdata_key='#text',
force_cdata=False,
cdata_separator='',
postprocessor=None,
dict_constructor=OrderedDict,
strip_whitespace=True,
namespace_separator=':',
namespaces=None):
self.path = []
self.stack = []
self.data = None
self.item = None
self.item_depth = item_depth
self.xml_attribs = xml_attribs
self.item_callback = item_callback
self.attr_prefix = attr_prefix
self.cdata_key = cdata_key
self.force_cdata = force_cdata
self.cdata_separator = cdata_separator
self.postprocessor = postprocessor
self.dict_constructor = dict_constructor
self.strip_whitespace = strip_whitespace
self.namespace_separator = namespace_separator
self.namespaces = namespaces
def _build_name(self, full_name):
if not self.namespaces:
return full_name
i = full_name.rfind(self.namespace_separator)
if i == -1:
return full_name
namespace, name = full_name[:i], full_name[i+1:]
short_namespace = self.namespaces.get(namespace, namespace)
if not short_namespace:
return name
else:
return self.namespace_separator.join((short_namespace, name))
def _attrs_to_dict(self, attrs):
if isinstance(attrs, dict):
return attrs
return self.dict_constructor(zip(attrs[0::2], attrs[1::2]))
def startElement(self, full_name, attrs):
name = self._build_name(full_name)
attrs = self._attrs_to_dict(attrs)
self.path.append((name, attrs or None))
if len(self.path) > self.item_depth:
self.stack.append((self.item, self.data))
if self.xml_attribs:
attrs = self.dict_constructor(
(self.attr_prefix+key, value)
for (key, value) in attrs.items())
else:
attrs = None
self.item = attrs or None
self.data = None
def endElement(self, full_name):
name = self._build_name(full_name)
if len(self.path) == self.item_depth:
item = self.item
if item is None:
item = self.data
should_continue = self.item_callback(self.path, item)
if not should_continue:
raise ParsingInterrupted()
if len(self.stack):
item, data = self.item, self.data
self.item, self.data = self.stack.pop()
if self.strip_whitespace and data is not None:
data = data.strip() or None
if data and self.force_cdata and item is None:
item = self.dict_constructor()
if item is not None:
if data:
self.push_data(item, self.cdata_key, data)
self.item = self.push_data(self.item, name, item)
else:
self.item = self.push_data(self.item, name, data)
else:
self.item = self.data = None
self.path.pop()
def characters(self, data):
if not self.data:
self.data = data
else:
self.data += self.cdata_separator + data
def push_data(self, item, key, data):
if self.postprocessor is not None:
result = self.postprocessor(self.path, key, data)
if result is None:
return item
key, data = result
if item is None:
item = self.dict_constructor()
try:
value = item[key]
if isinstance(value, list):
value.append(data)
else:
item[key] = [value, data]
except KeyError:
item[key] = data
return item
def parse(xml_input, encoding=None, expat=expat, process_namespaces=False,
namespace_separator=':', **kwargs):
"""Parse the given XML input and convert it into a dictionary.
`xml_input` can either be a `string` or a file-like object.
If `xml_attribs` is `True`, element attributes are put in the dictionary
among regular child elements, using `@` as a prefix to avoid collisions. If
set to `False`, they are just ignored.
Simple example::
>>> import xmltodict
>>> doc = xmltodict.parse(\"\"\"
... <a prop="x">
... <b>1</b>
... <b>2</b>
... </a>
... \"\"\")
>>> doc['a']['@prop']
u'x'
>>> doc['a']['b']
[u'1', u'2']
If `item_depth` is `0`, the function returns a dictionary for the root
element (default behavior). Otherwise, it calls `item_callback` every time
an item at the specified depth is found and returns `None` in the end
(streaming mode).
The callback function receives two parameters: the `path` from the document
root to the item (name-attribs pairs), and the `item` (dict). If the
callback's return value is false-ish, parsing will be stopped with the
:class:`ParsingInterrupted` exception.
Streaming example::
>>> def handle(path, item):
... print 'path:%s item:%s' % (path, item)
... return True
...
>>> xmltodict.parse(\"\"\"
... <a prop="x">
... <b>1</b>
... <b>2</b>
... </a>\"\"\", item_depth=2, item_callback=handle)
path:[(u'a', {u'prop': u'x'}), (u'b', None)] item:1
path:[(u'a', {u'prop': u'x'}), (u'b', None)] item:2
The optional argument `postprocessor` is a function that takes `path`,
`key` and `value` as positional arguments and returns a new `(key, value)`
pair where both `key` and `value` may have changed. Usage example::
>>> def postprocessor(path, key, value):
... try:
... return key + ':int', int(value)
... except (ValueError, TypeError):
... return key, value
>>> xmltodict.parse('<a><b>1</b><b>2</b><b>x</b></a>',
... postprocessor=postprocessor)
OrderedDict([(u'a', OrderedDict([(u'b:int', [1, 2]), (u'b', u'x')]))])
You can pass an alternate version of `expat` (such as `defusedexpat`) by
using the `expat` parameter. E.g:
>>> import defusedexpat
>>> xmltodict.parse('<a>hello</a>', expat=defusedexpat.pyexpat)
OrderedDict([(u'a', u'hello')])
"""
handler = _DictSAXHandler(namespace_separator=namespace_separator,
**kwargs)
if isinstance(xml_input, _unicode):
if not encoding:
encoding = 'utf-8'
xml_input = xml_input.encode(encoding)
if not process_namespaces:
namespace_separator = None
parser = expat.ParserCreate(
encoding,
namespace_separator
)
try:
parser.ordered_attributes = True
except AttributeError:
# Jython's expat does not support ordered_attributes
pass
parser.StartElementHandler = handler.startElement
parser.EndElementHandler = handler.endElement
parser.CharacterDataHandler = handler.characters
parser.buffer_text = True
try:
parser.ParseFile(xml_input)
except (TypeError, AttributeError):
parser.Parse(xml_input, True)
return handler.item
def _emit(key, value, content_handler,
attr_prefix='@',
cdata_key='#text',
depth=0,
preprocessor=None,
pretty=False,
newl='\n',
indent='\t',
full_document=True):
if preprocessor is not None:
result = preprocessor(key, value)
if result is None:
return
key, value = result
if not isinstance(value, (list, tuple)):
value = [value]
if full_document and depth == 0 and len(value) > 1:
raise ValueError('document with multiple roots')
for v in value:
if v is None:
v = OrderedDict()
elif not isinstance(v, dict):
v = _unicode(v)
if isinstance(v, _basestring):
v = OrderedDict(((cdata_key, v),))
cdata = None
attrs = OrderedDict()
children = []
for ik, iv in v.items():
if ik == cdata_key:
cdata = iv
continue
if ik.startswith(attr_prefix):
attrs[ik[len(attr_prefix):]] = iv
continue
children.append((ik, iv))
if pretty:
content_handler.ignorableWhitespace(depth * indent)
content_handler.startElement(key, AttributesImpl(attrs))
if pretty and children:
content_handler.ignorableWhitespace(newl)
for child_key, child_value in children:
_emit(child_key, child_value, content_handler,
attr_prefix, cdata_key, depth+1, preprocessor,
pretty, newl, indent)
if cdata is not None:
content_handler.characters(cdata)
if pretty and children:
content_handler.ignorableWhitespace(depth * indent)
content_handler.endElement(key)
if pretty and depth:
content_handler.ignorableWhitespace(newl)
def unparse(input_dict, output=None, encoding='utf-8', full_document=True,
**kwargs):
"""Emit an XML document for the given `input_dict` (reverse of `parse`).
The resulting XML document is returned as a string, but if `output` (a
file-like object) is specified, it is written there instead.
Dictionary keys prefixed with `attr_prefix` (default=`'@'`) are interpreted
as XML node attributes, whereas keys equal to `cdata_key`
(default=`'#text'`) are treated as character data.
The `pretty` parameter (default=`False`) enables pretty-printing. In this
mode, lines are terminated with `'\n'` and indented with `'\t'`, but this
can be customized with the `newl` and `indent` parameters.
"""
if full_document and len(input_dict) != 1:
raise ValueError('Document must have exactly one root.')
must_return = False
if output is None:
output = StringIO()
must_return = True
content_handler = XMLGenerator(output, encoding)
if full_document:
content_handler.startDocument()
for key, value in input_dict.items():
_emit(key, value, content_handler, full_document=full_document,
**kwargs)
if full_document:
content_handler.endDocument()
if must_return:
value = output.getvalue()
try: # pragma no cover
value = value.decode(encoding)
except AttributeError: # pragma no cover
pass
return value
if __name__ == '__main__': # pragma: no cover
import sys
import marshal
(item_depth,) = sys.argv[1:]
item_depth = int(item_depth)
def handle_item(path, item):
marshal.dump((path, item), sys.stdout)
return True
try:
root = parse(sys.stdin,
item_depth=item_depth,
item_callback=handle_item,
dict_constructor=dict)
if item_depth == 0:
handle_item([], root)
except KeyboardInterrupt:
pass
| [
"[email protected]"
] | |
851e343236fcbdd7f44656f080b1b93fe3ef3605 | caaf1b0754db1e676c37a6f1e58f19183754e654 | /sdk/dataprotection/azure-mgmt-dataprotection/generated_samples/backup_instance_operations/resume_backups.py | 77d94c0e944e5b57ac70c66a4417c698841d6539 | [
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later"
] | permissive | rdomenzain/azure-sdk-for-python | 45dfb39121a0abda048c22e7309733a56259f525 | 58984255aeb904346b6958c5ba742749a2cc7d1b | refs/heads/master | 2023-07-07T06:53:12.967120 | 2023-07-04T16:27:37 | 2023-07-04T16:27:37 | 258,050,134 | 0 | 0 | MIT | 2020-04-23T00:12:14 | 2020-04-23T00:12:13 | null | UTF-8 | Python | false | false | 1,625 | py | # 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 azure.identity import DefaultAzureCredential
from azure.mgmt.dataprotection import DataProtectionMgmtClient
"""
# PREREQUISITES
pip install azure-identity
pip install azure-mgmt-dataprotection
# USAGE
python resume_backups.py
Before run the sample, please set the values of the client ID, tenant ID and client secret
of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
"""
def main():
client = DataProtectionMgmtClient(
credential=DefaultAzureCredential(),
subscription_id="04cf684a-d41f-4550-9f70-7708a3a2283b",
)
client.backup_instances.begin_resume_backups(
resource_group_name="testrg",
vault_name="testvault",
backup_instance_name="testbi",
).result()
# x-ms-original-file: specification/dataprotection/resource-manager/Microsoft.DataProtection/stable/2023-01-01/examples/BackupInstanceOperations/ResumeBackups.json
if __name__ == "__main__":
main()
| [
"[email protected]"
] | |
afe8a18c571199fd1780bafd986503825bd29d7a | 711756b796d68035dc6a39060515200d1d37a274 | /output_exocyst/initial_28307.py | 0582f2480f2270b8a93822f63734d612f94f1bf3 | [] | no_license | batxes/exocyst_scripts | 8b109c279c93dd68c1d55ed64ad3cca93e3c95ca | a6c487d5053b9b67db22c59865e4ef2417e53030 | refs/heads/master | 2020-06-16T20:16:24.840725 | 2016-11-30T16:23:16 | 2016-11-30T16:23:16 | 75,075,164 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,960 | py | import _surface
import chimera
try:
import chimera.runCommand
except:
pass
from VolumePath import markerset as ms
try:
from VolumePath import Marker_Set, Link
new_marker_set=Marker_Set
except:
from VolumePath import volume_path_dialog
d= volume_path_dialog(True)
new_marker_set= d.new_marker_set
marker_sets={}
surf_sets={}
if "Sec3_GFPN" not in marker_sets:
s=new_marker_set('Sec3_GFPN')
marker_sets["Sec3_GFPN"]=s
s= marker_sets["Sec3_GFPN"]
mark=s.place_marker((535.641, 593.745, 553.103), (0.15, 0.4, 0.6), 18.4716)
if "Sec3_0" not in marker_sets:
s=new_marker_set('Sec3_0')
marker_sets["Sec3_0"]=s
s= marker_sets["Sec3_0"]
mark=s.place_marker((495, 355, 953), (0.21, 0.49, 0.72), 17.1475)
if "Sec3_1" not in marker_sets:
s=new_marker_set('Sec3_1')
marker_sets["Sec3_1"]=s
s= marker_sets["Sec3_1"]
mark=s.place_marker((575, 768, 495), (0.21, 0.49, 0.72), 17.1475)
if "Sec3_2" not in marker_sets:
s=new_marker_set('Sec3_2')
marker_sets["Sec3_2"]=s
s= marker_sets["Sec3_2"]
mark=s.place_marker((717, 602, 946), (0.21, 0.49, 0.72), 17.1475)
if "Sec3_3" not in marker_sets:
s=new_marker_set('Sec3_3')
marker_sets["Sec3_3"]=s
s= marker_sets["Sec3_3"]
mark=s.place_marker((677, 208, 177), (0.21, 0.49, 0.72), 17.1475)
if "Sec3_4" not in marker_sets:
s=new_marker_set('Sec3_4')
marker_sets["Sec3_4"]=s
s= marker_sets["Sec3_4"]
mark=s.place_marker((320, 428, 299), (0.21, 0.49, 0.72), 17.1475)
if "Sec3_5" not in marker_sets:
s=new_marker_set('Sec3_5')
marker_sets["Sec3_5"]=s
s= marker_sets["Sec3_5"]
mark=s.place_marker((867, 760, 334), (0.21, 0.49, 0.72), 17.1475)
if "Sec3_6" not in marker_sets:
s=new_marker_set('Sec3_6')
marker_sets["Sec3_6"]=s
s= marker_sets["Sec3_6"]
mark=s.place_marker((912, 50, 380), (0.21, 0.49, 0.72), 17.1475)
if "Sec3_GFPC" not in marker_sets:
s=new_marker_set('Sec3_GFPC')
marker_sets["Sec3_GFPC"]=s
s= marker_sets["Sec3_GFPC"]
mark=s.place_marker((521.31, 569.568, 598.694), (0.3, 0.6, 0.8), 18.4716)
if "Sec3_Anch" not in marker_sets:
s=new_marker_set('Sec3_Anch')
marker_sets["Sec3_Anch"]=s
s= marker_sets["Sec3_Anch"]
mark=s.place_marker((726.375, 562.158, 700.437), (0.3, 0.6, 0.8), 18.4716)
if "Sec5_GFPN" not in marker_sets:
s=new_marker_set('Sec5_GFPN')
marker_sets["Sec5_GFPN"]=s
s= marker_sets["Sec5_GFPN"]
mark=s.place_marker((566.996, 618.569, 556.866), (0.5, 0.3, 0.6), 18.4716)
if "Sec5_0" not in marker_sets:
s=new_marker_set('Sec5_0')
marker_sets["Sec5_0"]=s
s= marker_sets["Sec5_0"]
mark=s.place_marker((188, 654, 52), (0.6, 0.31, 0.64), 17.1475)
if "Sec5_1" not in marker_sets:
s=new_marker_set('Sec5_1')
marker_sets["Sec5_1"]=s
s= marker_sets["Sec5_1"]
mark=s.place_marker((897, 753, 836), (0.6, 0.31, 0.64), 17.1475)
if "Sec5_2" not in marker_sets:
s=new_marker_set('Sec5_2')
marker_sets["Sec5_2"]=s
s= marker_sets["Sec5_2"]
mark=s.place_marker((929, 146, 149), (0.6, 0.31, 0.64), 17.1475)
if "Sec5_3" not in marker_sets:
s=new_marker_set('Sec5_3')
marker_sets["Sec5_3"]=s
s= marker_sets["Sec5_3"]
mark=s.place_marker((857, 159, 789), (0.6, 0.31, 0.64), 17.1475)
if "Sec5_4" not in marker_sets:
s=new_marker_set('Sec5_4')
marker_sets["Sec5_4"]=s
s= marker_sets["Sec5_4"]
mark=s.place_marker((59, 242, 262), (0.6, 0.31, 0.64), 17.1475)
if "Sec5_5" not in marker_sets:
s=new_marker_set('Sec5_5')
marker_sets["Sec5_5"]=s
s= marker_sets["Sec5_5"]
mark=s.place_marker((320, 760, 670), (0.6, 0.31, 0.64), 17.1475)
if "Sec5_GFPC" not in marker_sets:
s=new_marker_set('Sec5_GFPC')
marker_sets["Sec5_GFPC"]=s
s= marker_sets["Sec5_GFPC"]
mark=s.place_marker((545.611, 538.15, 607.269), (0.7, 0.4, 0.7), 18.4716)
if "Sec6_GFPN" not in marker_sets:
s=new_marker_set('Sec6_GFPN')
marker_sets["Sec6_GFPN"]=s
s= marker_sets["Sec6_GFPN"]
mark=s.place_marker((547.051, 622.667, 621.128), (1, 1, 0), 18.4716)
if "Sec6_0" not in marker_sets:
s=new_marker_set('Sec6_0')
marker_sets["Sec6_0"]=s
s= marker_sets["Sec6_0"]
mark=s.place_marker((711, 555, 61), (1, 1, 0.2), 17.1475)
if "Sec6_1" not in marker_sets:
s=new_marker_set('Sec6_1')
marker_sets["Sec6_1"]=s
s= marker_sets["Sec6_1"]
mark=s.place_marker((979, 400, 73), (1, 1, 0.2), 17.1475)
if "Sec6_2" not in marker_sets:
s=new_marker_set('Sec6_2')
marker_sets["Sec6_2"]=s
s= marker_sets["Sec6_2"]
mark=s.place_marker((838, 825, 572), (1, 1, 0.2), 17.1475)
if "Sec6_3" not in marker_sets:
s=new_marker_set('Sec6_3')
marker_sets["Sec6_3"]=s
s= marker_sets["Sec6_3"]
mark=s.place_marker((751, 349, 11), (1, 1, 0.2), 17.1475)
if "Sec6_4" not in marker_sets:
s=new_marker_set('Sec6_4')
marker_sets["Sec6_4"]=s
s= marker_sets["Sec6_4"]
mark=s.place_marker((362, 888, 49), (1, 1, 0.2), 17.1475)
if "Sec6_5" not in marker_sets:
s=new_marker_set('Sec6_5')
marker_sets["Sec6_5"]=s
s= marker_sets["Sec6_5"]
mark=s.place_marker((435, 315, 332), (1, 1, 0.2), 17.1475)
if "Sec6_GFPC" not in marker_sets:
s=new_marker_set('Sec6_GFPC')
marker_sets["Sec6_GFPC"]=s
s= marker_sets["Sec6_GFPC"]
mark=s.place_marker((668.461, 556.663, 518.953), (1, 1, 0.4), 18.4716)
if "Sec6_Anch" not in marker_sets:
s=new_marker_set('Sec6_Anch')
marker_sets["Sec6_Anch"]=s
s= marker_sets["Sec6_Anch"]
mark=s.place_marker((617.586, 542.531, 361.748), (1, 1, 0.4), 18.4716)
if "Sec8_0" not in marker_sets:
s=new_marker_set('Sec8_0')
marker_sets["Sec8_0"]=s
s= marker_sets["Sec8_0"]
mark=s.place_marker((159, 613, 117), (0.65, 0.34, 0.16), 17.1475)
if "Sec8_1" not in marker_sets:
s=new_marker_set('Sec8_1')
marker_sets["Sec8_1"]=s
s= marker_sets["Sec8_1"]
mark=s.place_marker((950, 963, 193), (0.65, 0.34, 0.16), 17.1475)
if "Sec8_2" not in marker_sets:
s=new_marker_set('Sec8_2')
marker_sets["Sec8_2"]=s
s= marker_sets["Sec8_2"]
mark=s.place_marker((960, 263, 149), (0.65, 0.34, 0.16), 17.1475)
if "Sec8_3" not in marker_sets:
s=new_marker_set('Sec8_3')
marker_sets["Sec8_3"]=s
s= marker_sets["Sec8_3"]
mark=s.place_marker((122, 900, 660), (0.65, 0.34, 0.16), 17.1475)
if "Sec8_4" not in marker_sets:
s=new_marker_set('Sec8_4')
marker_sets["Sec8_4"]=s
s= marker_sets["Sec8_4"]
mark=s.place_marker((751, 976, 227), (0.65, 0.34, 0.16), 17.1475)
if "Sec8_5" not in marker_sets:
s=new_marker_set('Sec8_5')
marker_sets["Sec8_5"]=s
s= marker_sets["Sec8_5"]
mark=s.place_marker((564, 336, 719), (0.65, 0.34, 0.16), 17.1475)
if "Sec8_GFPC" not in marker_sets:
s=new_marker_set('Sec8_GFPC')
marker_sets["Sec8_GFPC"]=s
s= marker_sets["Sec8_GFPC"]
mark=s.place_marker((650.391, 496.924, 542.858), (0.7, 0.4, 0), 18.4716)
if "Sec8_Anch" not in marker_sets:
s=new_marker_set('Sec8_Anch')
marker_sets["Sec8_Anch"]=s
s= marker_sets["Sec8_Anch"]
mark=s.place_marker((662.636, 557.115, 753.535), (0.7, 0.4, 0), 18.4716)
if "Sec10_GFPN" not in marker_sets:
s=new_marker_set('Sec10_GFPN')
marker_sets["Sec10_GFPN"]=s
s= marker_sets["Sec10_GFPN"]
mark=s.place_marker((704.281, 503.596, 493.349), (0.2, 0.6, 0.2), 18.4716)
if "Sec10_0" not in marker_sets:
s=new_marker_set('Sec10_0')
marker_sets["Sec10_0"]=s
s= marker_sets["Sec10_0"]
mark=s.place_marker((424, 952, 298), (0.3, 0.69, 0.29), 17.1475)
if "Sec10_1" not in marker_sets:
s=new_marker_set('Sec10_1')
marker_sets["Sec10_1"]=s
s= marker_sets["Sec10_1"]
mark=s.place_marker((876, 77, 606), (0.3, 0.69, 0.29), 17.1475)
if "Sec10_2" not in marker_sets:
s=new_marker_set('Sec10_2')
marker_sets["Sec10_2"]=s
s= marker_sets["Sec10_2"]
mark=s.place_marker((983, 170, 963), (0.3, 0.69, 0.29), 17.1475)
if "Sec10_3" not in marker_sets:
s=new_marker_set('Sec10_3')
marker_sets["Sec10_3"]=s
s= marker_sets["Sec10_3"]
mark=s.place_marker((282, 193, 493), (0.3, 0.69, 0.29), 17.1475)
if "Sec10_4" not in marker_sets:
s=new_marker_set('Sec10_4')
marker_sets["Sec10_4"]=s
s= marker_sets["Sec10_4"]
mark=s.place_marker((179, 167, 362), (0.3, 0.69, 0.29), 17.1475)
if "Sec10_5" not in marker_sets:
s=new_marker_set('Sec10_5')
marker_sets["Sec10_5"]=s
s= marker_sets["Sec10_5"]
mark=s.place_marker((839, 203, 588), (0.3, 0.69, 0.29), 17.1475)
if "Sec10_GFPC" not in marker_sets:
s=new_marker_set('Sec10_GFPC')
marker_sets["Sec10_GFPC"]=s
s= marker_sets["Sec10_GFPC"]
mark=s.place_marker((540.06, 454.674, 611.871), (0.4, 0.75, 0.3), 18.4716)
if "Sec10_Anch" not in marker_sets:
s=new_marker_set('Sec10_Anch')
marker_sets["Sec10_Anch"]=s
s= marker_sets["Sec10_Anch"]
mark=s.place_marker((631.347, 641.621, 402.759), (0.4, 0.75, 0.3), 18.4716)
if "Sec15_GFPN" not in marker_sets:
s=new_marker_set('Sec15_GFPN')
marker_sets["Sec15_GFPN"]=s
s= marker_sets["Sec15_GFPN"]
mark=s.place_marker((568.942, 549.602, 529.226), (0.9, 0.5, 0.7), 18.4716)
if "Sec15_0" not in marker_sets:
s=new_marker_set('Sec15_0')
marker_sets["Sec15_0"]=s
s= marker_sets["Sec15_0"]
mark=s.place_marker((880, 522, 130), (0.97, 0.51, 0.75), 17.1475)
if "Sec15_1" not in marker_sets:
s=new_marker_set('Sec15_1')
marker_sets["Sec15_1"]=s
s= marker_sets["Sec15_1"]
mark=s.place_marker((91, 986, 592), (0.97, 0.51, 0.75), 17.1475)
if "Sec15_2" not in marker_sets:
s=new_marker_set('Sec15_2')
marker_sets["Sec15_2"]=s
s= marker_sets["Sec15_2"]
mark=s.place_marker((437, 34, 973), (0.97, 0.51, 0.75), 17.1475)
if "Sec15_3" not in marker_sets:
s=new_marker_set('Sec15_3')
marker_sets["Sec15_3"]=s
s= marker_sets["Sec15_3"]
mark=s.place_marker((273, 725, 385), (0.97, 0.51, 0.75), 17.1475)
if "Sec15_4" not in marker_sets:
s=new_marker_set('Sec15_4')
marker_sets["Sec15_4"]=s
s= marker_sets["Sec15_4"]
mark=s.place_marker((46, 373, 159), (0.97, 0.51, 0.75), 17.1475)
if "Sec15_5" not in marker_sets:
s=new_marker_set('Sec15_5')
marker_sets["Sec15_5"]=s
s= marker_sets["Sec15_5"]
mark=s.place_marker((69, 614, 808), (0.97, 0.51, 0.75), 17.1475)
if "Sec15_GFPC" not in marker_sets:
s=new_marker_set('Sec15_GFPC')
marker_sets["Sec15_GFPC"]=s
s= marker_sets["Sec15_GFPC"]
mark=s.place_marker((670.499, 448.5, 527.213), (1, 0.6, 0.8), 18.4716)
if "Sec15_Anch" not in marker_sets:
s=new_marker_set('Sec15_Anch')
marker_sets["Sec15_Anch"]=s
s= marker_sets["Sec15_Anch"]
mark=s.place_marker((528.487, 386.001, 467.526), (1, 0.6, 0.8), 18.4716)
if "Exo70_GFPN" not in marker_sets:
s=new_marker_set('Exo70_GFPN')
marker_sets["Exo70_GFPN"]=s
s= marker_sets["Exo70_GFPN"]
mark=s.place_marker((524.04, 568.181, 572.181), (0.8, 0, 0), 18.4716)
if "Exo70_0" not in marker_sets:
s=new_marker_set('Exo70_0')
marker_sets["Exo70_0"]=s
s= marker_sets["Exo70_0"]
mark=s.place_marker((262, 268, 542), (0.89, 0.1, 0.1), 17.1475)
if "Exo70_1" not in marker_sets:
s=new_marker_set('Exo70_1')
marker_sets["Exo70_1"]=s
s= marker_sets["Exo70_1"]
mark=s.place_marker((345, 349, 176), (0.89, 0.1, 0.1), 17.1475)
if "Exo70_2" not in marker_sets:
s=new_marker_set('Exo70_2')
marker_sets["Exo70_2"]=s
s= marker_sets["Exo70_2"]
mark=s.place_marker((589, 774, 941), (0.89, 0.1, 0.1), 17.1475)
if "Exo70_3" not in marker_sets:
s=new_marker_set('Exo70_3')
marker_sets["Exo70_3"]=s
s= marker_sets["Exo70_3"]
mark=s.place_marker((954, 801, 509), (0.89, 0.1, 0.1), 17.1475)
if "Exo70_4" not in marker_sets:
s=new_marker_set('Exo70_4')
marker_sets["Exo70_4"]=s
s= marker_sets["Exo70_4"]
mark=s.place_marker((787, 704, 774), (0.89, 0.1, 0.1), 17.1475)
if "Exo70_GFPC" not in marker_sets:
s=new_marker_set('Exo70_GFPC')
marker_sets["Exo70_GFPC"]=s
s= marker_sets["Exo70_GFPC"]
mark=s.place_marker((705.748, 474.225, 557.579), (1, 0.2, 0.2), 18.4716)
if "Exo70_Anch" not in marker_sets:
s=new_marker_set('Exo70_Anch')
marker_sets["Exo70_Anch"]=s
s= marker_sets["Exo70_Anch"]
mark=s.place_marker((477.603, 670.358, 384.03), (1, 0.2, 0.2), 18.4716)
if "Exo84_GFPN" not in marker_sets:
s=new_marker_set('Exo84_GFPN')
marker_sets["Exo84_GFPN"]=s
s= marker_sets["Exo84_GFPN"]
mark=s.place_marker((568.263, 615.714, 542.602), (0.9, 0.4, 0), 18.4716)
if "Exo84_0" not in marker_sets:
s=new_marker_set('Exo84_0')
marker_sets["Exo84_0"]=s
s= marker_sets["Exo84_0"]
mark=s.place_marker((879, 664, 955), (1, 0.5, 0), 17.1475)
if "Exo84_1" not in marker_sets:
s=new_marker_set('Exo84_1')
marker_sets["Exo84_1"]=s
s= marker_sets["Exo84_1"]
mark=s.place_marker((879, 595, 534), (1, 0.5, 0), 17.1475)
if "Exo84_2" not in marker_sets:
s=new_marker_set('Exo84_2')
marker_sets["Exo84_2"]=s
s= marker_sets["Exo84_2"]
mark=s.place_marker((355, 449, 160), (1, 0.5, 0), 17.1475)
if "Exo84_3" not in marker_sets:
s=new_marker_set('Exo84_3')
marker_sets["Exo84_3"]=s
s= marker_sets["Exo84_3"]
mark=s.place_marker((157, 290, 647), (1, 0.5, 0), 17.1475)
if "Exo84_GFPC" not in marker_sets:
s=new_marker_set('Exo84_GFPC')
marker_sets["Exo84_GFPC"]=s
s= marker_sets["Exo84_GFPC"]
mark=s.place_marker((568.791, 511.347, 608.299), (1, 0.6, 0.1), 18.4716)
if "Exo84_Anch" not in marker_sets:
s=new_marker_set('Exo84_Anch')
marker_sets["Exo84_Anch"]=s
s= marker_sets["Exo84_Anch"]
mark=s.place_marker((482.881, 430.091, 445.773), (1, 0.6, 0.1), 18.4716)
for k in surf_sets.keys():
chimera.openModels.add([surf_sets[k]])
| [
"[email protected]"
] | |
0031edfd68aaa9ff6c446eb3f2c7ca612fc31273 | a903fc8f24e4867a85dc85405421137360e360a1 | /PythonFiles/venv/Lib/site-packages/google/protobuf/empty_pb2.py | 65af2f0dafd2c2e5cac8aacd9c4243f628200bf6 | [] | no_license | CiBit2G/CiBit | 8c486d2aad672a0ec5aec57a0717418f08e3a8e0 | cedd24bccb31346ae2831655953e2ef6f9c5afa6 | refs/heads/Develop | 2023-08-10T10:51:56.447517 | 2021-01-08T22:08:33 | 2021-01-08T22:08:33 | 261,506,824 | 0 | 1 | null | 2023-07-23T15:08:58 | 2020-05-05T15:14:35 | Python | UTF-8 | Python | false | true | 1,853 | py | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: google/protobuf/empty.proto
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='google/protobuf/empty.proto',
package='google.protobuf',
syntax='proto3',
serialized_options=b'\n\023com.google.protobufB\nEmptyProtoP\001Z\'github.com/golang/protobuf/ptypes/empty\370\001\001\242\002\003GPB\252\002\036Google.Protobuf.WellKnownTypes',
serialized_pb=b'\n\x1bgoogle/protobuf/empty.proto\x12\x0fgoogle.protobuf\"\x07\n\x05\x45mptyBv\n\x13\x63om.google.protobufB\nEmptyProtoP\x01Z\'github.com/golang/protobuf/ptypes/empty\xf8\x01\x01\xa2\x02\x03GPB\xaa\x02\x1eGoogle.Protobuf.WellKnownTypesb\x06proto3'
)
_EMPTY = _descriptor.Descriptor(
name='Empty',
full_name='google.protobuf.Empty',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=48,
serialized_end=55,
)
DESCRIPTOR.message_types_by_name['Empty'] = _EMPTY
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
Empty = _reflection.GeneratedProtocolMessageType('Empty', (_message.Message,), {
'DESCRIPTOR' : _EMPTY,
'__module__' : 'google.protobuf.empty_pb2'
# @@protoc_insertion_point(class_scope:google.protobuf.Empty)
})
_sym_db.RegisterMessage(Empty)
DESCRIPTOR._options = None
# @@protoc_insertion_point(module_scope)
| [
"[email protected]"
] | |
7bfca6be76ece9e5856372d1f6dd3f4a2365041a | 993f18c21402d7a4ff21ddb7ff2ec6c80e466f20 | /onnx/reference/ops/op_rnn.py | 7b4e7bee98b8137c8317e850eb7461b161e56f64 | [
"Apache-2.0"
] | permissive | onnx/onnx | 10d3916803c7babff89ec0fa9045127bcccad376 | 8a475b34cb3875df311a46f57571646498f5bda7 | refs/heads/main | 2023-08-18T18:50:03.388353 | 2023-08-16T22:18:46 | 2023-08-16T22:18:46 | 102,692,863 | 16,164 | 4,150 | Apache-2.0 | 2023-09-14T17:10:38 | 2017-09-07T04:53:45 | Python | UTF-8 | Python | false | false | 4,972 | py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221,W0613
import numpy as np
from onnx.reference.op_run import OpRun
class CommonRNN(OpRun):
def __init__(self, onnx_node, run_params): # type: ignore
OpRun.__init__(self, onnx_node, run_params)
if self.direction in ("forward", "reverse"): # type: ignore
self.num_directions = 1 # type: ignore
elif self.direction == "bidirectional": # type: ignore
self.num_directions = 2 # type: ignore
else:
raise RuntimeError(f"Unknown direction {self.direction!r}.") # type: ignore
if (
self.activation_alpha is not None # type: ignore
and len(self.activation_alpha) != self.num_directions # type: ignore
):
raise RuntimeError(
f"activation_alpha must have the same size as num_directions={self.num_directions}." # type: ignore
)
if (
self.activation_beta is not None # type: ignore
and len(self.activation_beta) != self.num_directions # type: ignore
):
raise RuntimeError(
f"activation_beta must have the same size as num_directions={self.num_directions}." # type: ignore
)
self.f1 = self.choose_act(
self.activations[0], # type: ignore
self.activation_alpha[0] # type: ignore
if self.activation_alpha is not None and len(self.activation_alpha) > 0 # type: ignore
else None,
self.activation_beta[0] # type: ignore
if self.activation_beta is not None and len(self.activation_beta) > 0 # type: ignore
else None,
)
if len(self.activations) > 1: # type: ignore
self.f2 = self.choose_act(
self.activations[1], # type: ignore
self.activation_alpha[1] # type: ignore
if self.activation_alpha is not None and len(self.activation_alpha) > 1 # type: ignore
else None,
self.activation_beta[1] # type: ignore
if self.activation_beta is not None and len(self.activation_beta) > 1 # type: ignore
else None,
)
self.n_outputs = len(onnx_node.output)
def choose_act(self, name, alpha, beta): # type: ignore
if name in ("Tanh", "tanh"):
return self._f_tanh
if name in ("Affine", "affine"):
return lambda x: x * alpha + beta
raise RuntimeError(f"Unknown activation function {name!r}.")
def _f_tanh(self, x): # type: ignore
return np.tanh(x)
def _step(self, X, R, B, W, H_0): # type: ignore
h_list = []
H_t = H_0
for x in np.split(X, X.shape[0], axis=0):
H = self.f1(
np.dot(x, np.transpose(W))
+ np.dot(H_t, np.transpose(R))
+ np.add(*np.split(B, 2))
)
h_list.append(H)
H_t = H
concatenated = np.concatenate(h_list)
if self.num_directions == 1:
output = np.expand_dims(concatenated, 1)
return output, h_list[-1]
def _run( # type: ignore
self,
X,
W,
R,
B=None,
sequence_lens=None,
initial_h=None,
activation_alpha=None,
activation_beta=None,
activations=None,
clip=None,
direction=None,
hidden_size=None,
layout=None,
):
# TODO: support overridden attributes.
self.num_directions = W.shape[0]
if self.num_directions == 1:
R = np.squeeze(R, axis=0)
W = np.squeeze(W, axis=0)
if B is not None:
B = np.squeeze(B, axis=0)
if sequence_lens is not None:
sequence_lens = np.squeeze(sequence_lens, axis=0)
if initial_h is not None:
initial_h = np.squeeze(initial_h, axis=0)
hidden_size = R.shape[-1]
batch_size = X.shape[1]
X = X if layout == 0 else np.swapaxes(X, 0, 1)
b = B if B is not None else np.zeros(2 * hidden_size, dtype=X.dtype)
h_0 = (
initial_h
if initial_h is not None
else np.zeros((batch_size, hidden_size), dtype=X.dtype)
)
B = b
H_0 = h_0
else:
raise NotImplementedError(
f"Unsupported value {self.num_directions} for num_directions and operator {self.__class__.__name__!r}."
)
Y, Y_h = self._step(X, R, B, W, H_0)
if layout == 1:
Y = np.transpose(Y, [2, 0, 1, 3])
Y_h = Y[:, :, -1, :]
Y = Y.astype(X.dtype)
return (Y,) if self.n_outputs == 1 else (Y, Y_h)
class RNN_7(CommonRNN):
pass
class RNN_14(CommonRNN):
pass
| [
"[email protected]"
] | |
10307bb15711fd8b79dd45b49797dbad958413d1 | 98c6ea9c884152e8340605a706efefbea6170be5 | /examples/data/Assignment_3/blsmic004/question4.py | 71ab4ab7d932bf5d278d61266de70a716f569693 | [] | no_license | MrHamdulay/csc3-capstone | 479d659e1dcd28040e83ebd9e3374d0ccc0c6817 | 6f0fa0fa1555ceb1b0fb33f25e9694e68b6a53d2 | refs/heads/master | 2021-03-12T21:55:57.781339 | 2014-09-22T02:22:22 | 2014-09-22T02:22:22 | 22,372,174 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 541 | py | # Find palindromic primes between given values
# Michele Balestra BLSMIC004
# 23 March 2014
N = eval(input("Enter the starting point N:\n"))
M = eval(input("Enter the ending point M:\n"))
print("The palindromic primes are:")
for i in range(N+1,M):
strI = str(i)
if i==2:
print(i)
elif i==1: continue
elif i%2==0:continue
elif strI==strI[-1::-1]:
for j in range(2,int(i**0.5)+1):
if i%j==0:
break
else:
print(i)
else:
pass | [
"[email protected]"
] | |
a29d3e7de9b847c9928e92986e748f06fa3419d8 | f33192647643bf11f13936866d80d573189bd72f | /Easy/Kids With the Greatest Number of Candies.py | f499a7528ba409bd0f7b31b60b726b5016c81386 | [] | no_license | komalupatil/Leetcode_Solutions | d8a10bd8ea341dc51dbcdacfd1f2b9a4bb65032d | 62d20010ebdb91c15ecbcf5fb38e308511f26499 | refs/heads/master | 2022-05-19T00:13:31.540076 | 2022-04-23T01:45:11 | 2022-04-23T01:45:11 | 194,587,808 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 439 | py | #Leetcode 1431. Kids With the Greatest Number of Candies
class Solution:
def kidsWithCandies(self, candies: List[int], extraCandies: int) -> List[bool]:
maxCandy = max(candies)
result = []
for i in range(len(candies)):
if candies[i] == maxCandy or candies[i]+ extraCandies >= maxCandy:
result.append(True)
else:
result.append(False)
return result | [
"[email protected]"
] | |
45f46ed66d7154c90c87712a64c1ac8d6e923aa3 | aa0270b351402e421631ebc8b51e528448302fab | /sdk/databox/azure-mgmt-databox/azure/mgmt/databox/v2021_12_01/operations/_operations.py | 502f0faa6cfebf14fef2aa7238b3bc1804394cec | [
"MIT",
"LGPL-2.1-or-later",
"LicenseRef-scancode-generic-cla"
] | permissive | fangchen0601/azure-sdk-for-python | d04a22109d0ff8ff209c82e4154b7169b6cb2e53 | c2e11d6682e368b2f062e714490d2de42e1fed36 | refs/heads/master | 2023-05-11T16:53:26.317418 | 2023-05-04T20:02:16 | 2023-05-04T20:02:16 | 300,440,803 | 0 | 0 | MIT | 2020-10-16T18:45:29 | 2020-10-01T22:27:56 | null | UTF-8 | Python | false | false | 6,717 | py | # pylint: disable=too-many-lines
# 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.
# --------------------------------------------------------------------------
import sys
from typing import Any, Callable, Dict, Iterable, Optional, TypeVar
import urllib.parse
from azure.core.exceptions import (
ClientAuthenticationError,
HttpResponseError,
ResourceExistsError,
ResourceNotFoundError,
ResourceNotModifiedError,
map_error,
)
from azure.core.paging import ItemPaged
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from azure.core.utils import case_insensitive_dict
from azure.mgmt.core.exceptions import ARMErrorFormat
from .. import models as _models
from ..._serialization import Serializer
from .._vendor import DataBoxManagementClientMixinABC, _convert_request
if sys.version_info >= (3, 8):
from typing import Literal # pylint: disable=no-name-in-module, ungrouped-imports
else:
from typing_extensions import Literal # type: ignore # pylint: disable=ungrouped-imports
T = TypeVar("T")
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
def build_list_request(**kwargs: Any) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: Literal["2021-12-01"] = kwargs.pop("api_version", _params.pop("api-version", "2021-12-01"))
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop("template_url", "/providers/Microsoft.DataBox/operations")
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
# Construct headers
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
class Operations:
"""
.. warning::
**DO NOT** instantiate this class directly.
Instead, you should access the following operations through
:class:`~azure.mgmt.databox.v2021_12_01.DataBoxManagementClient`'s
:attr:`operations` attribute.
"""
models = _models
def __init__(self, *args, **kwargs):
input_args = list(args)
self._client = input_args.pop(0) if input_args else kwargs.pop("client")
self._config = input_args.pop(0) if input_args else kwargs.pop("config")
self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
@distributed_trace
def list(self, **kwargs: Any) -> Iterable["_models.Operation"]:
"""This method gets all the operations.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either Operation or the result of cls(response)
:rtype: ~azure.core.paging.ItemPaged[~azure.mgmt.databox.v2021_12_01.models.Operation]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version: Literal["2021-12-01"] = kwargs.pop("api_version", _params.pop("api-version", "2021-12-01"))
cls: ClsType[_models.OperationList] = kwargs.pop("cls", None)
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
def prepare_request(next_link=None):
if not next_link:
request = build_list_request(
api_version=api_version,
template_url=self.list.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
else:
# make call to next link with the client's api-version
_parsed_next_link = urllib.parse.urlparse(next_link)
_next_request_params = case_insensitive_dict(
{
key: [urllib.parse.quote(v) for v in value]
for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
}
)
_next_request_params["api-version"] = self._config.api_version
request = HttpRequest(
"GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
request.method = "GET"
return request
def extract_data(pipeline_response):
deserialized = self._deserialize("OperationList", pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem) # type: ignore
return deserialized.next_link or None, iter(list_of_elem)
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response: PipelineResponse = self._client._pipeline.run( # pylint: disable=protected-access
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)
error = self._deserialize.failsafe_deserialize(_models.ApiError, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)
list.metadata = {"url": "/providers/Microsoft.DataBox/operations"}
| [
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] | |
d99e70cf63ab0cd31386b32105d62353563e8104 | 7e099db730027a332cef52c4e539a1c3a39584d7 | /shell.py | 79226bed2f104aac2d8f90a5d23985b81099cf9f | [] | no_license | haricm/hasweb | 688108d01950822c9f3813a9a926c11e17529827 | 48e92c46855e4fa14f35090ae1e1c9ceffa5d75f | refs/heads/static | 2020-12-28T06:58:31.525138 | 2016-06-22T21:41:54 | 2016-06-22T21:41:54 | 51,294,404 | 0 | 0 | null | 2016-02-08T11:36:39 | 2016-02-08T11:36:38 | null | UTF-8 | Python | false | false | 181 | py | #! /usr/bin/env python
#! -*- coding: utf-8 -*-
import os
import readline
from pprint import pprint
from flask import *
from hasweb import *
os.environ['PYTHONINSPECT'] = 'True'
| [
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] | |
0f80ceed0a9b7196db673950e939e754400fa998 | 7b55cfc4ffa7678e4c7b8f2312831ebbd549e54f | /proj1/tests/other-tests/strictfp_tests/error/test_for_else_continue.py | b31c955db56843adb3d1a5c3bfa2c2f5a31bb97d | [] | no_license | czchen1/cs164-projects | 0d330efef85421e611a436b165428ba0ddfb3512 | a04cafbcaafd32e518227dacf89a6d7837bf9f57 | refs/heads/master | 2020-03-27T04:03:31.727524 | 2018-08-23T21:43:46 | 2018-08-23T21:43:46 | 145,909,148 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 41 | py | for _ in ():
pass
else:
continue
| [
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] | |
10dc94fce77ff1027f55411a178940bdfd04d4a5 | ec0b8bfe19b03e9c3bb13d9cfa9bd328fb9ca3f1 | /res/packages/scripts/scripts/common/Lib/plat-mac/lib-scriptpackages/Explorer/Netscape_Suite.py | 9dc8c9f099abf16d3e2caf20047680e160b01130 | [] | no_license | webiumsk/WOT-0.9.20.0 | de3d7441c5d442f085c47a89fa58a83f1cd783f2 | 811cb4e1bca271372a1d837a268b6e0e915368bc | refs/heads/master | 2021-01-20T22:11:45.505844 | 2017-08-29T20:11:38 | 2017-08-29T20:11:38 | 101,803,045 | 0 | 1 | null | null | null | null | WINDOWS-1250 | Python | false | false | 1,485 | py | # 2017.08.29 21:58:49 Střední Evropa (letní čas)
# Embedded file name: scripts/common/Lib/plat-mac/lib-scriptpackages/Explorer/Netscape_Suite.py
"""Suite Netscape Suite: Events defined by Netscape
Level 1, version 1
Generated from /Applications/Internet Explorer.app
AETE/AEUT resource version 1/0, language 0, script 0
"""
import aetools
import MacOS
_code = 'MOSS'
class Netscape_Suite_Events:
def Open_bookmark(self, _object = None, _attributes = {}, **_arguments):
"""Open bookmark: Opens a bookmark file
Required argument: If not available, reloads the current bookmark file
Keyword argument _attributes: AppleEvent attribute dictionary
"""
_code = 'MOSS'
_subcode = 'book'
if _arguments:
raise TypeError, 'No optional args expected'
_arguments['----'] = _object
_reply, _arguments, _attributes = self.send(_code, _subcode, _arguments, _attributes)
if _arguments.get('errn', 0):
raise aetools.Error, aetools.decodeerror(_arguments)
if _arguments.has_key('----'):
return _arguments['----']
_classdeclarations = {}
_propdeclarations = {}
_compdeclarations = {}
_enumdeclarations = {}
# okay decompyling c:\Users\PC\wotmods\files\originals\res\packages\scripts\scripts\common\Lib\plat-mac\lib-scriptpackages\Explorer\Netscape_Suite.pyc
# decompiled 1 files: 1 okay, 0 failed, 0 verify failed
# 2017.08.29 21:58:49 Střední Evropa (letní čas)
| [
"[email protected]"
] | |
d9c62814e709deaedc82713d90955bb4505d05c9 | 5759c0ed3219c06437ce5b39ef9ad92b5e191fed | /py/0814_binary_tree_pruning.py | 58027f3dc60a087ead471c4791aa442c2fa71a8e | [] | no_license | mengnan1994/Surrender-to-Reality | ba69df7c36112ad19f19157a9f368eae6340630f | 66232728ce49149188f863271ec2c57e426abb43 | refs/heads/master | 2022-02-25T01:34:49.526517 | 2019-09-22T17:21:28 | 2019-09-22T17:21:28 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,527 | py | """
We are given the head node root of a binary tree, where additionally every node's value is either a 0 or a 1.
Return the same tree where every subtree (of the given tree) not containing a 1 has been removed.
(Recall that the subtree of a node X is X, plus every node that is a descendant of X.)
Example 1:
Input: [1,null,0,0,1]
Output: [1,null,0,null,1]
Explanation:
Only the red nodes satisfy the property "every subtree not containing a 1".
The diagram on the right represents the answer.
Example 2:
Input: [1,0,1,0,0,0,1]
Output: [1,null,1,null,1]
Example 3:
Input: [1,1,0,1,1,0,1,0]
Output: [1,1,0,1,1,null,1]
Note:
1. The binary tree will have at most 100 nodes.
2. The value of each node will only be 0 or 1.
"""
# Definition for a binary tree node.
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def prune_tree(self, root):
"""
后序遍历，统计子树的节点值之和。若和为 0，剪枝
"""
if root.val == 0 and not root.left and not root.right:
return None
self._postorder_traverse(root)
return root
def _postorder_traverse(self, node : TreeNode):
if not node:
return 0
left = self._postorder_traverse(node.left)
right = self._postorder_traverse(node.right)
if not left:
node.left = None
if not right:
node.right = None
return left + right + node.val
| [
"[email protected]"
] | |
b8d25789b012978f89d53ff49aaf89fe33ba5b8e | 17acb8e20f9a24b16ce3651302fc2d7fc7b887a6 | /src/programy/storage/stores/nosql/mongo/store/users.py | 02d5a2bccdd7221a3337439e37f79782513550ca | [
"MIT"
] | permissive | cen-ai/program-y | 91052fdc11aec0f60311e3429895fac489d8ce54 | a753667638147544c54dbebd9f1c8f9ae7f2159e | refs/heads/master | 2020-03-22T15:11:07.896885 | 2018-10-15T22:13:58 | 2018-10-15T22:13:58 | 140,234,173 | 5 | 5 | NOASSERTION | 2019-01-03T09:09:07 | 2018-07-09T05:11:08 | Python | UTF-8 | Python | false | false | 2,230 | py | """
Copyright (c) 2016-2018 Keith Sterling http://www.keithsterling.com
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 programy.utils.logging.ylogger import YLogger
from programy.storage.stores.nosql.mongo.store.mongostore import MongoStore
from programy.storage.entities.user import UserStore
from programy.storage.stores.nosql.mongo.dao.user import User
class MongoUserStore(MongoStore, UserStore):
USERS = 'users'
USERID = 'userid'
CLIENT = 'client'
def __init__(self, storage_engine):
MongoStore.__init__(self, storage_engine)
def collection_name(self):
return MongoUserStore.USERS
def add_user(self, userid, client):
YLogger.info(self, "Adding user [%s] for client [%s]", userid, client)
user = User(userid, client)
self.add_document(user)
return True
def get_user(self, userid):
collection = self.collection()
user = collection.find_one({MongoUserStore.USERID: userid})
return user
def get_client_users(self, client):
collection = self.collection()
db_users = collection.find({MongoUserStore.CLIENT: client})
users = []
for user in db_users:
users.append(user)
return users
| [
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] | |
9c742413d38b0aaf50f7200c1696142e316f6e85 | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/verbs/_scrimps.py | 64415eec48625a7a2cc7517d1e7bb4590bc19881 | [
"MIT"
] | permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 238 | py |
from xai.brain.wordbase.verbs._scrimp import _SCRIMP
#calss header
class _SCRIMPS(_SCRIMP, ):
def __init__(self,):
_SCRIMP.__init__(self)
self.name = "SCRIMPS"
self.specie = 'verbs'
self.basic = "scrimp"
self.jsondata = {}
| [
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] | |
5a44fb3ce092fff1fc046dcecfc1d3f3eec40fcd | 134ff3c0719d4c0022eb0fb7c859bdbff5ca34b2 | /desktop/core/ext-py/Mako-0.3.3/mako/pygen.py | aada94d23e3be74ab01a823a6bece33ad1e46ed2 | [
"MIT",
"LicenseRef-scancode-proprietary-license",
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | permissive | civascu/hue | 22637f13a4cfc557716557661523131b6ac16da4 | 82f2de44789ff5a981ed725175bae7944832d1e9 | refs/heads/master | 2020-03-31T01:50:39.449966 | 2010-07-21T01:05:50 | 2010-07-21T01:07:15 | 788,284 | 0 | 0 | Apache-2.0 | 2019-02-04T07:03:12 | 2010-07-21T07:34:27 | Python | UTF-8 | Python | false | false | 9,774 | py | # pygen.py
# Copyright (C) 2006, 2007, 2008, 2009, 2010 Michael Bayer [email protected]
#
# This module is part of Mako and is released under
# the MIT License: http://www.opensource.org/licenses/mit-license.php
"""utilities for generating and formatting literal Python code."""
import re, string
from StringIO import StringIO
from mako import exceptions
class PythonPrinter(object):
def __init__(self, stream):
# indentation counter
self.indent = 0
# a stack storing information about why we incremented
# the indentation counter, to help us determine if we
# should decrement it
self.indent_detail = []
# the string of whitespace multiplied by the indent
# counter to produce a line
self.indentstring = " "
# the stream we are writing to
self.stream = stream
# a list of lines that represents a buffered "block" of code,
# which can be later printed relative to an indent level
self.line_buffer = []
self.in_indent_lines = False
self._reset_multi_line_flags()
def write(self, text):
self.stream.write(text)
def write_indented_block(self, block):
"""print a line or lines of python which already contain indentation.
The indentation of the total block of lines will be adjusted to that of
the current indent level."""
self.in_indent_lines = False
for l in re.split(r'\r?\n', block):
self.line_buffer.append(l)
def writelines(self, *lines):
"""print a series of lines of python."""
for line in lines:
self.writeline(line)
def writeline(self, line):
"""print a line of python, indenting it according to the current indent level.
this also adjusts the indentation counter according to the content of the line."""
if not self.in_indent_lines:
self._flush_adjusted_lines()
self.in_indent_lines = True
decreased_indent = False
if (line is None or
re.match(r"^\s*#",line) or
re.match(r"^\s*$", line)
):
hastext = False
else:
hastext = True
is_comment = line and len(line) and line[0] == '#'
# see if this line should decrease the indentation level
if (not decreased_indent and
not is_comment and
(not hastext or self._is_unindentor(line))
):
if self.indent > 0:
self.indent -=1
# if the indent_detail stack is empty, the user
# probably put extra closures - the resulting
# module wont compile.
if len(self.indent_detail) == 0:
raise exceptions.SyntaxException("Too many whitespace closures")
self.indent_detail.pop()
if line is None:
return
# write the line
self.stream.write(self._indent_line(line) + "\n")
# see if this line should increase the indentation level.
# note that a line can both decrase (before printing) and
# then increase (after printing) the indentation level.
if re.search(r":[ \t]*(?:#.*)?$", line):
# increment indentation count, and also
# keep track of what the keyword was that indented us,
# if it is a python compound statement keyword
# where we might have to look for an "unindent" keyword
match = re.match(r"^\s*(if|try|elif|while|for)", line)
if match:
# its a "compound" keyword, so we will check for "unindentors"
indentor = match.group(1)
self.indent +=1
self.indent_detail.append(indentor)
else:
indentor = None
# its not a "compound" keyword. but lets also
# test for valid Python keywords that might be indenting us,
# else assume its a non-indenting line
m2 = re.match(r"^\s*(def|class|else|elif|except|finally)", line)
if m2:
self.indent += 1
self.indent_detail.append(indentor)
def close(self):
"""close this printer, flushing any remaining lines."""
self._flush_adjusted_lines()
def _is_unindentor(self, line):
"""return true if the given line is an 'unindentor', relative to the last 'indent' event received."""
# no indentation detail has been pushed on; return False
if len(self.indent_detail) == 0:
return False
indentor = self.indent_detail[-1]
# the last indent keyword we grabbed is not a
# compound statement keyword; return False
if indentor is None:
return False
# if the current line doesnt have one of the "unindentor" keywords,
# return False
match = re.match(r"^\s*(else|elif|except|finally).*\:", line)
if not match:
return False
# whitespace matches up, we have a compound indentor,
# and this line has an unindentor, this
# is probably good enough
return True
# should we decide that its not good enough, heres
# more stuff to check.
#keyword = match.group(1)
# match the original indent keyword
#for crit in [
# (r'if|elif', r'else|elif'),
# (r'try', r'except|finally|else'),
# (r'while|for', r'else'),
#]:
# if re.match(crit[0], indentor) and re.match(crit[1], keyword): return True
#return False
def _indent_line(self, line, stripspace = ''):
"""indent the given line according to the current indent level.
stripspace is a string of space that will be truncated from the start of the line
before indenting."""
return re.sub(r"^%s" % stripspace, self.indentstring * self.indent, line)
def _reset_multi_line_flags(self):
"""reset the flags which would indicate we are in a backslashed or triple-quoted section."""
(self.backslashed, self.triplequoted) = (False, False)
def _in_multi_line(self, line):
"""return true if the given line is part of a multi-line block, via backslash or triple-quote."""
# we are only looking for explicitly joined lines here,
# not implicit ones (i.e. brackets, braces etc.). this is just
# to guard against the possibility of modifying the space inside
# of a literal multiline string with unfortunately placed whitespace
current_state = (self.backslashed or self.triplequoted)
if re.search(r"\\$", line):
self.backslashed = True
else:
self.backslashed = False
triples = len(re.findall(r"\"\"\"|\'\'\'", line))
if triples == 1 or triples % 2 != 0:
self.triplequoted = not self.triplequoted
return current_state
def _flush_adjusted_lines(self):
stripspace = None
self._reset_multi_line_flags()
for entry in self.line_buffer:
if self._in_multi_line(entry):
self.stream.write(entry + "\n")
else:
entry = entry.expandtabs()
if stripspace is None and re.search(r"^[ \t]*[^# \t]", entry):
stripspace = re.match(r"^([ \t]*)", entry).group(1)
self.stream.write(self._indent_line(entry, stripspace) + "\n")
self.line_buffer = []
self._reset_multi_line_flags()
def adjust_whitespace(text):
"""remove the left-whitespace margin of a block of Python code."""
state = [False, False]
(backslashed, triplequoted) = (0, 1)
def in_multi_line(line):
start_state = (state[backslashed] or state[triplequoted])
if re.search(r"\\$", line):
state[backslashed] = True
else:
state[backslashed] = False
def match(reg, t):
m = re.match(reg, t)
if m:
return m, t[len(m.group(0)):]
else:
return None, t
while line:
if state[triplequoted]:
m, line = match(r"%s" % state[triplequoted], line)
if m:
state[triplequoted] = False
else:
m, line = match(r".*?(?=%s|$)" % state[triplequoted], line)
else:
m, line = match(r'#', line)
if m:
return start_state
m, line = match(r"\"\"\"|\'\'\'", line)
if m:
state[triplequoted] = m.group(0)
continue
m, line = match(r".*?(?=\"\"\"|\'\'\'|#|$)", line)
return start_state
def _indent_line(line, stripspace = ''):
return re.sub(r"^%s" % stripspace, '', line)
lines = []
stripspace = None
for line in re.split(r'\r?\n', text):
if in_multi_line(line):
lines.append(line)
else:
line = line.expandtabs()
if stripspace is None and re.search(r"^[ \t]*[^# \t]", line):
stripspace = re.match(r"^([ \t]*)", line).group(1)
lines.append(_indent_line(line, stripspace))
return "\n".join(lines)
| [
"[email protected]"
] | |
eac6a2b1d70e266e04d31db1b3283e1f1ae7aaae | bebc317eebad1e0f136c2d959e5b9882e123d366 | /game/mario/src/data/level.py | 45618b67ab8331a23000646413fe4780b9d3a293 | [
"Apache-2.0"
] | permissive | GuillaumeFalourd/formulas-games | 62ac5b87e6868db29de022e52fd56993ec9c3c46 | 7530ef09cc85f30157e62b8761868c2a0eddb93d | refs/heads/main | 2023-07-14T04:33:11.666127 | 2021-08-29T23:21:16 | 2021-08-29T23:21:16 | 369,598,803 | 10 | 2 | null | null | null | null | UTF-8 | Python | false | false | 3,192 | py | from .sprites import level_1
from .basetypes import Vector2, Rectangle
from . import config as c
from .components.tiles import Question, Brick, Collider_Rect, Flagpole
from .components.items import *
from .components.enemies import *
#Colliders that don't possess velocity
static_colliders = []
#Colliders that possess velocity
dynamic_colliders = []
coins = []
super_mushrooms = []
enemies = []
#Fragments go here when a brick tile gets broken
brick_fragments = []
#Start and End tile for grouping large rows of tiles into one collider
start_tile = None
end_tile = None
#Read pixel data from level map and instantiate objects corresponding to pixel colors
for y in range(0, level_1.size[1]):
for x in range(0, level_1.size[0]):
color = level_1.getpixel((x, y))
pos = Vector2(x * c.TILE_SIZE, y * c.TILE_SIZE + 24)
#Black = Static ground collider, which are grouped together for optimizations
if color == c.BLACK:
if start_tile == None:
start_tile = pos
if end_tile == None:
if x + 1 > level_1.size[0]:
end_tile = pos
if level_1.getpixel((x + 1, y)) != c.BLACK:
end_tile = pos
if end_tile != None and start_tile != None:
w = end_tile.x - start_tile.x + c.TILE_SIZE
h = c.TILE_SIZE
rect = Rectangle(start_tile, w, h)
static_colliders.append(Collider_Rect(rect))
end_tile = None
start_tile = None
#Red = Pipe collider
elif color == c.RED:
h = c.SCREEN_SIZE.y - pos.y
w = 2 * c.TILE_SIZE
rect = Rectangle(pos, w, h)
static_colliders.append(Collider_Rect(rect))
#Yellow = Question tile with coin as item
elif color == c.YELLOW:
coin_rect = Rectangle(Vector2(pos.x, pos.y), 48, 42)
contents = Coin(coin_rect)
coins.append(contents)
rect = Rectangle(pos, c.TILE_SIZE, c.TILE_SIZE)
dynamic_colliders.append(Question(rect, contents))
#Gray = Brick tile
elif color == c.GRAY:
rect = Rectangle(pos, c.TILE_SIZE, c.TILE_SIZE)
dynamic_colliders.append(Brick(rect))
#Green = Question tile with mushroom as item
elif color == c.GREEN:
mushroom_rect = Rectangle(Vector2(pos.x, pos.y), c.TILE_SIZE, c.TILE_SIZE)
contents = Super_Mushroom(mushroom_rect, Vector2(c.MUSHROOM_START_VEL_X, 0))
super_mushrooms.append(contents)
rect = Rectangle(pos, c.TILE_SIZE, c.TILE_SIZE)
dynamic_colliders.append(Question(rect, contents))
#Brown = Goomba
elif color == c.BROWN:
rect = Rectangle(pos, c.TILE_SIZE, c.TILE_SIZE)
enemies.append(Goomba(rect, Vector2()))
elif color == c.PURPLE:
rect = Rectangle(Vector2(pos.x, pos.y - 24), 48, 72)
enemies.append(Turtle(rect, Vector2()))
#Instantiate flagpole
rect = Rectangle(Vector2(9504, 96), 48, 456)
flag_pos = Vector2(9480, 120)
c.flagpole = Flagpole(rect, flag_pos)
| [
"[email protected]"
] | |
3ebadc10fd17025f0d68ab97ada972030f456f03 | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2403/60592/233469.py | 9761f234d7673441623d7aa29b2be6f823ffb15b | [] | no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 260 | py | candies = int(input())
num_people = int(input())
i = 0
res = [0]*num_people
while(candies>0):
if candies>=i+1:
candies-=i+1
res[i%num_people] += i+1;
else:
res[i%num_people] += candies
candies = 0;
i = i+1
print(res) | [
"[email protected]"
] | |
c356b33c79658d26e2ebe9e93c3a76b76ff1259b | fbcb22a83a6e74ef68a06b3e3aa92f6a5b59beed | /iconfluence/interactive/interactive_checker.py | e30aa702c6a55ebb25f4c2da816426cb942821ae | [] | no_license | mwhittaker/enforced_invariant_confluence | f296b38152bf3e9282c5529069f141ea2cc028a6 | 1275d7330b2e2e1a091272dab458c3ac6ca655f6 | refs/heads/master | 2022-11-30T05:03:27.974549 | 2020-08-16T23:58:42 | 2020-08-16T23:58:42 | 71,737,634 | 5 | 0 | null | 2022-11-22T01:49:13 | 2016-10-24T00:13:33 | Python | UTF-8 | Python | false | false | 15,040 | py | from enum import Enum
from textwrap import wrap
from typing import List, Optional, Tuple
from colored import attr, fg
from orderedset import OrderedSet
import z3
from ..ast import (EVar, EBool, EBoolAnd, EBoolNot, Expr, Invariant,
typed_coerce)
from ..checker import Checker, Decision
from ..envs import ExprEnv, ValEnv, TypeEnv, Z3ExprEnv
from ..eval import eval_invariant
from ..state_explorer import StateExplorer
from ..typecheck import typecheck_expr, typecheck_invariant
from ..z3_ import compile
from ..z3_.fresh_name import FreshName
from ..z3_.model import InfiniteMap, InfiniteSet, model_to_state
from ..z3_.version_env import VersionEnv
from ..z3_.z3_util import scoped
class Label(Enum):
REACHABLE = "reachable"
UNREACHABLE = "unreachable"
class InteractiveChecker(Checker):
"""
An interactive invariant-confluence decision procedure.
Recall that a state based object O is (s0, T, I)-confluent if every (s0, T,
I)-reachable state satisfies the invariant. O is I-closed if
invariant-satisfying states are closed under join. I-closure always implies
(s0, T, I)-confluence, but the converse is not always true. However, if set
of invariant-satisfying points is a subset of the set of reachable points,
then invariant-confluence and invariant-closure are equivalent. This
interactive decision procedure relies on the user to iteratively refine the
invariant until it is a subset of the set of reachable points. It then
uses Z3 to check for invariant-closure.
>>> from .. import *
>>> checker = InteractiveChecker()
>>> x = checker.int_max('x', 0)
>>> y = checker.int_max('y', 0)
>>> checker.add_invariant('xy_leq_0', x * y <= 0)
>>> checker.add_transaction('x_inc', [x.assign(x + 1)])
>>> checker.add_transaction('y_dec', [y.assign(y - 1)])
>>> checker.check()
The following two states (i.e. lhs and rhs) satisfy the (refined) invariant, but
their join does not. Please use the lhs_reachable(), lhs_unreachable(),
rhs_reachable(), and rhs_unreachable() methods to label the states as reachable
or unreachable.
<BLANKLINE>
lhs = {'x': 0, 'y': 1}.
rhs [Label.REACHABLE] = {'x': 1, 'y': 0}.
lhs join rhs = {'x': 1, 'y': 1}.
<Decision.UNKNOWN: 'unknown'>
>>> checker.lhs_unreachable()
>>> checker.rhs_reachable()
>>> checker.refine_invariant(y <= 0)
>>> checker.check()
<Decision.YES: 'yes'>
"""
def __init__(self,
num_explored_states_per_step: int = 100,
verbose: bool = False) \
-> None:
Checker.__init__(self, verbose)
self.verbose = verbose
self.solver = z3.Solver()
self.fresh = FreshName()
self.invariant_refinements: List[Invariant] = []
# State explorer.
self.num_explored_states_per_step = num_explored_states_per_step
self.state_explorer = StateExplorer(self.crdt_env, self.s0_vals,
self.invariants, self.transactions)
# Counterexamples.
self.lhs: Optional[ValEnv] = None
self.lhs_label: Optional[Label] = None
self.rhs: Optional[ValEnv] = None
self.rhs_label: Optional[Label] = None
self.unreachable: List[ValEnv] = []
def __str__(self) -> str:
strings: List[str] = []
if len(self.invariant_refinements) > 0:
strings += ['Invariant Refinements']
for inv in self.invariant_refinements:
strings.append(f' {inv}')
strings += ['Reachable States']
strings += [f' ...']
for s in self.state_explorer.states[-10:]:
strings += [f' {s}']
if (len(self.unreachable) > 0):
strings += ['Unreachable States']
for s in self.unreachable:
strings += [f' {s}']
if (self.lhs is not None and
self.rhs is not None):
strings += ['Pending States']
lstr = f' [{self.lhs_label}]' if self.lhs_label is not None else ''
rstr = f' [{self.rhs_label}]' if self.rhs_label is not None else ''
strings.append(f' lhs = {self.lhs}{lstr}')
strings.append(f' rhs = {self.rhs}{rstr}')
return '\n'.join([Checker.__str__(self)] + strings)
def _wrap_print(self, s: str) -> None:
print('\n'.join(wrap(s, 80)))
def _compile_expr(self,
e: Expr,
venv: VersionEnv) -> Tuple[OrderedSet, z3.ExprRef]:
return compile.compile_expr(e, venv, self.type_env, self.fresh)
def _state_satisfies_invs(self, \
state: ValEnv, \
invs: List[Invariant]) \
-> bool:
return all(eval_invariant(inv, state) for inv in invs)
# TODO(mwhittaker): Replace with implementation in compile.
def _venv_satisfies_refined_i(self, venv: VersionEnv) -> OrderedSet:
zss = OrderedSet()
for inv in list(self.invariants.values()) + self.invariant_refinements:
inv_zss, inv_ze = self._compile_expr(inv, venv)
zss |= inv_zss
zss.add(inv_ze)
return zss
# TODO(mwhittaker): Replace with implementation in compile.
def _venv_doesnt_satisfy_refined_i(self, venv: VersionEnv) -> OrderedSet:
zss = OrderedSet()
zes = OrderedSet()
for inv in list(self.invariants.values()) + self.invariant_refinements:
inv_zss, inv_ze = self._compile_expr(inv, venv)
zss |= inv_zss
zes.add(inv_ze)
zss.add(z3.Not(z3.And(list(zes))))
return zss
def _model_to_state(self, model: z3.ModelRef, venv: VersionEnv) -> ValEnv:
names = {venv.get_name(name): name for name in self.type_env}
state = model_to_state(model, set(names.keys()))
for versioned_name, name in names.items():
state[name] = state[versioned_name]
del state[versioned_name]
return state
def _uninfinite_state(self, state: ValEnv) -> Optional[ValEnv]:
"""
state may have infinite sets and maps in it. _uninfinite_state converts
all such sets and maps to finite python sets and maps if possible. If
not possible, it returns None.
"""
finite_state: ValEnv = dict()
for k, v in state.items():
if isinstance(v, InfiniteSet) or isinstance(v, InfiniteMap):
if not v.finite():
return None
else:
finite_state[k] = v.get()
else:
finite_state[k] = v
return finite_state
def _model_to_exprs(self,
state: ValEnv,
tenv: TypeEnv) \
-> Optional[ExprEnv]:
finite_state = self._uninfinite_state(state)
if finite_state is None:
return None
return {name: typed_coerce(x, tenv[name])
for name, x in finite_state.items()}
def _known_reachable(self, state: ValEnv) -> bool:
"""
Returns whether we know that state is reachable. Note that if
_known_reachable reachable returns false, it doesn't necessarily mean
that state is unreachable.
"""
finite_state = self._uninfinite_state(state)
return (finite_state is not None and
finite_state in self.state_explorer.states)
def _record_state(self, state: ValEnv, label: Label) -> None:
if label == Label.UNREACHABLE:
self.unreachable.append(state)
exprs = self._model_to_exprs(state, self.type_env)
if exprs:
inv: Expr = EBool(True)
for name, e in exprs.items():
inv = EBoolAnd(inv, EVar(name).eq(e))
self.refine_invariant(EBoolNot(inv))
else:
assert label == Label.REACHABLE, label
finite_state = self._uninfinite_state(state)
if finite_state:
self.state_explorer.add(finite_state)
def _is_refined_i_closed(self) -> Decision:
with scoped(self.solver):
# Assert lhs satisfies invariant.
lhs_venv = VersionEnv('lhs')
zss = self._venv_satisfies_refined_i(lhs_venv)
# Assert rhs satisfies invariant.
rhs_venv = VersionEnv('rhs')
zss |= self._venv_satisfies_refined_i(rhs_venv)
# Compute join.
join_venv = VersionEnv('joined')
join_zss, join_venv = compile.compile_join(
lhs_venv, rhs_venv, join_venv, self.crdt_env, self.type_env,
self.fresh)
zss |= join_zss
# Assert that the join does NOT satisfy the invariant.
zss |= self._venv_doesnt_satisfy_refined_i(join_venv)
# Register assertions.
self.solver.add(list(zss))
# Display generated code.
if self.verbose:
print(f'{fg(206)}{self.solver.sexpr()}{attr(0)}')
# Check if we're I - NR closed.
result = self.solver.check()
# If z3 is stuck, we're stuck.
if result == z3.unknown:
print('Z3 got stuck!')
return Decision.UNKNOWN
# If there are no counterexamples, then we are invariant-closed.
if result == z3.unsat:
return Decision.YES
# Otherwise, we are are not invariant-closed, and we have a
# counterexample. Extract the counterexamples.
model = self.solver.model()
self.lhs = self._model_to_state(model, lhs_venv)
self.rhs = self._model_to_state(model, rhs_venv)
join = self._model_to_state(model, join_venv)
# Check if either state is reachable. If they both are, we are
# done.
if self._known_reachable(self.lhs):
self.lhs_reachable()
if self._known_reachable(self.rhs):
self.rhs_reachable()
if (self.lhs_label == Label.REACHABLE and
self.rhs_label == Label.REACHABLE):
m = ('The following states are both reachable and satisfy ' +
'the (refined) invariant, but their join does not. ' +
'Their join is also reachable, so the object is not ' +
'invariant confluent.')
self._wrap_print(m)
print(f' lhs = {self.lhs}')
print(f' rhs = {self.rhs}')
return Decision.NO
m = ('The following two states (i.e. lhs and rhs) satisfy the ' +
'(refined) invariant, but their join does not. Please use ' +
'the lhs_reachable(), lhs_unreachable(), rhs_reachable(), ' +
'and rhs_unreachable() methods to label the states as ' +
'reachable or unreachable.')
lstr = f' [{self.lhs_label}]' if self.lhs_label is not None else ''
rstr = f' [{self.rhs_label}]' if self.rhs_label is not None else ''
self._wrap_print(m)
print('')
print(f' lhs{lstr} = {self.lhs}.')
print(f' rhs{rstr} = {self.rhs}.')
print(f' lhs join rhs = {join}.')
return Decision.UNKNOWN
def _check(self) -> Decision:
# Make sure that both counterexamples are labelled, if they exist.
msg = ('State {0} is unlabelled. Call `{0}_reachable()` to label ' +
'the state as reachable or `{0}_unreachable()` to label the ' +
'state as unreachable.')
if (self.lhs is not None and self.lhs_label is None):
self._wrap_print(msg.format('lhs'))
return Decision.UNKNOWN
if (self.rhs is not None and self.rhs_label is None):
self._wrap_print(msg.format('rhs'))
return Decision.UNKNOWN
if (self.lhs is not None):
assert self.lhs_label is not None
assert self.rhs is not None
assert self.rhs_label is not None
# Record the unreachable counterexamples. Users can use the set of
# unreachable counterexamples to try and figure out how to refine
# the invariant. Reachable counterexamples are stored in
# self.state_explorer.
self._record_state(self.lhs, self.lhs_label)
self._record_state(self.rhs, self.rhs_label)
# If lhs and rhs are both reachable, then so is `lhs join rhs`.
# `lhs join rhs` does not satisfy the (refined) invariant, so if it
# is reachable, then we are not invariant-confluent.
if (self.lhs_label == Label.REACHABLE and
self.rhs_label == Label.REACHABLE):
m = ('The following states are both reachable and satisfy ' +
'the (refined) invariant, but their join does not. ' +
'Their join is also reachable, so the object is not ' +
'invariant confluent.')
print(f' lhs = {self.lhs}')
print(f' rhs = {self.rhs}')
return Decision.NO
else:
self.lhs = None
self.rhs = None
self.lhs_label = None
self.rhs_label = None
invs = list(self.invariants.values())
if not self._state_satisfies_invs(self.s0_vals, invs):
return Decision.NO
else:
if self.verbose:
n = self.num_explored_states_per_step
print(f'Exploring {n} states...')
print('')
self.state_explorer.explore(self.num_explored_states_per_step)
return self._is_refined_i_closed()
def lhs_reachable(self):
self.lhs_label = Label.REACHABLE
def lhs_unreachable(self):
self.lhs_label = Label.UNREACHABLE
def rhs_reachable(self):
self.rhs_label = Label.REACHABLE
def rhs_unreachable(self):
self.rhs_label = Label.UNREACHABLE
def refine_invariant(self, inv: Invariant):
inv = typecheck_invariant(inv, self.type_env)
# Ensure that the start state satisfies the invariant, unless it
# doesn't satisfy the original invariant to begin with.
invs = list(self.invariants.values())
if self._state_satisfies_invs(self.s0_vals, invs):
msg = (f'The initial state {self.s0_vals} satisfies the ' +
f'invariant, but does not satisfy the refined invariant ' +
f'{inv}. This means that you\'ve incorrectly refined the ' +
f'invariant. Double check your refinements and try again.')
invs = self.invariant_refinements + [inv]
assert self._state_satisfies_invs(self.s0_vals, invs), msg
self.invariant_refinements.append(inv)
| [
"[email protected]"
] | |
2f3a2709a16c0bcb0d3ed029c939daadb80c6b60 | 22a16f2fc3f2ddf92a620751530c2046ba2e9f1f | /api/dynamic_tests_v2/activation.py | 020b0bb91b00720d239a6152f8749c5e7cdbb632 | [] | no_license | ForFishes/benchmark | 8ebb8e13f44b2f3a350fe4325b03f7e5cab42065 | 56e070628ad67178cdfc67b47759020ff408300a | refs/heads/master | 2023-02-23T03:45:50.320413 | 2021-01-21T05:15:05 | 2021-01-21T05:15:05 | 331,538,261 | 0 | 2 | null | 2021-01-21T06:39:15 | 2021-01-21T06:39:14 | null | UTF-8 | Python | false | false | 1,913 | py | # Copyright (c) 2020 PaddlePaddle 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.
from common_import import *
class ActivationConfig(APIConfig):
def __init__(self):
super(ActivationConfig, self).__init__('activation')
self.api_name = 'cos'
self.api_list = {
'cos': 'cos',
'exp': 'exp',
'log': 'log',
'sin': 'sin',
'sinh': 'sinh',
'sqrt': 'sqrt',
'square': 'square',
'tanh': 'tanh'
}
class PDActivation(PaddleDynamicAPIBenchmarkBase):
def build_graph(self, config):
x = self.variable(name='x', shape=config.x_shape, dtype=config.x_dtype)
result = self.layers(config.api_name, x=x)
self.feed_list = [x]
self.fetch_list = [result]
if config.backward:
self.append_gradients(result, [x])
class TorchActivation(PytorchAPIBenchmarkBase):
def build_graph(self, config):
x = self.variable(name='x', shape=config.x_shape, dtype=config.x_dtype)
result = self.layers(config.api_name, x=x)
self.feed_list = [x]
self.fetch_list = [result]
if config.backward:
self.append_gradients(result, [x])
if __name__ == '__main__':
test_main(
pd_dy_obj=PDActivation(),
torch_obj=TorchActivation(),
config=ActivationConfig())
| [
"[email protected]"
] | |
188d1f6fc00b5a13136bb562cf24311d89cc6901 | 781e2692049e87a4256320c76e82a19be257a05d | /all_data/exercism_data/python/rna-transcription/d54f298dde914c7e9a732cbec50a20e1.py | 970f6d38930e8da150af549feb941eddf63bb6a5 | [] | no_license | itsolutionscorp/AutoStyle-Clustering | 54bde86fe6dbad35b568b38cfcb14c5ffaab51b0 | be0e2f635a7558f56c61bc0b36c6146b01d1e6e6 | refs/heads/master | 2020-12-11T07:27:19.291038 | 2016-03-16T03:18:00 | 2016-03-16T03:18:42 | 59,454,921 | 4 | 0 | null | 2016-05-23T05:40:56 | 2016-05-23T05:40:56 | null | UTF-8 | Python | false | false | 342 | py | class DNA:
def __init__(self, seq = None):
self.sequence = seq
# I guess this is inefficient as it declares the mapping every time a new object is created?
self.DNA_to_RNA = {
'A': 'U',
'G': 'C',
'C': 'G',
'T': 'A'
}
def to_rna(self):
return ''.join(self.DNA_to_RNA[c] for c in self.sequence)
| [
"[email protected]"
] | |
7f5c4e335dc2e63a6a9669b6e0f19872d9eb1f52 | 37d2eef4d6e58662b5a7af3eaf69726655cc0cf7 | /ryu/app/ofctl_rest.py | 9fbce30db029043cee58e91c40a5564e2c9225c4 | [
"Apache-2.0"
] | permissive | horms/ryu | 87904170c4a5d59a74ba65880d21e42c6612e983 | 8728c11e1280b9c6bd4b7a68c5d2fbe4ec5f2ad3 | refs/heads/master | 2023-08-09T10:30:25.056712 | 2014-03-10T02:44:21 | 2014-03-16T13:09:12 | 16,093,033 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 19,514 | py | # Copyright (C) 2012 Nippon Telegraph and Telephone Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import json
from webob import Response
from ryu.base import app_manager
from ryu.controller import ofp_event
from ryu.controller import dpset
from ryu.controller.handler import MAIN_DISPATCHER
from ryu.controller.handler import set_ev_cls
from ryu.ofproto import ofproto_v1_0
from ryu.ofproto import ofproto_v1_2
from ryu.ofproto import ofproto_v1_3
from ryu.lib import ofctl_v1_0
from ryu.lib import ofctl_v1_2
from ryu.lib import ofctl_v1_3
from ryu.app.wsgi import ControllerBase, WSGIApplication
LOG = logging.getLogger('ryu.app.ofctl_rest')
# REST API
#
## Retrieve the switch stats
#
# get the list of all switches
# GET /stats/switches
#
# get the desc stats of the switch
# GET /stats/desc/<dpid>
#
# get flows stats of the switch
# GET /stats/flow/<dpid>
#
# get ports stats of the switch
# GET /stats/port/<dpid>
#
# get meter features stats of the switch
# GET /stats/meterfeatures/<dpid>
#
# get meter config stats of the switch
# GET /stats/meterconfig/<dpid>
#
# get meters stats of the switch
# GET /stats/meter/<dpid>
#
# get group features stats of the switch
# GET /stats/groupfeatures/<dpid>
#
# get groups desc stats of the switch
# GET /stats/groupdesc/<dpid>
#
# get groups stats of the switch
# GET /stats/group/<dpid>
#
#
## Update the switch stats
#
# add a flow entry
# POST /stats/flowentry/add
#
# modify all matching flow entries
# POST /stats/flowentry/modify
#
# delete all matching flow entries
# POST /stats/flowentry/delete
#
# delete all flow entries of the switch
# DELETE /stats/flowentry/clear/<dpid>
#
# add a meter entry
# POST /stats/meterentry/add
#
# modify a meter entry
# POST /stats/meterentry/modify
#
# delete a meter entry
# POST /stats/meterentry/delete
#
# add a group entry
# POST /stats/groupentry/add
#
# modify a group entry
# POST /stats/groupentry/modify
#
# delete a group entry
# POST /stats/groupentry/delete
#
#
# send a experimeter message
# POST /stats/experimenter/<dpid>
class StatsController(ControllerBase):
def __init__(self, req, link, data, **config):
super(StatsController, self).__init__(req, link, data, **config)
self.dpset = data['dpset']
self.waiters = data['waiters']
def get_dpids(self, req, **_kwargs):
dps = self.dpset.dps.keys()
body = json.dumps(dps)
return (Response(content_type='application/json', body=body))
def get_desc_stats(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:
desc = ofctl_v1_0.get_desc_stats(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
desc = ofctl_v1_2.get_desc_stats(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
desc = ofctl_v1_3.get_desc_stats(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(desc)
return (Response(content_type='application/json', body=body))
def get_flow_stats(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:
flows = ofctl_v1_0.get_flow_stats(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
flows = ofctl_v1_2.get_flow_stats(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
flows = ofctl_v1_3.get_flow_stats(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(flows)
return (Response(content_type='application/json', body=body))
def get_port_stats(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:
ports = ofctl_v1_0.get_port_stats(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
ports = ofctl_v1_2.get_port_stats(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
ports = ofctl_v1_3.get_port_stats(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(ports)
return (Response(content_type='application/json', body=body))
def get_meter_features(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
meters = ofctl_v1_3.get_meter_features(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(meters)
return (Response(content_type='application/json', body=body))
def get_meter_config(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
meters = ofctl_v1_3.get_meter_config(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(meters)
return (Response(content_type='application/json', body=body))
def get_meter_stats(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
meters = ofctl_v1_3.get_meter_stats(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(meters)
return (Response(content_type='application/json', body=body))
def get_group_features(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
groups = ofctl_v1_2.get_group_features(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
groups = ofctl_v1_3.get_group_features(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(groups)
return Response(content_type='application/json', body=body)
def get_group_desc(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
groups = ofctl_v1_2.get_group_desc(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
groups = ofctl_v1_3.get_group_desc(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(groups)
return Response(content_type='application/json', body=body)
def get_group_stats(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
groups = ofctl_v1_2.get_group_stats(dp, self.waiters)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
groups = ofctl_v1_3.get_group_stats(dp, self.waiters)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
body = json.dumps(groups)
return Response(content_type='application/json', body=body)
def mod_flow_entry(self, req, cmd, **_kwargs):
try:
flow = eval(req.body)
except SyntaxError:
LOG.debug('invalid syntax %s', req.body)
return Response(status=400)
dpid = flow.get('dpid')
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if cmd == 'add':
cmd = dp.ofproto.OFPFC_ADD
elif cmd == 'modify':
cmd = dp.ofproto.OFPFC_MODIFY
elif cmd == 'delete':
cmd = dp.ofproto.OFPFC_DELETE
else:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:
ofctl_v1_0.mod_flow_entry(dp, flow, cmd)
elif dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
ofctl_v1_2.mod_flow_entry(dp, flow, cmd)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
ofctl_v1_3.mod_flow_entry(dp, flow, cmd)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
return Response(status=200)
def delete_flow_entry(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:
ofctl_v1_0.delete_flow_entry(dp)
elif dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
ofctl_v1_2.mod_flow_entry(dp, {}, dp.ofproto.OFPFC_DELETE)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
ofctl_v1_3.mod_flow_entry(dp, {}, dp.ofproto.OFPFC_DELETE)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
return Response(status=200)
def mod_meter_entry(self, req, cmd, **_kwargs):
try:
flow = eval(req.body)
except SyntaxError:
LOG.debug('invalid syntax %s', req.body)
return Response(status=400)
dpid = flow.get('dpid')
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION or \
dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
if cmd == 'add':
cmd = dp.ofproto.OFPMC_ADD
elif cmd == 'modify':
cmd = dp.ofproto.OFPMC_MODIFY
elif cmd == 'delete':
cmd = dp.ofproto.OFPMC_DELETE
else:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
ofctl_v1_3.mod_meter_entry(dp, flow, cmd)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
return Response(status=200)
def mod_group_entry(self, req, cmd, **_kwargs):
try:
group = eval(req.body)
except SyntaxError:
LOG.debug('invalid syntax %s', req.body)
return Response(status=400)
dpid = group.get('dpid')
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
if cmd == 'add':
cmd = dp.ofproto.OFPGC_ADD
elif cmd == 'modify':
cmd = dp.ofproto.OFPGC_MODIFY
elif cmd == 'delete':
cmd = dp.ofproto.OFPGC_DELETE
else:
return Response(status=404)
if dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
ofctl_v1_2.mod_group_entry(dp, group, cmd)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
ofctl_v1_3.mod_group_entry(dp, group, cmd)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
return Response(status=200)
def send_experimenter(self, req, dpid, **_kwargs):
dp = self.dpset.get(int(dpid))
if dp is None:
return Response(status=404)
try:
exp = eval(req.body)
except SyntaxError:
LOG.debug('invalid syntax %s', req.body)
return Response(status=400)
if dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
ofctl_v1_2.send_experimenter(dp, exp)
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
ofctl_v1_3.send_experimenter(dp, exp)
else:
LOG.debug('Unsupported OF protocol')
return Response(status=501)
return Response(status=200)
class RestStatsApi(app_manager.RyuApp):
OFP_VERSIONS = [ofproto_v1_0.OFP_VERSION,
ofproto_v1_2.OFP_VERSION,
ofproto_v1_3.OFP_VERSION]
_CONTEXTS = {
'dpset': dpset.DPSet,
'wsgi': WSGIApplication
}
def __init__(self, *args, **kwargs):
super(RestStatsApi, self).__init__(*args, **kwargs)
self.dpset = kwargs['dpset']
wsgi = kwargs['wsgi']
self.waiters = {}
self.data = {}
self.data['dpset'] = self.dpset
self.data['waiters'] = self.waiters
mapper = wsgi.mapper
wsgi.registory['StatsController'] = self.data
path = '/stats'
uri = path + '/switches'
mapper.connect('stats', uri,
controller=StatsController, action='get_dpids',
conditions=dict(method=['GET']))
uri = path + '/desc/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_desc_stats',
conditions=dict(method=['GET']))
uri = path + '/flow/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_flow_stats',
conditions=dict(method=['GET']))
uri = path + '/port/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_port_stats',
conditions=dict(method=['GET']))
uri = path + '/meterfeatures/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_meter_features',
conditions=dict(method=['GET']))
uri = path + '/meterconfig/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_meter_config',
conditions=dict(method=['GET']))
uri = path + '/meter/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_meter_stats',
conditions=dict(method=['GET']))
uri = path + '/groupfeatures/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_group_features',
conditions=dict(method=['GET']))
uri = path + '/groupdesc/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_group_desc',
conditions=dict(method=['GET']))
uri = path + '/group/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='get_group_stats',
conditions=dict(method=['GET']))
uri = path + '/flowentry/{cmd}'
mapper.connect('stats', uri,
controller=StatsController, action='mod_flow_entry',
conditions=dict(method=['POST']))
uri = path + '/flowentry/clear/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='delete_flow_entry',
conditions=dict(method=['DELETE']))
uri = path + '/meterentry/{cmd}'
mapper.connect('stats', uri,
controller=StatsController, action='mod_meter_entry',
conditions=dict(method=['POST']))
uri = path + '/groupentry/{cmd}'
mapper.connect('stats', uri,
controller=StatsController, action='mod_group_entry',
conditions=dict(method=['POST']))
uri = path + '/experimenter/{dpid}'
mapper.connect('stats', uri,
controller=StatsController, action='send_experimenter',
conditions=dict(method=['POST']))
def stats_reply_handler(self, ev):
msg = ev.msg
dp = msg.datapath
if dp.id not in self.waiters:
return
if msg.xid not in self.waiters[dp.id]:
return
lock, msgs = self.waiters[dp.id][msg.xid]
msgs.append(msg)
flags = 0
if dp.ofproto.OFP_VERSION == ofproto_v1_0.OFP_VERSION:
flags = dp.ofproto.OFPSF_REPLY_MORE
elif dp.ofproto.OFP_VERSION == ofproto_v1_2.OFP_VERSION:
flags = dp.ofproto.OFPSF_REPLY_MORE
elif dp.ofproto.OFP_VERSION == ofproto_v1_3.OFP_VERSION:
flags = dp.ofproto.OFPMPF_REPLY_MORE
if msg.flags & flags:
return
del self.waiters[dp.id][msg.xid]
lock.set()
@set_ev_cls(ofp_event.EventOFPStatsReply, MAIN_DISPATCHER)
def any_stats_reply_handler(self, ev):
# for OpenFlow 1.2
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPDescStatsReply, MAIN_DISPATCHER)
def desc_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPFlowStatsReply, MAIN_DISPATCHER)
def flow_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPPortStatsReply, MAIN_DISPATCHER)
def port_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPMeterStatsReply, MAIN_DISPATCHER)
def meter_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPMeterFeaturesStatsReply, MAIN_DISPATCHER)
def meter_features_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPMeterConfigStatsReply, MAIN_DISPATCHER)
def meter_config_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPGroupStatsReply, MAIN_DISPATCHER)
def group_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPGroupFeaturesStatsReply, MAIN_DISPATCHER)
def group_features_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
@set_ev_cls(ofp_event.EventOFPGroupDescStatsReply, MAIN_DISPATCHER)
def group_desc_stats_reply_handler(self, ev):
self.stats_reply_handler(ev)
| [
"[email protected]"
] | |
d5ad7a7cac14d5c97e9330e9f6ba0ea20956718b | 98c6ea9c884152e8340605a706efefbea6170be5 | /examples/data/Assignment_8/brnann016/question3.py | 75ec0cc3ee04626a026462b0fc5770c9ab077a6a | [] | no_license | MrHamdulay/csc3-capstone | 479d659e1dcd28040e83ebd9e3374d0ccc0c6817 | 6f0fa0fa1555ceb1b0fb33f25e9694e68b6a53d2 | refs/heads/master | 2021-03-12T21:55:57.781339 | 2014-09-22T02:22:22 | 2014-09-22T02:22:22 | 22,372,174 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 456 | py | # Assignment 8-question3
#Annika Brundyn
#encrypt function
def encrypt(message):
if len(message) == 1:
if message.islower():
if message == 'z':
return 'a'
else:
return chr(ord(message) + 1)
else:
return message
else:
return encrypt(message[0]) + encrypt(message[1:])
string = input("Enter a message:\n")
print("Encrypted message:")
print(encrypt(string)) | [
"[email protected]"
] | |
c05e933761f1a14bf307d77aeb5a24ae19f63d46 | 6123df2ee8648c7977c99564197f7834c7ea83a1 | /DataPreprocessing/排序算法/快速排序方法1.py | bbf8cdc0da6f5ccb2a18ba2805a1233b0aad786c | [] | no_license | JiaqiuWang/DataStatusPrediction | 2b66a24f992df64d93506f54e041d93282213c6e | 9eb3eff99f0f804857f3a1d70227f75c91a8258d | refs/heads/master | 2020-05-21T21:34:28.571549 | 2017-08-17T08:44:12 | 2017-08-17T08:44:12 | 84,649,900 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 769 | py |
# Method1
def quickSort(arr):
less = []
pivotList = []
more = []
if len(arr) <= 1:
return arr
else:
pivot = arr[0] # 将第一个值作为基准
print("pivot:", pivot)
for i in arr:
if i < pivot:
less.append(i)
elif i > pivot:
more.append(i)
else:
pivotList.append(i)
# 得到第一轮分组之后，继续讲分组进行下去
less = quickSort(less)
more = quickSort(more)
print("pivotList:", pivotList)
return less + pivotList + more
if __name__ == "__main__":
a = [4, 65, 2, -31, 0, 99, 83, 782, 1]
print("original list:", a)
print(quickSort(a))
| [
"[email protected]"
] | |
b546707ae6eb6405b3eefd2b37bb4147f39847f4 | fe3bc38d2a9f80a6b258e2c61dbe4557323a1d71 | /custom/ewsghana/south_migrations/0001_initial.py | 2a5bab415d8f95907823c7712377fcd7c3ce648e | [] | no_license | ekush/commcare-hq | 077eb3f525ffb7d1acca0848b9c7678baf776832 | 97a1f55f24f79224724b2ecdc7d5cea87d42f65b | refs/heads/master | 2021-01-17T22:25:09.734898 | 2015-08-25T23:07:49 | 2015-08-25T23:07:49 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,976 | py | # -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'FacilityInCharge'
db.create_table(u'ewsghana_facilityincharge', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('user_id', self.gf('django.db.models.fields.CharField')(max_length=128, db_index=True)),
('location', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['locations.SQLLocation'])),
))
db.send_create_signal(u'ewsghana', ['FacilityInCharge'])
def backwards(self, orm):
# Deleting model 'FacilityInCharge'
db.delete_table(u'ewsghana_facilityincharge')
models = {
u'ewsghana.facilityincharge': {
'Meta': {'object_name': 'FacilityInCharge'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'location': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['locations.SQLLocation']"}),
'user_id': ('django.db.models.fields.CharField', [], {'max_length': '128', 'db_index': 'True'})
},
u'locations.locationtype': {
'Meta': {'object_name': 'LocationType'},
'administrative': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'code': ('django.db.models.fields.SlugField', [], {'max_length': '50', 'null': 'True', 'db_index': 'False'}),
'domain': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}),
'emergency_level': ('django.db.models.fields.DecimalField', [], {'default': '0.5', 'max_digits': '10', 'decimal_places': '1'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'overstock_threshold': ('django.db.models.fields.DecimalField', [], {'default': '3.0', 'max_digits': '10', 'decimal_places': '1'}),
'parent_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['locations.LocationType']", 'null': 'True'}),
'shares_cases': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'understock_threshold': ('django.db.models.fields.DecimalField', [], {'default': '1.5', 'max_digits': '10', 'decimal_places': '1'}),
'view_descendants': ('django.db.models.fields.BooleanField', [], {'default': 'False'})
},
u'locations.sqllocation': {
'Meta': {'unique_together': "(('domain', 'site_code'),)", 'object_name': 'SQLLocation'},
'_products': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['products.SQLProduct']", 'null': 'True', 'symmetrical': 'False'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'domain': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}),
'external_id': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_archived': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'latitude': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '20', 'decimal_places': '10'}),
u'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}),
u'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}),
'location_id': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100', 'db_index': 'True'}),
'location_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['locations.LocationType']"}),
'longitude': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '20', 'decimal_places': '10'}),
'metadata': ('json_field.fields.JSONField', [], {'default': '{}'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True'}),
'parent': ('mptt.fields.TreeForeignKey', [], {'blank': 'True', 'related_name': "'children'", 'null': 'True', 'to': u"orm['locations.SQLLocation']"}),
u'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}),
'site_code': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'stocks_all_products': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'supply_point_id': ('django.db.models.fields.CharField', [], {'max_length': '255', 'unique': 'True', 'null': 'True', 'db_index': 'True'}),
u'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'})
},
u'products.sqlproduct': {
'Meta': {'object_name': 'SQLProduct'},
'category': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '100', 'null': 'True'}),
'code': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '100', 'null': 'True'}),
'cost': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '20', 'decimal_places': '5'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'default': "''", 'null': 'True'}),
'domain': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_archived': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True'}),
'product_data': ('json_field.fields.JSONField', [], {'default': '{}'}),
'product_id': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100', 'db_index': 'True'}),
'program_id': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '100', 'null': 'True'}),
'units': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '100', 'null': 'True'})
}
}
complete_apps = ['ewsghana'] | [
"[email protected]"
] | |
0e5519be5f6c9f0162340f3b13fe27ca4e7ef96e | d7016f69993570a1c55974582cda899ff70907ec | /sdk/containerservice/azure-mgmt-containerservice/azure/mgmt/containerservice/v2021_10_01/_container_service_client.py | 40fe62dd9a76da362a1f9a68e6ab090b8e1aa17f | [
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later"
] | permissive | kurtzeborn/azure-sdk-for-python | 51ca636ad26ca51bc0c9e6865332781787e6f882 | b23e71b289c71f179b9cf9b8c75b1922833a542a | refs/heads/main | 2023-03-21T14:19:50.299852 | 2023-02-15T13:30:47 | 2023-02-15T13:30:47 | 157,927,277 | 0 | 0 | MIT | 2022-07-19T08:05:23 | 2018-11-16T22:15:30 | Python | UTF-8 | Python | false | false | 6,786 | py | # 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 copy import deepcopy
from typing import Any, TYPE_CHECKING
from azure.core.rest import HttpRequest, HttpResponse
from azure.mgmt.core import ARMPipelineClient
from . import models as _models
from .._serialization import Deserializer, Serializer
from ._configuration import ContainerServiceClientConfiguration
from .operations import (
AgentPoolsOperations,
MaintenanceConfigurationsOperations,
ManagedClustersOperations,
Operations,
PrivateEndpointConnectionsOperations,
PrivateLinkResourcesOperations,
ResolvePrivateLinkServiceIdOperations,
SnapshotsOperations,
)
if TYPE_CHECKING:
# pylint: disable=unused-import,ungrouped-imports
from azure.core.credentials import TokenCredential
class ContainerServiceClient: # pylint: disable=client-accepts-api-version-keyword,too-many-instance-attributes
"""The Container Service Client.
:ivar operations: Operations operations
:vartype operations: azure.mgmt.containerservice.v2021_10_01.operations.Operations
:ivar managed_clusters: ManagedClustersOperations operations
:vartype managed_clusters:
azure.mgmt.containerservice.v2021_10_01.operations.ManagedClustersOperations
:ivar maintenance_configurations: MaintenanceConfigurationsOperations operations
:vartype maintenance_configurations:
azure.mgmt.containerservice.v2021_10_01.operations.MaintenanceConfigurationsOperations
:ivar agent_pools: AgentPoolsOperations operations
:vartype agent_pools: azure.mgmt.containerservice.v2021_10_01.operations.AgentPoolsOperations
:ivar private_endpoint_connections: PrivateEndpointConnectionsOperations operations
:vartype private_endpoint_connections:
azure.mgmt.containerservice.v2021_10_01.operations.PrivateEndpointConnectionsOperations
:ivar private_link_resources: PrivateLinkResourcesOperations operations
:vartype private_link_resources:
azure.mgmt.containerservice.v2021_10_01.operations.PrivateLinkResourcesOperations
:ivar resolve_private_link_service_id: ResolvePrivateLinkServiceIdOperations operations
:vartype resolve_private_link_service_id:
azure.mgmt.containerservice.v2021_10_01.operations.ResolvePrivateLinkServiceIdOperations
:ivar snapshots: SnapshotsOperations operations
:vartype snapshots: azure.mgmt.containerservice.v2021_10_01.operations.SnapshotsOperations
:param credential: Credential needed for the client to connect to Azure. Required.
:type credential: ~azure.core.credentials.TokenCredential
:param subscription_id: Subscription credentials which uniquely identify Microsoft Azure
subscription. The subscription ID forms part of the URI for every service call. Required.
:type subscription_id: str
:param base_url: Service URL. Default value is "https://management.azure.com".
:type base_url: str
:keyword api_version: Api Version. Default value is "2021-10-01". Note that overriding this
default value may result in unsupported behavior.
:paramtype api_version: str
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
"""
def __init__(
self,
credential: "TokenCredential",
subscription_id: str,
base_url: str = "https://management.azure.com",
**kwargs: Any
) -> None:
self._config = ContainerServiceClientConfiguration(
credential=credential, subscription_id=subscription_id, **kwargs
)
self._client = ARMPipelineClient(base_url=base_url, config=self._config, **kwargs)
client_models = {k: v for k, v in _models.__dict__.items() if isinstance(v, type)}
self._serialize = Serializer(client_models)
self._deserialize = Deserializer(client_models)
self._serialize.client_side_validation = False
self.operations = Operations(self._client, self._config, self._serialize, self._deserialize)
self.managed_clusters = ManagedClustersOperations(
self._client, self._config, self._serialize, self._deserialize
)
self.maintenance_configurations = MaintenanceConfigurationsOperations(
self._client, self._config, self._serialize, self._deserialize
)
self.agent_pools = AgentPoolsOperations(self._client, self._config, self._serialize, self._deserialize)
self.private_endpoint_connections = PrivateEndpointConnectionsOperations(
self._client, self._config, self._serialize, self._deserialize
)
self.private_link_resources = PrivateLinkResourcesOperations(
self._client, self._config, self._serialize, self._deserialize
)
self.resolve_private_link_service_id = ResolvePrivateLinkServiceIdOperations(
self._client, self._config, self._serialize, self._deserialize
)
self.snapshots = SnapshotsOperations(self._client, self._config, self._serialize, self._deserialize)
def _send_request(self, request: HttpRequest, **kwargs: Any) -> HttpResponse:
"""Runs the network request through the client's chained policies.
>>> from azure.core.rest import HttpRequest
>>> request = HttpRequest("GET", "https://www.example.org/")
<HttpRequest [GET], url: 'https://www.example.org/'>
>>> response = client._send_request(request)
<HttpResponse: 200 OK>
For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request
:param request: The network request you want to make. Required.
:type request: ~azure.core.rest.HttpRequest
:keyword bool stream: Whether the response payload will be streamed. Defaults to False.
:return: The response of your network call. Does not do error handling on your response.
:rtype: ~azure.core.rest.HttpResponse
"""
request_copy = deepcopy(request)
request_copy.url = self._client.format_url(request_copy.url)
return self._client.send_request(request_copy, **kwargs)
def close(self) -> None:
self._client.close()
def __enter__(self) -> "ContainerServiceClient":
self._client.__enter__()
return self
def __exit__(self, *exc_details) -> None:
self._client.__exit__(*exc_details)
| [
"[email protected]"
] | |
16f82b3f6d262e8dc868a376d1488222401592a2 | 4fca17a3dbc3e74ba7e46bd7869eb6d138e4c422 | /_0053_Maximum_Subarray.py | be2bb3203c47327d5a0a3576ae0dfaf79ab007fe | [] | no_license | mingweihe/leetcode | a2cfee0e004627b817a3c0321bb9c74128f8c1a7 | edff905f63ab95cdd40447b27a9c449c9cefec37 | refs/heads/master | 2021-06-19T07:46:46.897952 | 2021-05-02T05:13:17 | 2021-05-02T05:13:17 | 205,740,338 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 718 | py | class Solution(object):
def maxSubArray(self, nums):
"""
:type nums: List[int]
:rtype: int
"""
# Approach 2
for i in range(1, len(nums)):
if nums[i-1] > 0:
nums[i] = nums[i] + nums[i-1]
return max(nums)
# Approach 1
# if len(nums) == 1: return nums[0]
# return self.dfs(nums, 1, nums[0])
# def dfs(self, nums, index, accum):
# if len(nums) - 1 == index:
# return max(accum, accum + nums[index], nums[index])
# if accum < 0:
# return max(accum, self.dfs(nums, index + 1, nums[index]))
# return max(accum, self.dfs(nums, index + 1, nums[index] + accum))
| [
"[email protected]"
] | |
9b0e7bd06dbe8d684b607ce114803e57257e16aa | 9b9a02657812ea0cb47db0ae411196f0e81c5152 | /repoData/bradfitz-contributing/allPythonContent.py | 1b14ed542e15bd56f253645b9af4c72b62bedfa6 | [] | no_license | aCoffeeYin/pyreco | cb42db94a3a5fc134356c9a2a738a063d0898572 | 0ac6653219c2701c13c508c5c4fc9bc3437eea06 | refs/heads/master | 2020-12-14T14:10:05.763693 | 2016-06-27T05:15:15 | 2016-06-27T05:15:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 448,477 | py | __FILENAME__ = consumer
#!/usr/bin/python
#
# Copyright 2008 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
A sample OpenID consumer app for Google App Engine. Allows users to log into
other OpenID providers, then displays their OpenID login. Also stores and
displays the most recent logins.
Part of http://code.google.com/p/google-app-engine-samples/.
For more about OpenID, see:
http://openid.net/
http://openid.net/about.bml
Uses JanRain's Python OpenID library, version 2.1.1, licensed under the
Apache Software License 2.0:
http://openidenabled.com/python-openid/
The JanRain library includes a reference OpenID provider that can be used to
test this consumer. After starting the dev_appserver with this app, unpack the
JanRain library and run these commands from its root directory:
setenv PYTHONPATH .
python ./examples/server.py -s localhost
Then go to http://localhost:8080/ in your browser, type in
http://localhost:8000/test as your OpenID identifier, and click Verify.
"""
import datetime
import logging
import os
import re
import sys
import urlparse
import wsgiref.handlers
from google.appengine.ext import db
from google.appengine.ext import webapp
from google.appengine.ext.webapp import template
from openid import fetchers
from openid.consumer.consumer import Consumer
from openid.consumer import discover
from openid.extensions import pape, sreg
import fetcher
import store
import string
import random
import models
# Set to True if stack traces should be shown in the browser, etc.
_DEBUG = False
def GenKeyName(length=8, chars=string.letters + string.digits):
return ''.join([random.choice(chars) for i in xrange(length)])
class Session(db.Expando):
"""An in-progress OpenID login."""
claimed_id = db.StringProperty()
server_url = db.LinkProperty()
class Login(db.Model):
"""A completed OpenID login."""
status = db.StringProperty(choices=('success', 'cancel', 'failure'))
claimed_id = db.LinkProperty()
server_url = db.LinkProperty()
timestamp = db.DateTimeProperty(auto_now_add=True)
session = db.ReferenceProperty(Session)
class Handler(webapp.RequestHandler):
"""A base handler class with a couple OpenID-specific utilities."""
consumer = None
session = None
session_args = None
def __init__(self):
self.session_args = {}
def get_consumer(self):
"""Returns a Consumer instance.
"""
if not self.consumer:
fetchers.setDefaultFetcher(fetcher.UrlfetchFetcher())
if not self.load_session():
return
self.consumer = Consumer(self.session_args, store.DatastoreStore())
return self.consumer
def args_to_dict(self):
"""Converts the URL and POST parameters to a singly-valued dictionary.
Returns:
dict with the URL and POST body parameters
"""
req = self.request
return dict([(arg, req.get(arg)) for arg in req.arguments()])
def load_session(self):
"""Loads the current session.
"""
if not self.session:
id = self.request.get('session_id')
if id:
try:
self.session = db.get(db.Key.from_path('Session', int(id)))
assert self.session
except (AssertionError, db.Error), e:
self.report_error('Invalid session id: %d' % id)
return None
fields = self.session.dynamic_properties()
self.session_args = dict((f, getattr(self.session, f)) for f in fields)
else:
self.session_args = {}
self.session = Session()
self.session.claimed_id = self.request.get('openid')
return self.session
def store_session(self):
"""Stores the current session.
"""
assert self.session
for field, value in self.session_args.items():
setattr(self.session, field, value)
self.session.put()
def render(self, extra_values={}):
"""Renders the page, including the extra (optional) values.
Args:
template_name: string
The template to render.
extra_values: dict
Template values to provide to the template.
"""
logins = Login.gql('ORDER BY timestamp DESC').fetch(20)
for login in logins:
login.display_name = self.display_name(login.claimed_id)
login.friendly_time = self.relative_time(login.timestamp)
values = {
'response': {},
'openid': '',
'logins': logins,
}
values.update(extra_values)
cwd = os.path.dirname(__file__)
path = os.path.join(cwd, 'templates', 'base.html')
self.response.out.write(template.render(path, values, debug=_DEBUG))
def report_error(self, message, exception=None):
"""Shows an error HTML page.
Args:
message: string
A detailed error message.
"""
if exception:
logging.exception('Error: %s' % message)
self.render({'error': message})
def show_front_page(self):
"""Do an internal (non-302) redirect to the front page.
Preserves the user agent's requested URL.
"""
front_page = FrontPage()
front_page.request = self.request
front_page.response = self.response
front_page.get()
def relative_time(self, timestamp):
"""Returns a friendly string describing how long ago the timestamp was.
Args:
timestamp: a datetime
Returns:
string
"""
def format_number(num):
if num <= 9:
return {1: 'one', 2: 'two', 3: 'three', 4: 'four', 5: 'five',
6: 'six', 7: 'seven', 8: 'eight', 9: 'nine'}[num]
else:
return str(num)
delta = datetime.datetime.now() - timestamp
minutes = delta.seconds / 60
hours = minutes / 60
if delta.days > 1:
return '%s days ago' % format_number(delta.days)
elif delta.days == 1:
return 'yesterday'
elif hours > 1:
return '%s hours ago' % format_number(hours)
elif hours == 1:
return 'an hour ago'
elif minutes > 25:
return 'half an hour ago'
elif minutes > 5:
return '%s minutes ago' % format_number(minutes)
else:
return 'moments ago'
def display_name(self, openid_url):
"""Extracts a short, representative part of an OpenID URL for display.
For example, it returns "ryan" for:
ryan.com
www.ryan.com
ryan.provider.com
provider.com/ryan
provider.com/id/path/ryan
Adapted from Net::OpenID::Consumer, by Brad Fitzpatrick. See:
http://code.sixapart.com/svn/openid/trunk/perl/Net-OpenID-Consumer/lib/Net/OpenID/VerifiedIdentity.pm
Args:
openid_url: string
Returns:
string
"""
if not openid_url:
return 'None'
username_re = '[\w.+-]+'
scheme, host, path, params, query, frag = urlparse.urlparse(openid_url)
def sanitize(display_name):
if '@' in display_name:
# don't display full email addresses; use just the user name part
display_name = display_name[:display_name.index('@')]
return display_name
# is the username in the params?
match = re.search('(u|id|user|userid|user_id|profile)=(%s)' % username_re,
path)
if match:
return sanitize(match.group(2))
# is the username in the path?
path = path.split('/')
if re.match(username_re, path[-1]):
return sanitize(path[-1])
# use the hostname
host = host.split('.')
if len(host) == 1:
return host[0]
# strip common tlds and country code tlds
common_tlds = ('com', 'org', 'net', 'edu', 'info', 'biz', 'gov', 'mil')
if host[-1] in common_tlds or len(host[-1]) == 2:
host = host[:-1]
if host[-1] == 'co':
host = host[:-1]
# strip www prefix
if host[0] == 'www':
host = host[1:]
return sanitize('.'.join(host))
class FrontPage(Handler):
"""Show the default front page."""
def get(self):
self.render()
class StartHandler(Handler):
"""Handles a POST response to the OpenID login form."""
def post(self):
"""Handles login requests."""
logging.info(self.args_to_dict())
openid_url = self.request.get('openid_url')
if not openid_url:
self.report_error('Please enter an OpenID URL.')
return
logging.debug('Beginning discovery for OpenID %s' % openid_url)
try:
consumer = self.get_consumer()
if not consumer:
return
auth_request = consumer.begin(openid_url)
except discover.DiscoveryFailure, e:
self.report_error('Error during OpenID provider discovery.', e)
return
except discover.XRDSError, e:
self.report_error('Error parsing XRDS from provider.', e)
return
self.session.claimed_id = auth_request.endpoint.claimed_id
self.session.server_url = auth_request.endpoint.server_url
self.store_session()
sreg_request = sreg.SRegRequest(optional=['nickname', 'fullname', 'email'])
auth_request.addExtension(sreg_request)
pape_request = pape.Request([pape.AUTH_MULTI_FACTOR,
pape.AUTH_MULTI_FACTOR_PHYSICAL,
pape.AUTH_PHISHING_RESISTANT,
])
auth_request.addExtension(pape_request)
parts = list(urlparse.urlparse(self.request.uri))
parts[2] = 's/finish'
parts[4] = 'session_id=%d' % self.session.key().id()
parts[5] = ''
return_to = urlparse.urlunparse(parts)
realm = urlparse.urlunparse(parts[0:2] + [''] * 4)
redirect_url = auth_request.redirectURL(realm, return_to)
logging.debug('Redirecting to %s' % redirect_url)
self.response.set_status(302)
self.response.headers['Location'] = redirect_url
class FinishHandler(Handler):
"""Handle a redirect from the provider."""
def get(self):
args = self.args_to_dict()
consumer = self.get_consumer()
if not consumer:
return
if self.session.login_set.get():
self.render()
return
response = consumer.complete(args, self.request.uri)
assert response.status in Login.status.choices
if response.status == 'success':
sreg_data = sreg.SRegResponse.fromSuccessResponse(response).items()
pape_data = pape.Response.fromSuccessResponse(response)
self.session.claimed_id = response.endpoint.claimed_id
self.session.server_url = response.endpoint.server_url
elif response.status == 'failure':
logging.error(str(response))
logging.debug('Login status %s for claimed_id %s' %
(response.status, self.session.claimed_id))
if response.status != 'success':
self.render(locals())
return
session_id = GenKeyName(length=16)
login = Login(key_name=session_id,
status=response.status,
claimed_id=self.session.claimed_id,
server_url=self.session.server_url,
session=self.session.key())
login.put()
# update the login time
user = models.User(openid_user=login.claimed_id).GetOrCreateFromDatastore()
user.put()
self.response.headers.add_header('Set-Cookie',
'session=%s; path=/' % session_id)
# TODO(bradfitz: redirect to proper 'next' URL
self.redirect('/')
# Map URLs to our RequestHandler subclasses above
_URLS = [
('/s/openid', FrontPage),
('/s/startopenid', StartHandler),
('/s/finish', FinishHandler),
]
def main(argv):
application = webapp.WSGIApplication(_URLS, debug=_DEBUG)
wsgiref.handlers.CGIHandler().run(application)
if __name__ == '__main__':
main(sys.argv)
########NEW FILE########
__FILENAME__ = ElementInclude
#
# ElementTree
# $Id: ElementInclude.py 1862 2004-06-18 07:31:02Z Fredrik $
#
# limited xinclude support for element trees
#
# history:
# 2003-08-15 fl created
# 2003-11-14 fl fixed default loader
#
# Copyright (c) 2003-2004 by Fredrik Lundh. All rights reserved.
#
# [email protected]
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2004 by Fredrik Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
##
# Limited XInclude support for the ElementTree package.
##
import copy
import ElementTree
XINCLUDE = "{http://www.w3.org/2001/XInclude}"
XINCLUDE_INCLUDE = XINCLUDE + "include"
XINCLUDE_FALLBACK = XINCLUDE + "fallback"
##
# Fatal include error.
class FatalIncludeError(SyntaxError):
pass
##
# Default loader. This loader reads an included resource from disk.
#
# @param href Resource reference.
# @param parse Parse mode. Either "xml" or "text".
# @param encoding Optional text encoding.
# @return The expanded resource. If the parse mode is "xml", this
# is an ElementTree instance. If the parse mode is "text", this
# is a Unicode string. If the loader fails, it can return None
# or raise an IOError exception.
# @throws IOError If the loader fails to load the resource.
def default_loader(href, parse, encoding=None):
file = open(href)
if parse == "xml":
data = ElementTree.parse(file).getroot()
else:
data = file.read()
if encoding:
data = data.decode(encoding)
file.close()
return data
##
# Expand XInclude directives.
#
# @param elem Root element.
# @param loader Optional resource loader. If omitted, it defaults
# to {@link default_loader}. If given, it should be a callable
# that implements the same interface as <b>default_loader</b>.
# @throws FatalIncludeError If the function fails to include a given
# resource, or if the tree contains malformed XInclude elements.
# @throws IOError If the function fails to load a given resource.
def include(elem, loader=None):
if loader is None:
loader = default_loader
# look for xinclude elements
i = 0
while i < len(elem):
e = elem[i]
if e.tag == XINCLUDE_INCLUDE:
# process xinclude directive
href = e.get("href")
parse = e.get("parse", "xml")
if parse == "xml":
node = loader(href, parse)
if node is None:
raise FatalIncludeError(
"cannot load %r as %r" % (href, parse)
)
node = copy.copy(node)
if e.tail:
node.tail = (node.tail or "") + e.tail
elem[i] = node
elif parse == "text":
text = loader(href, parse, e.get("encoding"))
if text is None:
raise FatalIncludeError(
"cannot load %r as %r" % (href, parse)
)
if i:
node = elem[i-1]
node.tail = (node.tail or "") + text
else:
elem.text = (elem.text or "") + text + (e.tail or "")
del elem[i]
continue
else:
raise FatalIncludeError(
"unknown parse type in xi:include tag (%r)" % parse
)
elif e.tag == XINCLUDE_FALLBACK:
raise FatalIncludeError(
"xi:fallback tag must be child of xi:include (%r)" % e.tag
)
else:
include(e, loader)
i = i + 1
########NEW FILE########
__FILENAME__ = ElementPath
#
# ElementTree
# $Id: ElementPath.py 1858 2004-06-17 21:31:41Z Fredrik $
#
# limited xpath support for element trees
#
# history:
# 2003-05-23 fl created
# 2003-05-28 fl added support for // etc
# 2003-08-27 fl fixed parsing of periods in element names
#
# Copyright (c) 2003-2004 by Fredrik Lundh. All rights reserved.
#
# [email protected]
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2004 by Fredrik Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
##
# Implementation module for XPath support. There's usually no reason
# to import this module directly; the <b>ElementTree</b> does this for
# you, if needed.
##
import re
xpath_tokenizer = re.compile(
"(::|\.\.|\(\)|[/.*:\[\]\(\)@=])|((?:\{[^}]+\})?[^/:\[\]\(\)@=\s]+)|\s+"
).findall
class xpath_descendant_or_self:
pass
##
# Wrapper for a compiled XPath.
class Path:
##
# Create an Path instance from an XPath expression.
def __init__(self, path):
tokens = xpath_tokenizer(path)
# the current version supports 'path/path'-style expressions only
self.path = []
self.tag = None
if tokens and tokens[0][0] == "/":
raise SyntaxError("cannot use absolute path on element")
while tokens:
op, tag = tokens.pop(0)
if tag or op == "*":
self.path.append(tag or op)
elif op == ".":
pass
elif op == "/":
self.path.append(xpath_descendant_or_self())
continue
else:
raise SyntaxError("unsupported path syntax (%s)" % op)
if tokens:
op, tag = tokens.pop(0)
if op != "/":
raise SyntaxError(
"expected path separator (%s)" % (op or tag)
)
if self.path and isinstance(self.path[-1], xpath_descendant_or_self):
raise SyntaxError("path cannot end with //")
if len(self.path) == 1 and isinstance(self.path[0], type("")):
self.tag = self.path[0]
##
# Find first matching object.
def find(self, element):
tag = self.tag
if tag is None:
nodeset = self.findall(element)
if not nodeset:
return None
return nodeset[0]
for elem in element:
if elem.tag == tag:
return elem
return None
##
# Find text for first matching object.
def findtext(self, element, default=None):
tag = self.tag
if tag is None:
nodeset = self.findall(element)
if not nodeset:
return default
return nodeset[0].text or ""
for elem in element:
if elem.tag == tag:
return elem.text or ""
return default
##
# Find all matching objects.
def findall(self, element):
nodeset = [element]
index = 0
while 1:
try:
path = self.path[index]
index = index + 1
except IndexError:
return nodeset
set = []
if isinstance(path, xpath_descendant_or_self):
try:
tag = self.path[index]
if not isinstance(tag, type("")):
tag = None
else:
index = index + 1
except IndexError:
tag = None # invalid path
for node in nodeset:
new = list(node.getiterator(tag))
if new and new[0] is node:
set.extend(new[1:])
else:
set.extend(new)
else:
for node in nodeset:
for node in node:
if path == "*" or node.tag == path:
set.append(node)
if not set:
return []
nodeset = set
_cache = {}
##
# (Internal) Compile path.
def _compile(path):
p = _cache.get(path)
if p is not None:
return p
p = Path(path)
if len(_cache) >= 100:
_cache.clear()
_cache[path] = p
return p
##
# Find first matching object.
def find(element, path):
return _compile(path).find(element)
##
# Find text for first matching object.
def findtext(element, path, default=None):
return _compile(path).findtext(element, default)
##
# Find all matching objects.
def findall(element, path):
return _compile(path).findall(element)
########NEW FILE########
__FILENAME__ = ElementTree
#
# ElementTree
# $Id: ElementTree.py 2326 2005-03-17 07:45:21Z fredrik $
#
# light-weight XML support for Python 1.5.2 and later.
#
# history:
# 2001-10-20 fl created (from various sources)
# 2001-11-01 fl return root from parse method
# 2002-02-16 fl sort attributes in lexical order
# 2002-04-06 fl TreeBuilder refactoring, added PythonDoc markup
# 2002-05-01 fl finished TreeBuilder refactoring
# 2002-07-14 fl added basic namespace support to ElementTree.write
# 2002-07-25 fl added QName attribute support
# 2002-10-20 fl fixed encoding in write
# 2002-11-24 fl changed default encoding to ascii; fixed attribute encoding
# 2002-11-27 fl accept file objects or file names for parse/write
# 2002-12-04 fl moved XMLTreeBuilder back to this module
# 2003-01-11 fl fixed entity encoding glitch for us-ascii
# 2003-02-13 fl added XML literal factory
# 2003-02-21 fl added ProcessingInstruction/PI factory
# 2003-05-11 fl added tostring/fromstring helpers
# 2003-05-26 fl added ElementPath support
# 2003-07-05 fl added makeelement factory method
# 2003-07-28 fl added more well-known namespace prefixes
# 2003-08-15 fl fixed typo in ElementTree.findtext (Thomas Dartsch)
# 2003-09-04 fl fall back on emulator if ElementPath is not installed
# 2003-10-31 fl markup updates
# 2003-11-15 fl fixed nested namespace bug
# 2004-03-28 fl added XMLID helper
# 2004-06-02 fl added default support to findtext
# 2004-06-08 fl fixed encoding of non-ascii element/attribute names
# 2004-08-23 fl take advantage of post-2.1 expat features
# 2005-02-01 fl added iterparse implementation
# 2005-03-02 fl fixed iterparse support for pre-2.2 versions
#
# Copyright (c) 1999-2005 by Fredrik Lundh. All rights reserved.
#
# [email protected]
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2005 by Fredrik Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
__all__ = [
# public symbols
"Comment",
"dump",
"Element", "ElementTree",
"fromstring",
"iselement", "iterparse",
"parse",
"PI", "ProcessingInstruction",
"QName",
"SubElement",
"tostring",
"TreeBuilder",
"VERSION", "XML",
"XMLTreeBuilder",
]
##
# The <b>Element</b> type is a flexible container object, designed to
# store hierarchical data structures in memory. The type can be
# described as a cross between a list and a dictionary.
# <p>
# Each element has a number of properties associated with it:
# <ul>
# <li>a <i>tag</i>. This is a string identifying what kind of data
# this element represents (the element type, in other words).</li>
# <li>a number of <i>attributes</i>, stored in a Python dictionary.</li>
# <li>a <i>text</i> string.</li>
# <li>an optional <i>tail</i> string.</li>
# <li>a number of <i>child elements</i>, stored in a Python sequence</li>
# </ul>
#
# To create an element instance, use the {@link #Element} or {@link
# #SubElement} factory functions.
# <p>
# The {@link #ElementTree} class can be used to wrap an element
# structure, and convert it from and to XML.
##
import string, sys, re
class _SimpleElementPath:
# emulate pre-1.2 find/findtext/findall behaviour
def find(self, element, tag):
for elem in element:
if elem.tag == tag:
return elem
return None
def findtext(self, element, tag, default=None):
for elem in element:
if elem.tag == tag:
return elem.text or ""
return default
def findall(self, element, tag):
if tag[:3] == ".//":
return element.getiterator(tag[3:])
result = []
for elem in element:
if elem.tag == tag:
result.append(elem)
return result
try:
import ElementPath
except ImportError:
# FIXME: issue warning in this case?
ElementPath = _SimpleElementPath()
# TODO: add support for custom namespace resolvers/default namespaces
# TODO: add improved support for incremental parsing
VERSION = "1.2.6"
##
# Internal element class. This class defines the Element interface,
# and provides a reference implementation of this interface.
# <p>
# You should not create instances of this class directly. Use the
# appropriate factory functions instead, such as {@link #Element}
# and {@link #SubElement}.
#
# @see Element
# @see SubElement
# @see Comment
# @see ProcessingInstruction
class _ElementInterface:
# <tag attrib>text<child/>...</tag>tail
##
# (Attribute) Element tag.
tag = None
##
# (Attribute) Element attribute dictionary. Where possible, use
# {@link #_ElementInterface.get},
# {@link #_ElementInterface.set},
# {@link #_ElementInterface.keys}, and
# {@link #_ElementInterface.items} to access
# element attributes.
attrib = None
##
# (Attribute) Text before first subelement. This is either a
# string or the value None, if there was no text.
text = None
##
# (Attribute) Text after this element's end tag, but before the
# next sibling element's start tag. This is either a string or
# the value None, if there was no text.
tail = None # text after end tag, if any
def __init__(self, tag, attrib):
self.tag = tag
self.attrib = attrib
self._children = []
def __repr__(self):
return "<Element %s at %x>" % (self.tag, id(self))
##
# Creates a new element object of the same type as this element.
#
# @param tag Element tag.
# @param attrib Element attributes, given as a dictionary.
# @return A new element instance.
def makeelement(self, tag, attrib):
return Element(tag, attrib)
##
# Returns the number of subelements.
#
# @return The number of subelements.
def __len__(self):
return len(self._children)
##
# Returns the given subelement.
#
# @param index What subelement to return.
# @return The given subelement.
# @exception IndexError If the given element does not exist.
def __getitem__(self, index):
return self._children[index]
##
# Replaces the given subelement.
#
# @param index What subelement to replace.
# @param element The new element value.
# @exception IndexError If the given element does not exist.
# @exception AssertionError If element is not a valid object.
def __setitem__(self, index, element):
assert iselement(element)
self._children[index] = element
##
# Deletes the given subelement.
#
# @param index What subelement to delete.
# @exception IndexError If the given element does not exist.
def __delitem__(self, index):
del self._children[index]
##
# Returns a list containing subelements in the given range.
#
# @param start The first subelement to return.
# @param stop The first subelement that shouldn't be returned.
# @return A sequence object containing subelements.
def __getslice__(self, start, stop):
return self._children[start:stop]
##
# Replaces a number of subelements with elements from a sequence.
#
# @param start The first subelement to replace.
# @param stop The first subelement that shouldn't be replaced.
# @param elements A sequence object with zero or more elements.
# @exception AssertionError If a sequence member is not a valid object.
def __setslice__(self, start, stop, elements):
for element in elements:
assert iselement(element)
self._children[start:stop] = list(elements)
##
# Deletes a number of subelements.
#
# @param start The first subelement to delete.
# @param stop The first subelement to leave in there.
def __delslice__(self, start, stop):
del self._children[start:stop]
##
# Adds a subelement to the end of this element.
#
# @param element The element to add.
# @exception AssertionError If a sequence member is not a valid object.
def append(self, element):
assert iselement(element)
self._children.append(element)
##
# Inserts a subelement at the given position in this element.
#
# @param index Where to insert the new subelement.
# @exception AssertionError If the element is not a valid object.
def insert(self, index, element):
assert iselement(element)
self._children.insert(index, element)
##
# Removes a matching subelement. Unlike the <b>find</b> methods,
# this method compares elements based on identity, not on tag
# value or contents.
#
# @param element What element to remove.
# @exception ValueError If a matching element could not be found.
# @exception AssertionError If the element is not a valid object.
def remove(self, element):
assert iselement(element)
self._children.remove(element)
##
# Returns all subelements. The elements are returned in document
# order.
#
# @return A list of subelements.
# @defreturn list of Element instances
def getchildren(self):
return self._children
##
# Finds the first matching subelement, by tag name or path.
#
# @param path What element to look for.
# @return The first matching element, or None if no element was found.
# @defreturn Element or None
def find(self, path):
return ElementPath.find(self, path)
##
# Finds text for the first matching subelement, by tag name or path.
#
# @param path What element to look for.
# @param default What to return if the element was not found.
# @return The text content of the first matching element, or the
# default value no element was found. Note that if the element
# has is found, but has no text content, this method returns an
# empty string.
# @defreturn string
def findtext(self, path, default=None):
return ElementPath.findtext(self, path, default)
##
# Finds all matching subelements, by tag name or path.
#
# @param path What element to look for.
# @return A list or iterator containing all matching elements,
# in document order.
# @defreturn list of Element instances
def findall(self, path):
return ElementPath.findall(self, path)
##
# Resets an element. This function removes all subelements, clears
# all attributes, and sets the text and tail attributes to None.
def clear(self):
self.attrib.clear()
self._children = []
self.text = self.tail = None
##
# Gets an element attribute.
#
# @param key What attribute to look for.
# @param default What to return if the attribute was not found.
# @return The attribute value, or the default value, if the
# attribute was not found.
# @defreturn string or None
def get(self, key, default=None):
return self.attrib.get(key, default)
##
# Sets an element attribute.
#
# @param key What attribute to set.
# @param value The attribute value.
def set(self, key, value):
self.attrib[key] = value
##
# Gets a list of attribute names. The names are returned in an
# arbitrary order (just like for an ordinary Python dictionary).
#
# @return A list of element attribute names.
# @defreturn list of strings
def keys(self):
return self.attrib.keys()
##
# Gets element attributes, as a sequence. The attributes are
# returned in an arbitrary order.
#
# @return A list of (name, value) tuples for all attributes.
# @defreturn list of (string, string) tuples
def items(self):
return self.attrib.items()
##
# Creates a tree iterator. The iterator loops over this element
# and all subelements, in document order, and returns all elements
# with a matching tag.
# <p>
# If the tree structure is modified during iteration, the result
# is undefined.
#
# @param tag What tags to look for (default is to return all elements).
# @return A list or iterator containing all the matching elements.
# @defreturn list or iterator
def getiterator(self, tag=None):
nodes = []
if tag == "*":
tag = None
if tag is None or self.tag == tag:
nodes.append(self)
for node in self._children:
nodes.extend(node.getiterator(tag))
return nodes
# compatibility
_Element = _ElementInterface
##
# Element factory. This function returns an object implementing the
# standard Element interface. The exact class or type of that object
# is implementation dependent, but it will always be compatible with
# the {@link #_ElementInterface} class in this module.
# <p>
# The element name, attribute names, and attribute values can be
# either 8-bit ASCII strings or Unicode strings.
#
# @param tag The element name.
# @param attrib An optional dictionary, containing element attributes.
# @param **extra Additional attributes, given as keyword arguments.
# @return An element instance.
# @defreturn Element
def Element(tag, attrib={}, **extra):
attrib = attrib.copy()
attrib.update(extra)
return _ElementInterface(tag, attrib)
##
# Subelement factory. This function creates an element instance, and
# appends it to an existing element.
# <p>
# The element name, attribute names, and attribute values can be
# either 8-bit ASCII strings or Unicode strings.
#
# @param parent The parent element.
# @param tag The subelement name.
# @param attrib An optional dictionary, containing element attributes.
# @param **extra Additional attributes, given as keyword arguments.
# @return An element instance.
# @defreturn Element
def SubElement(parent, tag, attrib={}, **extra):
attrib = attrib.copy()
attrib.update(extra)
element = parent.makeelement(tag, attrib)
parent.append(element)
return element
##
# Comment element factory. This factory function creates a special
# element that will be serialized as an XML comment.
# <p>
# The comment string can be either an 8-bit ASCII string or a Unicode
# string.
#
# @param text A string containing the comment string.
# @return An element instance, representing a comment.
# @defreturn Element
def Comment(text=None):
element = Element(Comment)
element.text = text
return element
##
# PI element factory. This factory function creates a special element
# that will be serialized as an XML processing instruction.
#
# @param target A string containing the PI target.
# @param text A string containing the PI contents, if any.
# @return An element instance, representing a PI.
# @defreturn Element
def ProcessingInstruction(target, text=None):
element = Element(ProcessingInstruction)
element.text = target
if text:
element.text = element.text + " " + text
return element
PI = ProcessingInstruction
##
# QName wrapper. This can be used to wrap a QName attribute value, in
# order to get proper namespace handling on output.
#
# @param text A string containing the QName value, in the form {uri}local,
# or, if the tag argument is given, the URI part of a QName.
# @param tag Optional tag. If given, the first argument is interpreted as
# an URI, and this argument is interpreted as a local name.
# @return An opaque object, representing the QName.
class QName:
def __init__(self, text_or_uri, tag=None):
if tag:
text_or_uri = "{%s}%s" % (text_or_uri, tag)
self.text = text_or_uri
def __str__(self):
return self.text
def __hash__(self):
return hash(self.text)
def __cmp__(self, other):
if isinstance(other, QName):
return cmp(self.text, other.text)
return cmp(self.text, other)
##
# ElementTree wrapper class. This class represents an entire element
# hierarchy, and adds some extra support for serialization to and from
# standard XML.
#
# @param element Optional root element.
# @keyparam file Optional file handle or name. If given, the
# tree is initialized with the contents of this XML file.
class ElementTree:
def __init__(self, element=None, file=None):
assert element is None or iselement(element)
self._root = element # first node
if file:
self.parse(file)
##
# Gets the root element for this tree.
#
# @return An element instance.
# @defreturn Element
def getroot(self):
return self._root
##
# Replaces the root element for this tree. This discards the
# current contents of the tree, and replaces it with the given
# element. Use with care.
#
# @param element An element instance.
def _setroot(self, element):
assert iselement(element)
self._root = element
##
# Loads an external XML document into this element tree.
#
# @param source A file name or file object.
# @param parser An optional parser instance. If not given, the
# standard {@link XMLTreeBuilder} parser is used.
# @return The document root element.
# @defreturn Element
def parse(self, source, parser=None):
if not hasattr(source, "read"):
source = open(source, "rb")
if not parser:
parser = XMLTreeBuilder()
while 1:
data = source.read(32768)
if not data:
break
parser.feed(data)
self._root = parser.close()
return self._root
##
# Creates a tree iterator for the root element. The iterator loops
# over all elements in this tree, in document order.
#
# @param tag What tags to look for (default is to return all elements)
# @return An iterator.
# @defreturn iterator
def getiterator(self, tag=None):
assert self._root is not None
return self._root.getiterator(tag)
##
# Finds the first toplevel element with given tag.
# Same as getroot().find(path).
#
# @param path What element to look for.
# @return The first matching element, or None if no element was found.
# @defreturn Element or None
def find(self, path):
assert self._root is not None
if path[:1] == "/":
path = "." + path
return self._root.find(path)
##
# Finds the element text for the first toplevel element with given
# tag. Same as getroot().findtext(path).
#
# @param path What toplevel element to look for.
# @param default What to return if the element was not found.
# @return The text content of the first matching element, or the
# default value no element was found. Note that if the element
# has is found, but has no text content, this method returns an
# empty string.
# @defreturn string
def findtext(self, path, default=None):
assert self._root is not None
if path[:1] == "/":
path = "." + path
return self._root.findtext(path, default)
##
# Finds all toplevel elements with the given tag.
# Same as getroot().findall(path).
#
# @param path What element to look for.
# @return A list or iterator containing all matching elements,
# in document order.
# @defreturn list of Element instances
def findall(self, path):
assert self._root is not None
if path[:1] == "/":
path = "." + path
return self._root.findall(path)
##
# Writes the element tree to a file, as XML.
#
# @param file A file name, or a file object opened for writing.
# @param encoding Optional output encoding (default is US-ASCII).
def write(self, file, encoding="us-ascii"):
assert self._root is not None
if not hasattr(file, "write"):
file = open(file, "wb")
if not encoding:
encoding = "us-ascii"
elif encoding != "utf-8" and encoding != "us-ascii":
file.write("<?xml version='1.0' encoding='%s'?>\n" % encoding)
self._write(file, self._root, encoding, {})
def _write(self, file, node, encoding, namespaces):
# write XML to file
tag = node.tag
if tag is Comment:
file.write("<!-- %s -->" % _escape_cdata(node.text, encoding))
elif tag is ProcessingInstruction:
file.write("<?%s?>" % _escape_cdata(node.text, encoding))
else:
items = node.items()
xmlns_items = [] # new namespaces in this scope
try:
if isinstance(tag, QName) or tag[:1] == "{":
tag, xmlns = fixtag(tag, namespaces)
if xmlns: xmlns_items.append(xmlns)
except TypeError:
_raise_serialization_error(tag)
file.write("<" + _encode(tag, encoding))
if items or xmlns_items:
items.sort() # lexical order
for k, v in items:
try:
if isinstance(k, QName) or k[:1] == "{":
k, xmlns = fixtag(k, namespaces)
if xmlns: xmlns_items.append(xmlns)
except TypeError:
_raise_serialization_error(k)
try:
if isinstance(v, QName):
v, xmlns = fixtag(v, namespaces)
if xmlns: xmlns_items.append(xmlns)
except TypeError:
_raise_serialization_error(v)
file.write(" %s=\"%s\"" % (_encode(k, encoding),
_escape_attrib(v, encoding)))
for k, v in xmlns_items:
file.write(" %s=\"%s\"" % (_encode(k, encoding),
_escape_attrib(v, encoding)))
if node.text or len(node):
file.write(">")
if node.text:
file.write(_escape_cdata(node.text, encoding))
for n in node:
self._write(file, n, encoding, namespaces)
file.write("</" + _encode(tag, encoding) + ">")
else:
file.write(" />")
for k, v in xmlns_items:
del namespaces[v]
if node.tail:
file.write(_escape_cdata(node.tail, encoding))
# --------------------------------------------------------------------
# helpers
##
# Checks if an object appears to be a valid element object.
#
# @param An element instance.
# @return A true value if this is an element object.
# @defreturn flag
def iselement(element):
# FIXME: not sure about this; might be a better idea to look
# for tag/attrib/text attributes
return isinstance(element, _ElementInterface) or hasattr(element, "tag")
##
# Writes an element tree or element structure to sys.stdout. This
# function should be used for debugging only.
# <p>
# The exact output format is implementation dependent. In this
# version, it's written as an ordinary XML file.
#
# @param elem An element tree or an individual element.
def dump(elem):
# debugging
if not isinstance(elem, ElementTree):
elem = ElementTree(elem)
elem.write(sys.stdout)
tail = elem.getroot().tail
if not tail or tail[-1] != "\n":
sys.stdout.write("\n")
def _encode(s, encoding):
try:
return s.encode(encoding)
except AttributeError:
return s # 1.5.2: assume the string uses the right encoding
if sys.version[:3] == "1.5":
_escape = re.compile(r"[&<>\"\x80-\xff]+") # 1.5.2
else:
_escape = re.compile(eval(r'u"[&<>\"\u0080-\uffff]+"'))
_escape_map = {
"&": "&",
"<": "<",
">": ">",
'"': """,
}
_namespace_map = {
# "well-known" namespace prefixes
"http://www.w3.org/XML/1998/namespace": "xml",
"http://www.w3.org/1999/xhtml": "html",
"http://www.w3.org/1999/02/22-rdf-syntax-ns#": "rdf",
"http://schemas.xmlsoap.org/wsdl/": "wsdl",
}
def _raise_serialization_error(text):
raise TypeError(
"cannot serialize %r (type %s)" % (text, type(text).__name__)
)
def _encode_entity(text, pattern=_escape):
# map reserved and non-ascii characters to numerical entities
def escape_entities(m, map=_escape_map):
out = []
append = out.append
for char in m.group():
text = map.get(char)
if text is None:
text = "&#%d;" % ord(char)
append(text)
return string.join(out, "")
try:
return _encode(pattern.sub(escape_entities, text), "ascii")
except TypeError:
_raise_serialization_error(text)
#
# the following functions assume an ascii-compatible encoding
# (or "utf-16")
def _escape_cdata(text, encoding=None, replace=string.replace):
# escape character data
try:
if encoding:
try:
text = _encode(text, encoding)
except UnicodeError:
return _encode_entity(text)
text = replace(text, "&", "&")
text = replace(text, "<", "<")
text = replace(text, ">", ">")
return text
except (TypeError, AttributeError):
_raise_serialization_error(text)
def _escape_attrib(text, encoding=None, replace=string.replace):
# escape attribute value
try:
if encoding:
try:
text = _encode(text, encoding)
except UnicodeError:
return _encode_entity(text)
text = replace(text, "&", "&")
text = replace(text, "'", "'") # FIXME: overkill
text = replace(text, "\"", """)
text = replace(text, "<", "<")
text = replace(text, ">", ">")
return text
except (TypeError, AttributeError):
_raise_serialization_error(text)
def fixtag(tag, namespaces):
# given a decorated tag (of the form {uri}tag), return prefixed
# tag and namespace declaration, if any
if isinstance(tag, QName):
tag = tag.text
namespace_uri, tag = string.split(tag[1:], "}", 1)
prefix = namespaces.get(namespace_uri)
if prefix is None:
prefix = _namespace_map.get(namespace_uri)
if prefix is None:
prefix = "ns%d" % len(namespaces)
namespaces[namespace_uri] = prefix
if prefix == "xml":
xmlns = None
else:
xmlns = ("xmlns:%s" % prefix, namespace_uri)
else:
xmlns = None
return "%s:%s" % (prefix, tag), xmlns
##
# Parses an XML document into an element tree.
#
# @param source A filename or file object containing XML data.
# @param parser An optional parser instance. If not given, the
# standard {@link XMLTreeBuilder} parser is used.
# @return An ElementTree instance
def parse(source, parser=None):
tree = ElementTree()
tree.parse(source, parser)
return tree
##
# Parses an XML document into an element tree incrementally, and reports
# what's going on to the user.
#
# @param source A filename or file object containing XML data.
# @param events A list of events to report back. If omitted, only "end"
# events are reported.
# @return A (event, elem) iterator.
class iterparse:
def __init__(self, source, events=None):
if not hasattr(source, "read"):
source = open(source, "rb")
self._file = source
self._events = []
self._index = 0
self.root = self._root = None
self._parser = XMLTreeBuilder()
# wire up the parser for event reporting
parser = self._parser._parser
append = self._events.append
if events is None:
events = ["end"]
for event in events:
if event == "start":
try:
parser.ordered_attributes = 1
parser.specified_attributes = 1
def handler(tag, attrib_in, event=event, append=append,
start=self._parser._start_list):
append((event, start(tag, attrib_in)))
parser.StartElementHandler = handler
except AttributeError:
def handler(tag, attrib_in, event=event, append=append,
start=self._parser._start):
append((event, start(tag, attrib_in)))
parser.StartElementHandler = handler
elif event == "end":
def handler(tag, event=event, append=append,
end=self._parser._end):
append((event, end(tag)))
parser.EndElementHandler = handler
elif event == "start-ns":
def handler(prefix, uri, event=event, append=append):
try:
uri = _encode(uri, "ascii")
except UnicodeError:
pass
append((event, (prefix or "", uri)))
parser.StartNamespaceDeclHandler = handler
elif event == "end-ns":
def handler(prefix, event=event, append=append):
append((event, None))
parser.EndNamespaceDeclHandler = handler
def next(self):
while 1:
try:
item = self._events[self._index]
except IndexError:
if self._parser is None:
self.root = self._root
try:
raise StopIteration
except NameError:
raise IndexError
# load event buffer
del self._events[:]
self._index = 0
data = self._file.read(16384)
if data:
self._parser.feed(data)
else:
self._root = self._parser.close()
self._parser = None
else:
self._index = self._index + 1
return item
try:
iter
def __iter__(self):
return self
except NameError:
def __getitem__(self, index):
return self.next()
##
# Parses an XML document from a string constant. This function can
# be used to embed "XML literals" in Python code.
#
# @param source A string containing XML data.
# @return An Element instance.
# @defreturn Element
def XML(text):
parser = XMLTreeBuilder()
parser.feed(text)
return parser.close()
##
# Parses an XML document from a string constant, and also returns
# a dictionary which maps from element id:s to elements.
#
# @param source A string containing XML data.
# @return A tuple containing an Element instance and a dictionary.
# @defreturn (Element, dictionary)
def XMLID(text):
parser = XMLTreeBuilder()
parser.feed(text)
tree = parser.close()
ids = {}
for elem in tree.getiterator():
id = elem.get("id")
if id:
ids[id] = elem
return tree, ids
##
# Parses an XML document from a string constant. Same as {@link #XML}.
#
# @def fromstring(text)
# @param source A string containing XML data.
# @return An Element instance.
# @defreturn Element
fromstring = XML
##
# Generates a string representation of an XML element, including all
# subelements.
#
# @param element An Element instance.
# @return An encoded string containing the XML data.
# @defreturn string
def tostring(element, encoding=None):
class dummy:
pass
data = []
file = dummy()
file.write = data.append
ElementTree(element).write(file, encoding)
return string.join(data, "")
##
# Generic element structure builder. This builder converts a sequence
# of {@link #TreeBuilder.start}, {@link #TreeBuilder.data}, and {@link
# #TreeBuilder.end} method calls to a well-formed element structure.
# <p>
# You can use this class to build an element structure using a custom XML
# parser, or a parser for some other XML-like format.
#
# @param element_factory Optional element factory. This factory
# is called to create new Element instances, as necessary.
class TreeBuilder:
def __init__(self, element_factory=None):
self._data = [] # data collector
self._elem = [] # element stack
self._last = None # last element
self._tail = None # true if we're after an end tag
if element_factory is None:
element_factory = _ElementInterface
self._factory = element_factory
##
# Flushes the parser buffers, and returns the toplevel documen
# element.
#
# @return An Element instance.
# @defreturn Element
def close(self):
assert len(self._elem) == 0, "missing end tags"
assert self._last != None, "missing toplevel element"
return self._last
def _flush(self):
if self._data:
if self._last is not None:
text = string.join(self._data, "")
if self._tail:
assert self._last.tail is None, "internal error (tail)"
self._last.tail = text
else:
assert self._last.text is None, "internal error (text)"
self._last.text = text
self._data = []
##
# Adds text to the current element.
#
# @param data A string. This should be either an 8-bit string
# containing ASCII text, or a Unicode string.
def data(self, data):
self._data.append(data)
##
# Opens a new element.
#
# @param tag The element name.
# @param attrib A dictionary containing element attributes.
# @return The opened element.
# @defreturn Element
def start(self, tag, attrs):
self._flush()
self._last = elem = self._factory(tag, attrs)
if self._elem:
self._elem[-1].append(elem)
self._elem.append(elem)
self._tail = 0
return elem
##
# Closes the current element.
#
# @param tag The element name.
# @return The closed element.
# @defreturn Element
def end(self, tag):
self._flush()
self._last = self._elem.pop()
assert self._last.tag == tag,\
"end tag mismatch (expected %s, got %s)" % (
self._last.tag, tag)
self._tail = 1
return self._last
##
# Element structure builder for XML source data, based on the
# <b>expat</b> parser.
#
# @keyparam target Target object. If omitted, the builder uses an
# instance of the standard {@link #TreeBuilder} class.
# @keyparam html Predefine HTML entities. This flag is not supported
# by the current implementation.
# @see #ElementTree
# @see #TreeBuilder
class XMLTreeBuilder:
def __init__(self, html=0, target=None):
try:
from xml.parsers import expat
except ImportError:
raise ImportError(
"No module named expat; use SimpleXMLTreeBuilder instead"
)
self._parser = parser = expat.ParserCreate(None, "}")
if target is None:
target = TreeBuilder()
self._target = target
self._names = {} # name memo cache
# callbacks
parser.DefaultHandlerExpand = self._default
parser.StartElementHandler = self._start
parser.EndElementHandler = self._end
parser.CharacterDataHandler = self._data
# let expat do the buffering, if supported
try:
self._parser.buffer_text = 1
except AttributeError:
pass
# use new-style attribute handling, if supported
try:
self._parser.ordered_attributes = 1
self._parser.specified_attributes = 1
parser.StartElementHandler = self._start_list
except AttributeError:
pass
encoding = None
if not parser.returns_unicode:
encoding = "utf-8"
# target.xml(encoding, None)
self._doctype = None
self.entity = {}
def _fixtext(self, text):
# convert text string to ascii, if possible
try:
return _encode(text, "ascii")
except UnicodeError:
return text
def _fixname(self, key):
# expand qname, and convert name string to ascii, if possible
try:
name = self._names[key]
except KeyError:
name = key
if "}" in name:
name = "{" + name
self._names[key] = name = self._fixtext(name)
return name
def _start(self, tag, attrib_in):
fixname = self._fixname
tag = fixname(tag)
attrib = {}
for key, value in attrib_in.items():
attrib[fixname(key)] = self._fixtext(value)
return self._target.start(tag, attrib)
def _start_list(self, tag, attrib_in):
fixname = self._fixname
tag = fixname(tag)
attrib = {}
if attrib_in:
for i in range(0, len(attrib_in), 2):
attrib[fixname(attrib_in[i])] = self._fixtext(attrib_in[i+1])
return self._target.start(tag, attrib)
def _data(self, text):
return self._target.data(self._fixtext(text))
def _end(self, tag):
return self._target.end(self._fixname(tag))
def _default(self, text):
prefix = text[:1]
if prefix == "&":
# deal with undefined entities
try:
self._target.data(self.entity[text[1:-1]])
except KeyError:
from xml.parsers import expat
raise expat.error(
"undefined entity %s: line %d, column %d" %
(text, self._parser.ErrorLineNumber,
self._parser.ErrorColumnNumber)
)
elif prefix == "<" and text[:9] == "<!DOCTYPE":
self._doctype = [] # inside a doctype declaration
elif self._doctype is not None:
# parse doctype contents
if prefix == ">":
self._doctype = None
return
text = string.strip(text)
if not text:
return
self._doctype.append(text)
n = len(self._doctype)
if n > 2:
type = self._doctype[1]
if type == "PUBLIC" and n == 4:
name, type, pubid, system = self._doctype
elif type == "SYSTEM" and n == 3:
name, type, system = self._doctype
pubid = None
else:
return
if pubid:
pubid = pubid[1:-1]
self.doctype(name, pubid, system[1:-1])
self._doctype = None
##
# Handles a doctype declaration.
#
# @param name Doctype name.
# @param pubid Public identifier.
# @param system System identifier.
def doctype(self, name, pubid, system):
pass
##
# Feeds data to the parser.
#
# @param data Encoded data.
def feed(self, data):
self._parser.Parse(data, 0)
##
# Finishes feeding data to the parser.
#
# @return An element structure.
# @defreturn Element
def close(self):
self._parser.Parse("", 1) # end of data
tree = self._target.close()
del self._target, self._parser # get rid of circular references
return tree
########NEW FILE########
__FILENAME__ = HTMLTreeBuilder
#
# ElementTree
# $Id: HTMLTreeBuilder.py 2325 2005-03-16 15:50:43Z fredrik $
#
# a simple tree builder, for HTML input
#
# history:
# 2002-04-06 fl created
# 2002-04-07 fl ignore IMG and HR end tags
# 2002-04-07 fl added support for 1.5.2 and later
# 2003-04-13 fl added HTMLTreeBuilder alias
# 2004-12-02 fl don't feed non-ASCII charrefs/entities as 8-bit strings
# 2004-12-05 fl don't feed non-ASCII CDATA as 8-bit strings
#
# Copyright (c) 1999-2004 by Fredrik Lundh. All rights reserved.
#
# [email protected]
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2004 by Fredrik Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
##
# Tools to build element trees from HTML files.
##
import htmlentitydefs
import re, string, sys
import mimetools, StringIO
import ElementTree
AUTOCLOSE = "p", "li", "tr", "th", "td", "head", "body"
IGNOREEND = "img", "hr", "meta", "link", "br"
if sys.version[:3] == "1.5":
is_not_ascii = re.compile(r"[\x80-\xff]").search # 1.5.2
else:
is_not_ascii = re.compile(eval(r'u"[\u0080-\uffff]"')).search
try:
from HTMLParser import HTMLParser
except ImportError:
from sgmllib import SGMLParser
# hack to use sgmllib's SGMLParser to emulate 2.2's HTMLParser
class HTMLParser(SGMLParser):
# the following only works as long as this class doesn't
# provide any do, start, or end handlers
def unknown_starttag(self, tag, attrs):
self.handle_starttag(tag, attrs)
def unknown_endtag(self, tag):
self.handle_endtag(tag)
##
# ElementTree builder for HTML source code. This builder converts an
# HTML document or fragment to an ElementTree.
# <p>
# The parser is relatively picky, and requires balanced tags for most
# elements. However, elements belonging to the following group are
# automatically closed: P, LI, TR, TH, and TD. In addition, the
# parser automatically inserts end tags immediately after the start
# tag, and ignores any end tags for the following group: IMG, HR,
# META, and LINK.
#
# @keyparam builder Optional builder object. If omitted, the parser
# uses the standard <b>elementtree</b> builder.
# @keyparam encoding Optional character encoding, if known. If omitted,
# the parser looks for META tags inside the document. If no tags
# are found, the parser defaults to ISO-8859-1. Note that if your
# document uses a non-ASCII compatible encoding, you must decode
# the document before parsing.
#
# @see elementtree.ElementTree
class HTMLTreeBuilder(HTMLParser):
# FIXME: shouldn't this class be named Parser, not Builder?
def __init__(self, builder=None, encoding=None):
self.__stack = []
if builder is None:
builder = ElementTree.TreeBuilder()
self.__builder = builder
self.encoding = encoding or "iso-8859-1"
HTMLParser.__init__(self)
##
# Flushes parser buffers, and return the root element.
#
# @return An Element instance.
def close(self):
HTMLParser.close(self)
return self.__builder.close()
##
# (Internal) Handles start tags.
def handle_starttag(self, tag, attrs):
if tag == "meta":
# look for encoding directives
http_equiv = content = None
for k, v in attrs:
if k == "http-equiv":
http_equiv = string.lower(v)
elif k == "content":
content = v
if http_equiv == "content-type" and content:
# use mimetools to parse the http header
header = mimetools.Message(
StringIO.StringIO("%s: %s\n\n" % (http_equiv, content))
)
encoding = header.getparam("charset")
if encoding:
self.encoding = encoding
if tag in AUTOCLOSE:
if self.__stack and self.__stack[-1] == tag:
self.handle_endtag(tag)
self.__stack.append(tag)
attrib = {}
if attrs:
for k, v in attrs:
attrib[string.lower(k)] = v
self.__builder.start(tag, attrib)
if tag in IGNOREEND:
self.__stack.pop()
self.__builder.end(tag)
##
# (Internal) Handles end tags.
def handle_endtag(self, tag):
if tag in IGNOREEND:
return
lasttag = self.__stack.pop()
if tag != lasttag and lasttag in AUTOCLOSE:
self.handle_endtag(lasttag)
self.__builder.end(tag)
##
# (Internal) Handles character references.
def handle_charref(self, char):
if char[:1] == "x":
char = int(char[1:], 16)
else:
char = int(char)
if 0 <= char < 128:
self.__builder.data(chr(char))
else:
self.__builder.data(unichr(char))
##
# (Internal) Handles entity references.
def handle_entityref(self, name):
entity = htmlentitydefs.entitydefs.get(name)
if entity:
if len(entity) == 1:
entity = ord(entity)
else:
entity = int(entity[2:-1])
if 0 <= entity < 128:
self.__builder.data(chr(entity))
else:
self.__builder.data(unichr(entity))
else:
self.unknown_entityref(name)
##
# (Internal) Handles character data.
def handle_data(self, data):
if isinstance(data, type('')) and is_not_ascii(data):
# convert to unicode, but only if necessary
data = unicode(data, self.encoding, "ignore")
self.__builder.data(data)
##
# (Hook) Handles unknown entity references. The default action
# is to ignore unknown entities.
def unknown_entityref(self, name):
pass # ignore by default; override if necessary
##
# An alias for the <b>HTMLTreeBuilder</b> class.
TreeBuilder = HTMLTreeBuilder
##
# Parse an HTML document or document fragment.
#
# @param source A filename or file object containing HTML data.
# @param encoding Optional character encoding, if known. If omitted,
# the parser looks for META tags inside the document. If no tags
# are found, the parser defaults to ISO-8859-1.
# @return An ElementTree instance
def parse(source, encoding=None):
return ElementTree.parse(source, HTMLTreeBuilder(encoding=encoding))
if __name__ == "__main__":
import sys
ElementTree.dump(parse(open(sys.argv[1])))
########NEW FILE########
__FILENAME__ = SgmlopXMLTreeBuilder
#
# ElementTree
# $Id$
#
# A simple XML tree builder, based on the sgmlop library.
#
# Note that this version does not support namespaces. This may be
# changed in future versions.
#
# history:
# 2004-03-28 fl created
#
# Copyright (c) 1999-2004 by Fredrik Lundh. All rights reserved.
#
# [email protected]
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2004 by Fredrik Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
##
# Tools to build element trees from XML, based on the SGMLOP parser.
# <p>
# The current version does not support XML namespaces.
# <p>
# This tree builder requires the <b>sgmlop</b> extension module
# (available from
# <a href='http://effbot.org/downloads'>http://effbot.org/downloads</a>).
##
import ElementTree
##
# ElementTree builder for XML source data, based on the SGMLOP parser.
#
# @see elementtree.ElementTree
class TreeBuilder:
def __init__(self, html=0):
try:
import sgmlop
except ImportError:
raise RuntimeError("sgmlop parser not available")
self.__builder = ElementTree.TreeBuilder()
if html:
import htmlentitydefs
self.entitydefs.update(htmlentitydefs.entitydefs)
self.__parser = sgmlop.XMLParser()
self.__parser.register(self)
##
# Feeds data to the parser.
#
# @param data Encoded data.
def feed(self, data):
self.__parser.feed(data)
##
# Finishes feeding data to the parser.
#
# @return An element structure.
# @defreturn Element
def close(self):
self.__parser.close()
self.__parser = None
return self.__builder.close()
def finish_starttag(self, tag, attrib):
self.__builder.start(tag, attrib)
def finish_endtag(self, tag):
self.__builder.end(tag)
def handle_data(self, data):
self.__builder.data(data)
########NEW FILE########
__FILENAME__ = SimpleXMLTreeBuilder
#
# ElementTree
# $Id: SimpleXMLTreeBuilder.py 1862 2004-06-18 07:31:02Z Fredrik $
#
# A simple XML tree builder, based on Python's xmllib
#
# Note that due to bugs in xmllib, this builder does not fully support
# namespaces (unqualified attributes are put in the default namespace,
# instead of being left as is). Run this module as a script to find
# out if this affects your Python version.
#
# history:
# 2001-10-20 fl created
# 2002-05-01 fl added namespace support for xmllib
# 2002-08-17 fl added xmllib sanity test
#
# Copyright (c) 1999-2004 by Fredrik Lundh. All rights reserved.
#
# [email protected]
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2004 by Fredrik Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
##
# Tools to build element trees from XML files, using <b>xmllib</b>.
# This module can be used instead of the standard tree builder, for
# Python versions where "expat" is not available (such as 1.5.2).
# <p>
# Note that due to bugs in <b>xmllib</b>, the namespace support is
# not reliable (you can run the module as a script to find out exactly
# how unreliable it is on your Python version).
##
import xmllib, string
import ElementTree
##
# ElementTree builder for XML source data.
#
# @see elementtree.ElementTree
class TreeBuilder(xmllib.XMLParser):
def __init__(self, html=0):
self.__builder = ElementTree.TreeBuilder()
if html:
import htmlentitydefs
self.entitydefs.update(htmlentitydefs.entitydefs)
xmllib.XMLParser.__init__(self)
##
# Feeds data to the parser.
#
# @param data Encoded data.
def feed(self, data):
xmllib.XMLParser.feed(self, data)
##
# Finishes feeding data to the parser.
#
# @return An element structure.
# @defreturn Element
def close(self):
xmllib.XMLParser.close(self)
return self.__builder.close()
def handle_data(self, data):
self.__builder.data(data)
handle_cdata = handle_data
def unknown_starttag(self, tag, attrs):
attrib = {}
for key, value in attrs.items():
attrib[fixname(key)] = value
self.__builder.start(fixname(tag), attrib)
def unknown_endtag(self, tag):
self.__builder.end(fixname(tag))
def fixname(name, split=string.split):
# xmllib in 2.0 and later provides limited (and slightly broken)
# support for XML namespaces.
if " " not in name:
return name
return "{%s}%s" % tuple(split(name, " ", 1))
if __name__ == "__main__":
import sys
# sanity check: look for known namespace bugs in xmllib
p = TreeBuilder()
text = """\
<root xmlns='default'>
<tag attribute='value' />
</root>
"""
p.feed(text)
tree = p.close()
status = []
# check for bugs in the xmllib implementation
tag = tree.find("{default}tag")
if tag is None:
status.append("namespaces not supported")
if tag is not None and tag.get("{default}attribute"):
status.append("default namespace applied to unqualified attribute")
# report bugs
if status:
print "xmllib doesn't work properly in this Python version:"
for bug in status:
print "-", bug
else:
print "congratulations; no problems found in xmllib"
########NEW FILE########
__FILENAME__ = SimpleXMLWriter
#
# SimpleXMLWriter
# $Id: SimpleXMLWriter.py 2312 2005-03-02 18:13:39Z fredrik $
#
# a simple XML writer
#
# history:
# 2001-12-28 fl created
# 2002-11-25 fl fixed attribute encoding
# 2002-12-02 fl minor fixes for 1.5.2
# 2004-06-17 fl added pythondoc markup
# 2004-07-23 fl added flush method (from Jay Graves)
# 2004-10-03 fl added declaration method
#
# Copyright (c) 2001-2004 by Fredrik Lundh
#
# [email protected]
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The SimpleXMLWriter module is
#
# Copyright (c) 2001-2004 by Fredrik Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
##
# Tools to write XML files, without having to deal with encoding
# issues, well-formedness, etc.
# <p>
# The current version does not provide built-in support for
# namespaces. To create files using namespaces, you have to provide
# "xmlns" attributes and explicitly add prefixes to tags and
# attributes.
#
# <h3>Patterns</h3>
#
# The following example generates a small XHTML document.
# <pre>
#
# from elementtree.SimpleXMLWriter import XMLWriter
# import sys
#
# w = XMLWriter(sys.stdout)
#
# html = w.start("html")
#
# w.start("head")
# w.element("title", "my document")
# w.element("meta", name="generator", value="my application 1.0")
# w.end()
#
# w.start("body")
# w.element("h1", "this is a heading")
# w.element("p", "this is a paragraph")
#
# w.start("p")
# w.data("this is ")
# w.element("b", "bold")
# w.data(" and ")
# w.element("i", "italic")
# w.data(".")
# w.end("p")
#
# w.close(html)
# </pre>
##
import re, sys, string
try:
unicode("")
except NameError:
def encode(s, encoding):
# 1.5.2: application must use the right encoding
return s
_escape = re.compile(r"[&<>\"\x80-\xff]+") # 1.5.2
else:
def encode(s, encoding):
return s.encode(encoding)
_escape = re.compile(eval(r'u"[&<>\"\u0080-\uffff]+"'))
def encode_entity(text, pattern=_escape):
# map reserved and non-ascii characters to numerical entities
def escape_entities(m):
out = []
for char in m.group():
out.append("&#%d;" % ord(char))
return string.join(out, "")
return encode(pattern.sub(escape_entities, text), "ascii")
del _escape
#
# the following functions assume an ascii-compatible encoding
# (or "utf-16")
def escape_cdata(s, encoding=None, replace=string.replace):
s = replace(s, "&", "&")
s = replace(s, "<", "<")
s = replace(s, ">", ">")
if encoding:
try:
return encode(s, encoding)
except UnicodeError:
return encode_entity(s)
return s
def escape_attrib(s, encoding=None, replace=string.replace):
s = replace(s, "&", "&")
s = replace(s, "'", "'")
s = replace(s, "\"", """)
s = replace(s, "<", "<")
s = replace(s, ">", ">")
if encoding:
try:
return encode(s, encoding)
except UnicodeError:
return encode_entity(s)
return s
##
# XML writer class.
#
# @param file A file or file-like object. This object must implement
# a <b>write</b> method that takes an 8-bit string.
# @param encoding Optional encoding.
class XMLWriter:
def __init__(self, file, encoding="us-ascii"):
if not hasattr(file, "write"):
file = open(file, "w")
self.__write = file.write
if hasattr(file, "flush"):
self.flush = file.flush
self.__open = 0 # true if start tag is open
self.__tags = []
self.__data = []
self.__encoding = encoding
def __flush(self):
# flush internal buffers
if self.__open:
self.__write(">")
self.__open = 0
if self.__data:
data = string.join(self.__data, "")
self.__write(escape_cdata(data, self.__encoding))
self.__data = []
##
# Writes an XML declaration.
def declaration(self):
encoding = self.__encoding
if encoding == "us-ascii" or encoding == "utf-8":
self.__write("<?xml version='1.0'?>\n")
else:
self.__write("<?xml version='1.0' encoding='%s'?>\n" % encoding)
##
# Opens a new element. Attributes can be given as keyword
# arguments, or as a string/string dictionary. You can pass in
# 8-bit strings or Unicode strings; the former are assumed to use
# the encoding passed to the constructor. The method returns an
# opaque identifier that can be passed to the <b>close</b> method,
# to close all open elements up to and including this one.
#
# @param tag Element tag.
# @param attrib Attribute dictionary. Alternatively, attributes
# can be given as keyword arguments.
# @return An element identifier.
def start(self, tag, attrib={}, **extra):
self.__flush()
tag = escape_cdata(tag, self.__encoding)
self.__data = []
self.__tags.append(tag)
self.__write("<%s" % tag)
if attrib or extra:
attrib = attrib.copy()
attrib.update(extra)
attrib = attrib.items()
attrib.sort()
for k, v in attrib:
k = escape_cdata(k, self.__encoding)
v = escape_attrib(v, self.__encoding)
self.__write(" %s=\"%s\"" % (k, v))
self.__open = 1
return len(self.__tags)-1
##
# Adds a comment to the output stream.
#
# @param comment Comment text, as an 8-bit string or Unicode string.
def comment(self, comment):
self.__flush()
self.__write("<!-- %s -->\n" % escape_cdata(comment, self.__encoding))
##
# Adds character data to the output stream.
#
# @param text Character data, as an 8-bit string or Unicode string.
def data(self, text):
self.__data.append(text)
##
# Closes the current element (opened by the most recent call to
# <b>start</b>).
#
# @param tag Element tag. If given, the tag must match the start
# tag. If omitted, the current element is closed.
def end(self, tag=None):
if tag:
assert self.__tags, "unbalanced end(%s)" % tag
assert escape_cdata(tag, self.__encoding) == self.__tags[-1],\
"expected end(%s), got %s" % (self.__tags[-1], tag)
else:
assert self.__tags, "unbalanced end()"
tag = self.__tags.pop()
if self.__data:
self.__flush()
elif self.__open:
self.__open = 0
self.__write(" />")
return
self.__write("</%s>" % tag)
##
# Closes open elements, up to (and including) the element identified
# by the given identifier.
#
# @param id Element identifier, as returned by the <b>start</b> method.
def close(self, id):
while len(self.__tags) > id:
self.end()
##
# Adds an entire element. This is the same as calling <b>start</b>,
# <b>data</b>, and <b>end</b> in sequence. The <b>text</b> argument
# can be omitted.
def element(self, tag, text=None, attrib={}, **extra):
apply(self.start, (tag, attrib), extra)
if text:
self.data(text)
self.end()
##
# Flushes the output stream.
def flush(self):
pass # replaced by the constructor
########NEW FILE########
__FILENAME__ = TidyHTMLTreeBuilder
#
# ElementTree
# $Id: TidyHTMLTreeBuilder.py 2304 2005-03-01 17:42:41Z fredrik $
#
from elementtidy.TidyHTMLTreeBuilder import *
########NEW FILE########
__FILENAME__ = TidyTools
#
# ElementTree
# $Id: TidyTools.py 1862 2004-06-18 07:31:02Z Fredrik $
#
# tools to run the "tidy" command on an HTML or XHTML file, and return
# the contents as an XHTML element tree.
#
# history:
# 2002-10-19 fl added to ElementTree library; added getzonebody function
#
# Copyright (c) 1999-2004 by Fredrik Lundh. All rights reserved.
#
# [email protected]
# http://www.pythonware.com
#
##
# Tools to build element trees from HTML, using the external <b>tidy</b>
# utility.
##
import glob, string, os, sys
from ElementTree import ElementTree, Element
NS_XHTML = "{http://www.w3.org/1999/xhtml}"
##
# Convert an HTML or HTML-like file to XHTML, using the <b>tidy</b>
# command line utility.
#
# @param file Filename.
# @param new_inline_tags An optional list of valid but non-standard
# inline tags.
# @return An element tree, or None if not successful.
def tidy(file, new_inline_tags=None):
command = ["tidy", "-qn", "-asxml"]
if new_inline_tags:
command.append("--new-inline-tags")
command.append(string.join(new_inline_tags, ","))
# FIXME: support more tidy options!
# convert
os.system(
"%s %s >%s.out 2>%s.err" % (string.join(command), file, file, file)
)
# check that the result is valid XML
try:
tree = ElementTree()
tree.parse(file + ".out")
except:
print "*** %s:%s" % sys.exc_info()[:2]
print ("*** %s is not valid XML "
"(check %s.err for info)" % (file, file))
tree = None
else:
if os.path.isfile(file + ".out"):
os.remove(file + ".out")
if os.path.isfile(file + ".err"):
os.remove(file + ".err")
return tree
##
# Get document body from a an HTML or HTML-like file. This function
# uses the <b>tidy</b> function to convert HTML to XHTML, and cleans
# up the resulting XML tree.
#
# @param file Filename.
# @return A <b>body</b> element, or None if not successful.
def getbody(file, **options):
# get clean body from text file
# get xhtml tree
try:
tree = apply(tidy, (file,), options)
if tree is None:
return
except IOError, v:
print "***", v
return None
NS = NS_XHTML
# remove namespace uris
for node in tree.getiterator():
if node.tag.startswith(NS):
node.tag = node.tag[len(NS):]
body = tree.getroot().find("body")
return body
##
# Same as <b>getbody</b>, but turns plain text at the start of the
# document into an H1 tag. This function can be used to parse zone
# documents.
#
# @param file Filename.
# @return A <b>body</b> element, or None if not successful.
def getzonebody(file, **options):
body = getbody(file, **options)
if body is None:
return
if body.text and string.strip(body.text):
title = Element("h1")
title.text = string.strip(body.text)
title.tail = "\n\n"
body.insert(0, title)
body.text = None
return body
if __name__ == "__main__":
import sys
for arg in sys.argv[1:]:
for file in glob.glob(arg):
print file, "...", tidy(file)
########NEW FILE########
__FILENAME__ = XMLTreeBuilder
#
# ElementTree
# $Id: XMLTreeBuilder.py 2305 2005-03-01 17:43:09Z fredrik $
#
# an XML tree builder
#
# history:
# 2001-10-20 fl created
# 2002-05-01 fl added namespace support for xmllib
# 2002-07-27 fl require expat (1.5.2 code can use SimpleXMLTreeBuilder)
# 2002-08-17 fl use tag/attribute name memo cache
# 2002-12-04 fl moved XMLTreeBuilder to the ElementTree module
#
# Copyright (c) 1999-2004 by Fredrik Lundh. All rights reserved.
#
# [email protected]
# http://www.pythonware.com
#
# --------------------------------------------------------------------
# The ElementTree toolkit is
#
# Copyright (c) 1999-2004 by Fredrik Lundh
#
# By obtaining, using, and/or copying this software and/or its
# associated documentation, you agree that you have read, understood,
# and will comply with the following terms and conditions:
#
# Permission to use, copy, modify, and distribute this software and
# its associated documentation for any purpose and without fee is
# hereby granted, provided that the above copyright notice appears in
# all copies, and that both that copyright notice and this permission
# notice appear in supporting documentation, and that the name of
# Secret Labs AB or the author not be used in advertising or publicity
# pertaining to distribution of the software without specific, written
# prior permission.
#
# SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
# TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
# ABILITY AND FITNESS. IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
# BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
# DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
# WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
# OF THIS SOFTWARE.
# --------------------------------------------------------------------
##
# Tools to build element trees from XML files.
##
import ElementTree
##
# (obsolete) ElementTree builder for XML source data, based on the
# <b>expat</b> parser.
# <p>
# This class is an alias for ElementTree.XMLTreeBuilder. New code
# should use that version instead.
#
# @see elementtree.ElementTree
class TreeBuilder(ElementTree.XMLTreeBuilder):
pass
##
# (experimental) An alternate builder that supports manipulation of
# new elements.
class FancyTreeBuilder(TreeBuilder):
def __init__(self, html=0):
TreeBuilder.__init__(self, html)
self._parser.StartNamespaceDeclHandler = self._start_ns
self._parser.EndNamespaceDeclHandler = self._end_ns
self.namespaces = []
def _start(self, tag, attrib_in):
elem = TreeBuilder._start(self, tag, attrib_in)
self.start(elem)
def _start_list(self, tag, attrib_in):
elem = TreeBuilder._start_list(self, tag, attrib_in)
self.start(elem)
def _end(self, tag):
elem = TreeBuilder._end(self, tag)
self.end(elem)
def _start_ns(self, prefix, value):
self.namespaces.insert(0, (prefix, value))
def _end_ns(self, prefix):
assert self.namespaces.pop(0)[0] == prefix, "implementation confused"
##
# Hook method that's called when a new element has been opened.
# May access the <b>namespaces</b> attribute.
#
# @param element The new element. The tag name and attributes are,
# set, but it has no children, and the text and tail attributes
# are still empty.
def start(self, element):
pass
##
# Hook method that's called when a new element has been closed.
# May access the <b>namespaces</b> attribute.
#
# @param element The new element.
def end(self, element):
pass
########NEW FILE########
__FILENAME__ = fetcher
#!/usr/bin/python
#
# Copyright 2007, Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
An HTTPFetcher implementation that uses Google App Engine's urlfetch module.
HTTPFetcher is an interface defined in the top-level fetchers module in
JanRain's OpenID python library: http://openidenabled.com/python-openid/
For more, see openid/fetchers.py in that library.
"""
import logging
from openid import fetchers
from google.appengine.api import urlfetch
class UrlfetchFetcher(fetchers.HTTPFetcher):
"""An HTTPFetcher subclass that uses Google App Engine's urlfetch module.
"""
def fetch(self, url, body=None, headers=None):
"""
This performs an HTTP POST or GET, following redirects along
the way. If a body is specified, then the request will be a
POST. Otherwise, it will be a GET.
@param headers: HTTP headers to include with the request
@type headers: {str:str}
@return: An object representing the server's HTTP response. If
there are network or protocol errors, an exception will be
raised. HTTP error responses, like 404 or 500, do not
cause exceptions.
@rtype: L{HTTPResponse}
@raise Exception: Different implementations will raise
different errors based on the underlying HTTP library.
"""
if not fetchers._allowedURL(url):
raise ValueError('Bad URL scheme: %r' % (url,))
if not headers:
headers = {}
if body:
method = urlfetch.POST
if 'Content-Type' not in headers:
headers['Content-Type'] = 'application/x-www-form-urlencoded'
else:
method = urlfetch.GET
if not headers:
headers = {}
# follow up to 10 redirects
for i in range(10):
resp = urlfetch.fetch(url, body, method, headers)
if resp.status_code in (301, 302):
logging.debug('Following %d redirect to %s' %
(resp.status_code, resp.headers['location']))
url = resp.headers['location']
else:
break
return fetchers.HTTPResponse(url, resp.status_code, resp.headers,
resp.content)
########NEW FILE########
__FILENAME__ = filters
import re
from google.appengine.ext import webapp
register = webapp.template.create_template_register()
def linkify(text):
"""Escape tags, add line breaks, and linkify HTTP URLs."""
if not text:
return ""
text = text.replace('<', '<').replace('>', '>').replace("\n", '<br/>\n')
text = re.sub(r'\b((?:https?|irc|git)://[\w\-\/\?\&\=\.\:\%\#]+)',
lambda x: "<a href='%s'>%s</a>" % (x.group(1), x.group(1)),
text)
return text
register.filter(linkify)
########NEW FILE########
__FILENAME__ = main
#!/usr/bin/env python
#
# Copyright 2010 Brad Fitzpatrick
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
import logging
from google.appengine.api import users
from google.appengine.ext import db
from google.appengine.ext import webapp
from google.appengine.ext.webapp import template
from google.appengine.ext.webapp import util
import consumer
import models
import filters
webapp.template.register_template_library('filters')
def GetCurrentUser(request):
"""Returns a User entity (OpenID or Google) or None."""
user = users.get_current_user()
if user:
return models.User(google_user=user)
session_id = request.cookies.get('session', '')
if not session_id:
return None
login = consumer.Login.get_by_key_name(session_id)
if not login:
return None
return models.User(openid_user=login.claimed_id)
class IndexHandler(webapp.RequestHandler):
def get(self):
user = GetCurrentUser(self.request)
template_values = {
"user": user,
}
self.response.out.write(template.render("index.html", template_values))
class SiteHandler(webapp.RequestHandler):
def get(self):
self.response.out.write("I'm a site page.")
class LoginHandler(webapp.RequestHandler):
def get(self):
next_url = self.request.get("next")
if not re.match(r'^/[\w/]*$', next_url):
next_url = '/'
user = GetCurrentUser(self.request)
google_login_url = users.create_login_url('/s/notelogin?next=' + next_url)
template_values = {
"user": user,
"google_login_url": google_login_url,
}
self.response.out.write(template.render("login.html", template_values))
class NoteLoginHandler(webapp.RequestHandler):
"""Update a just-logged-in user's last_login property and send them along."""
def get(self):
next_url = self.request.get("next")
if not re.match(r'^/[\w/]*$', next_url):
next_url = '/'
user = GetCurrentUser(self.request)
if user:
user = user.GetOrCreateFromDatastore()
user.put() # updates time
self.redirect(next_url)
class LogoutHandler(webapp.RequestHandler):
def get(self):
next_url = self.request.get("next")
if not re.match(r'^/[\w/]*$', next_url):
next_url = '/'
user = GetCurrentUser(self.request)
if user:
user.LogOut(self, next_url)
else:
self.redirect(next_url)
class UserHandler(webapp.RequestHandler):
def get(self, user_key):
user = GetCurrentUser(self.request)
profile_user = models.User.get_by_key_name(user_key)
if not profile_user:
self.response.set_status(404)
return
can_edit = user and user.sha1_key == profile_user.sha1_key
edit_mode = can_edit and (self.request.get('mode') == "edit")
# get all the projects that this user maintains metadata for
pquery = db.Query(models.Project, keys_only=True)
pquery.filter('owner =', profile_user)
projects = [key.name() for key in pquery.fetch(500)]
url = ""
if profile_user.openid_user:
url = profile_user.openid_user
elif profile_user.url:
url = profile_user.url
template_values = {
"user": user, # logged-in user, or None
"edit_mode": edit_mode,
"can_edit": can_edit,
"profile_user": profile_user,
"user_key": user_key, # the sha1-ish thing
"projects": projects, # list(str), of project keys
"url": url,
}
self.response.out.write(template.render("user.html", template_values))
class CreateHandler(webapp.RequestHandler):
def get(self):
user = GetCurrentUser(self.request)
if not user:
self.redirect('/s/login?next=/s/create')
return
template_values = {
"user": user,
}
self.response.out.write(template.render("create.html", template_values))
def post(self):
user = GetCurrentUser(self.request)
if not user:
self.redirect('/s/login?next=/s/create')
return
def error(msg):
self.response.out.write("Error creating project:<ul><li>%s</li></ul>." %
msg)
return
project_key = self.request.get('project')
if not project_key:
return error("No project specified.")
if not re.match(r'^[a-z][a-z0-9\.\-]*[a-z0-9]$', project_key):
return error("Project name must match regular expression " +
"<tt>/^[a-z][a-z0-9\.\-]*[a-z0-9]$/</tt>.")
project = models.Project.get_by_key_name(project_key)
if project:
return error("Project already exists: <a href='/%s'>%s</a>" %
(project_key, project_key))
user = user.GetOrCreateFromDatastore()
project = models.Project(key_name=project_key,
owner=user)
project.put()
self.redirect("/%s" % project_key)
class ProjectHandler(webapp.RequestHandler):
def get(self, project_key):
user = GetCurrentUser(self.request)
project = models.Project.get_by_key_name(project_key)
if not project:
self.response.set_status(404)
can_edit = user and project and user.sha1_key == project.owner.sha1_key
edit_mode = can_edit and (self.request.get('mode') == "edit")
template_values = {
"user": user,
"project": project,
"edit_mode": edit_mode,
"can_edit": can_edit,
"project_key": project_key,
}
self.response.out.write(template.render("project.html", template_values))
class ProjectEditHandler(webapp.RequestHandler):
"""Handles POSTs to edit a project."""
def post(self):
user = GetCurrentUser(self.request)
project_key = self.request.get('project')
logging.info("project key: %s", project_key)
project = models.Project.get_by_key_name(project_key)
logging.info("project: %s", project)
if not project:
self.response.set_status(404)
return
can_edit = user and user.sha1_key == project.owner.sha1_key
if not can_edit:
self.response.set_status(403)
return
project.how_to = self.request.get("how_to")
project.code_repo = self.request.get("code_repo")
project.home_page = self.request.get("home_page")
project.bug_tracker = self.request.get("bug_tracker")
project.put()
self.redirect('/' + project_key)
class BrowseHandler(webapp.RequestHandler):
def get(self):
user = GetCurrentUser(self.request)
projects = models.Project.all().order('__key__')
if self.request.get("start"):
projects = projects.filter('__key__ >=',
db.Key.from_path(models.Project.kind(),
self.request.get("start")))
PAGE_SIZE = 25
projects = projects.fetch(PAGE_SIZE + 1)
next_page_project = None
if len(projects) > PAGE_SIZE:
next_page_project = projects[-1]
projects = projects[0:PAGE_SIZE]
template_values = {
"user": user,
"projects": projects,
"next_page_project": next_page_project,
}
self.response.out.write(template.render("browse.html", template_values))
def main():
application = webapp.WSGIApplication([
('/', IndexHandler),
('/s/create', CreateHandler),
('/s/login', LoginHandler),
('/s/logout', LogoutHandler),
('/s/editproject', ProjectEditHandler),
('/s/notelogin', NoteLoginHandler),
('/s/browse/?', BrowseHandler),
('/s/.*', SiteHandler),
(r'/u/([a-f0-9]{6,})', UserHandler),
(r'/([a-z][a-z0-9\.\-]*[a-z0-9])/?', ProjectHandler),
],
debug=True)
util.run_wsgi_app(application)
if __name__ == '__main__':
main()
########NEW FILE########
__FILENAME__ = models
#!/usr/bin/env python
#
# Copyright 2010 Brad Fitzpatrick
#
# 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 google.appengine.api import users
from google.appengine.ext import db
import logging
import sha
SALT = 'Contributing!'
class User(db.Model):
"""A user's global state, not specific to a project."""
# One of these will be set:
google_user = db.UserProperty(indexed=True, required=False)
openid_user = db.StringProperty(indexed=True, required=False)
url = db.StringProperty(indexed=False)
last_login = db.DateTimeProperty(auto_now=True)
@property
def last_login_short(self):
return str(self.last_login)[0:10]
@property
def display_name(self):
if self.google_user:
return self.google_user.email
if self.openid_user:
return self.openid_user
return "Unknown user type"
@property
def public_name(self):
if self.google_user:
email = self.google_user.email()
return email[0:email.find('@')+1] + "..."
if self.openid_user:
return self.openid_user
return "Unknown user type"
@property
def profile_page_url(self):
return "/u/" + self.sha1_key
@property
def sha1_key(self):
if self.google_user:
return sha.sha(self.google_user.email() + SALT).hexdigest()[0:8]
if self.openid_user:
return sha.sha(self.openid_user + SALT).hexdigest()[0:8]
return Exception("unknown user type")
def LogOut(self, handler, next_url):
if self.google_user:
handler.redirect(users.create_logout_url(next_url))
return
handler.response.headers.add_header(
'Set-Cookie', 'session=; path=/')
handler.redirect(next_url)
def GetOrCreateFromDatastore(self):
return User.get_or_insert(self.sha1_key,
google_user=self.google_user,
openid_user=self.openid_user)
class Project(db.Model):
"""A project which can be contributed to, with its metadata."""
pretty_name = db.StringProperty(required=False)
owner = db.ReferenceProperty(User, required=True)
last_edit = db.DateTimeProperty(auto_now=True)
how_to = db.TextProperty(default="")
code_repo = db.StringProperty(indexed=False, default="")
home_page = db.StringProperty(indexed=False, default="")
bug_tracker = db.StringProperty(indexed=False, default="")
@property
def name(self):
return self.key().name()
@property
def display_name(self):
if self.pretty_name:
return self.pretty_name
return self.name
@property
def last_edit_short(self):
return str(self.last_edit)[0:10]
class Contributor(db.Model):
"""A user-project tuple."""
user = db.ReferenceProperty(User, required=True)
project = db.ReferenceProperty(Project, required=True)
is_active = db.BooleanProperty()
role = db.StringProperty() # e.g. "Founder" freeform.
########NEW FILE########
__FILENAME__ = association
# -*- test-case-name: openid.test.test_association -*-
"""
This module contains code for dealing with associations between
consumers and servers. Associations contain a shared secret that is
used to sign C{openid.mode=id_res} messages.
Users of the library should not usually need to interact directly with
associations. The L{store<openid.store>},
L{server<openid.server.server>} and
L{consumer<openid.consumer.consumer>} objects will create and manage
the associations. The consumer and server code will make use of a
C{L{SessionNegotiator}} when managing associations, which enables
users to express a preference for what kind of associations should be
allowed, and what kind of exchange should be done to establish the
association.
@var default_negotiator: A C{L{SessionNegotiator}} that allows all
association types that are specified by the OpenID
specification. It prefers to use HMAC-SHA1/DH-SHA1, if it's
available. If HMAC-SHA256 is not supported by your Python runtime,
HMAC-SHA256 and DH-SHA256 will not be available.
@var encrypted_negotiator: A C{L{SessionNegotiator}} that
does not support C{'no-encryption'} associations. It prefers
HMAC-SHA1/DH-SHA1 association types if available.
"""
__all__ = [
'default_negotiator',
'encrypted_negotiator',
'SessionNegotiator',
'Association',
]
import time
from openid import cryptutil
from openid import kvform
from openid import oidutil
from openid.message import OPENID_NS
all_association_types = [
'HMAC-SHA1',
'HMAC-SHA256',
]
if hasattr(cryptutil, 'hmacSha256'):
supported_association_types = list(all_association_types)
default_association_order = [
('HMAC-SHA1', 'DH-SHA1'),
('HMAC-SHA1', 'no-encryption'),
('HMAC-SHA256', 'DH-SHA256'),
('HMAC-SHA256', 'no-encryption'),
]
only_encrypted_association_order = [
('HMAC-SHA1', 'DH-SHA1'),
('HMAC-SHA256', 'DH-SHA256'),
]
else:
supported_association_types = ['HMAC-SHA1']
default_association_order = [
('HMAC-SHA1', 'DH-SHA1'),
('HMAC-SHA1', 'no-encryption'),
]
only_encrypted_association_order = [
('HMAC-SHA1', 'DH-SHA1'),
]
def getSessionTypes(assoc_type):
"""Return the allowed session types for a given association type"""
assoc_to_session = {
'HMAC-SHA1': ['DH-SHA1', 'no-encryption'],
'HMAC-SHA256': ['DH-SHA256', 'no-encryption'],
}
return assoc_to_session.get(assoc_type, [])
def checkSessionType(assoc_type, session_type):
"""Check to make sure that this pair of assoc type and session
type are allowed"""
if session_type not in getSessionTypes(assoc_type):
raise ValueError(
'Session type %r not valid for assocation type %r'
% (session_type, assoc_type))
class SessionNegotiator(object):
"""A session negotiator controls the allowed and preferred
association types and association session types. Both the
C{L{Consumer<openid.consumer.consumer.Consumer>}} and
C{L{Server<openid.server.server.Server>}} use negotiators when
creating associations.
You can create and use negotiators if you:
- Do not want to do Diffie-Hellman key exchange because you use
transport-layer encryption (e.g. SSL)
- Want to use only SHA-256 associations
- Do not want to support plain-text associations over a non-secure
channel
It is up to you to set a policy for what kinds of associations to
accept. By default, the library will make any kind of association
that is allowed in the OpenID 2.0 specification.
Use of negotiators in the library
=================================
When a consumer makes an association request, it calls
C{L{getAllowedType}} to get the preferred association type and
association session type.
The server gets a request for a particular association/session
type and calls C{L{isAllowed}} to determine if it should
create an association. If it is supported, negotiation is
complete. If it is not, the server calls C{L{getAllowedType}} to
get an allowed association type to return to the consumer.
If the consumer gets an error response indicating that the
requested association/session type is not supported by the server
that contains an assocation/session type to try, it calls
C{L{isAllowed}} to determine if it should try again with the
given combination of association/session type.
@ivar allowed_types: A list of association/session types that are
allowed by the server. The order of the pairs in this list
determines preference. If an association/session type comes
earlier in the list, the library is more likely to use that
type.
@type allowed_types: [(str, str)]
"""
def __init__(self, allowed_types):
self.setAllowedTypes(allowed_types)
def copy(self):
return self.__class__(list(self.allowed_types))
def setAllowedTypes(self, allowed_types):
"""Set the allowed association types, checking to make sure
each combination is valid."""
for (assoc_type, session_type) in allowed_types:
checkSessionType(assoc_type, session_type)
self.allowed_types = allowed_types
def addAllowedType(self, assoc_type, session_type=None):
"""Add an association type and session type to the allowed
types list. The assocation/session pairs are tried in the
order that they are added."""
if self.allowed_types is None:
self.allowed_types = []
if session_type is None:
available = getSessionTypes(assoc_type)
if not available:
raise ValueError('No session available for association type %r'
% (assoc_type,))
for session_type in getSessionTypes(assoc_type):
self.addAllowedType(assoc_type, session_type)
else:
checkSessionType(assoc_type, session_type)
self.allowed_types.append((assoc_type, session_type))
def isAllowed(self, assoc_type, session_type):
"""Is this combination of association type and session type allowed?"""
assoc_good = (assoc_type, session_type) in self.allowed_types
matches = session_type in getSessionTypes(assoc_type)
return assoc_good and matches
def getAllowedType(self):
"""Get a pair of assocation type and session type that are
supported"""
try:
return self.allowed_types[0]
except IndexError:
return (None, None)
default_negotiator = SessionNegotiator(default_association_order)
encrypted_negotiator = SessionNegotiator(only_encrypted_association_order)
def getSecretSize(assoc_type):
if assoc_type == 'HMAC-SHA1':
return 20
elif assoc_type == 'HMAC-SHA256':
return 32
else:
raise ValueError('Unsupported association type: %r' % (assoc_type,))
class Association(object):
"""
This class represents an association between a server and a
consumer. In general, users of this library will never see
instances of this object. The only exception is if you implement
a custom C{L{OpenIDStore<openid.store.interface.OpenIDStore>}}.
If you do implement such a store, it will need to store the values
of the C{L{handle}}, C{L{secret}}, C{L{issued}}, C{L{lifetime}}, and
C{L{assoc_type}} instance variables.
@ivar handle: This is the handle the server gave this association.
@type handle: C{str}
@ivar secret: This is the shared secret the server generated for
this association.
@type secret: C{str}
@ivar issued: This is the time this association was issued, in
seconds since 00:00 GMT, January 1, 1970. (ie, a unix
timestamp)
@type issued: C{int}
@ivar lifetime: This is the amount of time this association is
good for, measured in seconds since the association was
issued.
@type lifetime: C{int}
@ivar assoc_type: This is the type of association this instance
represents. The only valid value of this field at this time
is C{'HMAC-SHA1'}, but new types may be defined in the future.
@type assoc_type: C{str}
@sort: __init__, fromExpiresIn, getExpiresIn, __eq__, __ne__,
handle, secret, issued, lifetime, assoc_type
"""
# The ordering and name of keys as stored by serialize
assoc_keys = [
'version',
'handle',
'secret',
'issued',
'lifetime',
'assoc_type',
]
_macs = {
'HMAC-SHA1': cryptutil.hmacSha1,
'HMAC-SHA256': cryptutil.hmacSha256,
}
def fromExpiresIn(cls, expires_in, handle, secret, assoc_type):
"""
This is an alternate constructor used by the OpenID consumer
library to create associations. C{L{OpenIDStore
<openid.store.interface.OpenIDStore>}} implementations
shouldn't use this constructor.
@param expires_in: This is the amount of time this association
is good for, measured in seconds since the association was
issued.
@type expires_in: C{int}
@param handle: This is the handle the server gave this
association.
@type handle: C{str}
@param secret: This is the shared secret the server generated
for this association.
@type secret: C{str}
@param assoc_type: This is the type of association this
instance represents. The only valid value of this field
at this time is C{'HMAC-SHA1'}, but new types may be
defined in the future.
@type assoc_type: C{str}
"""
issued = int(time.time())
lifetime = expires_in
return cls(handle, secret, issued, lifetime, assoc_type)
fromExpiresIn = classmethod(fromExpiresIn)
def __init__(self, handle, secret, issued, lifetime, assoc_type):
"""
This is the standard constructor for creating an association.
@param handle: This is the handle the server gave this
association.
@type handle: C{str}
@param secret: This is the shared secret the server generated
for this association.
@type secret: C{str}
@param issued: This is the time this association was issued,
in seconds since 00:00 GMT, January 1, 1970. (ie, a unix
timestamp)
@type issued: C{int}
@param lifetime: This is the amount of time this association
is good for, measured in seconds since the association was
issued.
@type lifetime: C{int}
@param assoc_type: This is the type of association this
instance represents. The only valid value of this field
at this time is C{'HMAC-SHA1'}, but new types may be
defined in the future.
@type assoc_type: C{str}
"""
if assoc_type not in all_association_types:
fmt = '%r is not a supported association type'
raise ValueError(fmt % (assoc_type,))
# secret_size = getSecretSize(assoc_type)
# if len(secret) != secret_size:
# fmt = 'Wrong size secret (%s bytes) for association type %s'
# raise ValueError(fmt % (len(secret), assoc_type))
self.handle = handle
self.secret = secret
self.issued = issued
self.lifetime = lifetime
self.assoc_type = assoc_type
def getExpiresIn(self, now=None):
"""
This returns the number of seconds this association is still
valid for, or C{0} if the association is no longer valid.
@return: The number of seconds this association is still valid
for, or C{0} if the association is no longer valid.
@rtype: C{int}
"""
if now is None:
now = int(time.time())
return max(0, self.issued + self.lifetime - now)
expiresIn = property(getExpiresIn)
def __eq__(self, other):
"""
This checks to see if two C{L{Association}} instances
represent the same association.
@return: C{True} if the two instances represent the same
association, C{False} otherwise.
@rtype: C{bool}
"""
return type(self) is type(other) and self.__dict__ == other.__dict__
def __ne__(self, other):
"""
This checks to see if two C{L{Association}} instances
represent different associations.
@return: C{True} if the two instances represent different
associations, C{False} otherwise.
@rtype: C{bool}
"""
return not (self == other)
def serialize(self):
"""
Convert an association to KV form.
@return: String in KV form suitable for deserialization by
deserialize.
@rtype: str
"""
data = {
'version':'2',
'handle':self.handle,
'secret':oidutil.toBase64(self.secret),
'issued':str(int(self.issued)),
'lifetime':str(int(self.lifetime)),
'assoc_type':self.assoc_type
}
assert len(data) == len(self.assoc_keys)
pairs = []
for field_name in self.assoc_keys:
pairs.append((field_name, data[field_name]))
return kvform.seqToKV(pairs, strict=True)
def deserialize(cls, assoc_s):
"""
Parse an association as stored by serialize().
inverse of serialize
@param assoc_s: Association as serialized by serialize()
@type assoc_s: str
@return: instance of this class
"""
pairs = kvform.kvToSeq(assoc_s, strict=True)
keys = []
values = []
for k, v in pairs:
keys.append(k)
values.append(v)
if keys != cls.assoc_keys:
raise ValueError('Unexpected key values: %r', keys)
version, handle, secret, issued, lifetime, assoc_type = values
if version != '2':
raise ValueError('Unknown version: %r' % version)
issued = int(issued)
lifetime = int(lifetime)
secret = oidutil.fromBase64(secret)
return cls(handle, secret, issued, lifetime, assoc_type)
deserialize = classmethod(deserialize)
def sign(self, pairs):
"""
Generate a signature for a sequence of (key, value) pairs
@param pairs: The pairs to sign, in order
@type pairs: sequence of (str, str)
@return: The binary signature of this sequence of pairs
@rtype: str
"""
kv = kvform.seqToKV(pairs)
try:
mac = self._macs[self.assoc_type]
except KeyError:
raise ValueError(
'Unknown association type: %r' % (self.assoc_type,))
return mac(self.secret, kv)
def getMessageSignature(self, message):
"""Return the signature of a message.
If I am not a sign-all association, the message must have a
signed list.
@return: the signature, base64 encoded
@rtype: str
@raises ValueError: If there is no signed list and I am not a sign-all
type of association.
"""
pairs = self._makePairs(message)
return oidutil.toBase64(self.sign(pairs))
def signMessage(self, message):
"""Add a signature (and a signed list) to a message.
@return: a new Message object with a signature
@rtype: L{openid.message.Message}
"""
if (message.hasKey(OPENID_NS, 'sig') or
message.hasKey(OPENID_NS, 'signed')):
raise ValueError('Message already has signed list or signature')
extant_handle = message.getArg(OPENID_NS, 'assoc_handle')
if extant_handle and extant_handle != self.handle:
raise ValueError("Message has a different association handle")
signed_message = message.copy()
signed_message.setArg(OPENID_NS, 'assoc_handle', self.handle)
message_keys = signed_message.toPostArgs().keys()
signed_list = [k[7:] for k in message_keys
if k.startswith('openid.')]
signed_list.append('signed')
signed_list.sort()
signed_message.setArg(OPENID_NS, 'signed', ','.join(signed_list))
sig = self.getMessageSignature(signed_message)
signed_message.setArg(OPENID_NS, 'sig', sig)
return signed_message
def checkMessageSignature(self, message):
"""Given a message with a signature, calculate a new signature
and return whether it matches the signature in the message.
@raises ValueError: if the message has no signature or no signature
can be calculated for it.
"""
message_sig = message.getArg(OPENID_NS, 'sig')
if not message_sig:
raise ValueError("%s has no sig." % (message,))
calculated_sig = self.getMessageSignature(message)
return calculated_sig == message_sig
def _makePairs(self, message):
signed = message.getArg(OPENID_NS, 'signed')
if not signed:
raise ValueError('Message has no signed list: %s' % (message,))
signed_list = signed.split(',')
pairs = []
data = message.toPostArgs()
for field in signed_list:
pairs.append((field, data.get('openid.' + field, '')))
return pairs
def __repr__(self):
return "<%s.%s %s %s>" % (
self.__class__.__module__,
self.__class__.__name__,
self.assoc_type,
self.handle)
########NEW FILE########
__FILENAME__ = consumer
# -*- test-case-name: openid.test.test_consumer -*-
"""OpenID support for Relying Parties (aka Consumers).
This module documents the main interface with the OpenID consumer
library. The only part of the library which has to be used and isn't
documented in full here is the store required to create an
C{L{Consumer}} instance. More on the abstract store type and
concrete implementations of it that are provided in the documentation
for the C{L{__init__<Consumer.__init__>}} method of the
C{L{Consumer}} class.
OVERVIEW
========
The OpenID identity verification process most commonly uses the
following steps, as visible to the user of this library:
1. The user enters their OpenID into a field on the consumer's
site, and hits a login button.
2. The consumer site discovers the user's OpenID provider using
the Yadis protocol.
3. The consumer site sends the browser a redirect to the
OpenID provider. This is the authentication request as
described in the OpenID specification.
4. The OpenID provider's site sends the browser a redirect
back to the consumer site. This redirect contains the
provider's response to the authentication request.
The most important part of the flow to note is the consumer's site
must handle two separate HTTP requests in order to perform the
full identity check.
LIBRARY DESIGN
==============
This consumer library is designed with that flow in mind. The
goal is to make it as easy as possible to perform the above steps
securely.
At a high level, there are two important parts in the consumer
library. The first important part is this module, which contains
the interface to actually use this library. The second is the
C{L{openid.store.interface}} module, which describes the
interface to use if you need to create a custom method for storing
the state this library needs to maintain between requests.
In general, the second part is less important for users of the
library to know about, as several implementations are provided
which cover a wide variety of situations in which consumers may
use the library.
This module contains a class, C{L{Consumer}}, with methods
corresponding to the actions necessary in each of steps 2, 3, and
4 described in the overview. Use of this library should be as easy
as creating an C{L{Consumer}} instance and calling the methods
appropriate for the action the site wants to take.
SESSIONS, STORES, AND STATELESS MODE
====================================
The C{L{Consumer}} object keeps track of two types of state:
1. State of the user's current authentication attempt. Things like
the identity URL, the list of endpoints discovered for that
URL, and in case where some endpoints are unreachable, the list
of endpoints already tried. This state needs to be held from
Consumer.begin() to Consumer.complete(), but it is only applicable
to a single session with a single user agent, and at the end of
the authentication process (i.e. when an OP replies with either
C{id_res} or C{cancel}) it may be discarded.
2. State of relationships with servers, i.e. shared secrets
(associations) with servers and nonces seen on signed messages.
This information should persist from one session to the next and
should not be bound to a particular user-agent.
These two types of storage are reflected in the first two arguments of
Consumer's constructor, C{session} and C{store}. C{session} is a
dict-like object and we hope your web framework provides you with one
of these bound to the user agent. C{store} is an instance of
L{openid.store.interface.OpenIDStore}.
Since the store does hold secrets shared between your application and the
OpenID provider, you should be careful about how you use it in a shared
hosting environment. If the filesystem or database permissions of your
web host allow strangers to read from them, do not store your data there!
If you have no safe place to store your data, construct your consumer
with C{None} for the store, and it will operate only in stateless mode.
Stateless mode may be slower, put more load on the OpenID provider, and
trusts the provider to keep you safe from replay attacks.
Several store implementation are provided, and the interface is
fully documented so that custom stores can be used as well. See
the documentation for the C{L{Consumer}} class for more
information on the interface for stores. The implementations that
are provided allow the consumer site to store the necessary data
in several different ways, including several SQL databases and
normal files on disk.
IMMEDIATE MODE
==============
In the flow described above, the user may need to confirm to the
OpenID provider that it's ok to disclose his or her identity.
The provider may draw pages asking for information from the user
before it redirects the browser back to the consumer's site. This
is generally transparent to the consumer site, so it is typically
ignored as an implementation detail.
There can be times, however, where the consumer site wants to get
a response immediately. When this is the case, the consumer can
put the library in immediate mode. In immediate mode, there is an
extra response possible from the server, which is essentially the
server reporting that it doesn't have enough information to answer
the question yet.
USING THIS LIBRARY
==================
Integrating this library into an application is usually a
relatively straightforward process. The process should basically
follow this plan:
Add an OpenID login field somewhere on your site. When an OpenID
is entered in that field and the form is submitted, it should make
a request to the your site which includes that OpenID URL.
First, the application should L{instantiate a Consumer<Consumer.__init__>}
with a session for per-user state and store for shared state.
using the store of choice.
Next, the application should call the 'C{L{begin<Consumer.begin>}}' method on the
C{L{Consumer}} instance. This method takes the OpenID URL. The
C{L{begin<Consumer.begin>}} method returns an C{L{AuthRequest}}
object.
Next, the application should call the
C{L{redirectURL<AuthRequest.redirectURL>}} method on the
C{L{AuthRequest}} object. The parameter C{return_to} is the URL
that the OpenID server will send the user back to after attempting
to verify his or her identity. The C{realm} parameter is the
URL (or URL pattern) that identifies your web site to the user
when he or she is authorizing it. Send a redirect to the
resulting URL to the user's browser.
That's the first half of the authentication process. The second
half of the process is done after the user's OpenID Provider sends the
user's browser a redirect back to your site to complete their
login.
When that happens, the user will contact your site at the URL
given as the C{return_to} URL to the
C{L{redirectURL<AuthRequest.redirectURL>}} call made
above. The request will have several query parameters added to
the URL by the OpenID provider as the information necessary to
finish the request.
Get an C{L{Consumer}} instance with the same session and store as
before and call its C{L{complete<Consumer.complete>}} method,
passing in all the received query arguments.
There are multiple possible return types possible from that
method. These indicate the whether or not the login was
successful, and include any additional information appropriate for
their type.
@var SUCCESS: constant used as the status for
L{SuccessResponse<openid.consumer.consumer.SuccessResponse>} objects.
@var FAILURE: constant used as the status for
L{FailureResponse<openid.consumer.consumer.FailureResponse>} objects.
@var CANCEL: constant used as the status for
L{CancelResponse<openid.consumer.consumer.CancelResponse>} objects.
@var SETUP_NEEDED: constant used as the status for
L{SetupNeededResponse<openid.consumer.consumer.SetupNeededResponse>}
objects.
"""
import cgi
import copy
import logging
from urlparse import urlparse, urldefrag
from openid import fetchers
from openid import oidutil
from openid.consumer.discover import discover, OpenIDServiceEndpoint, \
DiscoveryFailure, OPENID_1_0_TYPE, OPENID_1_1_TYPE, OPENID_2_0_TYPE
from openid.message import Message, OPENID_NS, OPENID2_NS, OPENID1_NS, \
IDENTIFIER_SELECT, no_default, BARE_NS
from openid import cryptutil
from openid import oidutil
from openid.association import Association, default_negotiator, \
SessionNegotiator
from openid.dh import DiffieHellman
from openid.store.nonce import mkNonce, split as splitNonce
from openid.yadis.manager import Discovery
__all__ = ['AuthRequest', 'Consumer', 'SuccessResponse',
'SetupNeededResponse', 'CancelResponse', 'FailureResponse',
'SUCCESS', 'FAILURE', 'CANCEL', 'SETUP_NEEDED',
]
def appEngineLoggingFunction(message, level=0):
# Level is unused.
logging.info(message)
oidutil.log = appEngineLoggingFunction
def makeKVPost(request_message, server_url):
"""Make a Direct Request to an OpenID Provider and return the
result as a Message object.
@raises openid.fetchers.HTTPFetchingError: if an error is
encountered in making the HTTP post.
@rtype: L{openid.message.Message}
"""
# XXX: TESTME
resp = fetchers.fetch(server_url, body=request_message.toURLEncoded())
# Process response in separate function that can be shared by async code.
return _httpResponseToMessage(resp, server_url)
def _httpResponseToMessage(response, server_url):
"""Adapt a POST response to a Message.
@type response: L{openid.fetchers.HTTPResponse}
@param response: Result of a POST to an OpenID endpoint.
@rtype: L{openid.message.Message}
@raises openid.fetchers.HTTPFetchingError: if the server returned a
status of other than 200 or 400.
@raises ServerError: if the server returned an OpenID error.
"""
# Should this function be named Message.fromHTTPResponse instead?
response_message = Message.fromKVForm(response.body)
if response.status == 400:
raise ServerError.fromMessage(response_message)
elif response.status != 200:
fmt = 'bad status code from server %s: %s'
error_message = fmt % (server_url, response.status)
raise fetchers.HTTPFetchingError(error_message)
return response_message
class Consumer(object):
"""An OpenID consumer implementation that performs discovery and
does session management.
@ivar consumer: an instance of an object implementing the OpenID
protocol, but doing no discovery or session management.
@type consumer: GenericConsumer
@ivar session: A dictionary-like object representing the user's
session data. This is used for keeping state of the OpenID
transaction when the user is redirected to the server.
@cvar session_key_prefix: A string that is prepended to session
keys to ensure that they are unique. This variable may be
changed to suit your application.
"""
session_key_prefix = "_openid_consumer_"
_token = 'last_token'
_discover = staticmethod(discover)
def __init__(self, session, store, consumer_class=None):
"""Initialize a Consumer instance.
You should create a new instance of the Consumer object with
every HTTP request that handles OpenID transactions.
@param session: See L{the session instance variable<openid.consumer.consumer.Consumer.session>}
@param store: an object that implements the interface in
C{L{openid.store.interface.OpenIDStore}}. Several
implementations are provided, to cover common database
environments.
@type store: C{L{openid.store.interface.OpenIDStore}}
@see: L{openid.store.interface}
@see: L{openid.store}
"""
self.session = session
if consumer_class is None:
consumer_class = GenericConsumer
self.consumer = consumer_class(store)
self._token_key = self.session_key_prefix + self._token
def begin(self, user_url, anonymous=False):
"""Start the OpenID authentication process. See steps 1-2 in
the overview at the top of this file.
@param user_url: Identity URL given by the user. This method
performs a textual transformation of the URL to try and
make sure it is normalized. For example, a user_url of
example.com will be normalized to http://example.com/
normalizing and resolving any redirects the server might
issue.
@type user_url: unicode
@param anonymous: Whether to make an anonymous request of the OpenID
provider. Such a request does not ask for an authorization
assertion for an OpenID identifier, but may be used with
extensions to pass other data. e.g. "I don't care who you are,
but I'd like to know your time zone."
@type anonymous: bool
@returns: An object containing the discovered information will
be returned, with a method for building a redirect URL to
the server, as described in step 3 of the overview. This
object may also be used to add extension arguments to the
request, using its
L{addExtensionArg<openid.consumer.consumer.AuthRequest.addExtensionArg>}
method.
@returntype: L{AuthRequest<openid.consumer.consumer.AuthRequest>}
@raises openid.consumer.discover.DiscoveryFailure: when I fail to
find an OpenID server for this URL. If the C{yadis} package
is available, L{openid.consumer.discover.DiscoveryFailure} is
an alias for C{yadis.discover.DiscoveryFailure}.
"""
disco = Discovery(self.session, user_url, self.session_key_prefix)
try:
service = disco.getNextService(self._discover)
except fetchers.HTTPFetchingError, why:
raise DiscoveryFailure(
'Error fetching XRDS document: %s' % (why[0],), None)
if service is None:
raise DiscoveryFailure(
'No usable OpenID services found for %s' % (user_url,), None)
else:
return self.beginWithoutDiscovery(service, anonymous)
def beginWithoutDiscovery(self, service, anonymous=False):
"""Start OpenID verification without doing OpenID server
discovery. This method is used internally by Consumer.begin
after discovery is performed, and exists to provide an
interface for library users needing to perform their own
discovery.
@param service: an OpenID service endpoint descriptor. This
object and factories for it are found in the
L{openid.consumer.discover} module.
@type service:
L{OpenIDServiceEndpoint<openid.consumer.discover.OpenIDServiceEndpoint>}
@returns: an OpenID authentication request object.
@rtype: L{AuthRequest<openid.consumer.consumer.AuthRequest>}
@See: Openid.consumer.consumer.Consumer.begin
@see: openid.consumer.discover
"""
auth_req = self.consumer.begin(service)
self.session[self._token_key] = auth_req.endpoint
try:
auth_req.setAnonymous(anonymous)
except ValueError, why:
raise ProtocolError(str(why))
return auth_req
def complete(self, query, return_to):
"""Called to interpret the server's response to an OpenID
request. It is called in step 4 of the flow described in the
consumer overview.
@param query: A dictionary of the query parameters for this
HTTP request.
@param return_to: The return URL used to invoke the
application. Extract the URL from your application's web
request framework and specify it here to have it checked
against the openid.return_to value in the response. If
the return_to URL check fails, the status of the
completion will be FAILURE.
@returns: a subclass of Response. The type of response is
indicated by the status attribute, which will be one of
SUCCESS, CANCEL, FAILURE, or SETUP_NEEDED.
@see: L{SuccessResponse<openid.consumer.consumer.SuccessResponse>}
@see: L{CancelResponse<openid.consumer.consumer.CancelResponse>}
@see: L{SetupNeededResponse<openid.consumer.consumer.SetupNeededResponse>}
@see: L{FailureResponse<openid.consumer.consumer.FailureResponse>}
"""
endpoint = self.session.get(self._token_key)
message = Message.fromPostArgs(query)
response = self.consumer.complete(message, endpoint, return_to)
try:
del self.session[self._token_key]
except KeyError:
pass
if (response.status in ['success', 'cancel'] and
response.identity_url is not None):
disco = Discovery(self.session,
response.identity_url,
self.session_key_prefix)
# This is OK to do even if we did not do discovery in
# the first place.
disco.cleanup(force=True)
return response
def setAssociationPreference(self, association_preferences):
"""Set the order in which association types/sessions should be
attempted. For instance, to only allow HMAC-SHA256
associations created with a DH-SHA256 association session:
>>> consumer.setAssociationPreference([('HMAC-SHA256', 'DH-SHA256')])
Any association type/association type pair that is not in this
list will not be attempted at all.
@param association_preferences: The list of allowed
(association type, association session type) pairs that
should be allowed for this consumer to use, in order from
most preferred to least preferred.
@type association_preferences: [(str, str)]
@returns: None
@see: C{L{openid.association.SessionNegotiator}}
"""
self.consumer.negotiator = SessionNegotiator(association_preferences)
class DiffieHellmanSHA1ConsumerSession(object):
session_type = 'DH-SHA1'
hash_func = staticmethod(cryptutil.sha1)
secret_size = 20
allowed_assoc_types = ['HMAC-SHA1']
def __init__(self, dh=None):
if dh is None:
dh = DiffieHellman.fromDefaults()
self.dh = dh
def getRequest(self):
cpub = cryptutil.longToBase64(self.dh.public)
args = {'dh_consumer_public': cpub}
if not self.dh.usingDefaultValues():
args.update({
'dh_modulus': cryptutil.longToBase64(self.dh.modulus),
'dh_gen': cryptutil.longToBase64(self.dh.generator),
})
return args
def extractSecret(self, response):
dh_server_public64 = response.getArg(
OPENID_NS, 'dh_server_public', no_default)
enc_mac_key64 = response.getArg(OPENID_NS, 'enc_mac_key', no_default)
dh_server_public = cryptutil.base64ToLong(dh_server_public64)
enc_mac_key = oidutil.fromBase64(enc_mac_key64)
return self.dh.xorSecret(dh_server_public, enc_mac_key, self.hash_func)
class DiffieHellmanSHA256ConsumerSession(DiffieHellmanSHA1ConsumerSession):
session_type = 'DH-SHA256'
hash_func = staticmethod(cryptutil.sha256)
secret_size = 32
allowed_assoc_types = ['HMAC-SHA256']
class PlainTextConsumerSession(object):
session_type = 'no-encryption'
allowed_assoc_types = ['HMAC-SHA1', 'HMAC-SHA256']
def getRequest(self):
return {}
def extractSecret(self, response):
mac_key64 = response.getArg(OPENID_NS, 'mac_key', no_default)
return oidutil.fromBase64(mac_key64)
class SetupNeededError(Exception):
"""Internally-used exception that indicates that an immediate-mode
request cancelled."""
def __init__(self, user_setup_url=None):
Exception.__init__(self, user_setup_url)
self.user_setup_url = user_setup_url
class ProtocolError(ValueError):
"""Exception that indicates that a message violated the
protocol. It is raised and caught internally to this file."""
class TypeURIMismatch(ProtocolError):
"""A protocol error arising from type URIs mismatching
"""
def __init__(self, expected, endpoint):
ProtocolError.__init__(self, expected, endpoint)
self.expected = expected
self.endpoint = endpoint
def __str__(self):
s = '<%s.%s: Required type %s not found in %s for endpoint %s>' % (
self.__class__.__module__, self.__class__.__name__,
self.expected, self.endpoint.type_uris, self.endpoint)
return s
class ServerError(Exception):
"""Exception that is raised when the server returns a 400 response
code to a direct request."""
def __init__(self, error_text, error_code, message):
Exception.__init__(self, error_text)
self.error_text = error_text
self.error_code = error_code
self.message = message
def fromMessage(cls, message):
"""Generate a ServerError instance, extracting the error text
and the error code from the message."""
error_text = message.getArg(
OPENID_NS, 'error', '<no error message supplied>')
error_code = message.getArg(OPENID_NS, 'error_code')
return cls(error_text, error_code, message)
fromMessage = classmethod(fromMessage)
class GenericConsumer(object):
"""This is the implementation of the common logic for OpenID
consumers. It is unaware of the application in which it is
running.
@ivar negotiator: An object that controls the kind of associations
that the consumer makes. It defaults to
C{L{openid.association.default_negotiator}}. Assign a
different negotiator to it if you have specific requirements
for how associations are made.
@type negotiator: C{L{openid.association.SessionNegotiator}}
"""
# The name of the query parameter that gets added to the return_to
# URL when using OpenID1. You can change this value if you want or
# need a different name, but don't make it start with openid,
# because it's not a standard protocol thing for OpenID1. For
# OpenID2, the library will take care of the nonce using standard
# OpenID query parameter names.
openid1_nonce_query_arg_name = 'janrain_nonce'
# Another query parameter that gets added to the return_to for
# OpenID 1; if the user's session state is lost, use this claimed
# identifier to do discovery when verifying the response.
openid1_return_to_identifier_name = 'openid1_claimed_id'
session_types = {
'DH-SHA1':DiffieHellmanSHA1ConsumerSession,
'DH-SHA256':DiffieHellmanSHA256ConsumerSession,
'no-encryption':PlainTextConsumerSession,
}
_discover = staticmethod(discover)
def __init__(self, store):
self.store = store
self.negotiator = default_negotiator.copy()
def begin(self, service_endpoint):
"""Create an AuthRequest object for the specified
service_endpoint. This method will create an association if
necessary."""
if self.store is None:
assoc = None
else:
assoc = self._getAssociation(service_endpoint)
request = AuthRequest(service_endpoint, assoc)
request.return_to_args[self.openid1_nonce_query_arg_name] = mkNonce()
if request.message.isOpenID1():
request.return_to_args[self.openid1_return_to_identifier_name] = \
request.endpoint.claimed_id
return request
def complete(self, message, endpoint, return_to):
"""Process the OpenID message, using the specified endpoint
and return_to URL as context. This method will handle any
OpenID message that is sent to the return_to URL.
"""
mode = message.getArg(OPENID_NS, 'mode', '<No mode set>')
modeMethod = getattr(self, '_complete_' + mode,
self._completeInvalid)
return modeMethod(message, endpoint, return_to)
def _complete_cancel(self, message, endpoint, _):
return CancelResponse(endpoint)
def _complete_error(self, message, endpoint, _):
error = message.getArg(OPENID_NS, 'error')
contact = message.getArg(OPENID_NS, 'contact')
reference = message.getArg(OPENID_NS, 'reference')
return FailureResponse(endpoint, error, contact=contact,
reference=reference)
def _complete_setup_needed(self, message, endpoint, _):
if not message.isOpenID2():
return self._completeInvalid(message, endpoint, _)
return SetupNeededResponse(endpoint)
def _complete_id_res(self, message, endpoint, return_to):
try:
self._checkSetupNeeded(message)
except SetupNeededError, why:
return SetupNeededResponse(endpoint, why.user_setup_url)
else:
try:
return self._doIdRes(message, endpoint, return_to)
except (ProtocolError, DiscoveryFailure), why:
return FailureResponse(endpoint, why[0])
def _completeInvalid(self, message, endpoint, _):
mode = message.getArg(OPENID_NS, 'mode', '<No mode set>')
return FailureResponse(endpoint,
'Invalid openid.mode: %r' % (mode,))
def _checkReturnTo(self, message, return_to):
"""Check an OpenID message and its openid.return_to value
against a return_to URL from an application. Return True on
success, False on failure.
"""
# Check the openid.return_to args against args in the original
# message.
try:
self._verifyReturnToArgs(message.toPostArgs())
except ProtocolError, why:
oidutil.log("Verifying return_to arguments: %s" % (why[0],))
return False
# Check the return_to base URL against the one in the message.
msg_return_to = message.getArg(OPENID_NS, 'return_to')
# The URL scheme, authority, and path MUST be the same between
# the two URLs.
app_parts = urlparse(return_to)
msg_parts = urlparse(msg_return_to)
# (addressing scheme, network location, path) must be equal in
# both URLs.
for part in range(0, 3):
if app_parts[part] != msg_parts[part]:
return False
return True
_makeKVPost = staticmethod(makeKVPost)
def _checkSetupNeeded(self, message):
"""Check an id_res message to see if it is a
checkid_immediate cancel response.
@raises SetupNeededError: if it is a checkid_immediate cancellation
"""
# In OpenID 1, we check to see if this is a cancel from
# immediate mode by the presence of the user_setup_url
# parameter.
if message.isOpenID1():
user_setup_url = message.getArg(OPENID1_NS, 'user_setup_url')
if user_setup_url is not None:
raise SetupNeededError(user_setup_url)
def _doIdRes(self, message, endpoint, return_to):
"""Handle id_res responses that are not cancellations of
immediate mode requests.
@param message: the response paramaters.
@param endpoint: the discovered endpoint object. May be None.
@raises ProtocolError: If the message contents are not
well-formed according to the OpenID specification. This
includes missing fields or not signing fields that should
be signed.
@raises DiscoveryFailure: If the subject of the id_res message
does not match the supplied endpoint, and discovery on the
identifier in the message fails (this should only happen
when using OpenID 2)
@returntype: L{Response}
"""
# Checks for presence of appropriate fields (and checks
# signed list fields)
self._idResCheckForFields(message)
if not self._checkReturnTo(message, return_to):
raise ProtocolError(
"return_to does not match return URL. Expected %r, got %r"
% (return_to, message.getArg(OPENID_NS, 'return_to')))
# Verify discovery information:
endpoint = self._verifyDiscoveryResults(message, endpoint)
oidutil.log("Received id_res response from %s using association %s" %
(endpoint.server_url,
message.getArg(OPENID_NS, 'assoc_handle')))
self._idResCheckSignature(message, endpoint.server_url)
# Will raise a ProtocolError if the nonce is bad
self._idResCheckNonce(message, endpoint)
signed_list_str = message.getArg(OPENID_NS, 'signed', no_default)
signed_list = signed_list_str.split(',')
signed_fields = ["openid." + s for s in signed_list]
return SuccessResponse(endpoint, message, signed_fields)
def _idResGetNonceOpenID1(self, message, endpoint):
"""Extract the nonce from an OpenID 1 response. Return the
nonce from the BARE_NS since we independently check the
return_to arguments are the same as those in the response
message.
See the openid1_nonce_query_arg_name class variable
@returns: The nonce as a string or None
"""
return message.getArg(BARE_NS, self.openid1_nonce_query_arg_name)
def _idResCheckNonce(self, message, endpoint):
if message.isOpenID1():
# This indicates that the nonce was generated by the consumer
nonce = self._idResGetNonceOpenID1(message, endpoint)
server_url = ''
else:
nonce = message.getArg(OPENID2_NS, 'response_nonce')
server_url = endpoint.server_url
if nonce is None:
raise ProtocolError('Nonce missing from response')
try:
timestamp, salt = splitNonce(nonce)
except ValueError, why:
raise ProtocolError('Malformed nonce: %s' % (why[0],))
if (self.store is not None and
not self.store.useNonce(server_url, timestamp, salt)):
raise ProtocolError('Nonce already used or out of range')
def _idResCheckSignature(self, message, server_url):
assoc_handle = message.getArg(OPENID_NS, 'assoc_handle')
if self.store is None:
assoc = None
else:
assoc = self.store.getAssociation(server_url, assoc_handle)
if assoc:
if assoc.getExpiresIn() <= 0:
# XXX: It might be a good idea sometimes to re-start the
# authentication with a new association. Doing it
# automatically opens the possibility for
# denial-of-service by a server that just returns expired
# associations (or really short-lived associations)
raise ProtocolError(
'Association with %s expired' % (server_url,))
if not assoc.checkMessageSignature(message):
raise ProtocolError('Bad signature')
else:
# It's not an association we know about. Stateless mode is our
# only possible path for recovery.
# XXX - async framework will not want to block on this call to
# _checkAuth.
if not self._checkAuth(message, server_url):
raise ProtocolError('Server denied check_authentication')
def _idResCheckForFields(self, message):
# XXX: this should be handled by the code that processes the
# response (that is, if a field is missing, we should not have
# to explicitly check that it's present, just make sure that
# the fields are actually being used by the rest of the code
# in tests). Although, which fields are signed does need to be
# checked somewhere.
basic_fields = ['return_to', 'assoc_handle', 'sig', 'signed']
basic_sig_fields = ['return_to', 'identity']
require_fields = {
OPENID2_NS: basic_fields + ['op_endpoint'],
OPENID1_NS: basic_fields + ['identity'],
}
require_sigs = {
OPENID2_NS: basic_sig_fields + ['response_nonce',
'claimed_id',
'assoc_handle',],
OPENID1_NS: basic_sig_fields,
}
for field in require_fields[message.getOpenIDNamespace()]:
if not message.hasKey(OPENID_NS, field):
raise ProtocolError('Missing required field %r' % (field,))
signed_list_str = message.getArg(OPENID_NS, 'signed', no_default)
signed_list = signed_list_str.split(',')
for field in require_sigs[message.getOpenIDNamespace()]:
# Field is present and not in signed list
if message.hasKey(OPENID_NS, field) and field not in signed_list:
raise ProtocolError('"%s" not signed' % (field,))
def _verifyReturnToArgs(query):
"""Verify that the arguments in the return_to URL are present in this
response.
"""
message = Message.fromPostArgs(query)
return_to = message.getArg(OPENID_NS, 'return_to')
if return_to is None:
raise ProtocolError('Response has no return_to')
parsed_url = urlparse(return_to)
rt_query = parsed_url[4]
parsed_args = cgi.parse_qsl(rt_query)
for rt_key, rt_value in parsed_args:
try:
value = query[rt_key]
if rt_value != value:
format = ("parameter %s value %r does not match "
"return_to's value %r")
raise ProtocolError(format % (rt_key, value, rt_value))
except KeyError:
format = "return_to parameter %s absent from query %r"
raise ProtocolError(format % (rt_key, query))
# Make sure all non-OpenID arguments in the response are also
# in the signed return_to.
bare_args = message.getArgs(BARE_NS)
for pair in bare_args.iteritems():
if pair not in parsed_args:
raise ProtocolError("Parameter %s not in return_to URL" % (pair[0],))
_verifyReturnToArgs = staticmethod(_verifyReturnToArgs)
def _verifyDiscoveryResults(self, resp_msg, endpoint=None):
"""
Extract the information from an OpenID assertion message and
verify it against the original
@param endpoint: The endpoint that resulted from doing discovery
@param resp_msg: The id_res message object
@returns: the verified endpoint
"""
if resp_msg.getOpenIDNamespace() == OPENID2_NS:
return self._verifyDiscoveryResultsOpenID2(resp_msg, endpoint)
else:
return self._verifyDiscoveryResultsOpenID1(resp_msg, endpoint)
def _verifyDiscoveryResultsOpenID2(self, resp_msg, endpoint):
to_match = OpenIDServiceEndpoint()
to_match.type_uris = [OPENID_2_0_TYPE]
to_match.claimed_id = resp_msg.getArg(OPENID2_NS, 'claimed_id')
to_match.local_id = resp_msg.getArg(OPENID2_NS, 'identity')
# Raises a KeyError when the op_endpoint is not present
to_match.server_url = resp_msg.getArg(
OPENID2_NS, 'op_endpoint', no_default)
# claimed_id and identifier must both be present or both
# be absent
if (to_match.claimed_id is None and
to_match.local_id is not None):
raise ProtocolError(
'openid.identity is present without openid.claimed_id')
elif (to_match.claimed_id is not None and
to_match.local_id is None):
raise ProtocolError(
'openid.claimed_id is present without openid.identity')
# This is a response without identifiers, so there's really no
# checking that we can do, so return an endpoint that's for
# the specified `openid.op_endpoint'
elif to_match.claimed_id is None:
return OpenIDServiceEndpoint.fromOPEndpointURL(to_match.server_url)
# The claimed ID doesn't match, so we have to do discovery
# again. This covers not using sessions, OP identifier
# endpoints and responses that didn't match the original
# request.
if not endpoint:
oidutil.log('No pre-discovered information supplied.')
endpoint = self._discoverAndVerify(to_match)
else:
# The claimed ID matches, so we use the endpoint that we
# discovered in initiation. This should be the most common
# case.
try:
self._verifyDiscoverySingle(endpoint, to_match)
except ProtocolError, e:
oidutil.log("Error attempting to use stored discovery information: " +
str(e))
oidutil.log("Attempting discovery to verify endpoint")
endpoint = self._discoverAndVerify(to_match)
# The endpoint we return should have the claimed ID from the
# message we just verified, fragment and all.
if endpoint.claimed_id != to_match.claimed_id:
endpoint = copy.copy(endpoint)
endpoint.claimed_id = to_match.claimed_id
return endpoint
def _verifyDiscoveryResultsOpenID1(self, resp_msg, endpoint):
claimed_id = resp_msg.getArg(BARE_NS, self.openid1_return_to_identifier_name)
if endpoint is None and claimed_id is None:
raise RuntimeError(
'When using OpenID 1, the claimed ID must be supplied, '
'either by passing it through as a return_to parameter '
'or by using a session, and supplied to the GenericConsumer '
'as the argument to complete()')
elif endpoint is not None and claimed_id is None:
claimed_id = endpoint.claimed_id
to_match = OpenIDServiceEndpoint()
to_match.type_uris = [OPENID_1_1_TYPE]
to_match.local_id = resp_msg.getArg(OPENID1_NS, 'identity')
# Restore delegate information from the initiation phase
to_match.claimed_id = claimed_id
if to_match.local_id is None:
raise ProtocolError('Missing required field openid.identity')
to_match_1_0 = copy.copy(to_match)
to_match_1_0.type_uris = [OPENID_1_0_TYPE]
if endpoint is not None:
try:
try:
self._verifyDiscoverySingle(endpoint, to_match)
except TypeURIMismatch:
self._verifyDiscoverySingle(endpoint, to_match_1_0)
except ProtocolError, e:
oidutil.log("Error attempting to use stored discovery information: " +
str(e))
oidutil.log("Attempting discovery to verify endpoint")
else:
return endpoint
# Endpoint is either bad (failed verification) or None
try:
return self._discoverAndVerify(to_match)
except TypeURIMismatch:
return self._discoverAndVerify(to_match_1_0)
def _verifyDiscoverySingle(self, endpoint, to_match):
"""Verify that the given endpoint matches the information
extracted from the OpenID assertion, and raise an exception if
there is a mismatch.
@type endpoint: openid.consumer.discover.OpenIDServiceEndpoint
@type to_match: openid.consumer.discover.OpenIDServiceEndpoint
@rtype: NoneType
@raises ProtocolError: when the endpoint does not match the
discovered information.
"""
# Every type URI that's in the to_match endpoint has to be
# present in the discovered endpoint.
for type_uri in to_match.type_uris:
if not endpoint.usesExtension(type_uri):
raise TypeURIMismatch(type_uri, endpoint)
# Fragments do not influence discovery, so we can't compare a
# claimed identifier with a fragment to discovered information.
defragged_claimed_id, _ = urldefrag(to_match.claimed_id)
if defragged_claimed_id != endpoint.claimed_id:
raise ProtocolError(
'Claimed ID does not match (different subjects!), '
'Expected %s, got %s' %
(defragged_claimed_id, endpoint.claimed_id))
if to_match.getLocalID() != endpoint.getLocalID():
raise ProtocolError('local_id mismatch. Expected %s, got %s' %
(to_match.getLocalID(), endpoint.getLocalID()))
# If the server URL is None, this must be an OpenID 1
# response, because op_endpoint is a required parameter in
# OpenID 2. In that case, we don't actually care what the
# discovered server_url is, because signature checking or
# check_auth should take care of that check for us.
if to_match.server_url is None:
assert to_match.preferredNamespace() == OPENID1_NS, (
"""The code calling this must ensure that OpenID 2
responses have a non-none `openid.op_endpoint' and
that it is set as the `server_url' attribute of the
`to_match' endpoint.""")
elif to_match.server_url != endpoint.server_url:
raise ProtocolError('OP Endpoint mismatch. Expected %s, got %s' %
(to_match.server_url, endpoint.server_url))
def _discoverAndVerify(self, to_match):
"""Given an endpoint object created from the information in an
OpenID response, perform discovery and verify the discovery
results, returning the matching endpoint that is the result of
doing that discovery.
@type to_match: openid.consumer.discover.OpenIDServiceEndpoint
@param to_match: The endpoint whose information we're confirming
@rtype: openid.consumer.discover.OpenIDServiceEndpoint
@returns: The result of performing discovery on the claimed
identifier in `to_match'
@raises DiscoveryFailure: when discovery fails.
"""
oidutil.log('Performing discovery on %s' % (to_match.claimed_id,))
_, services = self._discover(to_match.claimed_id)
if not services:
raise DiscoveryFailure('No OpenID information found at %s' %
(to_match.claimed_id,), None)
return self._verifyDiscoveredServices(services, to_match)
def _verifyDiscoveredServices(self, services, to_match):
"""See @L{_discoverAndVerify}"""
# Search the services resulting from discovery to find one
# that matches the information from the assertion
failure_messages = []
for endpoint in services:
try:
self._verifyDiscoverySingle(endpoint, to_match)
except ProtocolError, why:
failure_messages.append(str(why))
else:
# It matches, so discover verification has
# succeeded. Return this endpoint.
return endpoint
else:
oidutil.log('Discovery verification failure for %s' %
(to_match.claimed_id,))
for failure_message in failure_messages:
oidutil.log(' * Endpoint mismatch: ' + failure_message)
raise DiscoveryFailure(
'No matching endpoint found after discovering %s'
% (to_match.claimed_id,), None)
def _checkAuth(self, message, server_url):
"""Make a check_authentication request to verify this message.
@returns: True if the request is valid.
@rtype: bool
"""
oidutil.log('Using OpenID check_authentication')
request = self._createCheckAuthRequest(message)
if request is None:
return False
try:
response = self._makeKVPost(request, server_url)
except (fetchers.HTTPFetchingError, ServerError), e:
oidutil.log('check_authentication failed: %s' % (e[0],))
return False
else:
return self._processCheckAuthResponse(response, server_url)
def _createCheckAuthRequest(self, message):
"""Generate a check_authentication request message given an
id_res message.
"""
# Arguments that are always passed to the server and not
# included in the signature.
whitelist = ['assoc_handle', 'sig', 'signed', 'invalidate_handle']
check_args = {}
for k in whitelist:
val = message.getArg(OPENID_NS, k)
if val is not None:
check_args[k] = val
signed = message.getArg(OPENID_NS, 'signed')
if signed:
for k in signed.split(','):
val = message.getAliasedArg(k)
# Signed value is missing
if val is None:
oidutil.log('Missing signed field %r' % (k,))
return None
check_args[k] = val
check_args['mode'] = 'check_authentication'
return Message.fromOpenIDArgs(check_args)
def _processCheckAuthResponse(self, response, server_url):
"""Process the response message from a check_authentication
request, invalidating associations if requested.
"""
is_valid = response.getArg(OPENID_NS, 'is_valid', 'false')
invalidate_handle = response.getArg(OPENID_NS, 'invalidate_handle')
if invalidate_handle is not None:
oidutil.log(
'Received "invalidate_handle" from server %s' % (server_url,))
if self.store is None:
oidutil.log('Unexpectedly got invalidate_handle without '
'a store!')
else:
self.store.removeAssociation(server_url, invalidate_handle)
if is_valid == 'true':
return True
else:
oidutil.log('Server responds that checkAuth call is not valid')
return False
def _getAssociation(self, endpoint):
"""Get an association for the endpoint's server_url.
First try seeing if we have a good association in the
store. If we do not, then attempt to negotiate an association
with the server.
If we negotiate a good association, it will get stored.
@returns: A valid association for the endpoint's server_url or None
@rtype: openid.association.Association or NoneType
"""
assoc = self.store.getAssociation(endpoint.server_url)
if assoc is None or assoc.expiresIn <= 0:
assoc = self._negotiateAssociation(endpoint)
if assoc is not None:
self.store.storeAssociation(endpoint.server_url, assoc)
return assoc
def _negotiateAssociation(self, endpoint):
"""Make association requests to the server, attempting to
create a new association.
@returns: a new association object
@rtype: L{openid.association.Association}
"""
# Get our preferred session/association type from the negotiatior.
assoc_type, session_type = self.negotiator.getAllowedType()
try:
assoc = self._requestAssociation(
endpoint, assoc_type, session_type)
except ServerError, why:
supportedTypes = self._extractSupportedAssociationType(why,
endpoint,
assoc_type)
if supportedTypes is not None:
assoc_type, session_type = supportedTypes
# Attempt to create an association from the assoc_type
# and session_type that the server told us it
# supported.
try:
assoc = self._requestAssociation(
endpoint, assoc_type, session_type)
except ServerError, why:
# Do not keep trying, since it rejected the
# association type that it told us to use.
oidutil.log('Server %s refused its suggested association '
'type: session_type=%s, assoc_type=%s'
% (endpoint.server_url, session_type,
assoc_type))
return None
else:
return assoc
else:
return assoc
def _extractSupportedAssociationType(self, server_error, endpoint,
assoc_type):
"""Handle ServerErrors resulting from association requests.
@returns: If server replied with an C{unsupported-type} error,
return a tuple of supported C{association_type}, C{session_type}.
Otherwise logs the error and returns None.
@rtype: tuple or None
"""
# Any error message whose code is not 'unsupported-type'
# should be considered a total failure.
if server_error.error_code != 'unsupported-type' or \
server_error.message.isOpenID1():
oidutil.log(
'Server error when requesting an association from %r: %s'
% (endpoint.server_url, server_error.error_text))
return None
# The server didn't like the association/session type
# that we sent, and it sent us back a message that
# might tell us how to handle it.
oidutil.log(
'Unsupported association type %s: %s' % (assoc_type,
server_error.error_text,))
# Extract the session_type and assoc_type from the
# error message
assoc_type = server_error.message.getArg(OPENID_NS, 'assoc_type')
session_type = server_error.message.getArg(OPENID_NS, 'session_type')
if assoc_type is None or session_type is None:
oidutil.log('Server responded with unsupported association '
'session but did not supply a fallback.')
return None
elif not self.negotiator.isAllowed(assoc_type, session_type):
fmt = ('Server sent unsupported session/association type: '
'session_type=%s, assoc_type=%s')
oidutil.log(fmt % (session_type, assoc_type))
return None
else:
return assoc_type, session_type
def _requestAssociation(self, endpoint, assoc_type, session_type):
"""Make and process one association request to this endpoint's
OP endpoint URL.
@returns: An association object or None if the association
processing failed.
@raises ServerError: when the remote OpenID server returns an error.
"""
assoc_session, args = self._createAssociateRequest(
endpoint, assoc_type, session_type)
try:
response = self._makeKVPost(args, endpoint.server_url)
except fetchers.HTTPFetchingError, why:
oidutil.log('openid.associate request failed: %s' % (why[0],))
return None
try:
assoc = self._extractAssociation(response, assoc_session)
except KeyError, why:
oidutil.log('Missing required parameter in response from %s: %s'
% (endpoint.server_url, why[0]))
return None
except ProtocolError, why:
oidutil.log('Protocol error parsing response from %s: %s' % (
endpoint.server_url, why[0]))
return None
else:
return assoc
def _createAssociateRequest(self, endpoint, assoc_type, session_type):
"""Create an association request for the given assoc_type and
session_type.
@param endpoint: The endpoint whose server_url will be
queried. The important bit about the endpoint is whether
it's in compatiblity mode (OpenID 1.1)
@param assoc_type: The association type that the request
should ask for.
@type assoc_type: str
@param session_type: The session type that should be used in
the association request. The session_type is used to
create an association session object, and that session
object is asked for any additional fields that it needs to
add to the request.
@type session_type: str
@returns: a pair of the association session object and the
request message that will be sent to the server.
@rtype: (association session type (depends on session_type),
openid.message.Message)
"""
session_type_class = self.session_types[session_type]
assoc_session = session_type_class()
args = {
'mode': 'associate',
'assoc_type': assoc_type,
}
if not endpoint.compatibilityMode():
args['ns'] = OPENID2_NS
# Leave out the session type if we're in compatibility mode
# *and* it's no-encryption.
if (not endpoint.compatibilityMode() or
assoc_session.session_type != 'no-encryption'):
args['session_type'] = assoc_session.session_type
args.update(assoc_session.getRequest())
message = Message.fromOpenIDArgs(args)
return assoc_session, message
def _getOpenID1SessionType(self, assoc_response):
"""Given an association response message, extract the OpenID
1.X session type.
This function mostly takes care of the 'no-encryption' default
behavior in OpenID 1.
If the association type is plain-text, this function will
return 'no-encryption'
@returns: The association type for this message
@rtype: str
@raises KeyError: when the session_type field is absent.
"""
# If it's an OpenID 1 message, allow session_type to default
# to None (which signifies "no-encryption")
session_type = assoc_response.getArg(OPENID1_NS, 'session_type')
# Handle the differences between no-encryption association
# respones in OpenID 1 and 2:
# no-encryption is not really a valid session type for
# OpenID 1, but we'll accept it anyway, while issuing a
# warning.
if session_type == 'no-encryption':
oidutil.log('WARNING: OpenID server sent "no-encryption"'
'for OpenID 1.X')
# Missing or empty session type is the way to flag a
# 'no-encryption' response. Change the session type to
# 'no-encryption' so that it can be handled in the same
# way as OpenID 2 'no-encryption' respones.
elif session_type == '' or session_type is None:
session_type = 'no-encryption'
return session_type
def _extractAssociation(self, assoc_response, assoc_session):
"""Attempt to extract an association from the response, given
the association response message and the established
association session.
@param assoc_response: The association response message from
the server
@type assoc_response: openid.message.Message
@param assoc_session: The association session object that was
used when making the request
@type assoc_session: depends on the session type of the request
@raises ProtocolError: when data is malformed
@raises KeyError: when a field is missing
@rtype: openid.association.Association
"""
# Extract the common fields from the response, raising an
# exception if they are not found
assoc_type = assoc_response.getArg(
OPENID_NS, 'assoc_type', no_default)
assoc_handle = assoc_response.getArg(
OPENID_NS, 'assoc_handle', no_default)
# expires_in is a base-10 string. The Python parsing will
# accept literals that have whitespace around them and will
# accept negative values. Neither of these are really in-spec,
# but we think it's OK to accept them.
expires_in_str = assoc_response.getArg(
OPENID_NS, 'expires_in', no_default)
try:
expires_in = int(expires_in_str)
except ValueError, why:
raise ProtocolError('Invalid expires_in field: %s' % (why[0],))
# OpenID 1 has funny association session behaviour.
if assoc_response.isOpenID1():
session_type = self._getOpenID1SessionType(assoc_response)
else:
session_type = assoc_response.getArg(
OPENID2_NS, 'session_type', no_default)
# Session type mismatch
if assoc_session.session_type != session_type:
if (assoc_response.isOpenID1() and
session_type == 'no-encryption'):
# In OpenID 1, any association request can result in a
# 'no-encryption' association response. Setting
# assoc_session to a new no-encryption session should
# make the rest of this function work properly for
# that case.
assoc_session = PlainTextConsumerSession()
else:
# Any other mismatch, regardless of protocol version
# results in the failure of the association session
# altogether.
fmt = 'Session type mismatch. Expected %r, got %r'
message = fmt % (assoc_session.session_type, session_type)
raise ProtocolError(message)
# Make sure assoc_type is valid for session_type
if assoc_type not in assoc_session.allowed_assoc_types:
fmt = 'Unsupported assoc_type for session %s returned: %s'
raise ProtocolError(fmt % (assoc_session.session_type, assoc_type))
# Delegate to the association session to extract the secret
# from the response, however is appropriate for that session
# type.
try:
secret = assoc_session.extractSecret(assoc_response)
except ValueError, why:
fmt = 'Malformed response for %s session: %s'
raise ProtocolError(fmt % (assoc_session.session_type, why[0]))
return Association.fromExpiresIn(
expires_in, assoc_handle, secret, assoc_type)
class AuthRequest(object):
"""An object that holds the state necessary for generating an
OpenID authentication request. This object holds the association
with the server and the discovered information with which the
request will be made.
It is separate from the consumer because you may wish to add
things to the request before sending it on its way to the
server. It also has serialization options that let you encode the
authentication request as a URL or as a form POST.
"""
def __init__(self, endpoint, assoc):
"""
Creates a new AuthRequest object. This just stores each
argument in an appropriately named field.
Users of this library should not create instances of this
class. Instances of this class are created by the library
when needed.
"""
self.assoc = assoc
self.endpoint = endpoint
self.return_to_args = {}
self.message = Message()
self.message.setOpenIDNamespace(endpoint.preferredNamespace())
self._anonymous = False
def setAnonymous(self, is_anonymous):
"""Set whether this request should be made anonymously. If a
request is anonymous, the identifier will not be sent in the
request. This is only useful if you are making another kind of
request with an extension in this request.
Anonymous requests are not allowed when the request is made
with OpenID 1.
@raises ValueError: when attempting to set an OpenID1 request
as anonymous
"""
if is_anonymous and self.message.isOpenID1():
raise ValueError('OpenID 1 requests MUST include the '
'identifier in the request')
else:
self._anonymous = is_anonymous
def addExtension(self, extension_request):
"""Add an extension to this checkid request.
@param extension_request: An object that implements the
extension interface for adding arguments to an OpenID
message.
"""
extension_request.toMessage(self.message)
def addExtensionArg(self, namespace, key, value):
"""Add an extension argument to this OpenID authentication
request.
Use caution when adding arguments, because they will be
URL-escaped and appended to the redirect URL, which can easily
get quite long.
@param namespace: The namespace for the extension. For
example, the simple registration extension uses the
namespace C{sreg}.
@type namespace: str
@param key: The key within the extension namespace. For
example, the nickname field in the simple registration
extension's key is C{nickname}.
@type key: str
@param value: The value to provide to the server for this
argument.
@type value: str
"""
self.message.setArg(namespace, key, value)
def getMessage(self, realm, return_to=None, immediate=False):
"""Produce a L{openid.message.Message} representing this request.
@param realm: The URL (or URL pattern) that identifies your
web site to the user when she is authorizing it.
@type realm: str
@param return_to: The URL that the OpenID provider will send the
user back to after attempting to verify her identity.
Not specifying a return_to URL means that the user will not
be returned to the site issuing the request upon its
completion.
@type return_to: str
@param immediate: If True, the OpenID provider is to send back
a response immediately, useful for behind-the-scenes
authentication attempts. Otherwise the OpenID provider
may engage the user before providing a response. This is
the default case, as the user may need to provide
credentials or approve the request before a positive
response can be sent.
@type immediate: bool
@returntype: L{openid.message.Message}
"""
if return_to:
return_to = oidutil.appendArgs(return_to, self.return_to_args)
elif immediate:
raise ValueError(
'"return_to" is mandatory when using "checkid_immediate"')
elif self.message.isOpenID1():
raise ValueError('"return_to" is mandatory for OpenID 1 requests')
elif self.return_to_args:
raise ValueError('extra "return_to" arguments were specified, '
'but no return_to was specified')
if immediate:
mode = 'checkid_immediate'
else:
mode = 'checkid_setup'
message = self.message.copy()
if message.isOpenID1():
realm_key = 'trust_root'
else:
realm_key = 'realm'
message.updateArgs(OPENID_NS,
{
realm_key:realm,
'mode':mode,
'return_to':return_to,
})
if not self._anonymous:
if self.endpoint.isOPIdentifier():
# This will never happen when we're in compatibility
# mode, as long as isOPIdentifier() returns False
# whenever preferredNamespace() returns OPENID1_NS.
claimed_id = request_identity = IDENTIFIER_SELECT
else:
request_identity = self.endpoint.getLocalID()
claimed_id = self.endpoint.claimed_id
# This is true for both OpenID 1 and 2
message.setArg(OPENID_NS, 'identity', request_identity)
if message.isOpenID2():
message.setArg(OPENID2_NS, 'claimed_id', claimed_id)
if self.assoc:
message.setArg(OPENID_NS, 'assoc_handle', self.assoc.handle)
assoc_log_msg = 'with assocication %s' % (self.assoc.handle,)
else:
assoc_log_msg = 'using stateless mode.'
oidutil.log("Generated %s request to %s %s" %
(mode, self.endpoint.server_url, assoc_log_msg))
return message
def redirectURL(self, realm, return_to=None, immediate=False):
"""Returns a URL with an encoded OpenID request.
The resulting URL is the OpenID provider's endpoint URL with
parameters appended as query arguments. You should redirect
the user agent to this URL.
OpenID 2.0 endpoints also accept POST requests, see
C{L{shouldSendRedirect}} and C{L{formMarkup}}.
@param realm: The URL (or URL pattern) that identifies your
web site to the user when she is authorizing it.
@type realm: str
@param return_to: The URL that the OpenID provider will send the
user back to after attempting to verify her identity.
Not specifying a return_to URL means that the user will not
be returned to the site issuing the request upon its
completion.
@type return_to: str
@param immediate: If True, the OpenID provider is to send back
a response immediately, useful for behind-the-scenes
authentication attempts. Otherwise the OpenID provider
may engage the user before providing a response. This is
the default case, as the user may need to provide
credentials or approve the request before a positive
response can be sent.
@type immediate: bool
@returns: The URL to redirect the user agent to.
@returntype: str
"""
message = self.getMessage(realm, return_to, immediate)
return message.toURL(self.endpoint.server_url)
def formMarkup(self, realm, return_to=None, immediate=False,
form_tag_attrs=None):
"""Get html for a form to submit this request to the IDP.
@param form_tag_attrs: Dictionary of attributes to be added to
the form tag. 'accept-charset' and 'enctype' have defaults
that can be overridden. If a value is supplied for
'action' or 'method', it will be replaced.
@type form_tag_attrs: {unicode: unicode}
"""
message = self.getMessage(realm, return_to, immediate)
return message.toFormMarkup(self.endpoint.server_url,
form_tag_attrs)
def shouldSendRedirect(self):
"""Should this OpenID authentication request be sent as a HTTP
redirect or as a POST (form submission)?
@rtype: bool
"""
return self.endpoint.compatibilityMode()
FAILURE = 'failure'
SUCCESS = 'success'
CANCEL = 'cancel'
SETUP_NEEDED = 'setup_needed'
class Response(object):
status = None
def setEndpoint(self, endpoint):
self.endpoint = endpoint
if endpoint is None:
self.identity_url = None
else:
self.identity_url = endpoint.claimed_id
def getDisplayIdentifier(self):
"""Return the display identifier for this response.
"""
if self.endpoint is not None:
return self.endpoint.getDisplayIdentifier()
return None
class SuccessResponse(Response):
"""A response with a status of SUCCESS. Indicates that this request is a
successful acknowledgement from the OpenID server that the
supplied URL is, indeed controlled by the requesting agent.
@ivar identity_url: The identity URL that has been authenticated
@ivar endpoint: The endpoint that authenticated the identifier. You
may access other discovered information related to this endpoint,
such as the CanonicalID of an XRI, through this object.
@type endpoint: L{OpenIDServiceEndpoint<openid.consumer.discover.OpenIDServiceEndpoint>}
@ivar signed_fields: The arguments in the server's response that
were signed and verified.
@cvar status: SUCCESS
"""
status = SUCCESS
def __init__(self, endpoint, message, signed_fields=None):
# Don't use setEndpoint, because endpoint should never be None
# for a successfull transaction.
self.endpoint = endpoint
self.identity_url = endpoint.claimed_id
self.message = message
if signed_fields is None:
signed_fields = []
self.signed_fields = signed_fields
def isOpenID1(self):
"""Was this authentication response an OpenID 1 authentication
response?
"""
return self.message.isOpenID1()
def isSigned(self, ns_uri, ns_key):
"""Return whether a particular key is signed, regardless of
its namespace alias
"""
return self.message.getKey(ns_uri, ns_key) in self.signed_fields
def getSigned(self, ns_uri, ns_key, default=None):
"""Return the specified signed field if available,
otherwise return default
"""
if self.isSigned(ns_uri, ns_key):
return self.message.getArg(ns_uri, ns_key, default)
else:
return default
def getSignedNS(self, ns_uri):
"""Get signed arguments from the response message. Return a
dict of all arguments in the specified namespace. If any of
the arguments are not signed, return None.
"""
msg_args = self.message.getArgs(ns_uri)
for key in msg_args.iterkeys():
if not self.isSigned(ns_uri, key):
oidutil.log("SuccessResponse.getSignedNS: (%s, %s) not signed."
% (ns_uri, key))
return None
return msg_args
def extensionResponse(self, namespace_uri, require_signed):
"""Return response arguments in the specified namespace.
@param namespace_uri: The namespace URI of the arguments to be
returned.
@param require_signed: True if the arguments should be among
those signed in the response, False if you don't care.
If require_signed is True and the arguments are not signed,
return None.
"""
if require_signed:
return self.getSignedNS(namespace_uri)
else:
return self.message.getArgs(namespace_uri)
def getReturnTo(self):
"""Get the openid.return_to argument from this response.
This is useful for verifying that this request was initiated
by this consumer.
@returns: The return_to URL supplied to the server on the
initial request, or C{None} if the response did not contain
an C{openid.return_to} argument.
@returntype: str
"""
return self.getSigned(OPENID_NS, 'return_to')
def __eq__(self, other):
return (
(self.endpoint == other.endpoint) and
(self.identity_url == other.identity_url) and
(self.message == other.message) and
(self.signed_fields == other.signed_fields) and
(self.status == other.status))
def __ne__(self, other):
return not (self == other)
def __repr__(self):
return '<%s.%s id=%r signed=%r>' % (
self.__class__.__module__,
self.__class__.__name__,
self.identity_url, self.signed_fields)
class FailureResponse(Response):
"""A response with a status of FAILURE. Indicates that the OpenID
protocol has failed. This could be locally or remotely triggered.
@ivar identity_url: The identity URL for which authenitcation was
attempted, if it can be determined. Otherwise, None.
@ivar message: A message indicating why the request failed, if one
is supplied. otherwise, None.
@cvar status: FAILURE
"""
status = FAILURE
def __init__(self, endpoint, message=None, contact=None,
reference=None):
self.setEndpoint(endpoint)
self.message = message
self.contact = contact
self.reference = reference
def __repr__(self):
return "<%s.%s id=%r message=%r>" % (
self.__class__.__module__, self.__class__.__name__,
self.identity_url, self.message)
class CancelResponse(Response):
"""A response with a status of CANCEL. Indicates that the user
cancelled the OpenID authentication request.
@ivar identity_url: The identity URL for which authenitcation was
attempted, if it can be determined. Otherwise, None.
@cvar status: CANCEL
"""
status = CANCEL
def __init__(self, endpoint):
self.setEndpoint(endpoint)
class SetupNeededResponse(Response):
"""A response with a status of SETUP_NEEDED. Indicates that the
request was in immediate mode, and the server is unable to
authenticate the user without further interaction.
@ivar identity_url: The identity URL for which authenitcation was
attempted.
@ivar setup_url: A URL that can be used to send the user to the
server to set up for authentication. The user should be
redirected in to the setup_url, either in the current window
or in a new browser window. C{None} in OpenID 2.0.
@cvar status: SETUP_NEEDED
"""
status = SETUP_NEEDED
def __init__(self, endpoint, setup_url=None):
self.setEndpoint(endpoint)
self.setup_url = setup_url
########NEW FILE########
__FILENAME__ = discover
# -*- test-case-name: openid.test.test_discover -*-
"""Functions to discover OpenID endpoints from identifiers.
"""
__all__ = [
'DiscoveryFailure',
'OPENID_1_0_NS',
'OPENID_1_0_TYPE',
'OPENID_1_1_TYPE',
'OPENID_2_0_TYPE',
'OPENID_IDP_2_0_TYPE',
'OpenIDServiceEndpoint',
'discover',
]
import urlparse
from openid import oidutil, fetchers, urinorm
from openid import yadis
from openid.yadis.etxrd import nsTag, XRDSError, XRD_NS_2_0
from openid.yadis.services import applyFilter as extractServices
from openid.yadis.discover import discover as yadisDiscover
from openid.yadis.discover import DiscoveryFailure
from openid.yadis import xrires, filters
from openid.yadis import xri
from openid.consumer import html_parse
OPENID_1_0_NS = 'http://openid.net/xmlns/1.0'
OPENID_IDP_2_0_TYPE = 'http://specs.openid.net/auth/2.0/server'
OPENID_2_0_TYPE = 'http://specs.openid.net/auth/2.0/signon'
OPENID_1_1_TYPE = 'http://openid.net/signon/1.1'
OPENID_1_0_TYPE = 'http://openid.net/signon/1.0'
from openid.message import OPENID1_NS as OPENID_1_0_MESSAGE_NS
from openid.message import OPENID2_NS as OPENID_2_0_MESSAGE_NS
class OpenIDServiceEndpoint(object):
"""Object representing an OpenID service endpoint.
@ivar identity_url: the verified identifier.
@ivar canonicalID: For XRI, the persistent identifier.
"""
# OpenID service type URIs, listed in order of preference. The
# ordering of this list affects yadis and XRI service discovery.
openid_type_uris = [
OPENID_IDP_2_0_TYPE,
OPENID_2_0_TYPE,
OPENID_1_1_TYPE,
OPENID_1_0_TYPE,
]
def __init__(self):
self.claimed_id = None
self.server_url = None
self.type_uris = []
self.local_id = None
self.canonicalID = None
self.used_yadis = False # whether this came from an XRDS
self.display_identifier = None
def usesExtension(self, extension_uri):
return extension_uri in self.type_uris
def preferredNamespace(self):
if (OPENID_IDP_2_0_TYPE in self.type_uris or
OPENID_2_0_TYPE in self.type_uris):
return OPENID_2_0_MESSAGE_NS
else:
return OPENID_1_0_MESSAGE_NS
def supportsType(self, type_uri):
"""Does this endpoint support this type?
I consider C{/server} endpoints to implicitly support C{/signon}.
"""
return (
(type_uri in self.type_uris) or
(type_uri == OPENID_2_0_TYPE and self.isOPIdentifier())
)
def getDisplayIdentifier(self):
"""Return the display_identifier if set, else return the claimed_id.
"""
if self.display_identifier is None:
return self.claimed_id
return self.display_identifier
def compatibilityMode(self):
return self.preferredNamespace() != OPENID_2_0_MESSAGE_NS
def isOPIdentifier(self):
return OPENID_IDP_2_0_TYPE in self.type_uris
def parseService(self, yadis_url, uri, type_uris, service_element):
"""Set the state of this object based on the contents of the
service element."""
self.type_uris = type_uris
self.server_url = uri
self.used_yadis = True
if not self.isOPIdentifier():
# XXX: This has crappy implications for Service elements
# that contain both 'server' and 'signon' Types. But
# that's a pathological configuration anyway, so I don't
# think I care.
self.local_id = findOPLocalIdentifier(service_element,
self.type_uris)
self.claimed_id = yadis_url
def getLocalID(self):
"""Return the identifier that should be sent as the
openid.identity parameter to the server."""
# I looked at this conditional and thought "ah-hah! there's the bug!"
# but Python actually makes that one big expression somehow, i.e.
# "x is x is x" is not the same thing as "(x is x) is x".
# That's pretty weird, dude. -- kmt, 1/07
if (self.local_id is self.canonicalID is None):
return self.claimed_id
else:
return self.local_id or self.canonicalID
def fromBasicServiceEndpoint(cls, endpoint):
"""Create a new instance of this class from the endpoint
object passed in.
@return: None or OpenIDServiceEndpoint for this endpoint object"""
type_uris = endpoint.matchTypes(cls.openid_type_uris)
# If any Type URIs match and there is an endpoint URI
# specified, then this is an OpenID endpoint
if type_uris and endpoint.uri is not None:
openid_endpoint = cls()
openid_endpoint.parseService(
endpoint.yadis_url,
endpoint.uri,
endpoint.type_uris,
endpoint.service_element)
else:
openid_endpoint = None
return openid_endpoint
fromBasicServiceEndpoint = classmethod(fromBasicServiceEndpoint)
def fromHTML(cls, uri, html):
"""Parse the given document as HTML looking for an OpenID <link
rel=...>
@rtype: [OpenIDServiceEndpoint]
"""
discovery_types = [
(OPENID_2_0_TYPE, 'openid2.provider', 'openid2.local_id'),
(OPENID_1_1_TYPE, 'openid.server', 'openid.delegate'),
]
link_attrs = html_parse.parseLinkAttrs(html)
services = []
for type_uri, op_endpoint_rel, local_id_rel in discovery_types:
op_endpoint_url = html_parse.findFirstHref(
link_attrs, op_endpoint_rel)
if op_endpoint_url is None:
continue
service = cls()
service.claimed_id = uri
service.local_id = html_parse.findFirstHref(
link_attrs, local_id_rel)
service.server_url = op_endpoint_url
service.type_uris = [type_uri]
services.append(service)
return services
fromHTML = classmethod(fromHTML)
def fromXRDS(cls, uri, xrds):
"""Parse the given document as XRDS looking for OpenID services.
@rtype: [OpenIDServiceEndpoint]
@raises XRDSError: When the XRDS does not parse.
@since: 2.1.0
"""
return extractServices(uri, xrds, cls)
fromXRDS = classmethod(fromXRDS)
def fromDiscoveryResult(cls, discoveryResult):
"""Create endpoints from a DiscoveryResult.
@type discoveryResult: L{DiscoveryResult}
@rtype: list of L{OpenIDServiceEndpoint}
@raises XRDSError: When the XRDS does not parse.
@since: 2.1.0
"""
if discoveryResult.isXRDS():
method = cls.fromXRDS
else:
method = cls.fromHTML
return method(discoveryResult.normalized_uri,
discoveryResult.response_text)
fromDiscoveryResult = classmethod(fromDiscoveryResult)
def fromOPEndpointURL(cls, op_endpoint_url):
"""Construct an OP-Identifier OpenIDServiceEndpoint object for
a given OP Endpoint URL
@param op_endpoint_url: The URL of the endpoint
@rtype: OpenIDServiceEndpoint
"""
service = cls()
service.server_url = op_endpoint_url
service.type_uris = [OPENID_IDP_2_0_TYPE]
return service
fromOPEndpointURL = classmethod(fromOPEndpointURL)
def __str__(self):
return ("<%s.%s "
"server_url=%r "
"claimed_id=%r "
"local_id=%r "
"canonicalID=%r "
"used_yadis=%s "
">"
% (self.__class__.__module__, self.__class__.__name__,
self.server_url,
self.claimed_id,
self.local_id,
self.canonicalID,
self.used_yadis))
def findOPLocalIdentifier(service_element, type_uris):
"""Find the OP-Local Identifier for this xrd:Service element.
This considers openid:Delegate to be a synonym for xrd:LocalID if
both OpenID 1.X and OpenID 2.0 types are present. If only OpenID
1.X is present, it returns the value of openid:Delegate. If only
OpenID 2.0 is present, it returns the value of xrd:LocalID. If
there is more than one LocalID tag and the values are different,
it raises a DiscoveryFailure. This is also triggered when the
xrd:LocalID and openid:Delegate tags are different.
@param service_element: The xrd:Service element
@type service_element: ElementTree.Node
@param type_uris: The xrd:Type values present in this service
element. This function could extract them, but higher level
code needs to do that anyway.
@type type_uris: [str]
@raises DiscoveryFailure: when discovery fails.
@returns: The OP-Local Identifier for this service element, if one
is present, or None otherwise.
@rtype: str or unicode or NoneType
"""
# XXX: Test this function on its own!
# Build the list of tags that could contain the OP-Local Identifier
local_id_tags = []
if (OPENID_1_1_TYPE in type_uris or
OPENID_1_0_TYPE in type_uris):
local_id_tags.append(nsTag(OPENID_1_0_NS, 'Delegate'))
if OPENID_2_0_TYPE in type_uris:
local_id_tags.append(nsTag(XRD_NS_2_0, 'LocalID'))
# Walk through all the matching tags and make sure that they all
# have the same value
local_id = None
for local_id_tag in local_id_tags:
for local_id_element in service_element.findall(local_id_tag):
if local_id is None:
local_id = local_id_element.text
elif local_id != local_id_element.text:
format = 'More than one %r tag found in one service element'
message = format % (local_id_tag,)
raise DiscoveryFailure(message, None)
return local_id
def normalizeURL(url):
"""Normalize a URL, converting normalization failures to
DiscoveryFailure"""
try:
normalized = urinorm.urinorm(url)
except ValueError, why:
raise DiscoveryFailure('Normalizing identifier: %s' % (why[0],), None)
else:
return urlparse.urldefrag(normalized)[0]
def arrangeByType(service_list, preferred_types):
"""Rearrange service_list in a new list so services are ordered by
types listed in preferred_types. Return the new list."""
def enumerate(elts):
"""Return an iterable that pairs the index of an element with
that element.
For Python 2.2 compatibility"""
return zip(range(len(elts)), elts)
def bestMatchingService(service):
"""Return the index of the first matching type, or something
higher if no type matches.
This provides an ordering in which service elements that
contain a type that comes earlier in the preferred types list
come before service elements that come later. If a service
element has more than one type, the most preferred one wins.
"""
for i, t in enumerate(preferred_types):
if preferred_types[i] in service.type_uris:
return i
return len(preferred_types)
# Build a list with the service elements in tuples whose
# comparison will prefer the one with the best matching service
prio_services = [(bestMatchingService(s), orig_index, s)
for (orig_index, s) in enumerate(service_list)]
prio_services.sort()
# Now that the services are sorted by priority, remove the sort
# keys from the list.
for i in range(len(prio_services)):
prio_services[i] = prio_services[i][2]
return prio_services
def getOPOrUserServices(openid_services):
"""Extract OP Identifier services. If none found, return the
rest, sorted with most preferred first according to
OpenIDServiceEndpoint.openid_type_uris.
openid_services is a list of OpenIDServiceEndpoint objects.
Returns a list of OpenIDServiceEndpoint objects."""
op_services = arrangeByType(openid_services, [OPENID_IDP_2_0_TYPE])
openid_services = arrangeByType(openid_services,
OpenIDServiceEndpoint.openid_type_uris)
return op_services or openid_services
def discoverYadis(uri):
"""Discover OpenID services for a URI. Tries Yadis and falls back
on old-style <link rel='...'> discovery if Yadis fails.
@param uri: normalized identity URL
@type uri: str
@return: (claimed_id, services)
@rtype: (str, list(OpenIDServiceEndpoint))
@raises DiscoveryFailure: when discovery fails.
"""
# Might raise a yadis.discover.DiscoveryFailure if no document
# came back for that URI at all. I don't think falling back
# to OpenID 1.0 discovery on the same URL will help, so don't
# bother to catch it.
response = yadisDiscover(uri)
yadis_url = response.normalized_uri
body = response.response_text
try:
openid_services = OpenIDServiceEndpoint.fromXRDS(yadis_url, body)
except XRDSError:
# Does not parse as a Yadis XRDS file
openid_services = []
if not openid_services:
# Either not an XRDS or there are no OpenID services.
if response.isXRDS():
# if we got the Yadis content-type or followed the Yadis
# header, re-fetch the document without following the Yadis
# header, with no Accept header.
return discoverNoYadis(uri)
# Try to parse the response as HTML.
# <link rel="...">
openid_services = OpenIDServiceEndpoint.fromHTML(yadis_url, body)
return (yadis_url, getOPOrUserServices(openid_services))
def discoverXRI(iname):
endpoints = []
try:
canonicalID, services = xrires.ProxyResolver().query(
iname, OpenIDServiceEndpoint.openid_type_uris)
if canonicalID is None:
raise XRDSError('No CanonicalID found for XRI %r' % (iname,))
flt = filters.mkFilter(OpenIDServiceEndpoint)
for service_element in services:
endpoints.extend(flt.getServiceEndpoints(iname, service_element))
except XRDSError:
oidutil.log('xrds error on ' + iname)
for endpoint in endpoints:
# Is there a way to pass this through the filter to the endpoint
# constructor instead of tacking it on after?
endpoint.canonicalID = canonicalID
endpoint.claimed_id = canonicalID
endpoint.display_identifier = iname
# FIXME: returned xri should probably be in some normal form
return iname, getOPOrUserServices(endpoints)
def discoverNoYadis(uri):
http_resp = fetchers.fetch(uri)
if http_resp.status != 200:
raise DiscoveryFailure(
'HTTP Response status from identity URL host is not 200. '
'Got status %r' % (http_resp.status,), http_resp)
claimed_id = http_resp.final_url
openid_services = OpenIDServiceEndpoint.fromHTML(
claimed_id, http_resp.body)
return claimed_id, openid_services
def discoverURI(uri):
parsed = urlparse.urlparse(uri)
if parsed[0] and parsed[1]:
if parsed[0] not in ['http', 'https']:
raise DiscoveryFailure('URI scheme is not HTTP or HTTPS', None)
else:
uri = 'http://' + uri
uri = normalizeURL(uri)
claimed_id, openid_services = discoverYadis(uri)
claimed_id = normalizeURL(claimed_id)
return claimed_id, openid_services
def discover(identifier):
if xri.identifierScheme(identifier) == "XRI":
return discoverXRI(identifier)
else:
return discoverURI(identifier)
########NEW FILE########
__FILENAME__ = html_parse
"""
This module implements a VERY limited parser that finds <link> tags in
the head of HTML or XHTML documents and parses out their attributes
according to the OpenID spec. It is a liberal parser, but it requires
these things from the data in order to work:
- There must be an open <html> tag
- There must be an open <head> tag inside of the <html> tag
- Only <link>s that are found inside of the <head> tag are parsed
(this is by design)
- The parser follows the OpenID specification in resolving the
attributes of the link tags. This means that the attributes DO NOT
get resolved as they would by an XML or HTML parser. In particular,
only certain entities get replaced, and href attributes do not get
resolved relative to a base URL.
From http://openid.net/specs.bml#linkrel:
- The openid.server URL MUST be an absolute URL. OpenID consumers
MUST NOT attempt to resolve relative URLs.
- The openid.server URL MUST NOT include entities other than &,
<, >, and ".
The parser ignores SGML comments and <![CDATA[blocks]]>. Both kinds of
quoting are allowed for attributes.
The parser deals with invalid markup in these ways:
- Tag names are not case-sensitive
- The <html> tag is accepted even when it is not at the top level
- The <head> tag is accepted even when it is not a direct child of
the <html> tag, but a <html> tag must be an ancestor of the <head>
tag
- <link> tags are accepted even when they are not direct children of
the <head> tag, but a <head> tag must be an ancestor of the <link>
tag
- If there is no closing tag for an open <html> or <head> tag, the
remainder of the document is viewed as being inside of the tag. If
there is no closing tag for a <link> tag, the link tag is treated
as a short tag. Exceptions to this rule are that <html> closes
<html> and <body> or <head> closes <head>
- Attributes of the <link> tag are not required to be quoted.
- In the case of duplicated attribute names, the attribute coming
last in the tag will be the value returned.
- Any text that does not parse as an attribute within a link tag will
be ignored. (e.g. <link pumpkin rel='openid.server' /> will ignore
pumpkin)
- If there are more than one <html> or <head> tag, the parser only
looks inside of the first one.
- The contents of <script> tags are ignored entirely, except unclosed
<script> tags. Unclosed <script> tags are ignored.
- Any other invalid markup is ignored, including unclosed SGML
comments and unclosed <![CDATA[blocks.
"""
__all__ = ['parseLinkAttrs']
import re
flags = ( re.DOTALL # Match newlines with '.'
| re.IGNORECASE
| re.VERBOSE # Allow comments and whitespace in patterns
| re.UNICODE # Make \b respect Unicode word boundaries
)
# Stuff to remove before we start looking for tags
removed_re = re.compile(r'''
# Comments
<!--.*?-->
# CDATA blocks
| <!\[CDATA\[.*?\]\]>
# script blocks
| <script\b
# make sure script is not an XML namespace
(?!:)
[^>]*>.*?</script>
''', flags)
tag_expr = r'''
# Starts with the tag name at a word boundary, where the tag name is
# not a namespace
<%(tag_name)s\b(?!:)
# All of the stuff up to a ">", hopefully attributes.
(?P<attrs>[^>]*?)
(?: # Match a short tag
/>
| # Match a full tag
>
(?P<contents>.*?)
# Closed by
(?: # One of the specified close tags
</?%(closers)s\s*>
# End of the string
| \Z
)
)
'''
def tagMatcher(tag_name, *close_tags):
if close_tags:
options = '|'.join((tag_name,) + close_tags)
closers = '(?:%s)' % (options,)
else:
closers = tag_name
expr = tag_expr % locals()
return re.compile(expr, flags)
# Must contain at least an open html and an open head tag
html_find = tagMatcher('html')
head_find = tagMatcher('head', 'body')
link_find = re.compile(r'<link\b(?!:)', flags)
attr_find = re.compile(r'''
# Must start with a sequence of word-characters, followed by an equals sign
(?P<attr_name>\w+)=
# Then either a quoted or unquoted attribute
(?:
# Match everything that\'s between matching quote marks
(?P<qopen>["\'])(?P<q_val>.*?)(?P=qopen)
|
# If the value is not quoted, match up to whitespace
(?P<unq_val>(?:[^\s<>/]|/(?!>))+)
)
|
(?P<end_link>[<>])
''', flags)
# Entity replacement:
replacements = {
'amp':'&',
'lt':'<',
'gt':'>',
'quot':'"',
}
ent_replace = re.compile(r'&(%s);' % '|'.join(replacements.keys()))
def replaceEnt(mo):
"Replace the entities that are specified by OpenID"
return replacements.get(mo.group(1), mo.group())
def parseLinkAttrs(html):
"""Find all link tags in a string representing a HTML document and
return a list of their attributes.
@param html: the text to parse
@type html: str or unicode
@return: A list of dictionaries of attributes, one for each link tag
@rtype: [[(type(html), type(html))]]
"""
stripped = removed_re.sub('', html)
html_mo = html_find.search(stripped)
if html_mo is None or html_mo.start('contents') == -1:
return []
start, end = html_mo.span('contents')
head_mo = head_find.search(stripped, start, end)
if head_mo is None or head_mo.start('contents') == -1:
return []
start, end = head_mo.span('contents')
link_mos = link_find.finditer(stripped, head_mo.start(), head_mo.end())
matches = []
for link_mo in link_mos:
start = link_mo.start() + 5
link_attrs = {}
for attr_mo in attr_find.finditer(stripped, start):
if attr_mo.lastgroup == 'end_link':
break
# Either q_val or unq_val must be present, but not both
# unq_val is a True (non-empty) value if it is present
attr_name, q_val, unq_val = attr_mo.group(
'attr_name', 'q_val', 'unq_val')
attr_val = ent_replace.sub(replaceEnt, unq_val or q_val)
link_attrs[attr_name] = attr_val
matches.append(link_attrs)
return matches
def relMatches(rel_attr, target_rel):
"""Does this target_rel appear in the rel_str?"""
# XXX: TESTME
rels = rel_attr.strip().split()
for rel in rels:
rel = rel.lower()
if rel == target_rel:
return 1
return 0
def linkHasRel(link_attrs, target_rel):
"""Does this link have target_rel as a relationship?"""
# XXX: TESTME
rel_attr = link_attrs.get('rel')
return rel_attr and relMatches(rel_attr, target_rel)
def findLinksRel(link_attrs_list, target_rel):
"""Filter the list of link attributes on whether it has target_rel
as a relationship."""
# XXX: TESTME
matchesTarget = lambda attrs: linkHasRel(attrs, target_rel)
return filter(matchesTarget, link_attrs_list)
def findFirstHref(link_attrs_list, target_rel):
"""Return the value of the href attribute for the first link tag
in the list that has target_rel as a relationship."""
# XXX: TESTME
matches = findLinksRel(link_attrs_list, target_rel)
if not matches:
return None
first = matches[0]
return first.get('href')
########NEW FILE########
__FILENAME__ = cryptutil
"""Module containing a cryptographic-quality source of randomness and
other cryptographically useful functionality
Python 2.4 needs no external support for this module, nor does Python
2.3 on a system with /dev/urandom.
Other configurations will need a quality source of random bytes and
access to a function that will convert binary strings to long
integers. This module will work with the Python Cryptography Toolkit
(pycrypto) if it is present. pycrypto can be found with a search
engine, but is currently found at:
http://www.amk.ca/python/code/crypto
"""
__all__ = [
'base64ToLong',
'binaryToLong',
'hmacSha1',
'hmacSha256',
'longToBase64',
'longToBinary',
'randomString',
'randrange',
'sha1',
'sha256',
]
import hmac
import os
import random
from openid.oidutil import toBase64, fromBase64
try:
import hashlib
except ImportError:
import sha as sha1_module
try:
from Crypto.Hash import SHA256 as sha256_module
except ImportError:
sha256_module = None
else:
class HashContainer(object):
def __init__(self, hash_constructor):
self.new = hash_constructor
sha1_module = HashContainer(hashlib.sha1)
sha256_module = HashContainer(hashlib.sha256)
def hmacSha1(key, text):
return hmac.new(key, text, sha1_module).digest()
def sha1(s):
return sha1_module.new(s).digest()
if sha256_module is not None:
def hmacSha256(key, text):
return hmac.new(key, text, sha256_module).digest()
def sha256(s):
return sha256_module.new(s).digest()
SHA256_AVAILABLE = True
else:
_no_sha256 = NotImplementedError(
'Use Python 2.5, install pycrypto or install hashlib to use SHA256')
def hmacSha256(unused_key, unused_text):
raise _no_sha256
def sha256(s):
raise _no_sha256
SHA256_AVAILABLE = False
try:
from Crypto.Util.number import long_to_bytes, bytes_to_long
except ImportError:
import pickle
try:
# Check Python compatiblity by raising an exception on import
# if the needed functionality is not present. Present in
# Python >= 2.3
pickle.encode_long
pickle.decode_long
except AttributeError:
raise ImportError(
'No functionality for serializing long integers found')
# Present in Python >= 2.4
try:
reversed
except NameError:
def reversed(seq):
return map(seq.__getitem__, xrange(len(seq) - 1, -1, -1))
def longToBinary(l):
if l == 0:
return '\x00'
return ''.join(reversed(pickle.encode_long(l)))
def binaryToLong(s):
return pickle.decode_long(''.join(reversed(s)))
else:
# We have pycrypto
def longToBinary(l):
if l < 0:
raise ValueError('This function only supports positive integers')
bytes = long_to_bytes(l)
if ord(bytes[0]) > 127:
return '\x00' + bytes
else:
return bytes
def binaryToLong(bytes):
if not bytes:
raise ValueError('Empty string passed to strToLong')
if ord(bytes[0]) > 127:
raise ValueError('This function only supports positive integers')
return bytes_to_long(bytes)
# A cryptographically safe source of random bytes
try:
getBytes = os.urandom
except AttributeError:
try:
from Crypto.Util.randpool import RandomPool
except ImportError:
# Fall back on /dev/urandom, if present. It would be nice to
# have Windows equivalent here, but for now, require pycrypto
# on Windows.
try:
_urandom = file('/dev/urandom', 'rb')
except IOError:
raise ImportError('No adequate source of randomness found!')
else:
def getBytes(n):
bytes = []
while n:
chunk = _urandom.read(n)
n -= len(chunk)
bytes.append(chunk)
assert n >= 0
return ''.join(bytes)
else:
_pool = RandomPool()
def getBytes(n, pool=_pool):
if pool.entropy < n:
pool.randomize()
return pool.get_bytes(n)
# A randrange function that works for longs
try:
randrange = random.SystemRandom().randrange
except AttributeError:
# In Python 2.2's random.Random, randrange does not support
# numbers larger than sys.maxint for randrange. For simplicity,
# use this implementation for any Python that does not have
# random.SystemRandom
from math import log, ceil
_duplicate_cache = {}
def randrange(start, stop=None, step=1):
if stop is None:
stop = start
start = 0
r = (stop - start) // step
try:
(duplicate, nbytes) = _duplicate_cache[r]
except KeyError:
rbytes = longToBinary(r)
if rbytes[0] == '\x00':
nbytes = len(rbytes) - 1
else:
nbytes = len(rbytes)
mxrand = (256 ** nbytes)
# If we get a number less than this, then it is in the
# duplicated range.
duplicate = mxrand % r
if len(_duplicate_cache) > 10:
_duplicate_cache.clear()
_duplicate_cache[r] = (duplicate, nbytes)
while 1:
bytes = '\x00' + getBytes(nbytes)
n = binaryToLong(bytes)
# Keep looping if this value is in the low duplicated range
if n >= duplicate:
break
return start + (n % r) * step
def longToBase64(l):
return toBase64(longToBinary(l))
def base64ToLong(s):
return binaryToLong(fromBase64(s))
def randomString(length, chrs=None):
"""Produce a string of length random bytes, chosen from chrs."""
if chrs is None:
return getBytes(length)
else:
n = len(chrs)
return ''.join([chrs[randrange(n)] for _ in xrange(length)])
########NEW FILE########
__FILENAME__ = dh
from openid import cryptutil
from openid import oidutil
def strxor(x, y):
if len(x) != len(y):
raise ValueError('Inputs to strxor must have the same length')
xor = lambda (a, b): chr(ord(a) ^ ord(b))
return "".join(map(xor, zip(x, y)))
class DiffieHellman(object):
DEFAULT_MOD = 155172898181473697471232257763715539915724801966915404479707795314057629378541917580651227423698188993727816152646631438561595825688188889951272158842675419950341258706556549803580104870537681476726513255747040765857479291291572334510643245094715007229621094194349783925984760375594985848253359305585439638443L
DEFAULT_GEN = 2
def fromDefaults(cls):
return cls(cls.DEFAULT_MOD, cls.DEFAULT_GEN)
fromDefaults = classmethod(fromDefaults)
def __init__(self, modulus, generator):
self.modulus = long(modulus)
self.generator = long(generator)
self._setPrivate(cryptutil.randrange(1, modulus - 1))
def _setPrivate(self, private):
"""This is here to make testing easier"""
self.private = private
self.public = pow(self.generator, self.private, self.modulus)
def usingDefaultValues(self):
return (self.modulus == self.DEFAULT_MOD and
self.generator == self.DEFAULT_GEN)
def getSharedSecret(self, composite):
return pow(composite, self.private, self.modulus)
def xorSecret(self, composite, secret, hash_func):
dh_shared = self.getSharedSecret(composite)
hashed_dh_shared = hash_func(cryptutil.longToBinary(dh_shared))
return strxor(secret, hashed_dh_shared)
########NEW FILE########
__FILENAME__ = extension
from openid.message import Message
class Extension(object):
"""An interface for OpenID extensions.
@ivar ns_uri: The namespace to which to add the arguments for this
extension
"""
ns_uri = None
ns_alias = None
def getExtensionArgs(self):
"""Get the string arguments that should be added to an OpenID
message for this extension.
"""
raise NotImplementedError
def toMessage(self, message=None):
"""Add the arguments from this extension to the provided
message, or create a new message containing only those
arguments.
@returns: The message with the extension arguments added
"""
if message is None:
message = Message()
try:
message.namespaces.addAlias(self.ns_uri, self.ns_alias)
except KeyError:
if message.namespaces.getAlias(self.ns_uri) != self.ns_alias:
raise
message.updateArgs(self.ns_uri, self.getExtensionArgs())
return message
########NEW FILE########
__FILENAME__ = ax
# -*- test-case-name: openid.test.test_ax -*-
"""Implements the OpenID Attribute Exchange specification, version 1.0.
@since: 2.1.0
"""
__all__ = [
'AttributeRequest',
'FetchRequest',
'FetchResponse',
'StoreRequest',
'StoreResponse',
]
from openid import extension
from openid.server.trustroot import TrustRoot
from openid.message import NamespaceMap, OPENID_NS
# Use this as the 'count' value for an attribute in a FetchRequest to
# ask for as many values as the OP can provide.
UNLIMITED_VALUES = "unlimited"
# Minimum supported alias length in characters. Here for
# completeness.
MINIMUM_SUPPORTED_ALIAS_LENGTH = 32
def checkAlias(alias):
"""
Check an alias for invalid characters; raise AXError if any are
found. Return None if the alias is valid.
"""
if ',' in alias:
raise AXError("Alias %r must not contain comma" % (alias,))
if '.' in alias:
raise AXError("Alias %r must not contain period" % (alias,))
class AXError(ValueError):
"""Results from data that does not meet the attribute exchange 1.0
specification"""
class NotAXMessage(AXError):
"""Raised when there is no Attribute Exchange mode in the message."""
def __repr__(self):
return self.__class__.__name__
def __str__(self):
return self.__class__.__name__
class AXMessage(extension.Extension):
"""Abstract class containing common code for attribute exchange messages
@cvar ns_alias: The preferred namespace alias for attribute
exchange messages
@cvar mode: The type of this attribute exchange message. This must
be overridden in subclasses.
"""
# This class is abstract, so it's OK that it doesn't override the
# abstract method in Extension:
#
#pylint:disable-msg=W0223
ns_alias = 'ax'
mode = None
ns_uri = 'http://openid.net/srv/ax/1.0'
def _checkMode(self, ax_args):
"""Raise an exception if the mode in the attribute exchange
arguments does not match what is expected for this class.
@raises NotAXMessage: When there is no mode value in ax_args at all.
@raises AXError: When mode does not match.
"""
mode = ax_args.get('mode')
if mode != self.mode:
if not mode:
raise NotAXMessage()
else:
raise AXError(
'Expected mode %r; got %r' % (self.mode, mode))
def _newArgs(self):
"""Return a set of attribute exchange arguments containing the
basic information that must be in every attribute exchange
message.
"""
return {'mode':self.mode}
class AttrInfo(object):
"""Represents a single attribute in an attribute exchange
request. This should be added to an AXRequest object in order to
request the attribute.
@ivar required: Whether the attribute will be marked as required
when presented to the subject of the attribute exchange
request.
@type required: bool
@ivar count: How many values of this type to request from the
subject. Defaults to one.
@type count: int
@ivar type_uri: The identifier that determines what the attribute
represents and how it is serialized. For example, one type URI
representing dates could represent a Unix timestamp in base 10
and another could represent a human-readable string.
@type type_uri: str
@ivar alias: The name that should be given to this alias in the
request. If it is not supplied, a generic name will be
assigned. For example, if you want to call a Unix timestamp
value 'tstamp', set its alias to that value. If two attributes
in the same message request to use the same alias, the request
will fail to be generated.
@type alias: str or NoneType
"""
# It's OK that this class doesn't have public methods (it's just a
# holder for a bunch of attributes):
#
#pylint:disable-msg=R0903
def __init__(self, type_uri, count=1, required=False, alias=None):
self.required = required
self.count = count
self.type_uri = type_uri
self.alias = alias
if self.alias is not None:
checkAlias(self.alias)
def wantsUnlimitedValues(self):
"""
When processing a request for this attribute, the OP should
call this method to determine whether all available attribute
values were requested. If self.count == UNLIMITED_VALUES,
this returns True. Otherwise this returns False, in which
case self.count is an integer.
"""
return self.count == UNLIMITED_VALUES
def toTypeURIs(namespace_map, alias_list_s):
"""Given a namespace mapping and a string containing a
comma-separated list of namespace aliases, return a list of type
URIs that correspond to those aliases.
@param namespace_map: The mapping from namespace URI to alias
@type namespace_map: openid.message.NamespaceMap
@param alias_list_s: The string containing the comma-separated
list of aliases. May also be None for convenience.
@type alias_list_s: str or NoneType
@returns: The list of namespace URIs that corresponds to the
supplied list of aliases. If the string was zero-length or
None, an empty list will be returned.
@raise KeyError: If an alias is present in the list of aliases but
is not present in the namespace map.
"""
uris = []
if alias_list_s:
for alias in alias_list_s.split(','):
type_uri = namespace_map.getNamespaceURI(alias)
if type_uri is None:
raise KeyError(
'No type is defined for attribute name %r' % (alias,))
else:
uris.append(type_uri)
return uris
class FetchRequest(AXMessage):
"""An attribute exchange 'fetch_request' message. This message is
sent by a relying party when it wishes to obtain attributes about
the subject of an OpenID authentication request.
@ivar requested_attributes: The attributes that have been
requested thus far, indexed by the type URI.
@type requested_attributes: {str:AttrInfo}
@ivar update_url: A URL that will accept responses for this
attribute exchange request, even in the absence of the user
who made this request.
"""
mode = 'fetch_request'
def __init__(self, update_url=None):
AXMessage.__init__(self)
self.requested_attributes = {}
self.update_url = update_url
def add(self, attribute):
"""Add an attribute to this attribute exchange request.
@param attribute: The attribute that is being requested
@type attribute: C{L{AttrInfo}}
@returns: None
@raise KeyError: when the requested attribute is already
present in this fetch request.
"""
if attribute.type_uri in self.requested_attributes:
raise KeyError('The attribute %r has already been requested'
% (attribute.type_uri,))
self.requested_attributes[attribute.type_uri] = attribute
def getExtensionArgs(self):
"""Get the serialized form of this attribute fetch request.
@returns: The fetch request message parameters
@rtype: {unicode:unicode}
"""
aliases = NamespaceMap()
required = []
if_available = []
ax_args = self._newArgs()
for type_uri, attribute in self.requested_attributes.iteritems():
if attribute.alias is None:
alias = aliases.add(type_uri)
else:
# This will raise an exception when the second
# attribute with the same alias is added. I think it
# would be better to complain at the time that the
# attribute is added to this object so that the code
# that is adding it is identified in the stack trace,
# but it's more work to do so, and it won't be 100%
# accurate anyway, since the attributes are
# mutable. So for now, just live with the fact that
# we'll learn about the error later.
#
# The other possible approach is to hide the error and
# generate a new alias on the fly. I think that would
# probably be bad.
alias = aliases.addAlias(type_uri, attribute.alias)
if attribute.required:
required.append(alias)
else:
if_available.append(alias)
if attribute.count != 1:
ax_args['count.' + alias] = str(attribute.count)
ax_args['type.' + alias] = type_uri
if required:
ax_args['required'] = ','.join(required)
if if_available:
ax_args['if_available'] = ','.join(if_available)
return ax_args
def getRequiredAttrs(self):
"""Get the type URIs for all attributes that have been marked
as required.
@returns: A list of the type URIs for attributes that have
been marked as required.
@rtype: [str]
"""
required = []
for type_uri, attribute in self.requested_attributes.iteritems():
if attribute.required:
required.append(type_uri)
return required
def fromOpenIDRequest(cls, openid_request):
"""Extract a FetchRequest from an OpenID message
@param openid_request: The OpenID authentication request
containing the attribute fetch request
@type openid_request: C{L{openid.server.server.CheckIDRequest}}
@rtype: C{L{FetchRequest}} or C{None}
@returns: The FetchRequest extracted from the message or None, if
the message contained no AX extension.
@raises KeyError: if the AuthRequest is not consistent in its use
of namespace aliases.
@raises AXError: When parseExtensionArgs would raise same.
@see: L{parseExtensionArgs}
"""
message = openid_request.message
ax_args = message.getArgs(cls.ns_uri)
self = cls()
try:
self.parseExtensionArgs(ax_args)
except NotAXMessage, err:
return None
if self.update_url:
# Update URL must match the openid.realm of the underlying
# OpenID 2 message.
realm = message.getArg(OPENID_NS, 'realm',
message.getArg(OPENID_NS, 'return_to'))
if not realm:
raise AXError(("Cannot validate update_url %r " +
"against absent realm") % (self.update_url,))
tr = TrustRoot.parse(realm)
if not tr.validateURL(self.update_url):
raise AXError("Update URL %r failed validation against realm %r" %
(self.update_url, realm,))
return self
fromOpenIDRequest = classmethod(fromOpenIDRequest)
def parseExtensionArgs(self, ax_args):
"""Given attribute exchange arguments, populate this FetchRequest.
@param ax_args: Attribute Exchange arguments from the request.
As returned from L{Message.getArgs<openid.message.Message.getArgs>}.
@type ax_args: dict
@raises KeyError: if the message is not consistent in its use
of namespace aliases.
@raises NotAXMessage: If ax_args does not include an Attribute Exchange
mode.
@raises AXError: If the data to be parsed does not follow the
attribute exchange specification. At least when
'if_available' or 'required' is not specified for a
particular attribute type.
"""
# Raises an exception if the mode is not the expected value
self._checkMode(ax_args)
aliases = NamespaceMap()
for key, value in ax_args.iteritems():
if key.startswith('type.'):
alias = key[5:]
type_uri = value
aliases.addAlias(type_uri, alias)
count_key = 'count.' + alias
count_s = ax_args.get(count_key)
if count_s:
try:
count = int(count_s)
if count <= 0:
raise AXError("Count %r must be greater than zero, got %r" % (count_key, count_s,))
except ValueError:
if count_s != UNLIMITED_VALUES:
raise AXError("Invalid count value for %r: %r" % (count_key, count_s,))
count = count_s
else:
count = 1
self.add(AttrInfo(type_uri, alias=alias, count=count))
required = toTypeURIs(aliases, ax_args.get('required'))
for type_uri in required:
self.requested_attributes[type_uri].required = True
if_available = toTypeURIs(aliases, ax_args.get('if_available'))
all_type_uris = required + if_available
for type_uri in aliases.iterNamespaceURIs():
if type_uri not in all_type_uris:
raise AXError(
'Type URI %r was in the request but not '
'present in "required" or "if_available"' % (type_uri,))
self.update_url = ax_args.get('update_url')
def iterAttrs(self):
"""Iterate over the AttrInfo objects that are
contained in this fetch_request.
"""
return self.requested_attributes.itervalues()
def __iter__(self):
"""Iterate over the attribute type URIs in this fetch_request
"""
return iter(self.requested_attributes)
def has_key(self, type_uri):
"""Is the given type URI present in this fetch_request?
"""
return type_uri in self.requested_attributes
__contains__ = has_key
class AXKeyValueMessage(AXMessage):
"""An abstract class that implements a message that has attribute
keys and values. It contains the common code between
fetch_response and store_request.
"""
# This class is abstract, so it's OK that it doesn't override the
# abstract method in Extension:
#
#pylint:disable-msg=W0223
def __init__(self):
AXMessage.__init__(self)
self.data = {}
def addValue(self, type_uri, value):
"""Add a single value for the given attribute type to the
message. If there are already values specified for this type,
this value will be sent in addition to the values already
specified.
@param type_uri: The URI for the attribute
@param value: The value to add to the response to the relying
party for this attribute
@type value: unicode
@returns: None
"""
try:
values = self.data[type_uri]
except KeyError:
values = self.data[type_uri] = []
values.append(value)
def setValues(self, type_uri, values):
"""Set the values for the given attribute type. This replaces
any values that have already been set for this attribute.
@param type_uri: The URI for the attribute
@param values: A list of values to send for this attribute.
@type values: [unicode]
"""
self.data[type_uri] = values
def _getExtensionKVArgs(self, aliases=None):
"""Get the extension arguments for the key/value pairs
contained in this message.
@param aliases: An alias mapping. Set to None if you don't
care about the aliases for this request.
"""
if aliases is None:
aliases = NamespaceMap()
ax_args = {}
for type_uri, values in self.data.iteritems():
alias = aliases.add(type_uri)
ax_args['type.' + alias] = type_uri
ax_args['count.' + alias] = str(len(values))
for i, value in enumerate(values):
key = 'value.%s.%d' % (alias, i + 1)
ax_args[key] = value
return ax_args
def parseExtensionArgs(self, ax_args):
"""Parse attribute exchange key/value arguments into this
object.
@param ax_args: The attribute exchange fetch_response
arguments, with namespacing removed.
@type ax_args: {unicode:unicode}
@returns: None
@raises ValueError: If the message has bad values for
particular fields
@raises KeyError: If the namespace mapping is bad or required
arguments are missing
"""
self._checkMode(ax_args)
aliases = NamespaceMap()
for key, value in ax_args.iteritems():
if key.startswith('type.'):
type_uri = value
alias = key[5:]
checkAlias(alias)
aliases.addAlias(type_uri, alias)
for type_uri, alias in aliases.iteritems():
try:
count_s = ax_args['count.' + alias]
except KeyError:
value = ax_args['value.' + alias]
if value == u'':
values = []
else:
values = [value]
else:
count = int(count_s)
values = []
for i in range(1, count + 1):
value_key = 'value.%s.%d' % (alias, i)
value = ax_args[value_key]
values.append(value)
self.data[type_uri] = values
def getSingle(self, type_uri, default=None):
"""Get a single value for an attribute. If no value was sent
for this attribute, use the supplied default. If there is more
than one value for this attribute, this method will fail.
@type type_uri: str
@param type_uri: The URI for the attribute
@param default: The value to return if the attribute was not
sent in the fetch_response.
@returns: The value of the attribute in the fetch_response
message, or the default supplied
@rtype: unicode or NoneType
@raises ValueError: If there is more than one value for this
parameter in the fetch_response message.
@raises KeyError: If the attribute was not sent in this response
"""
values = self.data.get(type_uri)
if not values:
return default
elif len(values) == 1:
return values[0]
else:
raise AXError(
'More than one value present for %r' % (type_uri,))
def get(self, type_uri):
"""Get the list of values for this attribute in the
fetch_response.
XXX: what to do if the values are not present? default
parameter? this is funny because it's always supposed to
return a list, so the default may break that, though it's
provided by the user's code, so it might be okay. If no
default is supplied, should the return be None or []?
@param type_uri: The URI of the attribute
@returns: The list of values for this attribute in the
response. May be an empty list.
@rtype: [unicode]
@raises KeyError: If the attribute was not sent in the response
"""
return self.data[type_uri]
def count(self, type_uri):
"""Get the number of responses for a particular attribute in
this fetch_response message.
@param type_uri: The URI of the attribute
@returns: The number of values sent for this attribute
@raises KeyError: If the attribute was not sent in the
response. KeyError will not be raised if the number of
values was zero.
"""
return len(self.get(type_uri))
class FetchResponse(AXKeyValueMessage):
"""A fetch_response attribute exchange message
"""
mode = 'fetch_response'
def __init__(self, request=None, update_url=None):
"""
@param request: When supplied, I will use namespace aliases
that match those in this request. I will also check to
make sure I do not respond with attributes that were not
requested.
@type request: L{FetchRequest}
@param update_url: By default, C{update_url} is taken from the
request. But if you do not supply the request, you may set
the C{update_url} here.
@type update_url: str
"""
AXKeyValueMessage.__init__(self)
self.update_url = update_url
self.request = request
def getExtensionArgs(self):
"""Serialize this object into arguments in the attribute
exchange namespace
@returns: The dictionary of unqualified attribute exchange
arguments that represent this fetch_response.
@rtype: {unicode;unicode}
"""
aliases = NamespaceMap()
zero_value_types = []
if self.request is not None:
# Validate the data in the context of the request (the
# same attributes should be present in each, and the
# counts in the response must be no more than the counts
# in the request)
for type_uri in self.data:
if type_uri not in self.request:
raise KeyError(
'Response attribute not present in request: %r'
% (type_uri,))
for attr_info in self.request.iterAttrs():
# Copy the aliases from the request so that reading
# the response in light of the request is easier
if attr_info.alias is None:
aliases.add(attr_info.type_uri)
else:
aliases.addAlias(attr_info.type_uri, attr_info.alias)
try:
values = self.data[attr_info.type_uri]
except KeyError:
values = []
zero_value_types.append(attr_info)
if (attr_info.count != UNLIMITED_VALUES) and \
(attr_info.count < len(values)):
raise AXError(
'More than the number of requested values were '
'specified for %r' % (attr_info.type_uri,))
kv_args = self._getExtensionKVArgs(aliases)
# Add the KV args into the response with the args that are
# unique to the fetch_response
ax_args = self._newArgs()
# For each requested attribute, put its type/alias and count
# into the response even if no data were returned.
for attr_info in zero_value_types:
alias = aliases.getAlias(attr_info.type_uri)
kv_args['type.' + alias] = attr_info.type_uri
kv_args['count.' + alias] = '0'
update_url = ((self.request and self.request.update_url)
or self.update_url)
if update_url:
ax_args['update_url'] = update_url
ax_args.update(kv_args)
return ax_args
def parseExtensionArgs(self, ax_args):
"""@see: {Extension.parseExtensionArgs<openid.extension.Extension.parseExtensionArgs>}"""
super(FetchResponse, self).parseExtensionArgs(ax_args)
self.update_url = ax_args.get('update_url')
def fromSuccessResponse(cls, success_response, signed=True):
"""Construct a FetchResponse object from an OpenID library
SuccessResponse object.
@param success_response: A successful id_res response object
@type success_response: openid.consumer.consumer.SuccessResponse
@param signed: Whether non-signed args should be
processsed. If True (the default), only signed arguments
will be processsed.
@type signed: bool
@returns: A FetchResponse containing the data from the OpenID
message, or None if the SuccessResponse did not contain AX
extension data.
@raises AXError: when the AX data cannot be parsed.
"""
self = cls()
ax_args = success_response.extensionResponse(self.ns_uri, signed)
try:
self.parseExtensionArgs(ax_args)
except NotAXMessage, err:
return None
else:
return self
fromSuccessResponse = classmethod(fromSuccessResponse)
class StoreRequest(AXKeyValueMessage):
"""A store request attribute exchange message representation
"""
mode = 'store_request'
def __init__(self, aliases=None):
"""
@param aliases: The namespace aliases to use when making this
store request. Leave as None to use defaults.
"""
super(StoreRequest, self).__init__()
self.aliases = aliases
def getExtensionArgs(self):
"""
@see: L{Extension.getExtensionArgs<openid.extension.Extension.getExtensionArgs>}
"""
ax_args = self._newArgs()
kv_args = self._getExtensionKVArgs(self.aliases)
ax_args.update(kv_args)
return ax_args
class StoreResponse(AXMessage):
"""An indication that the store request was processed along with
this OpenID transaction.
"""
SUCCESS_MODE = 'store_response_success'
FAILURE_MODE = 'store_response_failure'
def __init__(self, succeeded=True, error_message=None):
AXMessage.__init__(self)
if succeeded and error_message is not None:
raise AXError('An error message may only be included in a '
'failing fetch response')
if succeeded:
self.mode = self.SUCCESS_MODE
else:
self.mode = self.FAILURE_MODE
self.error_message = error_message
def succeeded(self):
"""Was this response a success response?"""
return self.mode == self.SUCCESS_MODE
def getExtensionArgs(self):
"""@see: {Extension.getExtensionArgs<openid.extension.Extension.getExtensionArgs>}"""
ax_args = self._newArgs()
if not self.succeeded() and self.error_message:
ax_args['error'] = self.error_message
return ax_args
########NEW FILE########
__FILENAME__ = pape
"""An implementation of the OpenID Provider Authentication Policy
Extension 1.0
@see: http://openid.net/developers/specs/
@since: 2.1.0
"""
__all__ = [
'Request',
'Response',
'ns_uri',
'AUTH_PHISHING_RESISTANT',
'AUTH_MULTI_FACTOR',
'AUTH_MULTI_FACTOR_PHYSICAL',
]
from openid.extension import Extension
ns_uri = "http://specs.openid.net/extensions/pape/1.0"
AUTH_MULTI_FACTOR_PHYSICAL = \
'http://schemas.openid.net/pape/policies/2007/06/multi-factor-physical'
AUTH_MULTI_FACTOR = \
'http://schemas.openid.net/pape/policies/2007/06/multi-factor'
AUTH_PHISHING_RESISTANT = \
'http://schemas.openid.net/pape/policies/2007/06/phishing-resistant'
class Request(Extension):
"""A Provider Authentication Policy request, sent from a relying
party to a provider
@ivar preferred_auth_policies: The authentication policies that
the relying party prefers
@type preferred_auth_policies: [str]
@ivar max_auth_age: The maximum time, in seconds, that the relying
party wants to allow to have elapsed before the user must
re-authenticate
@type max_auth_age: int or NoneType
"""
ns_alias = 'pape'
def __init__(self, preferred_auth_policies=None, max_auth_age=None):
super(Request, self).__init__(self)
if not preferred_auth_policies:
preferred_auth_policies = []
self.preferred_auth_policies = preferred_auth_policies
self.max_auth_age = max_auth_age
def __nonzero__(self):
return bool(self.preferred_auth_policies or
self.max_auth_age is not None)
def addPolicyURI(self, policy_uri):
"""Add an acceptable authentication policy URI to this request
This method is intended to be used by the relying party to add
acceptable authentication types to the request.
@param policy_uri: The identifier for the preferred type of
authentication.
@see: http://openid.net/specs/openid-provider-authentication-policy-extension-1_0-01.html#auth_policies
"""
if policy_uri not in self.preferred_auth_policies:
self.preferred_auth_policies.append(policy_uri)
def getExtensionArgs(self):
"""@see: C{L{Extension.getExtensionArgs}}
"""
ns_args = {
'preferred_auth_policies':' '.join(self.preferred_auth_policies)
}
if self.max_auth_age is not None:
ns_args['max_auth_age'] = str(self.max_auth_age)
return ns_args
def fromOpenIDRequest(cls, request):
"""Instantiate a Request object from the arguments in a
C{checkid_*} OpenID message
"""
self = cls()
args = request.message.getArgs(self.ns_uri)
if args == {}:
return None
self.parseExtensionArgs(args)
return self
fromOpenIDRequest = classmethod(fromOpenIDRequest)
def parseExtensionArgs(self, args):
"""Set the state of this request to be that expressed in these
PAPE arguments
@param args: The PAPE arguments without a namespace
@rtype: None
@raises ValueError: When the max_auth_age is not parseable as
an integer
"""
# preferred_auth_policies is a space-separated list of policy URIs
self.preferred_auth_policies = []
policies_str = args.get('preferred_auth_policies')
if policies_str:
for uri in policies_str.split(' '):
if uri not in self.preferred_auth_policies:
self.preferred_auth_policies.append(uri)
# max_auth_age is base-10 integer number of seconds
max_auth_age_str = args.get('max_auth_age')
self.max_auth_age = None
if max_auth_age_str:
try:
self.max_auth_age = int(max_auth_age_str)
except ValueError:
pass
def preferredTypes(self, supported_types):
"""Given a list of authentication policy URIs that a provider
supports, this method returns the subsequence of those types
that are preferred by the relying party.
@param supported_types: A sequence of authentication policy
type URIs that are supported by a provider
@returns: The sub-sequence of the supported types that are
preferred by the relying party. This list will be ordered
in the order that the types appear in the supported_types
sequence, and may be empty if the provider does not prefer
any of the supported authentication types.
@returntype: [str]
"""
return filter(self.preferred_auth_policies.__contains__,
supported_types)
Request.ns_uri = ns_uri
class Response(Extension):
"""A Provider Authentication Policy response, sent from a provider
to a relying party
"""
ns_alias = 'pape'
def __init__(self, auth_policies=None, auth_age=None,
nist_auth_level=None):
super(Response, self).__init__(self)
if auth_policies:
self.auth_policies = auth_policies
else:
self.auth_policies = []
self.auth_age = auth_age
self.nist_auth_level = nist_auth_level
def addPolicyURI(self, policy_uri):
"""Add a authentication policy to this response
This method is intended to be used by the provider to add a
policy that the provider conformed to when authenticating the user.
@param policy_uri: The identifier for the preferred type of
authentication.
@see: http://openid.net/specs/openid-provider-authentication-policy-extension-1_0-01.html#auth_policies
"""
if policy_uri not in self.auth_policies:
self.auth_policies.append(policy_uri)
def fromSuccessResponse(cls, success_response):
"""Create a C{L{Response}} object from a successful OpenID
library response
(C{L{openid.consumer.consumer.SuccessResponse}}) response
message
@param success_response: A SuccessResponse from consumer.complete()
@type success_response: C{L{openid.consumer.consumer.SuccessResponse}}
@rtype: Response
@returns: A provider authentication policy response from the
data that was supplied with the C{id_res} response.
"""
self = cls()
# PAPE requires that the args be signed.
args = success_response.getSignedNS(self.ns_uri)
self.parseExtensionArgs(args)
return self
def parseExtensionArgs(self, args, strict=False):
"""Parse the provider authentication policy arguments into the
internal state of this object
@param args: unqualified provider authentication policy
arguments
@param strict: Whether to raise an exception when bad data is
encountered
@returns: None. The data is parsed into the internal fields of
this object.
"""
policies_str = args.get('auth_policies')
if policies_str:
self.auth_policies = policies_str.split(' ')
nist_level_str = args.get('nist_auth_level')
if nist_level_str:
try:
nist_level = int(nist_level_str)
except ValueError:
if strict:
raise ValueError('nist_auth_level must be an integer between '
'zero and four, inclusive')
else:
self.nist_auth_level = None
else:
if 0 <= nist_level < 5:
self.nist_auth_level = nist_level
auth_age_str = args.get('auth_age')
if auth_age_str:
try:
auth_age = int(auth_age_str)
except ValueError:
if strict:
raise
else:
if auth_age >= 0:
self.auth_age = auth_age
elif strict:
raise ValueError('Auth age must be above zero')
fromSuccessResponse = classmethod(fromSuccessResponse)
def getExtensionArgs(self):
"""@see: C{L{Extension.getExtensionArgs}}
"""
ns_args = {
'auth_policies':' '.join(self.auth_policies),
}
if self.nist_auth_level is not None:
if self.nist_auth_level not in range(0, 5):
raise ValueError('nist_auth_level must be an integer between '
'zero and four, inclusive')
ns_args['nist_auth_level'] = str(self.nist_auth_level)
if self.auth_age is not None:
if self.auth_age < 0:
raise ValueError('Auth age must be above zero')
ns_args['auth_age'] = str(int(self.auth_age))
return ns_args
Response.ns_uri = ns_uri
########NEW FILE########
__FILENAME__ = sreg
"""Simple registration request and response parsing and object representation
This module contains objects representing simple registration requests
and responses that can be used with both OpenID relying parties and
OpenID providers.
1. The relying party creates a request object and adds it to the
C{L{AuthRequest<openid.consumer.consumer.AuthRequest>}} object
before making the C{checkid_} request to the OpenID provider::
auth_request.addExtension(SRegRequest(required=['email']))
2. The OpenID provider extracts the simple registration request from
the OpenID request using C{L{SRegRequest.fromOpenIDRequest}},
gets the user's approval and data, creates a C{L{SRegResponse}}
object and adds it to the C{id_res} response::
sreg_req = SRegRequest.fromOpenIDRequest(checkid_request)
# [ get the user's approval and data, informing the user that
# the fields in sreg_response were requested ]
sreg_resp = SRegResponse.extractResponse(sreg_req, user_data)
sreg_resp.toMessage(openid_response.fields)
3. The relying party uses C{L{SRegResponse.fromSuccessResponse}} to
extract the data from the OpenID response::
sreg_resp = SRegResponse.fromSuccessResponse(success_response)
@since: 2.0
@var sreg_data_fields: The names of the data fields that are listed in
the sreg spec, and a description of them in English
@var sreg_uri: The preferred URI to use for the simple registration
namespace and XRD Type value
"""
from openid.message import registerNamespaceAlias, \
NamespaceAliasRegistrationError
from openid.extension import Extension
from openid import oidutil
try:
basestring #pylint:disable-msg=W0104
except NameError:
# For Python 2.2
basestring = (str, unicode) #pylint:disable-msg=W0622
__all__ = [
'SRegRequest',
'SRegResponse',
'data_fields',
'ns_uri',
'ns_uri_1_0',
'ns_uri_1_1',
'supportsSReg',
]
# The data fields that are listed in the sreg spec
data_fields = {
'fullname':'Full Name',
'nickname':'Nickname',
'dob':'Date of Birth',
'email':'E-mail Address',
'gender':'Gender',
'postcode':'Postal Code',
'country':'Country',
'language':'Language',
'timezone':'Time Zone',
}
def checkFieldName(field_name):
"""Check to see that the given value is a valid simple
registration data field name.
@raise ValueError: if the field name is not a valid simple
registration data field name
"""
if field_name not in data_fields:
raise ValueError('%r is not a defined simple registration field' %
(field_name,))
# URI used in the wild for Yadis documents advertising simple
# registration support
ns_uri_1_0 = 'http://openid.net/sreg/1.0'
# URI in the draft specification for simple registration 1.1
# <http://openid.net/specs/openid-simple-registration-extension-1_1-01.html>
ns_uri_1_1 = 'http://openid.net/extensions/sreg/1.1'
# This attribute will always hold the preferred URI to use when adding
# sreg support to an XRDS file or in an OpenID namespace declaration.
ns_uri = ns_uri_1_1
try:
registerNamespaceAlias(ns_uri_1_1, 'sreg')
except NamespaceAliasRegistrationError, e:
oidutil.log('registerNamespaceAlias(%r, %r) failed: %s' % (ns_uri_1_1,
'sreg', str(e),))
def supportsSReg(endpoint):
"""Does the given endpoint advertise support for simple
registration?
@param endpoint: The endpoint object as returned by OpenID discovery
@type endpoint: openid.consumer.discover.OpenIDEndpoint
@returns: Whether an sreg type was advertised by the endpoint
@rtype: bool
"""
return (endpoint.usesExtension(ns_uri_1_1) or
endpoint.usesExtension(ns_uri_1_0))
class SRegNamespaceError(ValueError):
"""The simple registration namespace was not found and could not
be created using the expected name (there's another extension
using the name 'sreg')
This is not I{illegal}, for OpenID 2, although it probably
indicates a problem, since it's not expected that other extensions
will re-use the alias that is in use for OpenID 1.
If this is an OpenID 1 request, then there is no recourse. This
should not happen unless some code has modified the namespaces for
the message that is being processed.
"""
def getSRegNS(message):
"""Extract the simple registration namespace URI from the given
OpenID message. Handles OpenID 1 and 2, as well as both sreg
namespace URIs found in the wild, as well as missing namespace
definitions (for OpenID 1)
@param message: The OpenID message from which to parse simple
registration fields. This may be a request or response message.
@type message: C{L{openid.message.Message}}
@returns: the sreg namespace URI for the supplied message. The
message may be modified to define a simple registration
namespace.
@rtype: C{str}
@raise ValueError: when using OpenID 1 if the message defines
the 'sreg' alias to be something other than a simple
registration type.
"""
# See if there exists an alias for one of the two defined simple
# registration types.
for sreg_ns_uri in [ns_uri_1_1, ns_uri_1_0]:
alias = message.namespaces.getAlias(sreg_ns_uri)
if alias is not None:
break
else:
# There is no alias for either of the types, so try to add
# one. We default to using the modern value (1.1)
sreg_ns_uri = ns_uri_1_1
try:
message.namespaces.addAlias(ns_uri_1_1, 'sreg')
except KeyError, why:
# An alias for the string 'sreg' already exists, but it's
# defined for something other than simple registration
raise SRegNamespaceError(why[0])
# we know that sreg_ns_uri defined, because it's defined in the
# else clause of the loop as well, so disable the warning
return sreg_ns_uri #pylint:disable-msg=W0631
class SRegRequest(Extension):
"""An object to hold the state of a simple registration request.
@ivar required: A list of the required fields in this simple
registration request
@type required: [str]
@ivar optional: A list of the optional fields in this simple
registration request
@type optional: [str]
@ivar policy_url: The policy URL that was provided with the request
@type policy_url: str or NoneType
@group Consumer: requestField, requestFields, getExtensionArgs, addToOpenIDRequest
@group Server: fromOpenIDRequest, parseExtensionArgs
"""
ns_alias = 'sreg'
def __init__(self, required=None, optional=None, policy_url=None,
sreg_ns_uri=ns_uri):
"""Initialize an empty simple registration request"""
Extension.__init__(self)
self.required = []
self.optional = []
self.policy_url = policy_url
self.ns_uri = sreg_ns_uri
if required:
self.requestFields(required, required=True, strict=True)
if optional:
self.requestFields(optional, required=False, strict=True)
# Assign getSRegNS to a static method so that it can be
# overridden for testing.
_getSRegNS = staticmethod(getSRegNS)
def fromOpenIDRequest(cls, request):
"""Create a simple registration request that contains the
fields that were requested in the OpenID request with the
given arguments
@param request: The OpenID request
@type request: openid.server.CheckIDRequest
@returns: The newly created simple registration request
@rtype: C{L{SRegRequest}}
"""
self = cls()
# Since we're going to mess with namespace URI mapping, don't
# mutate the object that was passed in.
message = request.message.copy()
self.ns_uri = self._getSRegNS(message)
args = message.getArgs(self.ns_uri)
self.parseExtensionArgs(args)
return self
fromOpenIDRequest = classmethod(fromOpenIDRequest)
def parseExtensionArgs(self, args, strict=False):
"""Parse the unqualified simple registration request
parameters and add them to this object.
This method is essentially the inverse of
C{L{getExtensionArgs}}. This method restores the serialized simple
registration request fields.
If you are extracting arguments from a standard OpenID
checkid_* request, you probably want to use C{L{fromOpenIDRequest}},
which will extract the sreg namespace and arguments from the
OpenID request. This method is intended for cases where the
OpenID server needs more control over how the arguments are
parsed than that method provides.
>>> args = message.getArgs(ns_uri)
>>> request.parseExtensionArgs(args)
@param args: The unqualified simple registration arguments
@type args: {str:str}
@param strict: Whether requests with fields that are not
defined in the simple registration specification should be
tolerated (and ignored)
@type strict: bool
@returns: None; updates this object
"""
for list_name in ['required', 'optional']:
required = (list_name == 'required')
items = args.get(list_name)
if items:
for field_name in items.split(','):
try:
self.requestField(field_name, required, strict)
except ValueError:
if strict:
raise
self.policy_url = args.get('policy_url')
def allRequestedFields(self):
"""A list of all of the simple registration fields that were
requested, whether they were required or optional.
@rtype: [str]
"""
return self.required + self.optional
def wereFieldsRequested(self):
"""Have any simple registration fields been requested?
@rtype: bool
"""
return bool(self.allRequestedFields())
def __contains__(self, field_name):
"""Was this field in the request?"""
return (field_name in self.required or
field_name in self.optional)
def requestField(self, field_name, required=False, strict=False):
"""Request the specified field from the OpenID user
@param field_name: the unqualified simple registration field name
@type field_name: str
@param required: whether the given field should be presented
to the user as being a required to successfully complete
the request
@param strict: whether to raise an exception when a field is
added to a request more than once
@raise ValueError: when the field requested is not a simple
registration field or strict is set and the field was
requested more than once
"""
checkFieldName(field_name)
if strict:
if field_name in self.required or field_name in self.optional:
raise ValueError('That field has already been requested')
else:
if field_name in self.required:
return
if field_name in self.optional:
if required:
self.optional.remove(field_name)
else:
return
if required:
self.required.append(field_name)
else:
self.optional.append(field_name)
def requestFields(self, field_names, required=False, strict=False):
"""Add the given list of fields to the request
@param field_names: The simple registration data fields to request
@type field_names: [str]
@param required: Whether these values should be presented to
the user as required
@param strict: whether to raise an exception when a field is
added to a request more than once
@raise ValueError: when a field requested is not a simple
registration field or strict is set and a field was
requested more than once
"""
if isinstance(field_names, basestring):
raise TypeError('Fields should be passed as a list of '
'strings (not %r)' % (type(field_names),))
for field_name in field_names:
self.requestField(field_name, required, strict=strict)
def getExtensionArgs(self):
"""Get a dictionary of unqualified simple registration
arguments representing this request.
This method is essentially the inverse of
C{L{parseExtensionArgs}}. This method serializes the simple
registration request fields.
@rtype: {str:str}
"""
args = {}
if self.required:
args['required'] = ','.join(self.required)
if self.optional:
args['optional'] = ','.join(self.optional)
if self.policy_url:
args['policy_url'] = self.policy_url
return args
class SRegResponse(Extension):
"""Represents the data returned in a simple registration response
inside of an OpenID C{id_res} response. This object will be
created by the OpenID server, added to the C{id_res} response
object, and then extracted from the C{id_res} message by the
Consumer.
@ivar data: The simple registration data, keyed by the unqualified
simple registration name of the field (i.e. nickname is keyed
by C{'nickname'})
@ivar ns_uri: The URI under which the simple registration data was
stored in the response message.
@group Server: extractResponse
@group Consumer: fromSuccessResponse
@group Read-only dictionary interface: keys, iterkeys, items, iteritems,
__iter__, get, __getitem__, keys, has_key
"""
ns_alias = 'sreg'
def __init__(self, data=None, sreg_ns_uri=ns_uri):
Extension.__init__(self)
if data is None:
self.data = {}
else:
self.data = data
self.ns_uri = sreg_ns_uri
def extractResponse(cls, request, data):
"""Take a C{L{SRegRequest}} and a dictionary of simple
registration values and create a C{L{SRegResponse}}
object containing that data.
@param request: The simple registration request object
@type request: SRegRequest
@param data: The simple registration data for this
response, as a dictionary from unqualified simple
registration field name to string (unicode) value. For
instance, the nickname should be stored under the key
'nickname'.
@type data: {str:str}
@returns: a simple registration response object
@rtype: SRegResponse
"""
self = cls()
self.ns_uri = request.ns_uri
for field in request.allRequestedFields():
value = data.get(field)
if value is not None:
self.data[field] = value
return self
extractResponse = classmethod(extractResponse)
# Assign getSRegArgs to a static method so that it can be
# overridden for testing
_getSRegNS = staticmethod(getSRegNS)
def fromSuccessResponse(cls, success_response, signed_only=True):
"""Create a C{L{SRegResponse}} object from a successful OpenID
library response
(C{L{openid.consumer.consumer.SuccessResponse}}) response
message
@param success_response: A SuccessResponse from consumer.complete()
@type success_response: C{L{openid.consumer.consumer.SuccessResponse}}
@param signed_only: Whether to process only data that was
signed in the id_res message from the server.
@type signed_only: bool
@rtype: SRegResponse
@returns: A simple registration response containing the data
that was supplied with the C{id_res} response.
"""
self = cls()
self.ns_uri = self._getSRegNS(success_response.message)
if signed_only:
args = success_response.getSignedNS(self.ns_uri)
else:
args = success_response.message.getArgs(self.ns_uri)
for field_name in data_fields:
if field_name in args:
self.data[field_name] = args[field_name]
return self
fromSuccessResponse = classmethod(fromSuccessResponse)
def getExtensionArgs(self):
"""Get the fields to put in the simple registration namespace
when adding them to an id_res message.
@see: openid.extension
"""
return self.data
# Read-only dictionary interface
def get(self, field_name, default=None):
"""Like dict.get, except that it checks that the field name is
defined by the simple registration specification"""
checkFieldName(field_name)
return self.data.get(field_name, default)
def items(self):
"""All of the data values in this simple registration response
"""
return self.data.items()
def iteritems(self):
return self.data.iteritems()
def keys(self):
return self.data.keys()
def iterkeys(self):
return self.data.iterkeys()
def has_key(self, key):
return key in self
def __contains__(self, field_name):
checkFieldName(field_name)
return field_name in self.data
def __iter__(self):
return iter(self.data)
def __getitem__(self, field_name):
checkFieldName(field_name)
return self.data[field_name]
def __nonzero__(self):
return bool(self.data)
########NEW FILE########
__FILENAME__ = fetchers
# -*- test-case-name: openid.test.test_fetchers -*-
"""
This module contains the HTTP fetcher interface and several implementations.
"""
__all__ = ['fetch', 'getDefaultFetcher', 'setDefaultFetcher', 'HTTPResponse',
'HTTPFetcher', 'createHTTPFetcher', 'HTTPFetchingError',
'HTTPError']
import urllib2
import time
import cStringIO
import sys
import openid
import openid.urinorm
# Try to import httplib2 for caching support
# http://bitworking.org/projects/httplib2/
try:
import httplib2
except ImportError:
# httplib2 not available
httplib2 = None
# try to import pycurl, which will let us use CurlHTTPFetcher
try:
import pycurl
except ImportError:
pycurl = None
USER_AGENT = "python-openid/%s (%s)" % (openid.__version__, sys.platform)
def fetch(url, body=None, headers=None):
"""Invoke the fetch method on the default fetcher. Most users
should need only this method.
@raises Exception: any exceptions that may be raised by the default fetcher
"""
fetcher = getDefaultFetcher()
return fetcher.fetch(url, body, headers)
def createHTTPFetcher():
"""Create a default HTTP fetcher instance
prefers Curl to urllib2."""
if pycurl is None:
fetcher = Urllib2Fetcher()
else:
fetcher = CurlHTTPFetcher()
return fetcher
# Contains the currently set HTTP fetcher. If it is set to None, the
# library will call createHTTPFetcher() to set it. Do not access this
# variable outside of this module.
_default_fetcher = None
def getDefaultFetcher():
"""Return the default fetcher instance
if no fetcher has been set, it will create a default fetcher.
@return: the default fetcher
@rtype: HTTPFetcher
"""
global _default_fetcher
if _default_fetcher is None:
setDefaultFetcher(createHTTPFetcher())
return _default_fetcher
def setDefaultFetcher(fetcher, wrap_exceptions=True):
"""Set the default fetcher
@param fetcher: The fetcher to use as the default HTTP fetcher
@type fetcher: HTTPFetcher
@param wrap_exceptions: Whether to wrap exceptions thrown by the
fetcher wil HTTPFetchingError so that they may be caught
easier. By default, exceptions will be wrapped. In general,
unwrapped fetchers are useful for debugging of fetching errors
or if your fetcher raises well-known exceptions that you would
like to catch.
@type wrap_exceptions: bool
"""
global _default_fetcher
if fetcher is None or not wrap_exceptions:
_default_fetcher = fetcher
else:
_default_fetcher = ExceptionWrappingFetcher(fetcher)
def usingCurl():
"""Whether the currently set HTTP fetcher is a Curl HTTP fetcher."""
return isinstance(getDefaultFetcher(), CurlHTTPFetcher)
class HTTPResponse(object):
"""XXX document attributes"""
headers = None
status = None
body = None
final_url = None
def __init__(self, final_url=None, status=None, headers=None, body=None):
self.final_url = final_url
self.status = status
self.headers = headers
self.body = body
def __repr__(self):
return "<%s status %s for %s>" % (self.__class__.__name__,
self.status,
self.final_url)
class HTTPFetcher(object):
"""
This class is the interface for openid HTTP fetchers. This
interface is only important if you need to write a new fetcher for
some reason.
"""
def fetch(self, url, body=None, headers=None):
"""
This performs an HTTP POST or GET, following redirects along
the way. If a body is specified, then the request will be a
POST. Otherwise, it will be a GET.
@param headers: HTTP headers to include with the request
@type headers: {str:str}
@return: An object representing the server's HTTP response. If
there are network or protocol errors, an exception will be
raised. HTTP error responses, like 404 or 500, do not
cause exceptions.
@rtype: L{HTTPResponse}
@raise Exception: Different implementations will raise
different errors based on the underlying HTTP library.
"""
raise NotImplementedError
def _allowedURL(url):
return url.startswith('http://') or url.startswith('https://')
class HTTPFetchingError(Exception):
"""Exception that is wrapped around all exceptions that are raised
by the underlying fetcher when using the ExceptionWrappingFetcher
@ivar why: The exception that caused this exception
"""
def __init__(self, why=None):
Exception.__init__(self, why)
self.why = why
class ExceptionWrappingFetcher(HTTPFetcher):
"""Fetcher that wraps another fetcher, causing all exceptions
@cvar uncaught_exceptions: Exceptions that should be exposed to the
user if they are raised by the fetch call
"""
uncaught_exceptions = (SystemExit, KeyboardInterrupt, MemoryError)
def __init__(self, fetcher):
self.fetcher = fetcher
def fetch(self, *args, **kwargs):
try:
return self.fetcher.fetch(*args, **kwargs)
except self.uncaught_exceptions:
raise
except:
import logging
logging.exception('qwert2')
exc_cls, exc_inst = sys.exc_info()[:2]
if exc_inst is None:
# string exceptions
exc_inst = exc_cls
raise HTTPFetchingError(why=exc_inst)
class Urllib2Fetcher(HTTPFetcher):
"""An C{L{HTTPFetcher}} that uses urllib2.
"""
# Parameterized for the benefit of testing frameworks, see
# http://trac.openidenabled.com/trac/ticket/85
urlopen = staticmethod(urllib2.urlopen)
def fetch(self, url, body=None, headers=None):
if not _allowedURL(url):
raise ValueError('Bad URL scheme: %r' % (url,))
if headers is None:
headers = {}
headers.setdefault(
'User-Agent',
"%s Python-urllib/%s" % (USER_AGENT, urllib2.__version__,))
req = urllib2.Request(url, data=body, headers=headers)
try:
f = self.urlopen(req)
try:
return self._makeResponse(f)
finally:
f.close()
except urllib2.HTTPError, why:
try:
return self._makeResponse(why)
finally:
why.close()
def _makeResponse(self, urllib2_response):
resp = HTTPResponse()
resp.body = urllib2_response.read()
resp.final_url = urllib2_response.geturl()
resp.headers = dict(urllib2_response.info().items())
if hasattr(urllib2_response, 'code'):
resp.status = urllib2_response.code
else:
resp.status = 200
return resp
class HTTPError(HTTPFetchingError):
"""
This exception is raised by the C{L{CurlHTTPFetcher}} when it
encounters an exceptional situation fetching a URL.
"""
pass
# XXX: define what we mean by paranoid, and make sure it is.
class CurlHTTPFetcher(HTTPFetcher):
"""
An C{L{HTTPFetcher}} that uses pycurl for fetching.
See U{http://pycurl.sourceforge.net/}.
"""
ALLOWED_TIME = 20 # seconds
def __init__(self):
HTTPFetcher.__init__(self)
if pycurl is None:
raise RuntimeError('Cannot find pycurl library')
def _parseHeaders(self, header_file):
header_file.seek(0)
# Remove the status line from the beginning of the input
unused_http_status_line = header_file.readline()
lines = [line.strip() for line in header_file]
# and the blank line from the end
empty_line = lines.pop()
if empty_line:
raise HTTPError("No blank line at end of headers: %r" % (line,))
headers = {}
for line in lines:
try:
name, value = line.split(':', 1)
except ValueError:
raise HTTPError(
"Malformed HTTP header line in response: %r" % (line,))
value = value.strip()
# HTTP headers are case-insensitive
name = name.lower()
headers[name] = value
return headers
def _checkURL(self, url):
# XXX: document that this can be overridden to match desired policy
# XXX: make sure url is well-formed and routeable
return _allowedURL(url)
def fetch(self, url, body=None, headers=None):
stop = int(time.time()) + self.ALLOWED_TIME
off = self.ALLOWED_TIME
if headers is None:
headers = {}
headers.setdefault('User-Agent',
"%s %s" % (USER_AGENT, pycurl.version,))
header_list = []
if headers is not None:
for header_name, header_value in headers.iteritems():
header_list.append('%s: %s' % (header_name, header_value))
c = pycurl.Curl()
try:
c.setopt(pycurl.NOSIGNAL, 1)
if header_list:
c.setopt(pycurl.HTTPHEADER, header_list)
# Presence of a body indicates that we should do a POST
if body is not None:
c.setopt(pycurl.POST, 1)
c.setopt(pycurl.POSTFIELDS, body)
while off > 0:
if not self._checkURL(url):
raise HTTPError("Fetching URL not allowed: %r" % (url,))
data = cStringIO.StringIO()
response_header_data = cStringIO.StringIO()
c.setopt(pycurl.WRITEFUNCTION, data.write)
c.setopt(pycurl.HEADERFUNCTION, response_header_data.write)
c.setopt(pycurl.TIMEOUT, off)
c.setopt(pycurl.URL, openid.urinorm.urinorm(url))
c.perform()
response_headers = self._parseHeaders(response_header_data)
code = c.getinfo(pycurl.RESPONSE_CODE)
if code in [301, 302, 303, 307]:
url = response_headers.get('location')
if url is None:
raise HTTPError(
'Redirect (%s) returned without a location' % code)
# Redirects are always GETs
c.setopt(pycurl.POST, 0)
# There is no way to reset POSTFIELDS to empty and
# reuse the connection, but we only use it once.
else:
resp = HTTPResponse()
resp.headers = response_headers
resp.status = code
resp.final_url = url
resp.body = data.getvalue()
return resp
off = stop - int(time.time())
raise HTTPError("Timed out fetching: %r" % (url,))
finally:
c.close()
class HTTPLib2Fetcher(HTTPFetcher):
"""A fetcher that uses C{httplib2} for performing HTTP
requests. This implementation supports HTTP caching.
@see: http://bitworking.org/projects/httplib2/
"""
def __init__(self, cache=None):
"""@param cache: An object suitable for use as an C{httplib2}
cache. If a string is passed, it is assumed to be a
directory name.
"""
if httplib2 is None:
raise RuntimeError('Cannot find httplib2 library. '
'See http://bitworking.org/projects/httplib2/')
super(HTTPLib2Fetcher, self).__init__()
# An instance of the httplib2 object that performs HTTP requests
self.httplib2 = httplib2.Http(cache)
# We want httplib2 to raise exceptions for errors, just like
# the other fetchers.
self.httplib2.force_exception_to_status_code = False
def fetch(self, url, body=None, headers=None):
"""Perform an HTTP request
@raises Exception: Any exception that can be raised by httplib2
@see: C{L{HTTPFetcher.fetch}}
"""
if body:
method = 'POST'
else:
method = 'GET'
# httplib2 doesn't check to make sure that the URL's scheme is
# 'http' so we do it here.
if not (url.startswith('http://') or url.startswith('https://')):
raise ValueError('URL is not a HTTP URL: %r' % (url,))
httplib2_response, content = self.httplib2.request(
url, method, body=body, headers=headers)
# Translate the httplib2 response to our HTTP response abstraction
# When a 400 is returned, there is no "content-location"
# header set. This seems like a bug to me. I can't think of a
# case where we really care about the final URL when it is an
# error response, but being careful about it can't hurt.
try:
final_url = httplib2_response['content-location']
except KeyError:
# We're assuming that no redirects occurred
assert not httplib2_response.previous
# And this should never happen for a successful response
assert httplib2_response.status != 200
final_url = url
return HTTPResponse(
body=content,
final_url=final_url,
headers=dict(httplib2_response.items()),
status=httplib2_response.status,
)
########NEW FILE########
__FILENAME__ = kvform
__all__ = ['seqToKV', 'kvToSeq', 'dictToKV', 'kvToDict']
from openid import oidutil
import types
def seqToKV(seq, strict=False):
"""Represent a sequence of pairs of strings as newline-terminated
key:value pairs. The pairs are generated in the order given.
@param seq: The pairs
@type seq: [(str, (unicode|str))]
@return: A string representation of the sequence
@rtype: str
"""
def err(msg):
formatted = 'seqToKV warning: %s: %r' % (msg, seq)
if strict:
raise ValueError(formatted)
else:
oidutil.log(formatted)
lines = []
for k, v in seq:
if isinstance(k, types.StringType):
k = k.decode('UTF8')
elif not isinstance(k, types.UnicodeType):
err('Converting key to string: %r' % k)
k = str(k)
if '\n' in k:
raise ValueError(
'Invalid input for seqToKV: key contains newline: %r' % (k,))
if ':' in k:
raise ValueError(
'Invalid input for seqToKV: key contains colon: %r' % (k,))
if k.strip() != k:
err('Key has whitespace at beginning or end: %r' % k)
if isinstance(v, types.StringType):
v = v.decode('UTF8')
elif not isinstance(v, types.UnicodeType):
err('Converting value to string: %r' % v)
v = str(v)
if '\n' in v:
raise ValueError(
'Invalid input for seqToKV: value contains newline: %r' % (v,))
if v.strip() != v:
err('Value has whitespace at beginning or end: %r' % v)
lines.append(k + ':' + v + '\n')
return ''.join(lines).encode('UTF8')
def kvToSeq(data, strict=False):
"""
After one parse, seqToKV and kvToSeq are inverses, with no warnings::
seq = kvToSeq(s)
seqToKV(kvToSeq(seq)) == seq
"""
def err(msg):
formatted = 'kvToSeq warning: %s: %r' % (msg, data)
if strict:
raise ValueError(formatted)
else:
oidutil.log(formatted)
lines = data.split('\n')
if lines[-1]:
err('Does not end in a newline')
else:
del lines[-1]
pairs = []
line_num = 0
for line in lines:
line_num += 1
# Ignore blank lines
if not line.strip():
continue
pair = line.split(':', 1)
if len(pair) == 2:
k, v = pair
k_s = k.strip()
if k_s != k:
fmt = ('In line %d, ignoring leading or trailing '
'whitespace in key %r')
err(fmt % (line_num, k))
if not k_s:
err('In line %d, got empty key' % (line_num,))
v_s = v.strip()
if v_s != v:
fmt = ('In line %d, ignoring leading or trailing '
'whitespace in value %r')
err(fmt % (line_num, v))
pairs.append((k_s.decode('UTF8'), v_s.decode('UTF8')))
else:
err('Line %d does not contain a colon' % line_num)
return pairs
def dictToKV(d):
seq = d.items()
seq.sort()
return seqToKV(seq)
def kvToDict(s):
return dict(kvToSeq(s))
########NEW FILE########
__FILENAME__ = message
"""Extension argument processing code
"""
__all__ = ['Message', 'NamespaceMap', 'no_default', 'registerNamespaceAlias',
'OPENID_NS', 'BARE_NS', 'OPENID1_NS', 'OPENID2_NS', 'SREG_URI',
'IDENTIFIER_SELECT']
import copy
import warnings
import urllib
from openid import oidutil
from openid import kvform
try:
ElementTree = oidutil.importElementTree()
except ImportError:
# No elementtree found, so give up, but don't fail to import,
# since we have fallbacks.
ElementTree = None
# This doesn't REALLY belong here, but where is better?
IDENTIFIER_SELECT = 'http://specs.openid.net/auth/2.0/identifier_select'
# URI for Simple Registration extension, the only commonly deployed
# OpenID 1.x extension, and so a special case
SREG_URI = 'http://openid.net/sreg/1.0'
# The OpenID 1.X namespace URI
OPENID1_NS = 'http://openid.net/signon/1.0'
# The OpenID 2.0 namespace URI
OPENID2_NS = 'http://specs.openid.net/auth/2.0'
# The namespace consisting of pairs with keys that are prefixed with
# "openid." but not in another namespace.
NULL_NAMESPACE = oidutil.Symbol('Null namespace')
# The null namespace, when it is an allowed OpenID namespace
OPENID_NS = oidutil.Symbol('OpenID namespace')
# The top-level namespace, excluding all pairs with keys that start
# with "openid."
BARE_NS = oidutil.Symbol('Bare namespace')
# Limit, in bytes, of identity provider and return_to URLs, including
# response payload. See OpenID 1.1 specification, Appendix D.
OPENID1_URL_LIMIT = 2047
# All OpenID protocol fields. Used to check namespace aliases.
OPENID_PROTOCOL_FIELDS = [
'ns', 'mode', 'error', 'return_to', 'contact', 'reference',
'signed', 'assoc_type', 'session_type', 'dh_modulus', 'dh_gen',
'dh_consumer_public', 'claimed_id', 'identity', 'realm',
'invalidate_handle', 'op_endpoint', 'response_nonce', 'sig',
'assoc_handle', 'trust_root', 'openid',
]
class UndefinedOpenIDNamespace(ValueError):
"""Raised if the generic OpenID namespace is accessed when there
is no OpenID namespace set for this message."""
# Sentinel used for Message implementation to indicate that getArg
# should raise an exception instead of returning a default.
no_default = object()
# Global namespace / alias registration map. See
# registerNamespaceAlias.
registered_aliases = {}
class NamespaceAliasRegistrationError(Exception):
"""
Raised when an alias or namespace URI has already been registered.
"""
pass
def registerNamespaceAlias(namespace_uri, alias):
"""
Registers a (namespace URI, alias) mapping in a global namespace
alias map. Raises NamespaceAliasRegistrationError if either the
namespace URI or alias has already been registered with a
different value. This function is required if you want to use a
namespace with an OpenID 1 message.
"""
global registered_aliases
if registered_aliases.get(alias) == namespace_uri:
return
if namespace_uri in registered_aliases.values():
raise NamespaceAliasRegistrationError, \
'Namespace uri %r already registered' % (namespace_uri,)
if alias in registered_aliases:
raise NamespaceAliasRegistrationError, \
'Alias %r already registered' % (alias,)
registered_aliases[alias] = namespace_uri
class Message(object):
"""
In the implementation of this object, None represents the global
namespace as well as a namespace with no key.
@cvar namespaces: A dictionary specifying specific
namespace-URI to alias mappings that should be used when
generating namespace aliases.
@ivar ns_args: two-level dictionary of the values in this message,
grouped by namespace URI. The first level is the namespace
URI.
"""
allowed_openid_namespaces = [OPENID1_NS, OPENID2_NS]
def __init__(self, openid_namespace=None):
"""Create an empty Message"""
self.args = {}
self.namespaces = NamespaceMap()
if openid_namespace is None:
self._openid_ns_uri = None
else:
self.setOpenIDNamespace(openid_namespace)
def fromPostArgs(cls, args):
"""Construct a Message containing a set of POST arguments"""
self = cls()
# Partition into "openid." args and bare args
openid_args = {}
for key, value in args.iteritems():
if isinstance(value, list):
raise TypeError("query dict must have one value for each key, "
"not lists of values. Query is %r" % (args,))
try:
prefix, rest = key.split('.', 1)
except ValueError:
prefix = None
if prefix != 'openid':
self.args[(BARE_NS, key)] = value
else:
openid_args[rest] = value
self._fromOpenIDArgs(openid_args)
return self
fromPostArgs = classmethod(fromPostArgs)
def fromOpenIDArgs(cls, openid_args):
"""Construct a Message from a parsed KVForm message"""
self = cls()
self._fromOpenIDArgs(openid_args)
return self
fromOpenIDArgs = classmethod(fromOpenIDArgs)
def _fromOpenIDArgs(self, openid_args):
global registered_aliases
ns_args = []
# Resolve namespaces
for rest, value in openid_args.iteritems():
try:
ns_alias, ns_key = rest.split('.', 1)
except ValueError:
ns_alias = NULL_NAMESPACE
ns_key = rest
if ns_alias == 'ns':
self.namespaces.addAlias(value, ns_key)
elif ns_alias == NULL_NAMESPACE and ns_key == 'ns':
# null namespace
self.namespaces.addAlias(value, NULL_NAMESPACE)
else:
ns_args.append((ns_alias, ns_key, value))
# Ensure that there is an OpenID namespace definition
openid_ns_uri = self.namespaces.getNamespaceURI(NULL_NAMESPACE)
if openid_ns_uri is None:
openid_ns_uri = OPENID1_NS
self.setOpenIDNamespace(openid_ns_uri)
# Actually put the pairs into the appropriate namespaces
for (ns_alias, ns_key, value) in ns_args:
ns_uri = self.namespaces.getNamespaceURI(ns_alias)
if ns_uri is None:
# Only try to map an alias to a default if it's an
# OpenID 1.x message.
if openid_ns_uri == OPENID1_NS:
for _alias, _uri in registered_aliases.iteritems():
if _alias == ns_alias:
ns_uri = _uri
break
if ns_uri is None:
ns_uri = openid_ns_uri
ns_key = '%s.%s' % (ns_alias, ns_key)
else:
self.namespaces.addAlias(ns_uri, ns_alias)
self.setArg(ns_uri, ns_key, value)
def setOpenIDNamespace(self, openid_ns_uri):
if openid_ns_uri not in self.allowed_openid_namespaces:
raise ValueError('Invalid null namespace: %r' % (openid_ns_uri,))
self.namespaces.addAlias(openid_ns_uri, NULL_NAMESPACE)
self._openid_ns_uri = openid_ns_uri
def getOpenIDNamespace(self):
return self._openid_ns_uri
def isOpenID1(self):
return self.getOpenIDNamespace() == OPENID1_NS
def isOpenID2(self):
return self.getOpenIDNamespace() == OPENID2_NS
def fromKVForm(cls, kvform_string):
"""Create a Message from a KVForm string"""
return cls.fromOpenIDArgs(kvform.kvToDict(kvform_string))
fromKVForm = classmethod(fromKVForm)
def copy(self):
return copy.deepcopy(self)
def toPostArgs(self):
"""Return all arguments with openid. in front of namespaced arguments.
"""
args = {}
# Add namespace definitions to the output
for ns_uri, alias in self.namespaces.iteritems():
if alias == NULL_NAMESPACE:
if ns_uri != OPENID1_NS:
args['openid.ns'] = ns_uri
else:
# drop the default null namespace definition. This
# potentially changes a message since we have no
# way of knowing whether it was explicitly
# specified at the time the message was
# parsed. The vast majority of the time, this will
# be the right thing to do. Possibly this could
# look in the signed list.
pass
else:
if self.getOpenIDNamespace() != OPENID1_NS:
ns_key = 'openid.ns.' + alias
args[ns_key] = ns_uri
for (ns_uri, ns_key), value in self.args.iteritems():
key = self.getKey(ns_uri, ns_key)
args[key] = value
return args
def toArgs(self):
"""Return all namespaced arguments, failing if any
non-namespaced arguments exist."""
# FIXME - undocumented exception
post_args = self.toPostArgs()
kvargs = {}
for k, v in post_args.iteritems():
if not k.startswith('openid.'):
raise ValueError(
'This message can only be encoded as a POST, because it '
'contains arguments that are not prefixed with "openid."')
else:
kvargs[k[7:]] = v
return kvargs
def toFormMarkup(self, action_url, form_tag_attrs=None,
submit_text="Continue"):
"""Generate HTML form markup that contains the values in this
message, to be HTTP POSTed as x-www-form-urlencoded UTF-8.
@param action_url: The URL to which the form will be POSTed
@type action_url: str
@param form_tag_attrs: Dictionary of attributes to be added to
the form tag. 'accept-charset' and 'enctype' have defaults
that can be overridden. If a value is supplied for
'action' or 'method', it will be replaced.
@type form_tag_attrs: {unicode: unicode}
@param submit_text: The text that will appear on the submit
button for this form.
@type submit_text: unicode
@returns: A string containing (X)HTML markup for a form that
encodes the values in this Message object.
@rtype: str or unicode
"""
if ElementTree is None:
raise RuntimeError('This function requires ElementTree.')
form = ElementTree.Element('form')
if form_tag_attrs:
for name, attr in form_tag_attrs.iteritems():
form.attrib[name] = attr
form.attrib['action'] = action_url
form.attrib['method'] = 'post'
form.attrib['accept-charset'] = 'UTF-8'
form.attrib['enctype'] = 'application/x-www-form-urlencoded'
for name, value in self.toPostArgs().iteritems():
attrs = {'type': 'hidden',
'name': name,
'value': value}
form.append(ElementTree.Element('input', attrs))
submit = ElementTree.Element(
'input', {'type':'submit', 'value':submit_text})
form.append(submit)
return ElementTree.tostring(form)
def toURL(self, base_url):
"""Generate a GET URL with the parameters in this message
attached as query parameters."""
return oidutil.appendArgs(base_url, self.toPostArgs())
def toKVForm(self):
"""Generate a KVForm string that contains the parameters in
this message. This will fail if the message contains arguments
outside of the 'openid.' prefix.
"""
return kvform.dictToKV(self.toArgs())
def toURLEncoded(self):
"""Generate an x-www-urlencoded string"""
args = self.toPostArgs().items()
args.sort()
return urllib.urlencode(args)
def _fixNS(self, namespace):
"""Convert an input value into the internally used values of
this object
@param namespace: The string or constant to convert
@type namespace: str or unicode or BARE_NS or OPENID_NS
"""
if namespace == OPENID_NS:
if self._openid_ns_uri is None:
raise UndefinedOpenIDNamespace('OpenID namespace not set')
else:
namespace = self._openid_ns_uri
if namespace != BARE_NS and type(namespace) not in [str, unicode]:
raise TypeError(
"Namespace must be BARE_NS, OPENID_NS or a string. got %r"
% (namespace,))
if namespace != BARE_NS and ':' not in namespace:
fmt = 'OpenID 2.0 namespace identifiers SHOULD be URIs. Got %r'
warnings.warn(fmt % (namespace,), DeprecationWarning)
if namespace == 'sreg':
fmt = 'Using %r instead of "sreg" as namespace'
warnings.warn(fmt % (SREG_URI,), DeprecationWarning,)
return SREG_URI
return namespace
def hasKey(self, namespace, ns_key):
namespace = self._fixNS(namespace)
return (namespace, ns_key) in self.args
def getKey(self, namespace, ns_key):
"""Get the key for a particular namespaced argument"""
namespace = self._fixNS(namespace)
if namespace == BARE_NS:
return ns_key
ns_alias = self.namespaces.getAlias(namespace)
# No alias is defined, so no key can exist
if ns_alias is None:
return None
if ns_alias == NULL_NAMESPACE:
tail = ns_key
else:
tail = '%s.%s' % (ns_alias, ns_key)
return 'openid.' + tail
def getArg(self, namespace, key, default=None):
"""Get a value for a namespaced key.
@param namespace: The namespace in the message for this key
@type namespace: str
@param key: The key to get within this namespace
@type key: str
@param default: The value to use if this key is absent from
this message. Using the special value
openid.message.no_default will result in this method
raising a KeyError instead of returning the default.
@rtype: str or the type of default
@raises KeyError: if default is no_default
@raises UndefinedOpenIDNamespace: if the message has not yet
had an OpenID namespace set
"""
namespace = self._fixNS(namespace)
args_key = (namespace, key)
try:
return self.args[args_key]
except KeyError:
if default is no_default:
raise KeyError((namespace, key))
else:
return default
def getArgs(self, namespace):
"""Get the arguments that are defined for this namespace URI
@returns: mapping from namespaced keys to values
@returntype: dict
"""
namespace = self._fixNS(namespace)
return dict([
(ns_key, value)
for ((pair_ns, ns_key), value)
in self.args.iteritems()
if pair_ns == namespace
])
def updateArgs(self, namespace, updates):
"""Set multiple key/value pairs in one call
@param updates: The values to set
@type updates: {unicode:unicode}
"""
namespace = self._fixNS(namespace)
for k, v in updates.iteritems():
self.setArg(namespace, k, v)
def setArg(self, namespace, key, value):
"""Set a single argument in this namespace"""
assert key is not None
assert value is not None
namespace = self._fixNS(namespace)
self.args[(namespace, key)] = value
if not (namespace is BARE_NS):
self.namespaces.add(namespace)
def delArg(self, namespace, key):
namespace = self._fixNS(namespace)
del self.args[(namespace, key)]
def __repr__(self):
return "<%s.%s %r>" % (self.__class__.__module__,
self.__class__.__name__,
self.args)
def __eq__(self, other):
return self.args == other.args
def __ne__(self, other):
return not (self == other)
def getAliasedArg(self, aliased_key, default=None):
if aliased_key == 'ns':
return self.getOpenIDNamespace()
if aliased_key.startswith('ns.'):
uri = self.namespaces.getNamespaceURI(aliased_key[3:])
if uri is None:
return default
else:
return uri
try:
alias, key = aliased_key.split('.', 1)
except ValueError:
# need more than x values to unpack
ns = None
else:
ns = self.namespaces.getNamespaceURI(alias)
if ns is None:
key = aliased_key
ns = self.getOpenIDNamespace()
return self.getArg(ns, key, default)
class NamespaceMap(object):
"""Maintains a bijective map between namespace uris and aliases.
"""
def __init__(self):
self.alias_to_namespace = {}
self.namespace_to_alias = {}
def getAlias(self, namespace_uri):
return self.namespace_to_alias.get(namespace_uri)
def getNamespaceURI(self, alias):
return self.alias_to_namespace.get(alias)
def iterNamespaceURIs(self):
"""Return an iterator over the namespace URIs"""
return iter(self.namespace_to_alias)
def iterAliases(self):
"""Return an iterator over the aliases"""
return iter(self.alias_to_namespace)
def iteritems(self):
"""Iterate over the mapping
@returns: iterator of (namespace_uri, alias)
"""
return self.namespace_to_alias.iteritems()
def addAlias(self, namespace_uri, desired_alias):
"""Add an alias from this namespace URI to the desired alias
"""
# Check that desired_alias is not an openid protocol field as
# per the spec.
assert desired_alias not in OPENID_PROTOCOL_FIELDS, \
"%r is not an allowed namespace alias" % (desired_alias,)
# Check that desired_alias does not contain a period as per
# the spec.
if type(desired_alias) in [str, unicode]:
assert '.' not in desired_alias, \
"%r must not contain a dot" % (desired_alias,)
# Check that there is not a namespace already defined for
# the desired alias
current_namespace_uri = self.alias_to_namespace.get(desired_alias)
if (current_namespace_uri is not None
and current_namespace_uri != namespace_uri):
fmt = ('Cannot map %r to alias %r. '
'%r is already mapped to alias %r')
msg = fmt % (
namespace_uri,
desired_alias,
current_namespace_uri,
desired_alias)
raise KeyError(msg)
# Check that there is not already a (different) alias for
# this namespace URI
alias = self.namespace_to_alias.get(namespace_uri)
if alias is not None and alias != desired_alias:
fmt = ('Cannot map %r to alias %r. '
'It is already mapped to alias %r')
raise KeyError(fmt % (namespace_uri, desired_alias, alias))
assert (desired_alias == NULL_NAMESPACE or
type(desired_alias) in [str, unicode]), repr(desired_alias)
self.alias_to_namespace[desired_alias] = namespace_uri
self.namespace_to_alias[namespace_uri] = desired_alias
return desired_alias
def add(self, namespace_uri):
"""Add this namespace URI to the mapping, without caring what
alias it ends up with"""
# See if this namespace is already mapped to an alias
alias = self.namespace_to_alias.get(namespace_uri)
if alias is not None:
return alias
# Fall back to generating a numerical alias
i = 0
while True:
alias = 'ext' + str(i)
try:
self.addAlias(namespace_uri, alias)
except KeyError:
i += 1
else:
return alias
assert False, "Not reached"
def isDefined(self, namespace_uri):
return namespace_uri in self.namespace_to_alias
def __contains__(self, namespace_uri):
return self.isDefined(namespace_uri)
########NEW FILE########
__FILENAME__ = oidutil
"""This module contains general utility code that is used throughout
the library.
For users of this library, the C{L{log}} function is probably the most
interesting.
"""
__all__ = ['log', 'appendArgs', 'toBase64', 'fromBase64']
import binascii
import sys
import urlparse
from urllib import urlencode
elementtree_modules = [
'lxml.etree',
'xml.etree.cElementTree',
'xml.etree.ElementTree',
'cElementTree',
'elementtree.ElementTree',
]
def importElementTree(module_names=None):
"""Find a working ElementTree implementation, trying the standard
places that such a thing might show up.
>>> ElementTree = importElementTree()
@param module_names: The names of modules to try to use as
ElementTree. Defaults to C{L{elementtree_modules}}
@returns: An ElementTree module
"""
if module_names is None:
module_names = elementtree_modules
for mod_name in module_names:
try:
ElementTree = __import__(mod_name, None, None, ['unused'])
except ImportError:
pass
else:
# Make sure it can actually parse XML
try:
ElementTree.XML('<unused/>')
except (SystemExit, MemoryError, AssertionError):
raise
except:
why = sys.exc_info()[1]
log('Not using ElementTree library %r because it failed to '
'parse a trivial document: %s' % (mod_name, why))
else:
return ElementTree
else:
raise
def log(message, level=0):
"""Handle a log message from the OpenID library.
This implementation writes the string it to C{sys.stderr},
followed by a newline.
Currently, the library does not use the second parameter to this
function, but that may change in the future.
To install your own logging hook::
from openid import oidutil
def myLoggingFunction(message, level):
...
oidutil.log = myLoggingFunction
@param message: A string containing a debugging message from the
OpenID library
@type message: str
@param level: The severity of the log message. This parameter is
currently unused, but in the future, the library may indicate
more important information with a higher level value.
@type level: int or None
@returns: Nothing.
"""
sys.stderr.write(message)
sys.stderr.write('\n')
def appendArgs(url, args):
"""Append query arguments to a HTTP(s) URL. If the URL already has
query arguemtns, these arguments will be added, and the existing
arguments will be preserved. Duplicate arguments will not be
detected or collapsed (both will appear in the output).
@param url: The url to which the arguments will be appended
@type url: str
@param args: The query arguments to add to the URL. If a
dictionary is passed, the items will be sorted before
appending them to the URL. If a sequence of pairs is passed,
the order of the sequence will be preserved.
@type args: A dictionary from string to string, or a sequence of
pairs of strings.
@returns: The URL with the parameters added
@rtype: str
"""
if hasattr(args, 'items'):
args = args.items()
args.sort()
else:
args = list(args)
if len(args) == 0:
return url
if '?' in url:
sep = '&'
else:
sep = '?'
# Map unicode to UTF-8 if present. Do not make any assumptions
# about the encodings of plain bytes (str).
i = 0
for k, v in args:
if type(k) is not str:
k = k.encode('UTF-8')
if type(v) is not str:
v = v.encode('UTF-8')
args[i] = (k, v)
i += 1
return '%s%s%s' % (url, sep, urlencode(args))
def toBase64(s):
"""Represent string s as base64, omitting newlines"""
return binascii.b2a_base64(s)[:-1]
def fromBase64(s):
try:
return binascii.a2b_base64(s)
except binascii.Error, why:
# Convert to a common exception type
raise ValueError(why[0])
def isAbsoluteHTTPURL(url):
"""Does this URL look like a http or https URL that has a host?
@param url: The url to check
@type url: str
@return: Whether the URL looks OK
@rtype: bool
"""
parts = urlparse.urlparse(url)
return parts[0] in ['http', 'https'] and parts[1]
class Symbol(object):
"""This class implements an object that compares equal to others
of the same type that have the same name. These are distict from
str or unicode objects.
"""
def __init__(self, name):
self.name = name
def __eq__(self, other):
return type(self) is type(other) and self.name == other.name
def __ne__(self, other):
return not (self == other)
def __hash__(self):
return hash((self.__class__, self.name))
def __repr__(self):
return '<Symbol %s>' % (self.name,)
########NEW FILE########
__FILENAME__ = sreg
"""moved to L{openid.extensions.sreg}"""
import warnings
warnings.warn("openid.sreg has moved to openid.extensions.sreg",
DeprecationWarning)
from openid.extensions.sreg import *
########NEW FILE########
__FILENAME__ = filestore
"""
This module contains an C{L{OpenIDStore}} implementation backed by
flat files.
"""
import string
import os
import os.path
import time
from errno import EEXIST, ENOENT
try:
from tempfile import mkstemp
except ImportError:
# Python < 2.3
import warnings
warnings.filterwarnings("ignore",
"tempnam is a potential security risk",
RuntimeWarning,
"openid.store.filestore")
def mkstemp(dir):
for _ in range(5):
name = os.tempnam(dir)
try:
fd = os.open(name, os.O_CREAT | os.O_EXCL | os.O_RDWR, 0600)
except OSError, why:
if why.errno != EEXIST:
raise
else:
return fd, name
raise RuntimeError('Failed to get temp file after 5 attempts')
from openid.association import Association
from openid.store.interface import OpenIDStore
from openid.store import nonce
from openid import cryptutil, oidutil
_filename_allowed = string.ascii_letters + string.digits + '.'
try:
# 2.4
set
except NameError:
try:
# 2.3
import sets
except ImportError:
# Python < 2.2
d = {}
for c in _filename_allowed:
d[c] = None
_isFilenameSafe = d.has_key
del d
else:
_isFilenameSafe = sets.Set(_filename_allowed).__contains__
else:
_isFilenameSafe = set(_filename_allowed).__contains__
def _safe64(s):
h64 = oidutil.toBase64(cryptutil.sha1(s))
h64 = h64.replace('+', '_')
h64 = h64.replace('/', '.')
h64 = h64.replace('=', '')
return h64
def _filenameEscape(s):
filename_chunks = []
for c in s:
if _isFilenameSafe(c):
filename_chunks.append(c)
else:
filename_chunks.append('_%02X' % ord(c))
return ''.join(filename_chunks)
def _removeIfPresent(filename):
"""Attempt to remove a file, returning whether the file existed at
the time of the call.
str -> bool
"""
try:
os.unlink(filename)
except OSError, why:
if why.errno == ENOENT:
# Someone beat us to it, but it's gone, so that's OK
return 0
else:
raise
else:
# File was present
return 1
def _ensureDir(dir_name):
"""Create dir_name as a directory if it does not exist. If it
exists, make sure that it is, in fact, a directory.
Can raise OSError
str -> NoneType
"""
try:
os.makedirs(dir_name)
except OSError, why:
if why.errno != EEXIST or not os.path.isdir(dir_name):
raise
class FileOpenIDStore(OpenIDStore):
"""
This is a filesystem-based store for OpenID associations and
nonces. This store should be safe for use in concurrent systems
on both windows and unix (excluding NFS filesystems). There are a
couple race conditions in the system, but those failure cases have
been set up in such a way that the worst-case behavior is someone
having to try to log in a second time.
Most of the methods of this class are implementation details.
People wishing to just use this store need only pay attention to
the C{L{__init__}} method.
Methods of this object can raise OSError if unexpected filesystem
conditions, such as bad permissions or missing directories, occur.
"""
def __init__(self, directory):
"""
Initializes a new FileOpenIDStore. This initializes the
nonce and association directories, which are subdirectories of
the directory passed in.
@param directory: This is the directory to put the store
directories in.
@type directory: C{str}
"""
# Make absolute
directory = os.path.normpath(os.path.abspath(directory))
self.nonce_dir = os.path.join(directory, 'nonces')
self.association_dir = os.path.join(directory, 'associations')
# Temp dir must be on the same filesystem as the assciations
# directory
self.temp_dir = os.path.join(directory, 'temp')
self.max_nonce_age = 6 * 60 * 60 # Six hours, in seconds
self._setup()
def _setup(self):
"""Make sure that the directories in which we store our data
exist.
() -> NoneType
"""
_ensureDir(self.nonce_dir)
_ensureDir(self.association_dir)
_ensureDir(self.temp_dir)
def _mktemp(self):
"""Create a temporary file on the same filesystem as
self.association_dir.
The temporary directory should not be cleaned if there are any
processes using the store. If there is no active process using
the store, it is safe to remove all of the files in the
temporary directory.
() -> (file, str)
"""
fd, name = mkstemp(dir=self.temp_dir)
try:
file_obj = os.fdopen(fd, 'wb')
return file_obj, name
except:
_removeIfPresent(name)
raise
def getAssociationFilename(self, server_url, handle):
"""Create a unique filename for a given server url and
handle. This implementation does not assume anything about the
format of the handle. The filename that is returned will
contain the domain name from the server URL for ease of human
inspection of the data directory.
(str, str) -> str
"""
if server_url.find('://') == -1:
raise ValueError('Bad server URL: %r' % server_url)
proto, rest = server_url.split('://', 1)
domain = _filenameEscape(rest.split('/', 1)[0])
url_hash = _safe64(server_url)
if handle:
handle_hash = _safe64(handle)
else:
handle_hash = ''
filename = '%s-%s-%s-%s' % (proto, domain, url_hash, handle_hash)
oidutil.log('filename for %s %s is %s' % (server_url, handle, filename))
return os.path.join(self.association_dir, filename)
def storeAssociation(self, server_url, association):
"""Store an association in the association directory.
(str, Association) -> NoneType
"""
association_s = association.serialize()
filename = self.getAssociationFilename(server_url, association.handle)
tmp_file, tmp = self._mktemp()
try:
try:
tmp_file.write(association_s)
os.fsync(tmp_file.fileno())
finally:
tmp_file.close()
try:
os.rename(tmp, filename)
except OSError, why:
if why.errno != EEXIST:
raise
# We only expect EEXIST to happen only on Windows. It's
# possible that we will succeed in unlinking the existing
# file, but not in putting the temporary file in place.
try:
os.unlink(filename)
except OSError, why:
if why.errno == ENOENT:
pass
else:
raise
# Now the target should not exist. Try renaming again,
# giving up if it fails.
os.rename(tmp, filename)
except:
# If there was an error, don't leave the temporary file
# around.
_removeIfPresent(tmp)
raise
def getAssociation(self, server_url, handle=None):
"""Retrieve an association. If no handle is specified, return
the association with the latest expiration.
(str, str or NoneType) -> Association or NoneType
"""
oidutil.log('getting association %s for url %s' % (handle, server_url))
if handle is None:
handle = ''
# The filename with the empty handle is a prefix of all other
# associations for the given server URL.
filename = self.getAssociationFilename(server_url, handle)
if handle:
return self._getAssociation(filename)
else:
association_files = os.listdir(self.association_dir)
matching_files = []
# strip off the path to do the comparison
name = os.path.basename(filename)
for association_file in association_files:
if association_file.startswith(name):
matching_files.append(association_file)
matching_associations = []
# read the matching files and sort by time issued
for name in matching_files:
full_name = os.path.join(self.association_dir, name)
association = self._getAssociation(full_name)
if association is not None:
matching_associations.append(
(association.issued, association))
matching_associations.sort()
# return the most recently issued one.
if matching_associations:
(_, assoc) = matching_associations[-1]
return assoc
else:
return None
def _getAssociation(self, filename):
oidutil.log('getting association from file %s' % filename)
try:
assoc_file = file(filename, 'rb')
except IOError, why:
if why.errno == ENOENT:
# No association exists for that URL and handle
return None
else:
raise
else:
try:
assoc_s = assoc_file.read()
finally:
assoc_file.close()
try:
association = Association.deserialize(assoc_s)
oidutil.log('got association %s' % association)
except ValueError:
_removeIfPresent(filename)
return None
# Clean up expired associations
if association.getExpiresIn() == 0:
_removeIfPresent(filename)
oidutil.log('association expired')
return None
else:
return association
def removeAssociation(self, server_url, handle):
"""Remove an association if it exists. Do nothing if it does not.
(str, str) -> bool
"""
assoc = self.getAssociation(server_url, handle)
if assoc is None:
return 0
else:
filename = self.getAssociationFilename(server_url, handle)
return _removeIfPresent(filename)
def useNonce(self, server_url, timestamp, salt):
"""Return whether this nonce is valid.
str -> bool
"""
if abs(timestamp - time.time()) > nonce.SKEW:
return False
if server_url:
proto, rest = server_url.split('://', 1)
else:
# Create empty proto / rest values for empty server_url,
# which is part of a consumer-generated nonce.
proto, rest = '', ''
domain = _filenameEscape(rest.split('/', 1)[0])
url_hash = _safe64(server_url)
salt_hash = _safe64(salt)
filename = '%08x-%s-%s-%s-%s' % (timestamp, proto, domain,
url_hash, salt_hash)
filename = os.path.join(self.nonce_dir, filename)
try:
fd = os.open(filename, os.O_CREAT | os.O_EXCL | os.O_WRONLY, 0200)
except OSError, why:
if why.errno == EEXIST:
return False
else:
raise
else:
os.close(fd)
return True
def _allAssocs(self):
all_associations = []
association_filenames = map(
lambda filename: os.path.join(self.association_dir, filename),
os.listdir(self.association_dir))
for association_filename in association_filenames:
try:
association_file = file(association_filename, 'rb')
except IOError, why:
if why.errno == ENOENT:
oidutil.log("%s disappeared during %s._allAssocs" % (
association_filename, self.__class__.__name__))
else:
raise
else:
try:
assoc_s = association_file.read()
finally:
association_file.close()
# Remove expired or corrupted associations
try:
association = Association.deserialize(assoc_s)
except ValueError:
_removeIfPresent(association_filename)
else:
all_associations.append(
(association_filename, association))
return all_associations
def cleanup(self):
"""Remove expired entries from the database. This is
potentially expensive, so only run when it is acceptable to
take time.
() -> NoneType
"""
self.cleanupAssociations()
self.cleanupNonces()
def cleanupAssociations(self):
removed = 0
for assoc_filename, assoc in self._allAssocs():
if assoc.getExpiresIn() == 0:
_removeIfPresent(assoc_filename)
removed += 1
return removed
def cleanupNonces(self):
nonces = os.listdir(self.nonce_dir)
now = time.time()
removed = 0
# Check all nonces for expiry
for nonce_fname in nonces:
timestamp = nonce_fname.split('-', 1)[0]
timestamp = int(timestamp, 16)
if abs(timestamp - now) > nonce.SKEW:
filename = os.path.join(self.nonce_dir, nonce_fname)
_removeIfPresent(filename)
removed += 1
return removed
########NEW FILE########
__FILENAME__ = interface
"""
This module contains the definition of the C{L{OpenIDStore}}
interface.
"""
class OpenIDStore(object):
"""
This is the interface for the store objects the OpenID library
uses. It is a single class that provides all of the persistence
mechanisms that the OpenID library needs, for both servers and
consumers.
@change: Version 2.0 removed the C{storeNonce}, C{getAuthKey}, and C{isDumb}
methods, and changed the behavior of the C{L{useNonce}} method
to support one-way nonces. It added C{L{cleanupNonces}},
C{L{cleanupAssociations}}, and C{L{cleanup}}.
@sort: storeAssociation, getAssociation, removeAssociation,
useNonce
"""
def storeAssociation(self, server_url, association):
"""
This method puts a C{L{Association
<openid.association.Association>}} object into storage,
retrievable by server URL and handle.
@param server_url: The URL of the identity server that this
association is with. Because of the way the server
portion of the library uses this interface, don't assume
there are any limitations on the character set of the
input string. In particular, expect to see unescaped
non-url-safe characters in the server_url field.
@type server_url: C{str}
@param association: The C{L{Association
<openid.association.Association>}} to store.
@type association: C{L{Association
<openid.association.Association>}}
@return: C{None}
@rtype: C{NoneType}
"""
raise NotImplementedError
def getAssociation(self, server_url, handle=None):
"""
This method returns an C{L{Association
<openid.association.Association>}} object from storage that
matches the server URL and, if specified, handle. It returns
C{None} if no such association is found or if the matching
association is expired.
If no handle is specified, the store may return any
association which matches the server URL. If multiple
associations are valid, the recommended return value for this
method is the one most recently issued.
This method is allowed (and encouraged) to garbage collect
expired associations when found. This method must not return
expired associations.
@param server_url: The URL of the identity server to get the
association for. Because of the way the server portion of
the library uses this interface, don't assume there are
any limitations on the character set of the input string.
In particular, expect to see unescaped non-url-safe
characters in the server_url field.
@type server_url: C{str}
@param handle: This optional parameter is the handle of the
specific association to get. If no specific handle is
provided, any valid association matching the server URL is
returned.
@type handle: C{str} or C{NoneType}
@return: The C{L{Association
<openid.association.Association>}} for the given identity
server.
@rtype: C{L{Association <openid.association.Association>}} or
C{NoneType}
"""
raise NotImplementedError
def removeAssociation(self, server_url, handle):
"""
This method removes the matching association if it's found,
and returns whether the association was removed or not.
@param server_url: The URL of the identity server the
association to remove belongs to. Because of the way the
server portion of the library uses this interface, don't
assume there are any limitations on the character set of
the input string. In particular, expect to see unescaped
non-url-safe characters in the server_url field.
@type server_url: C{str}
@param handle: This is the handle of the association to
remove. If there isn't an association found that matches
both the given URL and handle, then there was no matching
handle found.
@type handle: C{str}
@return: Returns whether or not the given association existed.
@rtype: C{bool} or C{int}
"""
raise NotImplementedError
def useNonce(self, server_url, timestamp, salt):
"""Called when using a nonce.
This method should return C{True} if the nonce has not been
used before, and store it for a while to make sure nobody
tries to use the same value again. If the nonce has already
been used or the timestamp is not current, return C{False}.
You may use L{openid.store.nonce.SKEW} for your timestamp window.
@change: In earlier versions, round-trip nonces were used and
a nonce was only valid if it had been previously stored
with C{storeNonce}. Version 2.0 uses one-way nonces,
requiring a different implementation here that does not
depend on a C{storeNonce} call. (C{storeNonce} is no
longer part of the interface.)
@param server_url: The URL of the server from which the nonce
originated.
@type server_url: C{str}
@param timestamp: The time that the nonce was created (to the
nearest second), in seconds since January 1 1970 UTC.
@type timestamp: C{int}
@param salt: A random string that makes two nonces from the
same server issued during the same second unique.
@type salt: str
@return: Whether or not the nonce was valid.
@rtype: C{bool}
"""
raise NotImplementedError
def cleanupNonces(self):
"""Remove expired nonces from the store.
Discards any nonce from storage that is old enough that its
timestamp would not pass L{useNonce}.
This method is not called in the normal operation of the
library. It provides a way for store admins to keep
their storage from filling up with expired data.
@return: the number of nonces expired.
@returntype: int
"""
raise NotImplementedError
def cleanupAssociations(self):
"""Remove expired associations from the store.
This method is not called in the normal operation of the
library. It provides a way for store admins to keep
their storage from filling up with expired data.
@return: the number of associations expired.
@returntype: int
"""
raise NotImplementedError
def cleanup(self):
"""Shortcut for C{L{cleanupNonces}()}, C{L{cleanupAssociations}()}.
This method is not called in the normal operation of the
library. It provides a way for store admins to keep
their storage from filling up with expired data.
"""
return self.cleanupNonces(), self.cleanupAssociations()
########NEW FILE########
__FILENAME__ = memstore
"""A simple store using only in-process memory."""
from openid.store import nonce
import copy
import time
class ServerAssocs(object):
def __init__(self):
self.assocs = {}
def set(self, assoc):
self.assocs[assoc.handle] = assoc
def get(self, handle):
return self.assocs.get(handle)
def remove(self, handle):
try:
del self.assocs[handle]
except KeyError:
return False
else:
return True
def best(self):
"""Returns association with the oldest issued date.
or None if there are no associations.
"""
best = None
for assoc in self.assocs.values():
if best is None or best.issued < assoc.issued:
best = assoc
return best
def cleanup(self):
"""Remove expired associations.
@return: tuple of (removed associations, remaining associations)
"""
remove = []
for handle, assoc in self.assocs.iteritems():
if assoc.getExpiresIn() == 0:
remove.append(handle)
for handle in remove:
del self.assocs[handle]
return len(remove), len(self.assocs)
class MemoryStore(object):
"""In-process memory store.
Use for single long-running processes. No persistence supplied.
"""
def __init__(self):
self.server_assocs = {}
self.nonces = {}
def _getServerAssocs(self, server_url):
try:
return self.server_assocs[server_url]
except KeyError:
assocs = self.server_assocs[server_url] = ServerAssocs()
return assocs
def storeAssociation(self, server_url, assoc):
assocs = self._getServerAssocs(server_url)
assocs.set(copy.deepcopy(assoc))
def getAssociation(self, server_url, handle=None):
assocs = self._getServerAssocs(server_url)
if handle is None:
return assocs.best()
else:
return assocs.get(handle)
def removeAssociation(self, server_url, handle):
assocs = self._getServerAssocs(server_url)
return assocs.remove(handle)
def useNonce(self, server_url, timestamp, salt):
if abs(timestamp - time.time()) > nonce.SKEW:
return False
anonce = (str(server_url), int(timestamp), str(salt))
if anonce in self.nonces:
return False
else:
self.nonces[anonce] = None
return True
def cleanupNonces(self):
now = time.time()
expired = []
for anonce in self.nonces.iterkeys():
if abs(anonce[1] - now) > nonce.SKEW:
# removing items while iterating over the set could be bad.
expired.append(anonce)
for anonce in expired:
del self.nonces[anonce]
return len(expired)
def cleanupAssociations(self):
remove_urls = []
removed_assocs = 0
for server_url, assocs in self.server_assocs.iteritems():
removed, remaining = assocs.cleanup()
removed_assocs += removed
if not remaining:
remove_urls.append(server_url)
# Remove entries from server_assocs that had none remaining.
for server_url in remove_urls:
del self.server_assocs[server_url]
return removed_assocs
def __eq__(self, other):
return ((self.server_assocs == other.server_assocs) and
(self.nonces == other.nonces))
def __ne__(self, other):
return not (self == other)
########NEW FILE########
__FILENAME__ = nonce
__all__ = [
'split',
'mkNonce',
'checkTimestamp',
]
from openid import cryptutil
from time import strptime, strftime, gmtime, time
from calendar import timegm
import string
NONCE_CHARS = string.ascii_letters + string.digits
# Keep nonces for five hours (allow five hours for the combination of
# request time and clock skew). This is probably way more than is
# necessary, but there is not much overhead in storing nonces.
SKEW = 60 * 60 * 5
time_fmt = '%Y-%m-%dT%H:%M:%SZ'
time_str_len = len('0000-00-00T00:00:00Z')
def split(nonce_string):
"""Extract a timestamp from the given nonce string
@param nonce_string: the nonce from which to extract the timestamp
@type nonce_string: str
@returns: A pair of a Unix timestamp and the salt characters
@returntype: (int, str)
@raises ValueError: if the nonce does not start with a correctly
formatted time string
"""
timestamp_str = nonce_string[:time_str_len]
try:
timestamp = timegm(strptime(timestamp_str, time_fmt))
except AssertionError: # Python 2.2
timestamp = -1
if timestamp < 0:
raise ValueError('time out of range')
return timestamp, nonce_string[time_str_len:]
def checkTimestamp(nonce_string, allowed_skew=SKEW, now=None):
"""Is the timestamp that is part of the specified nonce string
within the allowed clock-skew of the current time?
@param nonce_string: The nonce that is being checked
@type nonce_string: str
@param allowed_skew: How many seconds should be allowed for
completing the request, allowing for clock skew.
@type allowed_skew: int
@param now: The current time, as a Unix timestamp
@type now: int
@returntype: bool
@returns: Whether the timestamp is correctly formatted and within
the allowed skew of the current time.
"""
try:
stamp, _ = split(nonce_string)
except ValueError:
return False
else:
if now is None:
now = time()
# Time after which we should not use the nonce
past = now - allowed_skew
# Time that is too far in the future for us to allow
future = now + allowed_skew
# the stamp is not too far in the future and is not too far in
# the past
return past <= stamp <= future
def mkNonce(when=None):
"""Generate a nonce with the current timestamp
@param when: Unix timestamp representing the issue time of the
nonce. Defaults to the current time.
@type when: int
@returntype: str
@returns: A string that should be usable as a one-way nonce
@see: time
"""
salt = cryptutil.randomString(6, NONCE_CHARS)
if when is None:
t = gmtime()
else:
t = gmtime(when)
time_str = strftime(time_fmt, t)
return time_str + salt
########NEW FILE########
__FILENAME__ = sqlstore
"""
This module contains C{L{OpenIDStore}} implementations that use
various SQL databases to back them.
Example of how to initialize a store database::
python -c 'from openid.store import sqlstore; import pysqlite2.dbapi2; sqlstore.SQLiteStore(pysqlite2.dbapi2.connect("cstore.db")).createTables()'
"""
import re
import time
from openid.association import Association
from openid.store.interface import OpenIDStore
from openid.store import nonce
def _inTxn(func):
def wrapped(self, *args, **kwargs):
return self._callInTransaction(func, self, *args, **kwargs)
if hasattr(func, '__name__'):
try:
wrapped.__name__ = func.__name__[4:]
except TypeError:
pass
if hasattr(func, '__doc__'):
wrapped.__doc__ = func.__doc__
return wrapped
class SQLStore(OpenIDStore):
"""
This is the parent class for the SQL stores, which contains the
logic common to all of the SQL stores.
The table names used are determined by the class variables
C{L{settings_table}}, C{L{associations_table}}, and
C{L{nonces_table}}. To change the name of the tables used, pass
new table names into the constructor.
To create the tables with the proper schema, see the
C{L{createTables}} method.
This class shouldn't be used directly. Use one of its subclasses
instead, as those contain the code necessary to use a specific
database.
All methods other than C{L{__init__}} and C{L{createTables}}
should be considered implementation details.
@cvar settings_table: This is the default name of the table to
keep this store's settings in.
@cvar associations_table: This is the default name of the table to
keep associations in
@cvar nonces_table: This is the default name of the table to keep
nonces in.
@sort: __init__, createTables
"""
settings_table = 'oid_settings'
associations_table = 'oid_associations'
nonces_table = 'oid_nonces'
def __init__(self, conn, settings_table=None, associations_table=None,
nonces_table=None):
"""
This creates a new SQLStore instance. It requires an
established database connection be given to it, and it allows
overriding the default table names.
@param conn: This must be an established connection to a
database of the correct type for the SQLStore subclass
you're using.
@type conn: A python database API compatible connection
object.
@param settings_table: This is an optional parameter to
specify the name of the table used for this store's
settings. The default value is specified in
C{L{SQLStore.settings_table}}.
@type settings_table: C{str}
@param associations_table: This is an optional parameter to
specify the name of the table used for storing
associations. The default value is specified in
C{L{SQLStore.associations_table}}.
@type associations_table: C{str}
@param nonces_table: This is an optional parameter to specify
the name of the table used for storing nonces. The
default value is specified in C{L{SQLStore.nonces_table}}.
@type nonces_table: C{str}
"""
self.conn = conn
self.cur = None
self._statement_cache = {}
self._table_names = {
'settings': settings_table or self.settings_table,
'associations': associations_table or self.associations_table,
'nonces': nonces_table or self.nonces_table,
}
self.max_nonce_age = 6 * 60 * 60 # Six hours, in seconds
# DB API extension: search for "Connection Attributes .Error,
# .ProgrammingError, etc." in
# http://www.python.org/dev/peps/pep-0249/
if (hasattr(self.conn, 'IntegrityError') and
hasattr(self.conn, 'OperationalError')):
self.exceptions = self.conn
if not (hasattr(self.exceptions, 'IntegrityError') and
hasattr(self.exceptions, 'OperationalError')):
raise RuntimeError("Error using database connection module "
"(Maybe it can't be imported?)")
def blobDecode(self, blob):
"""Convert a blob as returned by the SQL engine into a str object.
str -> str"""
return blob
def blobEncode(self, s):
"""Convert a str object into the necessary object for storing
in the database as a blob."""
return s
def _getSQL(self, sql_name):
try:
return self._statement_cache[sql_name]
except KeyError:
sql = getattr(self, sql_name)
sql %= self._table_names
self._statement_cache[sql_name] = sql
return sql
def _execSQL(self, sql_name, *args):
sql = self._getSQL(sql_name)
self.cur.execute(sql, args)
def __getattr__(self, attr):
# if the attribute starts with db_, use a default
# implementation that looks up the appropriate SQL statement
# as an attribute of this object and executes it.
if attr[:3] == 'db_':
sql_name = attr[3:] + '_sql'
def func(*args):
return self._execSQL(sql_name, *args)
setattr(self, attr, func)
return func
else:
raise AttributeError('Attribute %r not found' % (attr,))
def _callInTransaction(self, func, *args, **kwargs):
"""Execute the given function inside of a transaction, with an
open cursor. If no exception is raised, the transaction is
comitted, otherwise it is rolled back."""
# No nesting of transactions
self.conn.rollback()
try:
self.cur = self.conn.cursor()
try:
ret = func(*args, **kwargs)
finally:
self.cur.close()
self.cur = None
except:
self.conn.rollback()
raise
else:
self.conn.commit()
return ret
def txn_createTables(self):
"""
This method creates the database tables necessary for this
store to work. It should not be called if the tables already
exist.
"""
self.db_create_nonce()
self.db_create_assoc()
self.db_create_settings()
createTables = _inTxn(txn_createTables)
def txn_storeAssociation(self, server_url, association):
"""Set the association for the server URL.
Association -> NoneType
"""
a = association
self.db_set_assoc(
server_url,
a.handle,
self.blobEncode(a.secret),
a.issued,
a.lifetime,
a.assoc_type)
storeAssociation = _inTxn(txn_storeAssociation)
def txn_getAssociation(self, server_url, handle=None):
"""Get the most recent association that has been set for this
server URL and handle.
str -> NoneType or Association
"""
if handle is not None:
self.db_get_assoc(server_url, handle)
else:
self.db_get_assocs(server_url)
rows = self.cur.fetchall()
if len(rows) == 0:
return None
else:
associations = []
for values in rows:
assoc = Association(*values)
assoc.secret = self.blobDecode(assoc.secret)
if assoc.getExpiresIn() == 0:
self.txn_removeAssociation(server_url, assoc.handle)
else:
associations.append((assoc.issued, assoc))
if associations:
associations.sort()
return associations[-1][1]
else:
return None
getAssociation = _inTxn(txn_getAssociation)
def txn_removeAssociation(self, server_url, handle):
"""Remove the association for the given server URL and handle,
returning whether the association existed at all.
(str, str) -> bool
"""
self.db_remove_assoc(server_url, handle)
return self.cur.rowcount > 0 # -1 is undefined
removeAssociation = _inTxn(txn_removeAssociation)
def txn_useNonce(self, server_url, timestamp, salt):
"""Return whether this nonce is present, and if it is, then
remove it from the set.
str -> bool"""
if abs(timestamp - time.time()) > nonce.SKEW:
return False
try:
self.db_add_nonce(server_url, timestamp, salt)
except self.exceptions.IntegrityError:
# The key uniqueness check failed
return False
else:
# The nonce was successfully added
return True
useNonce = _inTxn(txn_useNonce)
def txn_cleanupNonces(self):
self.db_clean_nonce(int(time.time()) - nonce.SKEW)
return self.cur.rowcount
cleanupNonces = _inTxn(txn_cleanupNonces)
def txn_cleanupAssociations(self):
self.db_clean_assoc(int(time.time()))
return self.cur.rowcount
cleanupAssociations = _inTxn(txn_cleanupAssociations)
class SQLiteStore(SQLStore):
"""
This is an SQLite-based specialization of C{L{SQLStore}}.
To create an instance, see C{L{SQLStore.__init__}}. To create the
tables it will use, see C{L{SQLStore.createTables}}.
All other methods are implementation details.
"""
create_nonce_sql = """
CREATE TABLE %(nonces)s (
server_url VARCHAR,
timestamp INTEGER,
salt CHAR(40),
UNIQUE(server_url, timestamp, salt)
);
"""
create_assoc_sql = """
CREATE TABLE %(associations)s
(
server_url VARCHAR(2047),
handle VARCHAR(255),
secret BLOB(128),
issued INTEGER,
lifetime INTEGER,
assoc_type VARCHAR(64),
PRIMARY KEY (server_url, handle)
);
"""
create_settings_sql = """
CREATE TABLE %(settings)s
(
setting VARCHAR(128) UNIQUE PRIMARY KEY,
value BLOB(20)
);
"""
set_assoc_sql = ('INSERT OR REPLACE INTO %(associations)s '
'VALUES (?, ?, ?, ?, ?, ?);')
get_assocs_sql = ('SELECT handle, secret, issued, lifetime, assoc_type '
'FROM %(associations)s WHERE server_url = ?;')
get_assoc_sql = (
'SELECT handle, secret, issued, lifetime, assoc_type '
'FROM %(associations)s WHERE server_url = ? AND handle = ?;')
get_expired_sql = ('SELECT server_url '
'FROM %(associations)s WHERE issued + lifetime < ?;')
remove_assoc_sql = ('DELETE FROM %(associations)s '
'WHERE server_url = ? AND handle = ?;')
clean_assoc_sql = 'DELETE FROM %(associations)s WHERE issued + lifetime < ?;'
add_nonce_sql = 'INSERT INTO %(nonces)s VALUES (?, ?, ?);'
clean_nonce_sql = 'DELETE FROM %(nonces)s WHERE timestamp < ?;'
def blobDecode(self, buf):
return str(buf)
def blobEncode(self, s):
return buffer(s)
def useNonce(self, *args, **kwargs):
# Older versions of the sqlite wrapper do not raise
# IntegrityError as they should, so we have to detect the
# message from the OperationalError.
try:
return super(SQLiteStore, self).useNonce(*args, **kwargs)
except self.exceptions.OperationalError, why:
if re.match('^columns .* are not unique$', why[0]):
return False
else:
raise
class MySQLStore(SQLStore):
"""
This is a MySQL-based specialization of C{L{SQLStore}}.
Uses InnoDB tables for transaction support.
To create an instance, see C{L{SQLStore.__init__}}. To create the
tables it will use, see C{L{SQLStore.createTables}}.
All other methods are implementation details.
"""
try:
import MySQLdb as exceptions
except ImportError:
exceptions = None
create_nonce_sql = """
CREATE TABLE %(nonces)s (
server_url BLOB,
timestamp INTEGER,
salt CHAR(40),
PRIMARY KEY (server_url(255), timestamp, salt)
)
TYPE=InnoDB;
"""
create_assoc_sql = """
CREATE TABLE %(associations)s
(
server_url BLOB,
handle VARCHAR(255),
secret BLOB,
issued INTEGER,
lifetime INTEGER,
assoc_type VARCHAR(64),
PRIMARY KEY (server_url(255), handle)
)
TYPE=InnoDB;
"""
create_settings_sql = """
CREATE TABLE %(settings)s
(
setting VARCHAR(128) UNIQUE PRIMARY KEY,
value BLOB
)
TYPE=InnoDB;
"""
set_assoc_sql = ('REPLACE INTO %(associations)s '
'VALUES (%%s, %%s, %%s, %%s, %%s, %%s);')
get_assocs_sql = ('SELECT handle, secret, issued, lifetime, assoc_type'
' FROM %(associations)s WHERE server_url = %%s;')
get_expired_sql = ('SELECT server_url '
'FROM %(associations)s WHERE issued + lifetime < %%s;')
get_assoc_sql = (
'SELECT handle, secret, issued, lifetime, assoc_type'
' FROM %(associations)s WHERE server_url = %%s AND handle = %%s;')
remove_assoc_sql = ('DELETE FROM %(associations)s '
'WHERE server_url = %%s AND handle = %%s;')
clean_assoc_sql = 'DELETE FROM %(associations)s WHERE issued + lifetime < %%s;'
add_nonce_sql = 'INSERT INTO %(nonces)s VALUES (%%s, %%s, %%s);'
clean_nonce_sql = 'DELETE FROM %(nonces)s WHERE timestamp < %%s;'
def blobDecode(self, blob):
if type(blob) is str:
# Versions of MySQLdb >= 1.2.2
return blob
else:
# Versions of MySQLdb prior to 1.2.2 (as far as we can tell)
return blob.tostring()
class PostgreSQLStore(SQLStore):
"""
This is a PostgreSQL-based specialization of C{L{SQLStore}}.
To create an instance, see C{L{SQLStore.__init__}}. To create the
tables it will use, see C{L{SQLStore.createTables}}.
All other methods are implementation details.
"""
try:
import psycopg as exceptions
except ImportError:
# psycopg2 has the dbapi extension where the exception classes
# are available on the connection object. A psycopg2
# connection will use the correct exception classes because of
# this, and a psycopg connection will fall through to use the
# psycopg imported above.
exceptions = None
create_nonce_sql = """
CREATE TABLE %(nonces)s (
server_url VARCHAR(2047),
timestamp INTEGER,
salt CHAR(40),
PRIMARY KEY (server_url, timestamp, salt)
);
"""
create_assoc_sql = """
CREATE TABLE %(associations)s
(
server_url VARCHAR(2047),
handle VARCHAR(255),
secret BYTEA,
issued INTEGER,
lifetime INTEGER,
assoc_type VARCHAR(64),
PRIMARY KEY (server_url, handle),
CONSTRAINT secret_length_constraint CHECK (LENGTH(secret) <= 128)
);
"""
create_settings_sql = """
CREATE TABLE %(settings)s
(
setting VARCHAR(128) UNIQUE PRIMARY KEY,
value BYTEA,
CONSTRAINT value_length_constraint CHECK (LENGTH(value) <= 20)
);
"""
def db_set_assoc(self, server_url, handle, secret, issued, lifetime, assoc_type):
"""
Set an association. This is implemented as a method because
REPLACE INTO is not supported by PostgreSQL (and is not
standard SQL).
"""
result = self.db_get_assoc(server_url, handle)
rows = self.cur.fetchall()
if len(rows):
# Update the table since this associations already exists.
return self.db_update_assoc(secret, issued, lifetime, assoc_type,
server_url, handle)
else:
# Insert a new record because this association wasn't
# found.
return self.db_new_assoc(server_url, handle, secret, issued,
lifetime, assoc_type)
new_assoc_sql = ('INSERT INTO %(associations)s '
'VALUES (%%s, %%s, %%s, %%s, %%s, %%s);')
update_assoc_sql = ('UPDATE %(associations)s SET '
'secret = %%s, issued = %%s, '
'lifetime = %%s, assoc_type = %%s '
'WHERE server_url = %%s AND handle = %%s;')
get_assocs_sql = ('SELECT handle, secret, issued, lifetime, assoc_type'
' FROM %(associations)s WHERE server_url = %%s;')
get_expired_sql = ('SELECT server_url '
'FROM %(associations)s WHERE issued + lifetime < %%s;')
get_assoc_sql = (
'SELECT handle, secret, issued, lifetime, assoc_type'
' FROM %(associations)s WHERE server_url = %%s AND handle = %%s;')
remove_assoc_sql = ('DELETE FROM %(associations)s '
'WHERE server_url = %%s AND handle = %%s;')
clean_assoc_sql = 'DELETE FROM %(associations)s WHERE issued + lifetime < %%s;'
add_nonce_sql = 'INSERT INTO %(nonces)s VALUES (%%s, %%s, %%s);'
clean_nonce_sql = 'DELETE FROM %(nonces)s WHERE timestamp < %%s;'
def blobEncode(self, blob):
try:
from psycopg2 import Binary
except ImportError:
from psycopg import Binary
return Binary(blob)
########NEW FILE########
__FILENAME__ = urinorm
import re
# from appendix B of rfc 3986 (http://www.ietf.org/rfc/rfc3986.txt)
uri_pattern = r'^(([^:/?#]+):)?(//([^/?#]*))?([^?#]*)(\?([^#]*))?(#(.*))?'
uri_re = re.compile(uri_pattern)
authority_pattern = r'^([^@]*@)?([^:]*)(:.*)?'
authority_re = re.compile(authority_pattern)
pct_encoded_pattern = r'%([0-9A-Fa-f]{2})'
pct_encoded_re = re.compile(pct_encoded_pattern)
try:
unichr(0x10000)
except ValueError:
# narrow python build
UCSCHAR = [
(0xA0, 0xD7FF),
(0xF900, 0xFDCF),
(0xFDF0, 0xFFEF),
]
IPRIVATE = [
(0xE000, 0xF8FF),
]
else:
UCSCHAR = [
(0xA0, 0xD7FF),
(0xF900, 0xFDCF),
(0xFDF0, 0xFFEF),
(0x10000, 0x1FFFD),
(0x20000, 0x2FFFD),
(0x30000, 0x3FFFD),
(0x40000, 0x4FFFD),
(0x50000, 0x5FFFD),
(0x60000, 0x6FFFD),
(0x70000, 0x7FFFD),
(0x80000, 0x8FFFD),
(0x90000, 0x9FFFD),
(0xA0000, 0xAFFFD),
(0xB0000, 0xBFFFD),
(0xC0000, 0xCFFFD),
(0xD0000, 0xDFFFD),
(0xE1000, 0xEFFFD),
]
IPRIVATE = [
(0xE000, 0xF8FF),
(0xF0000, 0xFFFFD),
(0x100000, 0x10FFFD),
]
_unreserved = [False] * 256
for _ in range(ord('A'), ord('Z') + 1): _unreserved[_] = True
for _ in range(ord('0'), ord('9') + 1): _unreserved[_] = True
for _ in range(ord('a'), ord('z') + 1): _unreserved[_] = True
_unreserved[ord('-')] = True
_unreserved[ord('.')] = True
_unreserved[ord('_')] = True
_unreserved[ord('~')] = True
_escapeme_re = re.compile('[%s]' % (''.join(
map(lambda (m, n): u'%s-%s' % (unichr(m), unichr(n)),
UCSCHAR + IPRIVATE)),))
def _pct_escape_unicode(char_match):
c = char_match.group()
return ''.join(['%%%X' % (ord(octet),) for octet in c.encode('utf-8')])
def _pct_encoded_replace_unreserved(mo):
try:
i = int(mo.group(1), 16)
if _unreserved[i]:
return chr(i)
else:
return mo.group().upper()
except ValueError:
return mo.group()
def _pct_encoded_replace(mo):
try:
return chr(int(mo.group(1), 16))
except ValueError:
return mo.group()
def remove_dot_segments(path):
result_segments = []
while path:
if path.startswith('../'):
path = path[3:]
elif path.startswith('./'):
path = path[2:]
elif path.startswith('/./'):
path = path[2:]
elif path == '/.':
path = '/'
elif path.startswith('/../'):
path = path[3:]
if result_segments:
result_segments.pop()
elif path == '/..':
path = '/'
if result_segments:
result_segments.pop()
elif path == '..' or path == '.':
path = ''
else:
i = 0
if path[0] == '/':
i = 1
i = path.find('/', i)
if i == -1:
i = len(path)
result_segments.append(path[:i])
path = path[i:]
return ''.join(result_segments)
def urinorm(uri):
if isinstance(uri, unicode):
uri = _escapeme_re.sub(_pct_escape_unicode, uri).encode('ascii')
uri_mo = uri_re.match(uri)
scheme = uri_mo.group(2)
if scheme is None:
raise ValueError('No scheme specified')
scheme = scheme.lower()
if scheme not in ('http', 'https'):
raise ValueError('Not an absolute HTTP or HTTPS URI: %r' % (uri,))
authority = uri_mo.group(4)
if authority is None:
raise ValueError('Not an absolute URI: %r' % (uri,))
authority_mo = authority_re.match(authority)
if authority_mo is None:
raise ValueError('URI does not have a valid authority: %r' % (uri,))
userinfo, host, port = authority_mo.groups()
if userinfo is None:
userinfo = ''
if '%' in host:
host = host.lower()
host = pct_encoded_re.sub(_pct_encoded_replace, host)
host = unicode(host, 'utf-8').encode('idna')
else:
host = host.lower()
if port:
if (port == ':' or
(scheme == 'http' and port == ':80') or
(scheme == 'https' and port == ':443')):
port = ''
else:
port = ''
authority = userinfo + host + port
path = uri_mo.group(5)
path = pct_encoded_re.sub(_pct_encoded_replace_unreserved, path)
path = remove_dot_segments(path)
if not path:
path = '/'
query = uri_mo.group(6)
if query is None:
query = ''
fragment = uri_mo.group(8)
if fragment is None:
fragment = ''
return scheme + '://' + authority + path + query + fragment
########NEW FILE########
__FILENAME__ = accept
"""Functions for generating and parsing HTTP Accept: headers for
supporting server-directed content negotiation.
"""
def generateAcceptHeader(*elements):
"""Generate an accept header value
[str or (str, float)] -> str
"""
parts = []
for element in elements:
if type(element) is str:
qs = "1.0"
mtype = element
else:
mtype, q = element
q = float(q)
if q > 1 or q <= 0:
raise ValueError('Invalid preference factor: %r' % q)
qs = '%0.1f' % (q,)
parts.append((qs, mtype))
parts.sort()
chunks = []
for q, mtype in parts:
if q == '1.0':
chunks.append(mtype)
else:
chunks.append('%s; q=%s' % (mtype, q))
return ', '.join(chunks)
def parseAcceptHeader(value):
"""Parse an accept header, ignoring any accept-extensions
returns a list of tuples containing main MIME type, MIME subtype,
and quality markdown.
str -> [(str, str, float)]
"""
chunks = [chunk.strip() for chunk in value.split(',')]
accept = []
for chunk in chunks:
parts = [s.strip() for s in chunk.split(';')]
mtype = parts.pop(0)
if '/' not in mtype:
# This is not a MIME type, so ignore the bad data
continue
main, sub = mtype.split('/', 1)
for ext in parts:
if '=' in ext:
k, v = ext.split('=', 1)
if k == 'q':
try:
q = float(v)
break
except ValueError:
# Ignore poorly formed q-values
pass
else:
q = 1.0
accept.append((q, main, sub))
accept.sort()
accept.reverse()
return [(main, sub, q) for (q, main, sub) in accept]
def matchTypes(accept_types, have_types):
"""Given the result of parsing an Accept: header, and the
available MIME types, return the acceptable types with their
quality markdowns.
For example:
>>> acceptable = parseAcceptHeader('text/html, text/plain; q=0.5')
>>> matchTypes(acceptable, ['text/plain', 'text/html', 'image/jpeg'])
[('text/html', 1.0), ('text/plain', 0.5)]
Type signature: ([(str, str, float)], [str]) -> [(str, float)]
"""
if not accept_types:
# Accept all of them
default = 1
else:
default = 0
match_main = {}
match_sub = {}
for (main, sub, q) in accept_types:
if main == '*':
default = max(default, q)
continue
elif sub == '*':
match_main[main] = max(match_main.get(main, 0), q)
else:
match_sub[(main, sub)] = max(match_sub.get((main, sub), 0), q)
accepted_list = []
order_maintainer = 0
for mtype in have_types:
main, sub = mtype.split('/')
if (main, sub) in match_sub:
q = match_sub[(main, sub)]
else:
q = match_main.get(main, default)
if q:
accepted_list.append((1 - q, order_maintainer, q, mtype))
order_maintainer += 1
accepted_list.sort()
return [(mtype, q) for (_, _, q, mtype) in accepted_list]
def getAcceptable(accept_header, have_types):
"""Parse the accept header and return a list of available types in
preferred order. If a type is unacceptable, it will not be in the
resulting list.
This is a convenience wrapper around matchTypes and
parseAcceptHeader.
(str, [str]) -> [str]
"""
accepted = parseAcceptHeader(accept_header)
preferred = matchTypes(accepted, have_types)
return [mtype for (mtype, _) in preferred]
########NEW FILE########
__FILENAME__ = constants
__all__ = ['YADIS_HEADER_NAME', 'YADIS_CONTENT_TYPE', 'YADIS_ACCEPT_HEADER']
from openid.yadis.accept import generateAcceptHeader
YADIS_HEADER_NAME = 'X-XRDS-Location'
YADIS_CONTENT_TYPE = 'application/xrds+xml'
# A value suitable for using as an accept header when performing YADIS
# discovery, unless the application has special requirements
YADIS_ACCEPT_HEADER = generateAcceptHeader(
('text/html', 0.3),
('application/xhtml+xml', 0.5),
(YADIS_CONTENT_TYPE, 1.0),
)
########NEW FILE########
__FILENAME__ = discover
# -*- test-case-name: openid.test.test_yadis_discover -*-
__all__ = ['discover', 'DiscoveryResult', 'DiscoveryFailure']
from cStringIO import StringIO
from openid import fetchers
from openid.yadis.constants import \
YADIS_HEADER_NAME, YADIS_CONTENT_TYPE, YADIS_ACCEPT_HEADER
from openid.yadis.parsehtml import MetaNotFound, findHTMLMeta
class DiscoveryFailure(Exception):
"""Raised when a YADIS protocol error occurs in the discovery process"""
identity_url = None
def __init__(self, message, http_response):
Exception.__init__(self, message)
self.http_response = http_response
class DiscoveryResult(object):
"""Contains the result of performing Yadis discovery on a URI"""
# The URI that was passed to the fetcher
request_uri = None
# The result of following redirects from the request_uri
normalized_uri = None
# The URI from which the response text was returned (set to
# None if there was no XRDS document found)
xrds_uri = None
# The content-type returned with the response_text
content_type = None
# The document returned from the xrds_uri
response_text = None
def __init__(self, request_uri):
"""Initialize the state of the object
sets all attributes to None except the request_uri
"""
self.request_uri = request_uri
def usedYadisLocation(self):
"""Was the Yadis protocol's indirection used?"""
return self.normalized_uri != self.xrds_uri
def isXRDS(self):
"""Is the response text supposed to be an XRDS document?"""
return (self.usedYadisLocation() or
self.content_type == YADIS_CONTENT_TYPE)
def discover(uri):
"""Discover services for a given URI.
@param uri: The identity URI as a well-formed http or https
URI. The well-formedness and the protocol are not checked, but
the results of this function are undefined if those properties
do not hold.
@return: DiscoveryResult object
@raises Exception: Any exception that can be raised by fetching a URL with
the given fetcher.
@raises DiscoveryFailure: When the HTTP response does not have a 200 code.
"""
result = DiscoveryResult(uri)
resp = fetchers.fetch(uri, headers={'Accept': YADIS_ACCEPT_HEADER})
if resp.status != 200:
raise DiscoveryFailure(
'HTTP Response status from identity URL host is not 200. '
'Got status %r' % (resp.status,), resp)
# Note the URL after following redirects
result.normalized_uri = resp.final_url
# Attempt to find out where to go to discover the document
# or if we already have it
result.content_type = resp.headers.get('content-type')
result.xrds_uri = whereIsYadis(resp)
if result.xrds_uri and result.usedYadisLocation():
resp = fetchers.fetch(result.xrds_uri)
if resp.status != 200:
exc = DiscoveryFailure(
'HTTP Response status from Yadis host is not 200. '
'Got status %r' % (resp.status,), resp)
exc.identity_url = result.normalized_uri
raise exc
result.content_type = resp.headers.get('content-type')
result.response_text = resp.body
return result
def whereIsYadis(resp):
"""Given a HTTPResponse, return the location of the Yadis document.
May be the URL just retrieved, another URL, or None, if I can't
find any.
[non-blocking]
@returns: str or None
"""
# Attempt to find out where to go to discover the document
# or if we already have it
content_type = resp.headers.get('content-type')
# According to the spec, the content-type header must be an exact
# match, or else we have to look for an indirection.
if (content_type and
content_type.split(';', 1)[0].lower() == YADIS_CONTENT_TYPE):
return resp.final_url
else:
# Try the header
yadis_loc = resp.headers.get(YADIS_HEADER_NAME.lower())
if not yadis_loc:
# Parse as HTML if the header is missing.
#
# XXX: do we want to do something with content-type, like
# have a whitelist or a blacklist (for detecting that it's
# HTML)?
try:
yadis_loc = findHTMLMeta(StringIO(resp.body))
except MetaNotFound:
pass
return yadis_loc
########NEW FILE########
__FILENAME__ = etxrd
# -*- test-case-name: yadis.test.test_etxrd -*-
"""
ElementTree interface to an XRD document.
"""
__all__ = [
'nsTag',
'mkXRDTag',
'isXRDS',
'parseXRDS',
'getCanonicalID',
'getYadisXRD',
'getPriorityStrict',
'getPriority',
'prioSort',
'iterServices',
'expandService',
'expandServices',
]
import sys
import random
from datetime import datetime
from time import strptime
from openid.oidutil import importElementTree
ElementTree = importElementTree()
# the different elementtree modules don't have a common exception
# model. We just want to be able to catch the exceptions that signify
# malformed XML data and wrap them, so that the other library code
# doesn't have to know which XML library we're using.
try:
# Make the parser raise an exception so we can sniff out the type
# of exceptions
ElementTree.XML('> purposely malformed XML <')
except (SystemExit, MemoryError, AssertionError, ImportError):
raise
except:
XMLError = sys.exc_info()[0]
from openid.yadis import xri
class XRDSError(Exception):
"""An error with the XRDS document."""
# The exception that triggered this exception
reason = None
class XRDSFraud(XRDSError):
"""Raised when there's an assertion in the XRDS that it does not have
the authority to make.
"""
def parseXRDS(text):
"""Parse the given text as an XRDS document.
@return: ElementTree containing an XRDS document
@raises XRDSError: When there is a parse error or the document does
not contain an XRDS.
"""
try:
element = ElementTree.XML(text)
except XMLError, why:
exc = XRDSError('Error parsing document as XML')
exc.reason = why
raise exc
else:
tree = ElementTree.ElementTree(element)
if not isXRDS(tree):
raise XRDSError('Not an XRDS document')
return tree
XRD_NS_2_0 = 'xri://$xrd*($v*2.0)'
XRDS_NS = 'xri://$xrds'
def nsTag(ns, t):
return '{%s}%s' % (ns, t)
def mkXRDTag(t):
"""basestring -> basestring
Create a tag name in the XRD 2.0 XML namespace suitable for using
with ElementTree
"""
return nsTag(XRD_NS_2_0, t)
def mkXRDSTag(t):
"""basestring -> basestring
Create a tag name in the XRDS XML namespace suitable for using
with ElementTree
"""
return nsTag(XRDS_NS, t)
# Tags that are used in Yadis documents
root_tag = mkXRDSTag('XRDS')
service_tag = mkXRDTag('Service')
xrd_tag = mkXRDTag('XRD')
type_tag = mkXRDTag('Type')
uri_tag = mkXRDTag('URI')
expires_tag = mkXRDTag('Expires')
# Other XRD tags
canonicalID_tag = mkXRDTag('CanonicalID')
def isXRDS(xrd_tree):
"""Is this document an XRDS document?"""
root = xrd_tree.getroot()
return root.tag == root_tag
def getYadisXRD(xrd_tree):
"""Return the XRD element that should contain the Yadis services"""
xrd = None
# for the side-effect of assigning the last one in the list to the
# xrd variable
for xrd in xrd_tree.findall(xrd_tag):
pass
# There were no elements found, or else xrd would be set to the
# last one
if xrd is None:
raise XRDSError('No XRD present in tree')
return xrd
def getXRDExpiration(xrd_element, default=None):
"""Return the expiration date of this XRD element, or None if no
expiration was specified.
@type xrd_element: ElementTree node
@param default: The value to use as the expiration if no
expiration was specified in the XRD.
@rtype: datetime.datetime
@raises ValueError: If the xrd:Expires element is present, but its
contents are not formatted according to the specification.
"""
expires_element = xrd_element.find(expires_tag)
if expires_element is None:
return default
else:
expires_string = expires_element.text
# Will raise ValueError if the string is not the expected format
expires_time = strptime(expires_string, "%Y-%m-%dT%H:%M:%SZ")
return datetime(*expires_time[0:6])
def getCanonicalID(iname, xrd_tree):
"""Return the CanonicalID from this XRDS document.
@param iname: the XRI being resolved.
@type iname: unicode
@param xrd_tree: The XRDS output from the resolver.
@type xrd_tree: ElementTree
@returns: The XRI CanonicalID or None.
@returntype: unicode or None
"""
xrd_list = xrd_tree.findall(xrd_tag)
xrd_list.reverse()
try:
canonicalID = xri.XRI(xrd_list[0].findall(canonicalID_tag)[-1].text)
except IndexError:
return None
childID = canonicalID
for xrd in xrd_list[1:]:
# XXX: can't use rsplit until we require python >= 2.4.
parent_sought = childID[:childID.rindex('!')]
parent_list = [xri.XRI(c.text) for c in xrd.findall(canonicalID_tag)]
if parent_sought not in parent_list:
raise XRDSFraud("%r can not come from any of %s" % (parent_sought,
parent_list))
childID = parent_sought
root = xri.rootAuthority(iname)
if not xri.providerIsAuthoritative(root, childID):
raise XRDSFraud("%r can not come from root %r" % (childID, root))
return canonicalID
class _Max(object):
"""Value that compares greater than any other value.
Should only be used as a singleton. Implemented for use as a
priority value for when a priority is not specified."""
def __cmp__(self, other):
if other is self:
return 0
return 1
Max = _Max()
def getPriorityStrict(element):
"""Get the priority of this element.
Raises ValueError if the value of the priority is invalid. If no
priority is specified, it returns a value that compares greater
than any other value.
"""
prio_str = element.get('priority')
if prio_str is not None:
prio_val = int(prio_str)
if prio_val >= 0:
return prio_val
else:
raise ValueError('Priority values must be non-negative integers')
# Any errors in parsing the priority fall through to here
return Max
def getPriority(element):
"""Get the priority of this element
Returns Max if no priority is specified or the priority value is invalid.
"""
try:
return getPriorityStrict(element)
except ValueError:
return Max
def prioSort(elements):
"""Sort a list of elements that have priority attributes"""
# Randomize the services before sorting so that equal priority
# elements are load-balanced.
random.shuffle(elements)
prio_elems = [(getPriority(e), e) for e in elements]
prio_elems.sort()
sorted_elems = [s for (_, s) in prio_elems]
return sorted_elems
def iterServices(xrd_tree):
"""Return an iterable over the Service elements in the Yadis XRD
sorted by priority"""
xrd = getYadisXRD(xrd_tree)
return prioSort(xrd.findall(service_tag))
def sortedURIs(service_element):
"""Given a Service element, return a list of the contents of all
URI tags in priority order."""
return [uri_element.text for uri_element
in prioSort(service_element.findall(uri_tag))]
def getTypeURIs(service_element):
"""Given a Service element, return a list of the contents of all
Type tags"""
return [type_element.text for type_element
in service_element.findall(type_tag)]
def expandService(service_element):
"""Take a service element and expand it into an iterator of:
([type_uri], uri, service_element)
"""
uris = sortedURIs(service_element)
if not uris:
uris = [None]
expanded = []
for uri in uris:
type_uris = getTypeURIs(service_element)
expanded.append((type_uris, uri, service_element))
return expanded
def expandServices(service_elements):
"""Take a sorted iterator of service elements and expand it into a
sorted iterator of:
([type_uri], uri, service_element)
There may be more than one item in the resulting list for each
service element if there is more than one URI or type for a
service, but each triple will be unique.
If there is no URI or Type for a Service element, it will not
appear in the result.
"""
expanded = []
for service_element in service_elements:
expanded.extend(expandService(service_element))
return expanded
########NEW FILE########
__FILENAME__ = filters
"""This module contains functions and classes used for extracting
endpoint information out of a Yadis XRD file using the ElementTree XML
parser.
"""
__all__ = [
'BasicServiceEndpoint',
'mkFilter',
'IFilter',
'TransformFilterMaker',
'CompoundFilter',
]
from openid.yadis.etxrd import expandService
class BasicServiceEndpoint(object):
"""Generic endpoint object that contains parsed service
information, as well as a reference to the service element from
which it was generated. If there is more than one xrd:Type or
xrd:URI in the xrd:Service, this object represents just one of
those pairs.
This object can be used as a filter, because it implements
fromBasicServiceEndpoint.
The simplest kind of filter you can write implements
fromBasicServiceEndpoint, which takes one of these objects.
"""
def __init__(self, yadis_url, type_uris, uri, service_element):
self.type_uris = type_uris
self.yadis_url = yadis_url
self.uri = uri
self.service_element = service_element
def matchTypes(self, type_uris):
"""Query this endpoint to see if it has any of the given type
URIs. This is useful for implementing other endpoint classes
that e.g. need to check for the presence of multiple versions
of a single protocol.
@param type_uris: The URIs that you wish to check
@type type_uris: iterable of str
@return: all types that are in both in type_uris and
self.type_uris
"""
return [uri for uri in type_uris if uri in self.type_uris]
def fromBasicServiceEndpoint(endpoint):
"""Trivial transform from a basic endpoint to itself. This
method exists to allow BasicServiceEndpoint to be used as a
filter.
If you are subclassing this object, re-implement this function.
@param endpoint: An instance of BasicServiceEndpoint
@return: The object that was passed in, with no processing.
"""
return endpoint
fromBasicServiceEndpoint = staticmethod(fromBasicServiceEndpoint)
class IFilter(object):
"""Interface for Yadis filter objects. Other filter-like things
are convertable to this class."""
def getServiceEndpoints(self, yadis_url, service_element):
"""Returns an iterator of endpoint objects"""
raise NotImplementedError
class TransformFilterMaker(object):
"""Take a list of basic filters and makes a filter that transforms
the basic filter into a top-level filter. This is mostly useful
for the implementation of mkFilter, which should only be needed
for special cases or internal use by this library.
This object is useful for creating simple filters for services
that use one URI and are specified by one Type (we expect most
Types will fit this paradigm).
Creates a BasicServiceEndpoint object and apply the filter
functions to it until one of them returns a value.
"""
def __init__(self, filter_functions):
"""Initialize the filter maker's state
@param filter_functions: The endpoint transformer functions to
apply to the basic endpoint. These are called in turn
until one of them does not return None, and the result of
that transformer is returned.
"""
self.filter_functions = filter_functions
def getServiceEndpoints(self, yadis_url, service_element):
"""Returns an iterator of endpoint objects produced by the
filter functions."""
endpoints = []
# Do an expansion of the service element by xrd:Type and xrd:URI
for type_uris, uri, _ in expandService(service_element):
# Create a basic endpoint object to represent this
# yadis_url, Service, Type, URI combination
endpoint = BasicServiceEndpoint(
yadis_url, type_uris, uri, service_element)
e = self.applyFilters(endpoint)
if e is not None:
endpoints.append(e)
return endpoints
def applyFilters(self, endpoint):
"""Apply filter functions to an endpoint until one of them
returns non-None."""
for filter_function in self.filter_functions:
e = filter_function(endpoint)
if e is not None:
# Once one of the filters has returned an
# endpoint, do not apply any more.
return e
return None
class CompoundFilter(object):
"""Create a new filter that applies a set of filters to an endpoint
and collects their results.
"""
def __init__(self, subfilters):
self.subfilters = subfilters
def getServiceEndpoints(self, yadis_url, service_element):
"""Generate all endpoint objects for all of the subfilters of
this filter and return their concatenation."""
endpoints = []
for subfilter in self.subfilters:
endpoints.extend(
subfilter.getServiceEndpoints(yadis_url, service_element))
return endpoints
# Exception raised when something is not able to be turned into a filter
filter_type_error = TypeError(
'Expected a filter, an endpoint, a callable or a list of any of these.')
def mkFilter(parts):
"""Convert a filter-convertable thing into a filter
@param parts: a filter, an endpoint, a callable, or a list of any of these.
"""
# Convert the parts into a list, and pass to mkCompoundFilter
if parts is None:
parts = [BasicServiceEndpoint]
try:
parts = list(parts)
except TypeError:
return mkCompoundFilter([parts])
else:
return mkCompoundFilter(parts)
def mkCompoundFilter(parts):
"""Create a filter out of a list of filter-like things
Used by mkFilter
@param parts: list of filter, endpoint, callable or list of any of these
"""
# Separate into a list of callables and a list of filter objects
transformers = []
filters = []
for subfilter in parts:
try:
subfilter = list(subfilter)
except TypeError:
# If it's not an iterable
if hasattr(subfilter, 'getServiceEndpoints'):
# It's a full filter
filters.append(subfilter)
elif hasattr(subfilter, 'fromBasicServiceEndpoint'):
# It's an endpoint object, so put its endpoint
# conversion attribute into the list of endpoint
# transformers
transformers.append(subfilter.fromBasicServiceEndpoint)
elif callable(subfilter):
# It's a simple callable, so add it to the list of
# endpoint transformers
transformers.append(subfilter)
else:
raise filter_type_error
else:
filters.append(mkCompoundFilter(subfilter))
if transformers:
filters.append(TransformFilterMaker(transformers))
if len(filters) == 1:
return filters[0]
else:
return CompoundFilter(filters)
########NEW FILE########
__FILENAME__ = manager
class YadisServiceManager(object):
"""Holds the state of a list of selected Yadis services, managing
storing it in a session and iterating over the services in order."""
def __init__(self, starting_url, yadis_url, services, session_key):
# The URL that was used to initiate the Yadis protocol
self.starting_url = starting_url
# The URL after following redirects (the identifier)
self.yadis_url = yadis_url
# List of service elements
self.services = list(services)
self.session_key = session_key
# Reference to the current service object
self._current = None
def __len__(self):
"""How many untried services remain?"""
return len(self.services)
def __iter__(self):
return self
def next(self):
"""Return the next service
self.current() will continue to return that service until the
next call to this method."""
try:
self._current = self.services.pop(0)
except IndexError:
raise StopIteration
else:
return self._current
def current(self):
"""Return the current service.
Returns None if there are no services left.
"""
return self._current
def forURL(self, url):
return url in [self.starting_url, self.yadis_url]
def started(self):
"""Has the first service been returned?"""
return self._current is not None
def store(self, session):
"""Store this object in the session, by its session key."""
session[self.session_key] = self
class Discovery(object):
"""State management for discovery.
High-level usage pattern is to call .getNextService(discover) in
order to find the next available service for this user for this
session. Once a request completes, call .finish() to clean up the
session state.
@ivar session: a dict-like object that stores state unique to the
requesting user-agent. This object must be able to store
serializable objects.
@ivar url: the URL that is used to make the discovery request
@ivar session_key_suffix: The suffix that will be used to identify
this object in the session object.
"""
DEFAULT_SUFFIX = 'auth'
PREFIX = '_yadis_services_'
def __init__(self, session, url, session_key_suffix=None):
"""Initialize a discovery object"""
self.session = session
self.url = url
if session_key_suffix is None:
session_key_suffix = self.DEFAULT_SUFFIX
self.session_key_suffix = session_key_suffix
def getNextService(self, discover):
"""Return the next authentication service for the pair of
user_input and session. This function handles fallback.
@param discover: a callable that takes a URL and returns a
list of services
@type discover: str -> [service]
@return: the next available service
"""
manager = self.getManager()
if manager is not None and not manager:
self.destroyManager()
if not manager:
yadis_url, services = discover(self.url)
manager = self.createManager(services, yadis_url)
if manager:
service = manager.next()
manager.store(self.session)
else:
service = None
return service
def cleanup(self, force=False):
"""Clean up Yadis-related services in the session and return
the most-recently-attempted service from the manager, if one
exists.
@param force: True if the manager should be deleted regardless
of whether it's a manager for self.url.
@return: current service endpoint object or None if there is
no current service
"""
manager = self.getManager(force=force)
if manager is not None:
service = manager.current()
self.destroyManager(force=force)
else:
service = None
return service
### Lower-level methods
def getSessionKey(self):
"""Get the session key for this starting URL and suffix
@return: The session key
@rtype: str
"""
return self.PREFIX + self.session_key_suffix
def getManager(self, force=False):
"""Extract the YadisServiceManager for this object's URL and
suffix from the session.
@param force: True if the manager should be returned
regardless of whether it's a manager for self.url.
@return: The current YadisServiceManager, if it's for this
URL, or else None
"""
manager = self.session.get(self.getSessionKey())
if (manager is not None and (manager.forURL(self.url) or force)):
return manager
else:
return None
def createManager(self, services, yadis_url=None):
"""Create a new YadisService Manager for this starting URL and
suffix, and store it in the session.
@raises KeyError: When I already have a manager.
@return: A new YadisServiceManager or None
"""
key = self.getSessionKey()
if self.getManager():
raise KeyError('There is already a %r manager for %r' %
(key, self.url))
if not services:
return None
manager = YadisServiceManager(self.url, yadis_url, services, key)
manager.store(self.session)
return manager
def destroyManager(self, force=False):
"""Delete any YadisServiceManager with this starting URL and
suffix from the session.
If there is no service manager or the service manager is for a
different URL, it silently does nothing.
@param force: True if the manager should be deleted regardless
of whether it's a manager for self.url.
"""
if self.getManager(force=force) is not None:
key = self.getSessionKey()
del self.session[key]
########NEW FILE########
__FILENAME__ = parsehtml
__all__ = ['findHTMLMeta', 'MetaNotFound']
from HTMLParser import HTMLParser, HTMLParseError
import htmlentitydefs
import re
from openid.yadis.constants import YADIS_HEADER_NAME
# Size of the chunks to search at a time (also the amount that gets
# read at a time)
CHUNK_SIZE = 1024 * 16 # 16 KB
class ParseDone(Exception):
"""Exception to hold the URI that was located when the parse is
finished. If the parse finishes without finding the URI, set it to
None."""
class MetaNotFound(Exception):
"""Exception to hold the content of the page if we did not find
the appropriate <meta> tag"""
re_flags = re.IGNORECASE | re.UNICODE | re.VERBOSE
ent_pat = r'''
&
(?: \#x (?P<hex> [a-f0-9]+ )
| \# (?P<dec> \d+ )
| (?P<word> \w+ )
)
;'''
ent_re = re.compile(ent_pat, re_flags)
def substituteMO(mo):
if mo.lastgroup == 'hex':
codepoint = int(mo.group('hex'), 16)
elif mo.lastgroup == 'dec':
codepoint = int(mo.group('dec'))
else:
assert mo.lastgroup == 'word'
codepoint = htmlentitydefs.name2codepoint.get(mo.group('word'))
if codepoint is None:
return mo.group()
else:
return unichr(codepoint)
def substituteEntities(s):
return ent_re.sub(substituteMO, s)
class YadisHTMLParser(HTMLParser):
"""Parser that finds a meta http-equiv tag in the head of a html
document.
When feeding in data, if the tag is matched or it will never be
found, the parser will raise ParseDone with the uri as the first
attribute.
Parsing state diagram
=====================
Any unlisted input does not affect the state::
1, 2, 5 8
+--------------------------+ +-+
| | | |
4 | 3 1, 2, 5, 7 v | v
TOP -> HTML -> HEAD ----------> TERMINATED
| | ^ | ^ ^
| | 3 | | | |
| +------------+ +-> FOUND ------+ |
| 6 8 |
| 1, 2 |
+------------------------------------+
1. any of </body>, </html>, </head> -> TERMINATE
2. <body> -> TERMINATE
3. <head> -> HEAD
4. <html> -> HTML
5. <html> -> TERMINATE
6. <meta http-equiv='X-XRDS-Location'> -> FOUND
7. <head> -> TERMINATE
8. Any input -> TERMINATE
"""
TOP = 0
HTML = 1
HEAD = 2
FOUND = 3
TERMINATED = 4
def __init__(self):
HTMLParser.__init__(self)
self.phase = self.TOP
def _terminate(self):
self.phase = self.TERMINATED
raise ParseDone(None)
def handle_endtag(self, tag):
# If we ever see an end of head, body, or html, bail out right away.
# [1]
if tag in ['head', 'body', 'html']:
self._terminate()
def handle_starttag(self, tag, attrs):
# if we ever see a start body tag, bail out right away, since
# we want to prevent the meta tag from appearing in the body
# [2]
if tag=='body':
self._terminate()
if self.phase == self.TOP:
# At the top level, allow a html tag or a head tag to move
# to the head or html phase
if tag == 'head':
# [3]
self.phase = self.HEAD
elif tag == 'html':
# [4]
self.phase = self.HTML
elif self.phase == self.HTML:
# if we are in the html tag, allow a head tag to move to
# the HEAD phase. If we get another html tag, then bail
# out
if tag == 'head':
# [3]
self.phase = self.HEAD
elif tag == 'html':
# [5]
self._terminate()
elif self.phase == self.HEAD:
# If we are in the head phase, look for the appropriate
# meta tag. If we get a head or body tag, bail out.
if tag == 'meta':
attrs_d = dict(attrs)
http_equiv = attrs_d.get('http-equiv', '').lower()
if http_equiv == YADIS_HEADER_NAME.lower():
raw_attr = attrs_d.get('content')
yadis_loc = substituteEntities(raw_attr)
# [6]
self.phase = self.FOUND
raise ParseDone(yadis_loc)
elif tag in ['head', 'html']:
# [5], [7]
self._terminate()
def feed(self, chars):
# [8]
if self.phase in [self.TERMINATED, self.FOUND]:
self._terminate()
return HTMLParser.feed(self, chars)
def findHTMLMeta(stream):
"""Look for a meta http-equiv tag with the YADIS header name.
@param stream: Source of the html text
@type stream: Object that implements a read() method that works
like file.read
@return: The URI from which to fetch the XRDS document
@rtype: str
@raises MetaNotFound: raised with the content that was
searched as the first parameter.
"""
parser = YadisHTMLParser()
chunks = []
while 1:
chunk = stream.read(CHUNK_SIZE)
if not chunk:
# End of file
break
chunks.append(chunk)
try:
parser.feed(chunk)
except HTMLParseError, why:
# HTML parse error, so bail
chunks.append(stream.read())
break
except ParseDone, why:
uri = why[0]
if uri is None:
# Parse finished, but we may need the rest of the file
chunks.append(stream.read())
break
else:
return uri
content = ''.join(chunks)
raise MetaNotFound(content)
########NEW FILE########
__FILENAME__ = services
# -*- test-case-name: openid.test.test_services -*-
from openid.yadis.filters import mkFilter
from openid.yadis.discover import discover, DiscoveryFailure
from openid.yadis.etxrd import parseXRDS, iterServices, XRDSError
def getServiceEndpoints(input_url, flt=None):
"""Perform the Yadis protocol on the input URL and return an
iterable of resulting endpoint objects.
@param flt: A filter object or something that is convertable to
a filter object (using mkFilter) that will be used to generate
endpoint objects. This defaults to generating BasicEndpoint
objects.
@param input_url: The URL on which to perform the Yadis protocol
@return: The normalized identity URL and an iterable of endpoint
objects generated by the filter function.
@rtype: (str, [endpoint])
@raises DiscoveryFailure: when Yadis fails to obtain an XRDS document.
"""
result = discover(input_url)
try:
endpoints = applyFilter(result.normalized_uri,
result.response_text, flt)
except XRDSError, err:
raise DiscoveryFailure(str(err), None)
return (result.normalized_uri, endpoints)
def applyFilter(normalized_uri, xrd_data, flt=None):
"""Generate an iterable of endpoint objects given this input data,
presumably from the result of performing the Yadis protocol.
@param normalized_uri: The input URL, after following redirects,
as in the Yadis protocol.
@param xrd_data: The XML text the XRDS file fetched from the
normalized URI.
@type xrd_data: str
"""
flt = mkFilter(flt)
et = parseXRDS(xrd_data)
endpoints = []
for service_element in iterServices(et):
endpoints.extend(
flt.getServiceEndpoints(normalized_uri, service_element))
return endpoints
########NEW FILE########
__FILENAME__ = xri
# -*- test-case-name: openid.test.test_xri -*-
"""Utility functions for handling XRIs.
@see: XRI Syntax v2.0 at the U{OASIS XRI Technical Committee<http://www.oasis-open.org/committees/tc_home.php?wg_abbrev=xri>}
"""
import re
XRI_AUTHORITIES = ['!', '=', '@', '+', '$', '(']
try:
unichr(0x10000)
except ValueError:
# narrow python build
UCSCHAR = [
(0xA0, 0xD7FF),
(0xF900, 0xFDCF),
(0xFDF0, 0xFFEF),
]
IPRIVATE = [
(0xE000, 0xF8FF),
]
else:
UCSCHAR = [
(0xA0, 0xD7FF),
(0xF900, 0xFDCF),
(0xFDF0, 0xFFEF),
(0x10000, 0x1FFFD),
(0x20000, 0x2FFFD),
(0x30000, 0x3FFFD),
(0x40000, 0x4FFFD),
(0x50000, 0x5FFFD),
(0x60000, 0x6FFFD),
(0x70000, 0x7FFFD),
(0x80000, 0x8FFFD),
(0x90000, 0x9FFFD),
(0xA0000, 0xAFFFD),
(0xB0000, 0xBFFFD),
(0xC0000, 0xCFFFD),
(0xD0000, 0xDFFFD),
(0xE1000, 0xEFFFD),
]
IPRIVATE = [
(0xE000, 0xF8FF),
(0xF0000, 0xFFFFD),
(0x100000, 0x10FFFD),
]
_escapeme_re = re.compile('[%s]' % (''.join(
map(lambda (m, n): u'%s-%s' % (unichr(m), unichr(n)),
UCSCHAR + IPRIVATE)),))
def identifierScheme(identifier):
"""Determine if this identifier is an XRI or URI.
@returns: C{"XRI"} or C{"URI"}
"""
if identifier.startswith('xri://') or (
identifier and identifier[0] in XRI_AUTHORITIES):
return "XRI"
else:
return "URI"
def toIRINormal(xri):
"""Transform an XRI to IRI-normal form."""
if not xri.startswith('xri://'):
xri = 'xri://' + xri
return escapeForIRI(xri)
_xref_re = re.compile('\((.*?)\)')
def _escape_xref(xref_match):
"""Escape things that need to be escaped if they're in a cross-reference.
"""
xref = xref_match.group()
xref = xref.replace('/', '%2F')
xref = xref.replace('?', '%3F')
xref = xref.replace('#', '%23')
return xref
def escapeForIRI(xri):
"""Escape things that need to be escaped when transforming to an IRI."""
xri = xri.replace('%', '%25')
xri = _xref_re.sub(_escape_xref, xri)
return xri
def toURINormal(xri):
"""Transform an XRI to URI normal form."""
return iriToURI(toIRINormal(xri))
def _percentEscapeUnicode(char_match):
c = char_match.group()
return ''.join(['%%%X' % (ord(octet),) for octet in c.encode('utf-8')])
def iriToURI(iri):
"""Transform an IRI to a URI by escaping unicode."""
# According to RFC 3987, section 3.1, "Mapping of IRIs to URIs"
return _escapeme_re.sub(_percentEscapeUnicode, iri)
def providerIsAuthoritative(providerID, canonicalID):
"""Is this provider ID authoritative for this XRI?
@returntype: bool
"""
# XXX: can't use rsplit until we require python >= 2.4.
lastbang = canonicalID.rindex('!')
parent = canonicalID[:lastbang]
return parent == providerID
def rootAuthority(xri):
"""Return the root authority for an XRI.
Example::
rootAuthority("xri://@example") == "xri://@"
@type xri: unicode
@returntype: unicode
"""
if xri.startswith('xri://'):
xri = xri[6:]
authority = xri.split('/', 1)[0]
if authority[0] == '(':
# Cross-reference.
# XXX: This is incorrect if someone nests cross-references so there
# is another close-paren in there. Hopefully nobody does that
# before we have a real xriparse function. Hopefully nobody does
# that *ever*.
root = authority[:authority.index(')') + 1]
elif authority[0] in XRI_AUTHORITIES:
# Other XRI reference.
root = authority[0]
else:
# IRI reference. XXX: Can IRI authorities have segments?
segments = authority.split('!')
segments = reduce(list.__add__,
map(lambda s: s.split('*'), segments))
root = segments[0]
return XRI(root)
def XRI(xri):
"""An XRI object allowing comparison of XRI.
Ideally, this would do full normalization and provide comparsion
operators as per XRI Syntax. Right now, it just does a bit of
canonicalization by ensuring the xri scheme is present.
@param xri: an xri string
@type xri: unicode
"""
if not xri.startswith('xri://'):
xri = 'xri://' + xri
return xri
########NEW FILE########
__FILENAME__ = xrires
# -*- test-case-name: openid.test.test_xrires -*-
"""XRI resolution.
"""
from urllib import urlencode
from openid import fetchers
from openid.yadis import etxrd
from openid.yadis.xri import toURINormal
from openid.yadis.services import iterServices
DEFAULT_PROXY = 'http://proxy.xri.net/'
class ProxyResolver(object):
"""Python interface to a remote XRI proxy resolver.
"""
def __init__(self, proxy_url=DEFAULT_PROXY):
self.proxy_url = proxy_url
def queryURL(self, xri, service_type=None):
"""Build a URL to query the proxy resolver.
@param xri: An XRI to resolve.
@type xri: unicode
@param service_type: The service type to resolve, if you desire
service endpoint selection. A service type is a URI.
@type service_type: str
@returns: a URL
@returntype: str
"""
# Trim off the xri:// prefix. The proxy resolver didn't accept it
# when this code was written, but that may (or may not) change for
# XRI Resolution 2.0 Working Draft 11.
qxri = toURINormal(xri)[6:]
hxri = self.proxy_url + qxri
args = {
# XXX: If the proxy resolver will ensure that it doesn't return
# bogus CanonicalIDs (as per Steve's message of 15 Aug 2006
# 11:13:42), then we could ask for application/xrd+xml instead,
# which would give us a bit less to process.
'_xrd_r': 'application/xrds+xml',
}
if service_type:
args['_xrd_t'] = service_type
else:
# Don't perform service endpoint selection.
args['_xrd_r'] += ';sep=false'
query = _appendArgs(hxri, args)
return query
def query(self, xri, service_types):
"""Resolve some services for an XRI.
Note: I don't implement any service endpoint selection beyond what
the resolver I'm querying does, so the Services I return may well
include Services that were not of the types you asked for.
May raise fetchers.HTTPFetchingError or L{etxrd.XRDSError} if
the fetching or parsing don't go so well.
@param xri: An XRI to resolve.
@type xri: unicode
@param service_types: A list of services types to query for. Service
types are URIs.
@type service_types: list of str
@returns: tuple of (CanonicalID, Service elements)
@returntype: (unicode, list of C{ElementTree.Element}s)
"""
# FIXME: No test coverage!
services = []
# Make a seperate request to the proxy resolver for each service
# type, as, if it is following Refs, it could return a different
# XRDS for each.
canonicalID = None
for service_type in service_types:
url = self.queryURL(xri, service_type)
response = fetchers.fetch(url)
if response.status != 200:
# XXX: sucks to fail silently.
# print "response not OK:", response
continue
et = etxrd.parseXRDS(response.body)
canonicalID = etxrd.getCanonicalID(xri, et)
some_services = list(iterServices(et))
services.extend(some_services)
# TODO:
# * If we do get hits for multiple service_types, we're almost
# certainly going to have duplicated service entries and
# broken priority ordering.
return canonicalID, services
def _appendArgs(url, args):
"""Append some arguments to an HTTP query.
"""
# to be merged with oidutil.appendArgs when we combine the projects.
if hasattr(args, 'items'):
args = args.items()
args.sort()
if len(args) == 0:
return url
# According to XRI Resolution section "QXRI query parameters":
#
# """If the original QXRI had a null query component (only a leading
# question mark), or a query component consisting of only question
# marks, one additional leading question mark MUST be added when
# adding any XRI resolution parameters."""
if '?' in url.rstrip('?'):
sep = '&'
else:
sep = '?'
return '%s%s%s' % (url, sep, urlencode(args))
########NEW FILE########
__FILENAME__ = store
#!/usr/bin/python
#
# Copyright 2007, Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
An OpenIDStore implementation that uses the datastore as its backing store.
Stores associations, nonces, and authentication tokens.
OpenIDStore is an interface from JanRain's OpenID python library:
http://openidenabled.com/python-openid/
For more, see openid/store/interface.py in that library.
"""
import datetime
from openid.association import Association as OpenIDAssociation
from openid.store.interface import OpenIDStore
from openid.store import nonce
from google.appengine.ext import db
# number of associations and nonces to clean up in a single request.
CLEANUP_BATCH_SIZE = 50
class Association(db.Model):
"""An association with another OpenID server, either a consumer or a provider.
"""
url = db.LinkProperty()
handle = db.StringProperty()
association = db.TextProperty()
created = db.DateTimeProperty(auto_now_add=True)
class UsedNonce(db.Model):
"""An OpenID nonce that has been used.
"""
server_url = db.LinkProperty()
timestamp = db.DateTimeProperty()
salt = db.StringProperty()
class DatastoreStore(OpenIDStore):
"""An OpenIDStore implementation that uses the datastore. See
openid/store/interface.py for in-depth descriptions of the methods.
They follow the OpenID python library's style, not Google's style, since
they override methods defined in the OpenIDStore class.
"""
def storeAssociation(self, server_url, association):
"""
This method puts a C{L{Association <openid.association.Association>}}
object into storage, retrievable by server URL and handle.
"""
assoc = Association(url=server_url,
handle=association.handle,
association=association.serialize())
assoc.put()
def getAssociation(self, server_url, handle=None):
"""
This method returns an C{L{Association <openid.association.Association>}}
object from storage that matches the server URL and, if specified, handle.
It returns C{None} if no such association is found or if the matching
association is expired.
If no handle is specified, the store may return any association which
matches the server URL. If multiple associations are valid, the
recommended return value for this method is the one that will remain valid
for the longest duration.
"""
query = Association.all().filter('url', server_url)
if handle:
query.filter('handle', handle)
results = query.fetch(1)
if results:
association = OpenIDAssociation.deserialize(results[0].association)
if association.getExpiresIn() > 0:
# hasn't expired yet
return association
return None
def removeAssociation(self, server_url, handle):
"""
This method removes the matching association if it's found, and returns
whether the association was removed or not.
"""
query = Association.gql('WHERE url = :1 AND handle = :2',
server_url, handle)
return self._delete_first(query)
def useNonce(self, server_url, timestamp, salt):
"""Called when using a nonce.
This method should return C{True} if the nonce has not been
used before, and store it for a while to make sure nobody
tries to use the same value again. If the nonce has already
been used or the timestamp is not current, return C{False}.
You may use L{openid.store.nonce.SKEW} for your timestamp window.
@change: In earlier versions, round-trip nonces were used and
a nonce was only valid if it had been previously stored
with C{storeNonce}. Version 2.0 uses one-way nonces,
requiring a different implementation here that does not
depend on a C{storeNonce} call. (C{storeNonce} is no
longer part of the interface.)
@param server_url: The URL of the server from which the nonce
originated.
@type server_url: C{str}
@param timestamp: The time that the nonce was created (to the
nearest second), in seconds since January 1 1970 UTC.
@type timestamp: C{int}
@param salt: A random string that makes two nonces from the
same server issued during the same second unique.
@type salt: str
@return: Whether or not the nonce was valid.
@rtype: C{bool}
"""
query = UsedNonce.gql(
'WHERE server_url = :1 AND salt = :2 AND timestamp >= :3',
server_url, salt, self._expiration_datetime())
return query.fetch(1) == []
def cleanupNonces(self):
"""Remove expired nonces from the store.
Discards any nonce from storage that is old enough that its
timestamp would not pass L{useNonce}.
This method is not called in the normal operation of the
library. It provides a way for store admins to keep
their storage from filling up with expired data.
@return: the number of nonces expired.
@returntype: int
"""
query = UsedNonce.gql('WHERE timestamp < :1', self._expiration_datetime())
return self._cleanup_batch(query)
def cleanupAssociations(self):
"""Remove expired associations from the store.
This method is not called in the normal operation of the
library. It provides a way for store admins to keep
their storage from filling up with expired data.
@return: the number of associations expired.
@returntype: int
"""
query = Association.gql('WHERE created < :1', self._expiration_datetime())
return self._cleanup_batch(query)
def cleanup(self):
"""Shortcut for C{L{cleanupNonces}()}, C{L{cleanupAssociations}()}.
This method is not called in the normal operation of the
library. It provides a way for store admins to keep
their storage from filling up with expired data.
"""
return self.cleanupNonces(), self.cleanupAssociations()
def _delete_first(self, query):
"""Deletes the first result for the given query.
Returns True if an entity was deleted, false if no entity could be deleted
or if the query returned no results.
"""
results = query.fetch(1)
if results:
try:
results[0].delete()
return True
except db.Error:
return False
else:
return False
def _cleanup_batch(self, query):
"""Deletes the first batch of entities that match the given query.
Returns the number of entities that were deleted.
"""
to_delete = list(query.fetch(CLEANUP_BATCH_SIZE))
# can't use batch delete since they're all root entities :/
for entity in to_delete:
entity.delete()
return len(to_delete)
def _expiration_datetime(self):
"""Returns the current expiration date for nonces and associations.
"""
return datetime.datetime.now() - datetime.timedelta(seconds=nonce.SKEW)
########NEW FILE########
| [
"[email protected]"
] | |
2a3cb3835f9c19bc32575a438d0a76d34366cd63 | e23a4f57ce5474d468258e5e63b9e23fb6011188 | /095_os_and_sys/_exercises/templates/Programming_Python/04_File and Directory Tools/04_002_Ensuring file closure Exception handlers and context managers.py | 1f41b027f691c845469a9997d5f06857f17bb216 | [] | no_license | syurskyi/Python_Topics | 52851ecce000cb751a3b986408efe32f0b4c0835 | be331826b490b73f0a176e6abed86ef68ff2dd2b | refs/heads/master | 2023-06-08T19:29:16.214395 | 2023-05-29T17:09:11 | 2023-05-29T17:09:11 | 220,583,118 | 3 | 2 | null | 2023-02-16T03:08:10 | 2019-11-09T02:58:47 | Python | UTF-8 | Python | false | false | 609 | py | # myfile = open(filename, 'w')
# ___
# ...process myfile...
# finally:
# myfile.close()
#
# with open(filename, 'w') as myfile:
# ...process myfile, auto-closed on statement exit...
#
# myfile = open(filename, 'w') # traditional form
# ...process myfile...
# myfile.close()
# with open(filename) as myfile: # context manager form
# ...process myfile...
#
# with A() as a, B() as b:
# ...statements...
#
# with A() as a:
# with B() as b:
# ...statements...
#
# with open('data') as fin, open('results', 'w') as fout:
# for line in fin:
# fout.write(transform(line))
| [
"[email protected]"
] | |
3688f07341087091eea0c443b07b0e57449140dd | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02699/s068976853.py | b2a893ced4293b4b0b5cd753623e0bc052df3729 | [] | no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 233 | py | import math
def fact(n):
ans = 1
for i in range(2, n+1):
ans*= i
return ans
def comb(n, c):
return fact(n)//(fact(n-c)*c)
s,w = map(int, input().split())
if(s >w):
print('safe')
else:
print('unsafe')
| [
"[email protected]"
] | |
7588f5b215b1669a718d62698603a3e391129fd8 | 925767da3d33266364f4af5ea5ac161245c598ad | /deshi/deshi/settings.py | 8ce101a6eb8b87962b35c3d1f54201f270f08ed8 | [] | no_license | NicholasPiano/karate-master | facce4100ac92c0f39f1328ee2838126c153538d | 137ebbd1f274dd79a367f9bbab3651ea365fce83 | refs/heads/master | 2016-09-05T11:24:34.843555 | 2014-08-23T19:24:09 | 2014-08-23T19:24:09 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,969 | py | """
Django settings for deshi project.
For more information on this file, see
https://docs.djangoproject.com/en/1.6/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.6/ref/settings/
"""
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
BASE_DIR = os.path.dirname(os.path.dirname(__file__))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.6/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = '5^*pq(en6o74-3b&8mjn*46jqm*g1o2+5f8s9ws*+8m#7)mleu'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
TEMPLATE_DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = (
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
)
MIDDLEWARE_CLASSES = (
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
)
ROOT_URLCONF = 'deshi.urls'
WSGI_APPLICATION = 'deshi.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.6/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Internationalization
# https://docs.djangoproject.com/en/1.6/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.6/howto/static-files/
STATIC_URL = '/static/'
| [
"[email protected]"
] | |
f8c73640ebe25fa7c4573ab066b1ab063467fe7c | 248c535f3612c646bccadecafdca649fd788bb1f | /.history/app/models_20210927050245.py | ca7953fdc6715ad70a6fdf536c24ba6ca96e517f | [
"MIT"
] | permissive | GraceOswal/pitch-perfect | 3b923e4de5fff1a405dcb54374a1ba0522232025 | d781c6e0f55c11f2a5e5dceb952f6b2de3c47c3b | refs/heads/master | 2023-08-16T01:42:18.742154 | 2021-10-01T06:59:11 | 2021-10-01T06:59:11 | 410,224,294 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 105 | py | from . import db
# connect class user to pitchperfect database
class User(db.Model):
__table__ = '' | [
"[email protected]"
] | |
38271f663558957f927383295f723ae4eee88114 | 74be814f7cd10d3c91a53460bd6698aa8bc95704 | /剑指offer/面试题28. 对称的二叉树.py | 11a5e592f54dc79fb7ac584d9dd760cf25be2339 | [] | no_license | weiyuyan/LeetCode | 7202f7422bc3bef6bd35ea299550b51905401656 | 19db0e78826d3e3d27d2574abd9d461eb41458d1 | refs/heads/master | 2020-12-03T17:10:53.738507 | 2020-05-27T08:28:36 | 2020-05-27T08:28:36 | 231,402,839 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,587 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# author：ShidongDu time:2020/2/17
'''
请实现一个函数，用来判断一棵二叉树是不是对称的。如果一棵二叉树和它的镜像一样，那么它是对称的。
例如，二叉树 [1,2,2,3,4,4,3] 是对称的。
1
/ \
2 2
/ \ / \
3 4 4 3
但是下面这个 [1,2,2,null,3,null,3] 则不是镜像对称的:
1
/ \
2 2
\ \
3 3
示例 1：
输入：root = [1,2,2,3,4,4,3]
输出：true
示例 2：
输入：root = [1,2,2,null,3,null,3]
输出：false
限制：
0 <= 节点个数 <= 1000
注意：本题与主站 101 题相同：https://leetcode-cn.com/problems/symmetric-tree/
'''
# Definition for a binary tree node.
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def isSymmetric(self, root: TreeNode) -> bool:
return self.is_Symmetrical(root, root)
def is_Symmetrical(self, root1: TreeNode, root2: TreeNode):
# 全为空，自然返回True
if not root1 and not root2: return True
# 一个为空，另一个不为空，返回False
if not root1 or not root2: return False
# 两个节点的值不相同，返回False
if root1.val != root2.val: return False
# 全不为空
# root1 按照 根-左-右 的先序遍历来， root2按照 根-右-左 的对称先序遍历来
return self.is_Symmetrical(root1.left, root2.right) and self.is_Symmetrical(root1.right, root2.left)
| [
"[email protected]"
] | |
8baeb5e0f4856447b4b776a2cfd340d842904525 | 350db570521d3fc43f07df645addb9d6e648c17e | /0191_Number_of_1_Bits/solution_test.py | c90810e481c8eb1413275fac582e235cd1db37c7 | [] | no_license | benjaminhuanghuang/ben-leetcode | 2efcc9185459a1dd881c6e2ded96c42c5715560a | a2cd0dc5e098080df87c4fb57d16877d21ca47a3 | refs/heads/master | 2022-12-10T02:30:06.744566 | 2022-11-27T04:06:52 | 2022-11-27T04:06:52 | 236,252,145 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 364 | py |
'''
191. Number of 1 Bits
Level: Easy
https://leetcode.com/problems/number-of-1-bits
'''
import unittest
class TestSum(unittest.TestCase):
def test_sum(self):
self.assertEqual(sum([1, 2, 3]), 6, "Should be 6")
def test_sum_tuple(self):
self.assertEqual(sum((1, 2, 2)), 6, "Should be 6")
if __name__ == '__main__':
unittest.main() | [
"[email protected]"
] | |
85bf6770328eb00c91e0cc62a4016f0dc533d828 | a6982e37fdc5359fa89b9f0587f75ff4889a849d | /venv/Lib/site-packages/pandas/tests/series/indexing/test_loc.py | 088406e0a1db670b2a260bce1a69e073370b2a34 | [
"Apache-2.0"
] | permissive | 1pani/fund-rank-dashboard | 5d7ca6107013d5f82e7c54b3199d5506f34175c3 | 95c17bbee6e09ae4eb5738c519d3b6c651e8257c | refs/heads/master | 2022-12-21T19:36:05.446703 | 2019-10-19T09:45:30 | 2019-10-19T09:45:30 | 137,461,621 | 4 | 1 | Apache-2.0 | 2022-12-15T09:19:53 | 2018-06-15T08:26:46 | Python | UTF-8 | Python | false | false | 4,078 | py | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import pytest
import numpy as np
import pandas as pd
from pandas import (Series, Timestamp)
from pandas.compat import lrange
from pandas.util.testing import (assert_series_equal)
def test_loc_getitem(test_data):
inds = test_data.series.index[[3, 4, 7]]
assert_series_equal(
test_data.series.loc[inds],
test_data.series.reindex(inds))
assert_series_equal(test_data.series.iloc[5::2], test_data.series[5::2])
# slice with indices
d1, d2 = test_data.ts.index[[5, 15]]
result = test_data.ts.loc[d1:d2]
expected = test_data.ts.truncate(d1, d2)
assert_series_equal(result, expected)
# boolean
mask = test_data.series > test_data.series.median()
assert_series_equal(test_data.series.loc[mask], test_data.series[mask])
# ask for index value
assert test_data.ts.loc[d1] == test_data.ts[d1]
assert test_data.ts.loc[d2] == test_data.ts[d2]
def test_loc_getitem_not_monotonic(test_data):
d1, d2 = test_data.ts.index[[5, 15]]
ts2 = test_data.ts[::2][[1, 2, 0]]
pytest.raises(KeyError, ts2.loc.__getitem__, slice(d1, d2))
pytest.raises(KeyError, ts2.loc.__setitem__, slice(d1, d2), 0)
def test_loc_getitem_setitem_integer_slice_keyerrors():
s = Series(np.random.randn(10), index=lrange(0, 20, 2))
# this is OK
cp = s.copy()
cp.iloc[4:10] = 0
assert (cp.iloc[4:10] == 0).all()
# so is this
cp = s.copy()
cp.iloc[3:11] = 0
assert (cp.iloc[3:11] == 0).values.all()
result = s.iloc[2:6]
result2 = s.loc[3:11]
expected = s.reindex([4, 6, 8, 10])
assert_series_equal(result, expected)
assert_series_equal(result2, expected)
# non-monotonic, raise KeyError
s2 = s.iloc[lrange(5) + lrange(5, 10)[::-1]]
pytest.raises(KeyError, s2.loc.__getitem__, slice(3, 11))
pytest.raises(KeyError, s2.loc.__setitem__, slice(3, 11), 0)
def test_loc_getitem_iterator(test_data):
idx = iter(test_data.series.index[:10])
result = test_data.series.loc[idx]
assert_series_equal(result, test_data.series[:10])
def test_loc_setitem_boolean(test_data):
mask = test_data.series > test_data.series.median()
result = test_data.series.copy()
result.loc[mask] = 0
expected = test_data.series
expected[mask] = 0
assert_series_equal(result, expected)
def test_loc_setitem_corner(test_data):
inds = list(test_data.series.index[[5, 8, 12]])
test_data.series.loc[inds] = 5
pytest.raises(Exception, test_data.series.loc.__setitem__,
inds + ['foo'], 5)
def test_basic_setitem_with_labels(test_data):
indices = test_data.ts.index[[5, 10, 15]]
cp = test_data.ts.copy()
exp = test_data.ts.copy()
cp[indices] = 0
exp.loc[indices] = 0
assert_series_equal(cp, exp)
cp = test_data.ts.copy()
exp = test_data.ts.copy()
cp[indices[0]:indices[2]] = 0
exp.loc[indices[0]:indices[2]] = 0
assert_series_equal(cp, exp)
# integer indexes, be careful
s = Series(np.random.randn(10), index=lrange(0, 20, 2))
inds = [0, 4, 6]
arr_inds = np.array([0, 4, 6])
cp = s.copy()
exp = s.copy()
s[inds] = 0
s.loc[inds] = 0
assert_series_equal(cp, exp)
cp = s.copy()
exp = s.copy()
s[arr_inds] = 0
s.loc[arr_inds] = 0
assert_series_equal(cp, exp)
inds_notfound = [0, 4, 5, 6]
arr_inds_notfound = np.array([0, 4, 5, 6])
pytest.raises(Exception, s.__setitem__, inds_notfound, 0)
pytest.raises(Exception, s.__setitem__, arr_inds_notfound, 0)
# GH12089
# with tz for values
s = Series(pd.date_range("2011-01-01", periods=3, tz="US/Eastern"),
index=['a', 'b', 'c'])
s2 = s.copy()
expected = Timestamp('2011-01-03', tz='US/Eastern')
s2.loc['a'] = expected
result = s2.loc['a']
assert result == expected
s2 = s.copy()
s2.iloc[0] = expected
result = s2.iloc[0]
assert result == expected
s2 = s.copy()
s2['a'] = expected
result = s2['a']
assert result == expected
| [
"[email protected]"
] | |
5254c10860b55877dc9906e14f80581bf5a37ac8 | 3dedcaa542529534b53f0612458b3b5dcaaa8d49 | /icevision/models/mmdet/common/mask/two_stage/__init__.py | 939fe5d03f1b56bfade4f752f82d41414d35a7a5 | [
"Apache-2.0"
] | permissive | jerbly/icevision | 20801e7acb6ef7dd8efe58ac9a398967689cbc9c | f97edd52c152f438d02bcce8a2e40b90dc304d01 | refs/heads/master | 2023-04-01T08:16:28.088970 | 2021-04-13T12:15:40 | 2021-04-13T12:15:40 | 314,865,574 | 0 | 1 | Apache-2.0 | 2023-03-29T15:45:46 | 2020-11-21T17:25:37 | Python | UTF-8 | Python | false | false | 114 | py | from icevision.models.mmdet.common.mask import *
from icevision.models.mmdet.common.mask.two_stage.model import *
| [
"[email protected]"
] | |
31406d7aa715df53849ac4f2affbec4743320fcb | 04097eda667d795407c71dbf7b8c408a55661e35 | /prb_srv_x_T_legacy.py | 7a9109fbb219f71ff13fa21410bbe1f7dff3579f | [] | no_license | synsrv/src_thrs-dyn-analysis | b0f76aded935093ebd15a1aa0d525ca07bbe98ee | 9a286ec6f8259a6f7b73fb75ffe99c965263f4a7 | refs/heads/master | 2020-06-22T02:32:26.379781 | 2019-07-18T15:22:28 | 2019-07-18T15:22:28 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,114 | py |
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as pl
from matplotlib import rc
rc('text', usetex=True)
pl.rcParams['text.latex.preamble'] = [
r'\usepackage{tgheros}',
r'\usepackage{sansmath}',
r'\sansmath'
r'\usepackage{siunitx}',
r'\sisetup{detect-all}',
]
import argparse, sys, os, itertools, pickle
import numpy as np
data_dirs = sorted(['data/'+pth for pth in next(os.walk("data/"))[1]])
fig, ax = pl.subplots()
bin_w = 1
for dpath in data_dirs:
try:
with open(dpath+'/namespace.p', 'rb') as pfile:
nsp=pickle.load(pfile)
with open(dpath+'/lts.p', 'rb') as pfile:
lts_df=np.array(pickle.load(pfile))
# discard synapses present at beginning
lts_df = lts_df[lts_df[:,1]>0]
# only take synapses grown in first half of simulation
t_split = nsp['Nsteps']/2
lts_df = lts_df[lts_df[:,3]<t_split]
lts = lts_df[:,2] - lts_df[:,3]
assert np.min(lts) > 0
lts[lts>t_split]=t_split
bins = np.arange(1,t_split+bin_w,bin_w)
counts, edges = np.histogram(lts,
bins=bins,
density=False)
srv = 1. - np.cumsum(counts)/float(np.sum(counts))
label = str(nsp['bn_sig'])
centers = (edges[:-1] + edges[1:])/2.
ax.plot(centers, srv, '.', label=label)
except FileNotFoundError:
print(dpath[-4:], "reports: Error loading namespace")
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('lifetime [steps]')
ax.set_ylabel('relative frequency')
directory = 'figures/prb_srv_single/'
if not os.path.exists(directory):
os.makedirs(directory)
pl.legend()
fname = dpath[-4:]
fig.savefig(directory+'/'+fname+'.png', dpi=150,
bbox_inches='tight')
| [
"[email protected]"
] | |
a56fc1a4e5b64686820edca3055851034da5cf31 | ade10d077441c1d36d027c05df458e942b904a1d | /scripts/classifcation_pos_all_vs_one.py | cbd024b089929aa4abb8a5011aef697193f32b07 | [
"MIT"
] | permissive | nmningmei/metacognition | dabadca913e404994f8d53167e5f36f551590def | 734082e247cc7fc9d277563e2676e10692617a3f | refs/heads/master | 2022-07-14T06:51:42.508132 | 2021-11-14T09:40:13 | 2021-11-14T09:40:13 | 197,187,294 | 2 | 1 | MIT | 2022-06-21T23:01:56 | 2019-07-16T12:12:11 | Python | UTF-8 | Python | false | false | 6,544 | py | # -*- coding: utf-8 -*-
"""
Created on Sun Jul 15 16:02:16 2018
@author: ning
"""
import os
working_dir = ''
import pandas as pd
pd.options.mode.chained_assignment = None
import numpy as np
from utils import (classification_simple_logistic)
saving_dir = '../results/all_vs_one'
if not os.path.exists(saving_dir):
os.mkdir(saving_dir)
# Exp 1
for participant in ['AC', 'CL', 'FW', 'HB', 'KK', 'LM', 'MC', 'MP1', 'MP2', 'NN', 'RP','SD', 'TJ', 'TS', 'WT']:
experiment = 'pos'
df = pd.read_csv(os.path.join(working_dir,'../data/PoSdata.csv'))
df = df[df.columns[1:]]
df.columns = ['participant',
'blocks',
'trials',
'firstgabor',
'success',
'tilted',
'correct',
'RT_correct',
'awareness',
'RT_awareness',
'confidence',
'RT_confidence']
df_sub = df[df['participant'] == participant]
# make sure all the attributes are either 0 or 1
df_sub.loc[:,'success' ] = df_sub.loc[:,'success' ].values - 1
df_sub.loc[:,'awareness' ] = df_sub.loc[:,'awareness' ].values - 1
df_sub.loc[:,'confidence'] = df_sub.loc[:,'confidence'].values - 1
# use success, awareness, and confidence as features
np.random.seed(12345)
# use all judgement features
feature_names = [
'correct',
'awareness',
'confidence',]
target_name = 'success'
results = dict(sub = [],
model = [],
score = [],
window = [],
chance = [],
feature = [],
)
for n_back in np.arange(1,5): # loop through the number of trials looking back
# this is the part that is redundent and the code is long
results = classification_simple_logistic(
df_sub,
feature_names,
target_name,
results,
participant,
experiment,
window=n_back,
chance = False,
)
temp = pd.DataFrame(results)
temp.to_csv(os.path.join(saving_dir,'pos_3_1_features (experiment score)_{}.csv'.format(participant)),index=False) # save as a csv
# use correct as features
feature_names = [
'correct',
]
target_name = 'success'
results = dict(sub = [],
model = [],
score = [],
window = [],
chance = [],
feature = [],
)
for n_back in np.arange(1,5): # loop through the number of trials looking back
# this is the part that is redundent and the code is long
results = classification_simple_logistic(
df_sub,
feature_names,
target_name,
results,
participant,
experiment,
window=n_back,
chance = False,
)
temp = pd.DataFrame(results)
temp.to_csv(os.path.join(saving_dir,'pos_correct_features (experiment score)_{}.csv'.format(participant)),index=False) # save as a csv
np.random.seed(12345)
# use awareness as features
feature_names = [
'awareness',]
target_name = 'success'
results = dict(sub = [],
model = [],
score = [],
window = [],
chance = [],
feature = [],
)
for n_back in np.arange(1,5): # loop through the number of trials looking back
# this is the part that is redundent and the code is long
results = classification_simple_logistic(
df_sub,
feature_names,
target_name,
results,
participant,
experiment,
window=n_back,
chance = False,
)
temp = pd.DataFrame(results)
temp.to_csv(os.path.join(saving_dir,'pos_awareness_features (experiment score)_{}.csv'.format(participant)),index=False) # save as a csv
# use confidence as features
feature_names = [
'confidence',]
target_name = 'success'
results = dict(sub = [],
model = [],
score = [],
window = [],
chance = [],
feature = [],
)
for n_back in np.arange(1,5): # loop through the number of trials looking back
# this is the part that is redundent and the code is long
results = classification_simple_logistic(
df_sub,
feature_names,
target_name,
results,
participant,
experiment,
window=n_back,
chance = False,
)
temp = pd.DataFrame(results)
temp.to_csv(os.path.join(saving_dir,'pos_confidence_features (experiment score)_{}.csv'.format(participant)),index=False) # save as a csv
| [
"[email protected]"
] | |
a2bfe17cf36e9ac576e0431a529c25c270f4c096 | 6ef3b1919e7acbc72e5706b2dc6d716f8929e3d2 | /transformers/training_args_tf.py | 94e5c3f320ec74ca7352f0d00c07971e15815b7f | [
"MIT"
] | permissive | linshaoxin-maker/taas | 04f7dcc7c0d2818718e6b245531e017ca5370231 | 34e11fab167a7beb78fbe6991ff8721dc9208793 | refs/heads/main | 2023-01-19T20:58:04.459980 | 2020-11-27T02:28:36 | 2020-11-27T02:28:36 | 329,522,465 | 6 | 0 | MIT | 2021-01-14T06:02:08 | 2021-01-14T06:02:07 | null | UTF-8 | Python | false | false | 9,501 | py | import warnings
from dataclasses import dataclass, field
from typing import Tuple
from .file_utils import cached_property, is_tf_available, tf_required
from .training_args import TrainingArguments
from .utils import logging
logger = logging.get_logger(__name__)
if is_tf_available():
import tensorflow as tf
@dataclass
class TFTrainingArguments(TrainingArguments):
"""
TrainingArguments is the subset of the arguments we use in our example scripts
**which relate to the training loop itself**.
Using :class:`~transformers.HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on the command line.
Parameters:
output_dir (:obj:`str`):
The output directory where the model predictions and checkpoints will be written.
overwrite_output_dir (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`True`, overwrite the content of the output directory. Use this to continue training if
:obj:`output_dir` points to a checkpoint directory.
do_train (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to run training or not.
do_eval (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to run evaluation on the dev set or not.
do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to run predictions on the test set or not.
evaluate_during_training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to run evaluation during training at each logging step or not.
per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8):
The batch size per GPU/TPU core/CPU for training.
per_device_eval_batch_size (:obj:`int`, `optional`, defaults to 8):
The batch size per GPU/TPU core/CPU for evaluation.
gradient_accumulation_steps: (:obj:`int`, `optional`, defaults to 1):
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.
learning_rate (:obj:`float`, `optional`, defaults to 5e-5):
The initial learning rate for Adam.
weight_decay (:obj:`float`, `optional`, defaults to 0):
The weight decay to apply (if not zero).
adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8):
Epsilon for the Adam optimizer.
max_grad_norm (:obj:`float`, `optional`, defaults to 1.0):
Maximum gradient norm (for gradient clipping).
num_train_epochs(:obj:`float`, `optional`, defaults to 3.0):
Total number of training epochs to perform.
max_steps (:obj:`int`, `optional`, defaults to -1):
If set to a positive number, the total number of training steps to perform. Overrides
:obj:`num_train_epochs`.
warmup_steps (:obj:`int`, `optional`, defaults to 0):
Number of steps used for a linear warmup from 0 to :obj:`learning_rate`.
logging_dir (:obj:`str`, `optional`):
Tensorboard log directory. Will default to `runs/**CURRENT_DATETIME_HOSTNAME**`.
logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`):
Wheter to log and evalulate the first :obj:`global_step` or not.
logging_steps (:obj:`int`, `optional`, defaults to 500):
Number of update steps between two logs.
save_steps (:obj:`int`, `optional`, defaults to 500):
Number of updates steps before two checkpoint saves.
save_total_limit (:obj:`int`, `optional`):
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
:obj:`output_dir`.
no_cuda (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to not use CUDA even when it is available or not.
seed (:obj:`int`, `optional`, defaults to 42):
Random seed for initialization.
fp16 (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to use 16-bit (mixed) precision training (through NVIDIA apex) instead of 32-bit training.
fp16_opt_level (:obj:`str`, `optional`, defaults to 'O1'):
For :obj:`fp16` training, apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details
on the `apex documentation <https://nvidia.github.io/apex/amp.html>`__.
local_rank (:obj:`int`, `optional`, defaults to -1):
During distributed training, the rank of the process.
tpu_num_cores (:obj:`int`, `optional`):
When training on TPU, the mumber of TPU cores (automatically passed by launcher script).
debug (:obj:`bool`, `optional`, defaults to :obj:`False`):
Wheter to activate the trace to record computation graphs and profiling information or not.
dataloader_drop_last (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
or not.
eval_steps (:obj:`int`, `optional`, defaults to 1000):
Number of update steps before two evaluations.
past_index (:obj:`int`, `optional`, defaults to -1):
Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can
make use of the past hidden states for their predictions. If this argument is set to a positive int, the
``Trainer`` will use the corresponding output (usually index 2) as the past state and feed it to the model
at the next training step under the keyword argument ``mems``.
tpu_name (:obj:`str`, `optional`):
The name of the TPU the process is running on.
run_name (:obj:`str`, `optional`):
A descriptor for the run. Notably used for wandb logging.
"""
tpu_name: str = field(
default=None,
metadata={"help": "Name of TPU"},
)
@cached_property
@tf_required
def _setup_strategy(self) -> Tuple["tf.distribute.Strategy", int]:
logger.info("Tensorflow: setting up strategy")
gpus = tf.config.list_physical_devices("GPU")
if self.no_cuda:
strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
else:
try:
if self.tpu_name:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name)
else:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
elif len(gpus) == 0:
strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
elif len(gpus) == 1:
strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
elif len(gpus) > 1:
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
strategy = tf.distribute.MirroredStrategy()
else:
raise ValueError("Cannot find the proper strategy please check your environment properties.")
return strategy
@property
@tf_required
def strategy(self) -> "tf.distribute.Strategy":
"""
The strategy used for distributed training.
"""
return self._setup_strategy
@property
@tf_required
def n_replicas(self) -> int:
"""
The number of replicas (CPUs, GPUs or TPU cores) used in this training.
"""
return self._setup_strategy.num_replicas_in_sync
@property
def train_batch_size(self) -> int:
"""
The actual batch size for training (may differ from :obj:`per_gpu_train_batch_size` in distributed training).
"""
if self.per_gpu_train_batch_size:
logger.warning(
"Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
"version. Using `--per_device_train_batch_size` is preferred."
)
per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
return per_device_batch_size * self.n_replicas
@property
def eval_batch_size(self) -> int:
"""
The actual batch size for evaluation (may differ from :obj:`per_gpu_eval_batch_size` in distributed training).
"""
if self.per_gpu_eval_batch_size:
logger.warning(
"Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
"version. Using `--per_device_eval_batch_size` is preferred."
)
per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
return per_device_batch_size * self.n_replicas
@property
@tf_required
def n_gpu(self) -> int:
"""
The number of replicas (CPUs, GPUs or TPU cores) used in this training.
"""
warnings.warn(
"The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.",
FutureWarning,
)
return self._setup_strategy.num_replicas_in_sync
| [
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] | |
1f2da7d9fbaf2354d8c00fea11e387e58c256aaa | d03ce5c5c3750e60ba9baa958e5445b98ba14462 | /user/views.py | f25749f7fb2516d57bb3384433e3d5e046bc0143 | [] | no_license | alxayeed/covid-room-finder | dbd22ebb702bf588384d6fb8d917c29568cc22a6 | 3ff541d76e342d6bd0b3d7221577b06ec0806837 | refs/heads/main | 2023-03-30T12:33:57.152717 | 2021-02-21T13:08:00 | 2021-02-21T13:08:00 | 339,812,785 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,407 | py | from django.shortcuts import render, redirect
from django.http import HttpResponse
from .forms import RegisterForm
from django.contrib.auth import login, logout, authenticate
from django.contrib import messages
def register_user(request):
form = RegisterForm()
if request.method == "POST":
form = RegisterForm(request.POST)
if form.is_valid():
username = form.cleaned_data.get('username')
password = form.cleaned_data.get('password')
form.save()
login(request, authenticate(
request, username=username, password=password))
return redirect('index')
else:
return render(request, 'user/register_form.html', {'form': form})
return render(request, 'user/register_form.html', {'form': form})
def login_user(request):
if request.method == 'POST':
username = request.POST.get('username')
password = request.POST.get('password')
user = authenticate(request, username=username, password=password)
if user is not None:
login(request, user)
print(request.user.is_authenticated)
return redirect('index')
else:
messages.error(request, 'Username or Password is incorrect')
return render(request, 'user/login.html')
def logout_user(request):
logout(request)
return redirect('login')
| [
"[email protected]"
] | |
fe79280e5ce2a4759497da529a8e4910958ffdf0 | f543c5593b40622a219f7c2961b2cdf43766eae4 | /tfx/examples/penguin/penguin_pipeline_kubeflow_gcp.py | 5772acd21124664170f449da68336be4c7828572 | [
"Apache-2.0"
] | permissive | 2k0ri/tfx | 939e52d481d6319e30296bce5740f4724304cbac | 7474ba28560293fa32c2e5ceffa0f82ae5dc3b86 | refs/heads/master | 2023-02-22T06:06:26.654711 | 2021-01-27T21:59:38 | 2021-01-27T22:00:27 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 16,556 | py | # Copyright 2020 Google LLC. 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.
"""Penguin example using TFX."""
import copy
import os
from typing import Dict, List, Optional, Text
from absl import app
from absl import flags
import tensorflow_model_analysis as tfma
from tfx.components import CsvExampleGen
from tfx.components import Evaluator
from tfx.components import ExampleValidator
from tfx.components import Pusher
from tfx.components import ResolverNode
from tfx.components import SchemaGen
from tfx.components import StatisticsGen
from tfx.components import Trainer
from tfx.components import Transform
from tfx.dsl.components.base import executor_spec
from tfx.dsl.experimental import latest_blessed_model_resolver
from tfx.extensions.google_cloud_ai_platform.pusher import executor as ai_platform_pusher_executor
from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor
from tfx.extensions.google_cloud_ai_platform.tuner.component import Tuner
from tfx.orchestration import data_types
from tfx.orchestration import pipeline
from tfx.orchestration.kubeflow import kubeflow_dag_runner
from tfx.proto import tuner_pb2
from tfx.types import Channel
from tfx.types.standard_artifacts import Model
from tfx.types.standard_artifacts import ModelBlessing
from tfx.utils.dsl_utils import external_input
FLAGS = flags.FLAGS
flags.DEFINE_bool('distributed_training', False,
'If True, enable distributed training.')
_pipeline_name = 'penguin_kubeflow_gcp'
# Directory and data locations (uses Google Cloud Storage).
_input_bucket = 'gs://my-bucket'
_output_bucket = 'gs://my-bucket'
_data_root = os.path.join(_input_bucket, 'penguin', 'data')
# Directory and data locations. This example assumes all of the
# example code and metadata library is relative to $HOME, but you can store
# these files anywhere on your local filesystem.
_tfx_root = os.path.join(_output_bucket, 'tfx')
_pipeline_root = os.path.join(_tfx_root, _pipeline_name)
# Google Cloud Platform project id to use when deploying this pipeline.
# This project configuration is for running Dataflow, AIP Training service and
# Prediction service. Note that the AIP Vizier service (CloudTuner) is
# separately configured in the module file.
_project_id = 'my-gcp-project'
# Python module file to inject customized logic into the TFX components. The
# Transform, Trainer and Tuner all require user-defined functions to run
# successfully. Copy this from the current directory to a GCS bucket and update
# the location below.
_module_file = os.path.join(_input_bucket, 'penguin',
'penguin_utils_cloud_tuner.py')
# Region to use for Dataflow jobs and AI Platform jobs.
# Dataflow: https://cloud.google.com/dataflow/docs/concepts/regional-endpoints
# AI Platform: https://cloud.google.com/ml-engine/docs/tensorflow/regions
_gcp_region = 'us-central1'
# A dict which contains the training job parameters to be passed to Google
# Cloud AI Platform. For the full set of parameters supported by Google Cloud AI
# Platform, refer to
# https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Job
_ai_platform_training_args = {
'project': _project_id,
'region': _gcp_region,
# Starting from TFX 0.14, training on AI Platform uses custom containers:
# https://cloud.google.com/ml-engine/docs/containers-overview
# You can specify a custom container here. If not specified, TFX will use a
# a public container image matching the installed version of TFX.
# 'masterConfig': { 'imageUri': 'gcr.io/my-project/my-container' },
# Note that if you do specify a custom container, ensure the entrypoint
# calls into TFX's run_executor script (tfx/scripts/run_executor.py)
# Both CloudTuner and the Google Cloud AI Platform extensions Tuner
# component can be used together, in which case it allows distributed
# parallel tuning backed by AI Platform Vizier's hyperparameter search
# algorithm. However, in order to do so, the Cloud AI Platform Job must be
# given access to the AI Platform Vizier service.
# https://cloud.google.com/ai-platform/training/docs/custom-service-account#custom
# Then, you should specify the custom service account for the training job.
'serviceAccount': '<SA_NAME>@my-gcp-project.iam.gserviceaccount.com',
}
_pusher_custom_config = {
# A dict which contains the serving job parameters to be passed to Google
# Cloud AI Platform. For the full set of parameters supported by Google
# Cloud AI Platform, refer to
# https://cloud.google.com/ml-engine/reference/rest/v1/projects.models
ai_platform_pusher_executor.SERVING_ARGS_KEY: {
'model_name': 'penguin',
'project_id': _project_id,
'machine_type': 'n1-standard-8',
},
# Regional endpoint for prediction service. See
# https://cloud.google.com/ai-platform/prediction/docs/regional-endpoints#using_regional_endpoints
ai_platform_pusher_executor.ENDPOINT_ARGS_KEY:
'https://%s-ml.googleapis.com' % _gcp_region,
}
def create_pipeline(
pipeline_name: Text,
pipeline_root: Text,
data_root: Text,
module_file: Text,
ai_platform_training_args: Dict[Text, Text],
pusher_custom_config: Dict[Text, Text],
enable_tuning: bool,
beam_pipeline_args: Optional[List[Text]] = None) -> pipeline.Pipeline:
"""Implements the penguin pipeline with TFX and Kubeflow Pipeline.
Args:
pipeline_name: name of the TFX pipeline being created.
pipeline_root: root directory of the pipeline. Should be a valid GCS path.
data_root: uri of the penguin data.
module_file: uri of the module files used in Trainer and Transform
components.
ai_platform_training_args: Args of CAIP training job. Please refer to
https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#Job
for detailed description.
pusher_custom_config: Custom configs passed to pusher.
enable_tuning: If True, the hyperparameter tuning through CloudTuner is
enabled.
beam_pipeline_args: Optional list of beam pipeline options. Please refer to
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#setting-other-cloud-dataflow-pipeline-options.
When this argument is not provided, the default is to use GCP
DataflowRunner with 50GB disk size as specified in this function. If an
empty list is passed in, default specified by Beam will be used, which can
be found at
https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#setting-other-cloud-dataflow-pipeline-options
Returns:
A TFX pipeline object.
"""
examples = external_input(data_root)
# Beam args to run data processing on DataflowRunner.
#
# TODO(b/151114974): Remove `disk_size_gb` flag after default is increased.
# TODO(b/151116587): Remove `shuffle_mode` flag after default is changed.
# TODO(b/156874687): Remove `machine_type` after IP addresses are no longer a
# scaling bottleneck.
if beam_pipeline_args is None:
beam_pipeline_args = [
'--runner=DataflowRunner',
'--project=' + _project_id,
'--temp_location=' + os.path.join(_output_bucket, 'tmp'),
'--region=' + _gcp_region,
# Temporary overrides of defaults.
'--disk_size_gb=50',
'--experiments=shuffle_mode=auto',
'--machine_type=e2-standard-8',
]
# Number of epochs in training.
train_steps = data_types.RuntimeParameter(
name='train_steps',
default=100,
ptype=int,
)
# Number of epochs in evaluation.
eval_steps = data_types.RuntimeParameter(
name='eval_steps',
default=50,
ptype=int,
)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
schema_gen = SchemaGen(
statistics=statistics_gen.outputs['statistics'], infer_feature_shape=True)
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=module_file)
# Update ai_platform_training_args if distributed training was enabled.
# Number of worker machines used in distributed training.
worker_count = data_types.RuntimeParameter(
name='worker_count',
default=2,
ptype=int,
)
# Type of worker machines used in distributed training.
worker_type = data_types.RuntimeParameter(
name='worker_type',
default='standard',
ptype=str,
)
local_training_args = copy.deepcopy(ai_platform_training_args)
if FLAGS.distributed_training:
local_training_args.update({
# You can specify the machine types, the number of replicas for workers
# and parameter servers.
# https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#ScaleTier
'scaleTier': 'CUSTOM',
'masterType': 'large_model',
'workerType': worker_type,
'parameterServerType': 'standard',
'workerCount': worker_count,
'parameterServerCount': 1,
})
# Tunes the hyperparameters for model training based on user-provided Python
# function. Note that once the hyperparameters are tuned, you can drop the
# Tuner component from pipeline and feed Trainer with tuned hyperparameters.
if enable_tuning:
# The Tuner component launches 1 AIP Training job for flock management.
# For example, 3 workers (defined by num_parallel_trials) in the flock
# management AIP Training job, each runs Tuner.Executor.
# Then, 3 AIP Training Jobs (defined by local_training_args) are invoked
# from each worker in the flock management Job for Trial execution.
tuner = Tuner(
module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps},
tune_args=tuner_pb2.TuneArgs(
# num_parallel_trials=3 means that 3 search loops are
# running in parallel.
# Each tuner may include a distributed training job which can be
# specified in local_training_args above (e.g. 1 PS + 2 workers).
num_parallel_trials=3),
custom_config={
# Configures Cloud AI Platform-specific configs . For details, see
# https://cloud.google.com/ai-platform/training/docs/reference/rest/v1/projects.jobs#traininginput.
ai_platform_trainer_executor.TRAINING_ARGS_KEY:
local_training_args
})
# Uses user-provided Python function that trains a model.
trainer = Trainer(
custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_trainer_executor.GenericExecutor),
module_file=module_file,
examples=transform.outputs['transformed_examples'],
transform_graph=transform.outputs['transform_graph'],
schema=schema_gen.outputs['schema'],
# If Tuner is in the pipeline, Trainer can take Tuner's output
# best_hyperparameters artifact as input and utilize it in the user module
# code.
#
# If there isn't Tuner in the pipeline, either use ImporterNode to import
# a previous Tuner's output to feed to Trainer, or directly use the tuned
# hyperparameters in user module code and set hyperparameters to None
# here.
#
# Example of ImporterNode,
# hparams_importer = ImporterNode(
# instance_name='import_hparams',
# source_uri='path/to/best_hyperparameters.txt',
# artifact_type=HyperParameters)
# ...
# hyperparameters = hparams_importer.outputs['result'],
hyperparameters=(tuner.outputs['best_hyperparameters']
if enable_tuning else None),
train_args={'num_steps': train_steps},
eval_args={'num_steps': eval_steps},
custom_config={
ai_platform_trainer_executor.TRAINING_ARGS_KEY:
local_training_args
})
# Get the latest blessed model for model validation.
model_resolver = ResolverNode(
instance_name='latest_blessed_model_resolver',
resolver_class=latest_blessed_model_resolver.LatestBlessedModelResolver,
model=Channel(type=Model),
model_blessing=Channel(type=ModelBlessing))
# Uses TFMA to compute an evaluation statistics over features of a model and
# perform quality validation of a candidate model (compared to a baseline).
eval_config = tfma.EvalConfig(
model_specs=[tfma.ModelSpec(label_key='species')],
slicing_specs=[tfma.SlicingSpec()],
metrics_specs=[
tfma.MetricsSpec(metrics=[
tfma.MetricConfig(
class_name='SparseCategoricalAccuracy',
threshold=tfma.MetricThreshold(
value_threshold=tfma.GenericValueThreshold(
lower_bound={'value': 0.6}),
change_threshold=tfma.GenericChangeThreshold(
direction=tfma.MetricDirection.HIGHER_IS_BETTER,
absolute={'value': -1e-10})))
])
])
evaluator = Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
baseline_model=model_resolver.outputs['model'],
# Change threshold will be ignored if there is no baseline (first run).
eval_config=eval_config)
pusher = Pusher(
custom_executor_spec=executor_spec.ExecutorClassSpec(
ai_platform_pusher_executor.Executor),
model=trainer.outputs['model'],
model_blessing=evaluator.outputs['blessing'],
custom_config=pusher_custom_config,
)
components = [
example_gen,
statistics_gen,
schema_gen,
example_validator,
transform,
trainer,
model_resolver,
evaluator,
pusher,
]
if enable_tuning:
components.append(tuner)
return pipeline.Pipeline(
pipeline_name=pipeline_name,
pipeline_root=pipeline_root,
components=components,
enable_cache=True,
beam_pipeline_args=beam_pipeline_args)
def main(unused_argv):
# Metadata config. The defaults works work with the installation of
# KF Pipelines using Kubeflow. If installing KF Pipelines using the
# lightweight deployment option, you may need to override the defaults.
metadata_config = kubeflow_dag_runner.get_default_kubeflow_metadata_config()
# This pipeline automatically injects the Kubeflow TFX image if the
# environment variable 'KUBEFLOW_TFX_IMAGE' is defined. The tfx
# cli tool exports the environment variable to pass to the pipelines.
tfx_image = os.environ.get('KUBEFLOW_TFX_IMAGE', None)
runner_config = kubeflow_dag_runner.KubeflowDagRunnerConfig(
kubeflow_metadata_config=metadata_config,
# Specify custom docker image to use.
tfx_image=tfx_image)
kubeflow_dag_runner.KubeflowDagRunner(config=runner_config).run(
create_pipeline(
pipeline_name=_pipeline_name,
pipeline_root=_pipeline_root,
data_root=_data_root,
module_file=_module_file,
enable_tuning=True,
ai_platform_training_args=_ai_platform_training_args,
pusher_custom_config=_pusher_custom_config,
))
# $ tfx pipeline create \
# --pipeline-path=penguin_pipeline_kubeflow_gcp.py
# https://github.com/tensorflow/tfx/blob/master/docs/guide/cli.md#create
if __name__ == '__main__':
app.run(main)
| [
"[email protected]"
] | |
75155b68d0584f73062c82fc4a19c12263c70205 | 5d3bd6a1227e383e57e16a4ecaaf5d2d727d1d72 | /tools/sapp/sapp/tests/interactive_test.py | 8e68b00cdaa6c4a4564ce3b320de95a01a717dd6 | [
"MIT"
] | permissive | KevinHock/pyre-check | 75e918f89a4bfe28a82546364d7aba94a83a56e9 | 09b0f53e23353b16a59ef76d8e5c5b78bdf08447 | refs/heads/master | 2020-12-04T17:08:07.931289 | 2020-02-22T01:51:13 | 2020-02-22T01:52:45 | 231,847,877 | 4 | 1 | MIT | 2020-01-05T00:32:13 | 2020-01-05T00:32:12 | null | UTF-8 | Python | false | false | 84,783 | py | #!/usr/bin/env python3
import os
import sys
from datetime import datetime
from io import StringIO
from typing import List
from unittest import TestCase
from unittest.mock import mock_open, patch
from sqlalchemy.orm import Session
from ..db import DB, DBType
from ..decorators import UserError
from ..interactive import (
Interactive,
IssueQueryResult,
TraceFrameQueryResult,
TraceTuple,
)
from ..models import (
DBID,
Issue,
IssueInstance,
IssueInstanceSharedTextAssoc,
IssueInstanceTraceFrameAssoc,
Run,
RunStatus,
SharedText,
SharedTextKind,
SourceLocation,
TraceFrame,
TraceFrameLeafAssoc,
TraceKind,
)
from ..pysa_taint_parser import Parser
from .fake_object_generator import FakeObjectGenerator
class InteractiveTest(TestCase):
def setUp(self) -> None:
self.db = DB(DBType.MEMORY)
self.interactive = Interactive(
database=self.db, repository_directory="", parser_class=Parser
)
self.stdout = StringIO()
self.stderr = StringIO()
sys.stdout = self.stdout # redirect output
sys.stderr = self.stderr # redirect output
self.fakes = FakeObjectGenerator()
def tearDown(self) -> None:
sys.stdout = sys.__stdout__ # reset redirect
sys.stderr = sys.__stderr__ # reset redirect
def _clear_stdout(self):
self.stdout = StringIO()
sys.stdout = self.stdout
def _add_to_session(self, session, data):
if not isinstance(data, list):
session.add(data)
return
for row in data:
session.add(row)
def _frame_to_query_result(
self, session: Session, trace_frame: TraceFrame
) -> TraceFrameQueryResult:
caller = (
session.query(SharedText.contents)
.filter(SharedText.id == trace_frame.caller_id)
.scalar()
)
callee = (
session.query(SharedText.contents)
.filter(SharedText.id == trace_frame.callee_id)
.scalar()
)
filename = (
session.query(SharedText.contents)
.filter(SharedText.id == trace_frame.filename_id)
.scalar()
)
return TraceFrameQueryResult(
id=trace_frame.id,
caller=caller,
caller_port=trace_frame.caller_port,
callee=callee,
callee_port=trace_frame.callee_port,
caller_id=trace_frame.caller_id,
callee_id=trace_frame.callee_id,
callee_location=trace_frame.callee_location,
kind=trace_frame.kind,
filename=filename,
)
def testState(self):
self.interactive.current_run_id = 1
self.interactive.current_issue_instance_id = 2
self.interactive.current_frame_id = 3
self.interactive.sources = {1}
self.interactive.sinks = {2}
self.interactive.state()
output = self.stdout.getvalue()
self.assertIn("Database: memory:sapp.db", output)
self.assertIn("Repository directory: ", output)
self.assertIn("Current run: 1", output)
self.assertIn("Current issue instance: 2", output)
self.assertIn("Current trace frame: 3", output)
self.assertIn("Sources filter: {1}", output)
self.assertIn("Sinks filter: {2}", output)
def testListIssuesBasic(self):
run = self.fakes.run()
self.fakes.issue()
self.fakes.instance(
message="message1", filename="file.py", callable="module.function1"
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.issues()
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertIn("Code: 6016", output)
self.assertIn("Message: message1", output)
self.assertIn("Callable: module.function1", output)
self.assertIn("Location: file.py:6|7|8", output)
def testListIssuesFromLatestRun(self):
self.fakes.issue()
run1 = self.fakes.run()
self.fakes.instance() # part of run1
self.fakes.save_all(self.db) # early flush to resolve DBID's
run2 = self.fakes.run()
self.fakes.instance() # part of run2
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run1)
session.add(run2)
session.commit()
self.interactive.setup()
self.interactive.issues()
output = self.stdout.getvalue().strip()
self.assertNotIn("Issue 1", output)
self.assertIn("Issue 2", output)
def _list_issues_filter_setup(self):
run = self.fakes.run()
issue1 = self.fakes.issue()
self.fakes.instance(
issue_id=issue1.id,
callable="module.sub.function1",
filename="module/sub.py",
min_trace_length_to_sources=1,
min_trace_length_to_sinks=1,
)
self.fakes.save_all(self.db)
issue2 = self.fakes.issue()
self.fakes.instance(
issue_id=issue2.id,
callable="module.sub.function2",
filename="module/sub.py",
min_trace_length_to_sources=2,
min_trace_length_to_sinks=2,
)
self.fakes.save_all(self.db)
issue3 = self.fakes.issue()
self.fakes.instance(
issue_id=issue3.id,
callable="module.function3",
filename="module/__init__.py",
min_trace_length_to_sources=3,
min_trace_length_to_sinks=3,
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
def testListIssuesFilterCodes(self):
self._list_issues_filter_setup()
self.interactive.setup()
self.interactive.issues(codes="a string")
stderr = self.stderr.getvalue().strip()
self.assertIn("'codes' should be", stderr)
self.interactive.issues(codes=6016)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(codes=[6017, 6018])
output = self.stdout.getvalue().strip()
self.assertNotIn("Issue 1", output)
self.assertIn("Issue 2", output)
self.assertIn("Issue 3", output)
def testListIssuesFilterCallables(self):
self._list_issues_filter_setup()
self.interactive.setup()
self.interactive.issues(callables=1234)
stderr = self.stderr.getvalue().strip()
self.assertIn("'callables' should be", stderr)
self.interactive.issues(callables="%sub%")
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(callables=["%function3"])
output = self.stdout.getvalue().strip()
self.assertNotIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertIn("Issue 3", output)
def testListIssuesFilterFilenames(self):
self._list_issues_filter_setup()
self.interactive.setup()
self.interactive.issues(filenames=1234)
stderr = self.stderr.getvalue().strip()
self.assertIn("'filenames' should be", stderr)
self.interactive.issues(filenames="module/s%")
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(filenames=["%__init__.py"])
output = self.stdout.getvalue().strip()
self.assertNotIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertIn("Issue 3", output)
def testListIssuesFilterMinTraceLength(self):
self._list_issues_filter_setup()
self.interactive.setup()
self.interactive.issues(exact_trace_length_to_sources="1")
stderr = self.stderr.getvalue().strip()
self.assertIn("'exact_trace_length_to_sources' should be", stderr)
self._clear_stdout()
self.interactive.issues(exact_trace_length_to_sinks="1")
stderr = self.stderr.getvalue().strip()
self.assertIn("'exact_trace_length_to_sinks' should be", stderr)
self._clear_stdout()
self.interactive.issues(max_trace_length_to_sources="1")
stderr = self.stderr.getvalue().strip()
self.assertIn("'max_trace_length_to_sources' should be", stderr)
self._clear_stdout()
self.interactive.issues(max_trace_length_to_sinks="1")
stderr = self.stderr.getvalue().strip()
self.assertIn("'max_trace_length_to_sinks' should be", stderr)
self._clear_stdout()
self.interactive.issues(
exact_trace_length_to_sources=1, max_trace_length_to_sources=1
)
stderr = self.stderr.getvalue().strip()
self.assertIn("can't be set together", stderr)
self._clear_stdout()
self.interactive.issues(
exact_trace_length_to_sinks=1, max_trace_length_to_sinks=1
)
stderr = self.stderr.getvalue().strip()
self.assertIn("can't be set together", stderr)
self._clear_stdout()
self.interactive.issues(exact_trace_length_to_sources=1)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(exact_trace_length_to_sinks=1)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(exact_trace_length_to_sources=[1, 2])
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(exact_trace_length_to_sinks=[1, 2])
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(max_trace_length_to_sources=1)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(max_trace_length_to_sinks=1)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(max_trace_length_to_sources=2)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(max_trace_length_to_sinks=2)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(
max_trace_length_to_sources=1, max_trace_length_to_sinks=1
)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
self.interactive.issues(
max_trace_length_to_sources=1, max_trace_length_to_sinks=2
)
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
self.assertNotIn("Issue 3", output)
self._clear_stdout()
def testNoRunsFound(self):
self.interactive.setup()
stderr = self.stderr.getvalue().strip()
self.assertIn("No runs found.", stderr)
def testListRuns(self):
runs = [
Run(id=1, date=datetime.now(), status=RunStatus.FINISHED),
Run(id=2, date=datetime.now(), status=RunStatus.INCOMPLETE),
Run(id=3, date=datetime.now(), status=RunStatus.FINISHED),
]
with self.db.make_session() as session:
self._add_to_session(session, runs)
session.commit()
self.interactive.setup()
self.interactive.runs()
output = self.stdout.getvalue().strip()
self.assertIn("Run 1", output)
self.assertNotIn("Run 2", output)
self.assertIn("Run 3", output)
def testSetRun(self):
self.fakes.issue()
run1 = self.fakes.run()
self.fakes.instance(message="Issue message")
self.fakes.save_all(self.db)
run2 = self.fakes.run()
self.fakes.instance(message="Issue message")
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run1)
session.add(run2)
session.commit()
self.interactive.setup()
self.interactive.run(1)
self.interactive.issues()
output = self.stdout.getvalue().strip()
self.assertIn("Issue 1", output)
self.assertNotIn("Issue 2", output)
def testSetRunNonExistent(self):
runs = [
Run(id=1, date=datetime.now(), status=RunStatus.FINISHED),
Run(id=2, date=datetime.now(), status=RunStatus.INCOMPLETE),
]
with self.db.make_session() as session:
self._add_to_session(session, runs)
session.commit()
self.interactive.setup()
self.interactive.run(2)
self.interactive.run(3)
stderr = self.stderr.getvalue().strip()
self.assertIn("Run 2 doesn't exist", stderr)
self.assertIn("Run 3 doesn't exist", stderr)
def testSetLatestRun(self):
runs = [
Run(id=1, date=datetime.now(), status=RunStatus.FINISHED, kind="a"),
Run(id=2, date=datetime.now(), status=RunStatus.FINISHED, kind="a"),
Run(id=3, date=datetime.now(), status=RunStatus.FINISHED, kind="a"),
Run(id=4, date=datetime.now(), status=RunStatus.FINISHED, kind="b"),
Run(id=5, date=datetime.now(), status=RunStatus.FINISHED, kind="b"),
Run(id=6, date=datetime.now(), status=RunStatus.FINISHED, kind="c"),
]
with self.db.make_session() as session:
self._add_to_session(session, runs)
session.commit()
self.interactive.latest_run("c")
self.assertEqual(self.interactive.current_run_id, 6)
self.interactive.latest_run("b")
self.assertEqual(self.interactive.current_run_id, 5)
self.interactive.latest_run("a")
self.assertEqual(self.interactive.current_run_id, 3)
self.interactive.latest_run("d")
self.assertEqual(self.interactive.current_run_id, 3)
self.assertIn("No runs with kind 'd'", self.stderr.getvalue())
def testSetIssue(self):
run = self.fakes.run()
self.fakes.issue()
self.fakes.instance(message="Issue message")
self.fakes.instance(message="Issue message")
self.fakes.instance(message="Issue message")
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.issue(2)
self.assertEqual(self.interactive.current_issue_instance_id, 2)
stdout = self.stdout.getvalue().strip()
self.assertNotIn("Issue 1", stdout)
self.assertIn("Issue 2", stdout)
self.assertNotIn("Issue 3", stdout)
self.interactive.issue(1)
self.assertEqual(self.interactive.current_issue_instance_id, 1)
stdout = self.stdout.getvalue().strip()
self.assertIn("Issue 1", stdout)
self.assertNotIn("Issue 3", stdout)
def testSetIssueNonExistent(self):
run = self.fakes.run()
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.issue(1)
stderr = self.stderr.getvalue().strip()
self.assertIn("Issue 1 doesn't exist", stderr)
def testSetIssueUpdatesRun(self):
self.fakes.issue()
run1 = self.fakes.run()
self.fakes.instance()
self.fakes.save_all(self.db)
run2 = self.fakes.run()
self.fakes.instance()
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run1)
session.add(run2)
session.commit()
self.interactive.setup()
self.assertEqual(int(self.interactive.current_run_id), 2)
self.interactive.issue(1)
self.assertEqual(int(self.interactive.current_run_id), 1)
def testGetSources(self):
self.fakes.instance()
source1 = self.fakes.source("source1")
source2 = self.fakes.source("source2")
self.fakes.source("source3")
self.fakes.save_all(self.db)
assocs = [
IssueInstanceSharedTextAssoc(
shared_text_id=source1.id, issue_instance_id=1
),
IssueInstanceSharedTextAssoc(
shared_text_id=source2.id, issue_instance_id=1
),
]
with self.db.make_session() as session:
self._add_to_session(session, assocs)
session.commit()
self.interactive.setup()
sources = self.interactive._get_leaves_issue_instance(
session, 1, SharedTextKind.SOURCE
)
self.assertEqual(len(sources), 2)
self.assertIn("source1", sources)
self.assertIn("source2", sources)
def testGetSinks(self):
self.fakes.instance()
sink1 = self.fakes.sink("sink1")
sink2 = self.fakes.sink("sink2")
self.fakes.sink("sink3")
self.fakes.save_all(self.db)
assocs = [
IssueInstanceSharedTextAssoc(shared_text_id=sink1.id, issue_instance_id=1),
IssueInstanceSharedTextAssoc(shared_text_id=sink2.id, issue_instance_id=1),
]
with self.db.make_session() as session:
self._add_to_session(session, assocs)
session.commit()
self.interactive.setup()
sinks = self.interactive._get_leaves_issue_instance(
session, 1, SharedTextKind.SINK
)
self.assertEqual(len(sinks), 2)
self.assertIn("sink1", sinks)
self.assertIn("sink2", sinks)
def testGetFeatures(self):
self.fakes.instance()
feature1 = self.fakes.feature("via:feature1")
feature2 = self.fakes.feature("via:feature2")
self.fakes.feature("via:feature3")
self.fakes.save_all(self.db)
assocs = [
IssueInstanceSharedTextAssoc(
shared_text_id=feature1.id, issue_instance_id=1
),
IssueInstanceSharedTextAssoc(
shared_text_id=feature2.id, issue_instance_id=1
),
]
with self.db.make_session() as session:
self._add_to_session(session, assocs)
session.commit()
self.interactive.setup()
features = self.interactive._get_leaves_issue_instance(
session, 1, SharedTextKind.FEATURE
)
self.assertEqual(len(features), 2)
self.assertIn("via:feature1", features)
self.assertIn("via:feature2", features)
def _basic_trace_frames(self):
return [
self.fakes.precondition(
caller="call1",
caller_port="root",
callee="call2",
callee_port="param0",
location=(1, 1, 1),
),
self.fakes.precondition(
caller="call2",
caller_port="param0",
callee="leaf",
callee_port="sink",
location=(1, 2, 1),
),
]
def testNextTraceFrames(self):
run = self.fakes.run()
frames = self._basic_trace_frames()
sink = self.fakes.sink("sink1")
self.fakes.saver.add(
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=sink.id, trace_length=1
)
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.sinks = {"sink1"}
next_frames = self.interactive._next_forward_trace_frames(
session, frames[0], set()
)
self.assertEqual(len(next_frames), 1)
self.assertEqual(int(next_frames[0].id), int(frames[1].id))
def testNextTraceFramesMultipleRuns(self):
run1 = self.fakes.run()
frames = self._basic_trace_frames()
self.fakes.save_all(self.db)
run2 = self.fakes.run()
frames.extend(self._basic_trace_frames())
sink = self.fakes.sink("sink1")
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=sink.id, trace_length=0
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[3].id, leaf_id=sink.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run1)
session.add(run2)
session.commit()
self.interactive.setup()
self.interactive.sinks = {"sink1"}
next_frames = self.interactive._next_forward_trace_frames(
session, frames[2], set()
)
self.assertEqual(len(next_frames), 1)
self.assertEqual(int(next_frames[0].id), int(frames[3].id))
def testNextTraceFramesBackwards(self):
run = self.fakes.run()
frames = [
self.fakes.precondition(
caller="call1",
caller_port="root",
callee="call3",
callee_port="param1",
location=(1, 1, 1),
),
self.fakes.precondition(
caller="call3",
caller_port="param1",
callee="leaf",
callee_port="sink",
location=(1, 2, 1),
),
]
sink = self.fakes.sink("sink1")
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=sink.id, trace_length=1
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=sink.id, trace_length=1
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.sinks = {"sink1"}
next_frames = self.interactive._next_backward_trace_frames(
session, frames[1], set()
)
self.assertEqual(len(next_frames), 1)
self.assertEqual(int(next_frames[0].id), int(frames[0].id))
def testNavigateTraceFrames(self):
run = self.fakes.run()
frames = self._basic_trace_frames()
sink = self.fakes.sink("sink1")
self.fakes.saver.add(
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=sink.id, trace_length=1
)
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.sinks = {"sink1"}
result = self.interactive._navigate_trace_frames(session, [frames[0]])
self.assertEqual(len(result), 2)
self.assertEqual(int(result[0][0].id), int(frames[0].id))
self.assertEqual(int(result[1][0].id), int(frames[1].id))
def testNavigateTraceFramesDetectsCycle(self):
"""This test checks that we don't get stuck in a cycle. Without cycle
detection code, this test will go from 1->2->1->2->... . With cycle
detection code it goes 1->2->3->4.
"""
run = self.fakes.run()
frames = [
self.fakes.precondition(
caller="call1",
caller_port="param1",
callee="call2",
callee_port="param2",
),
self.fakes.precondition(
caller="call2",
caller_port="param2",
callee="call1",
callee_port="param1",
),
self.fakes.precondition(
caller="call1",
caller_port="param1",
callee="call3",
callee_port="param3",
),
self.fakes.precondition(
caller="call3", caller_port="param3", callee="leaf", callee_port="sink"
),
]
sink = self.fakes.sink("sink")
self.fakes.saver.add_all(
[
# This trace_length 0 is part of a bug.
# See models.py:TraceFrameLeafAssoc.trace_length
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=sink.id, trace_length=0
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=sink.id, trace_length=1
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[2].id, leaf_id=sink.id, trace_length=1
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[3].id, leaf_id=sink.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.sinks = {"sink"}
trace = self.interactive._navigate_trace_frames(session, [frames[0]])
self.assertEqual(len(frames), 4)
self.assertEqual(
[int(frame.id) for frame, _branches in trace],
[int(frame.id) for frame in frames],
)
def testCreateTraceTuples(self):
# reverse order
postcondition_traces = [
(
TraceFrameQueryResult(
id=DBID(1),
callee="call3",
callee_port="result",
filename="file3.py",
callee_location=SourceLocation(1, 1, 3),
caller="main",
caller_port="root",
),
1,
),
(
TraceFrameQueryResult(
id=DBID(2),
callee="call2",
callee_port="result",
caller="dummy caller",
caller_port="dummy caller",
filename="file2.py",
callee_location=SourceLocation(1, 1, 2),
),
2,
),
(
TraceFrameQueryResult(
id=DBID(3),
callee="leaf",
callee_port="source",
caller="dummy caller",
caller_port="dummy caller",
filename="file1.py",
callee_location=SourceLocation(1, 1, 1),
),
3,
),
]
trace_tuples = self.interactive._create_trace_tuples(postcondition_traces)
self.assertEqual(len(trace_tuples), 3)
self.assertEqual(
trace_tuples,
[
TraceTuple(postcondition_traces[0][0], 1),
TraceTuple(postcondition_traces[1][0], 2),
TraceTuple(postcondition_traces[2][0], 3),
],
)
def testOutputTraceTuples(self):
trace_tuples = [
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(1),
caller="unused",
caller_port="unused",
callee="leaf",
callee_port="source",
filename="file1.py",
callee_location=SourceLocation(1, 1, 1),
)
),
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(2),
caller="unused",
caller_port="unused",
callee="call2",
callee_port="result",
filename="file2.py",
callee_location=SourceLocation(1, 1, 2),
)
),
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(3),
caller="unused",
caller_port="unused",
callee="call3",
callee_port="result",
filename="file3.py",
callee_location=SourceLocation(1, 1, 3),
)
),
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(4),
caller="unused",
caller_port="unused",
callee="main",
callee_port="root",
filename="file4.py",
callee_location=SourceLocation(1, 1, 4),
)
),
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(5),
caller="unused",
caller_port="unused",
callee="call4",
callee_port="param0",
filename="file4.py",
callee_location=SourceLocation(1, 1, 4),
)
),
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(6),
caller="unused",
caller_port="unused",
callee="call5",
callee_port="param1",
filename="file5.py",
callee_location=SourceLocation(1, 1, 5),
)
),
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(7),
caller="unused",
caller_port="unused",
callee="leaf",
callee_port="sink",
filename="file6.py",
callee_location=SourceLocation(1, 1, 6),
)
),
]
self.interactive.current_trace_frame_index = 1
self.interactive._output_trace_tuples(trace_tuples)
output = self.stdout.getvalue()
self.assertEqual(
output.split("\n"),
[
" # ⎇ [callable] [port] [location]",
" 1 leaf source file1.py:1|1|1",
" --> 2 call2 result file2.py:1|1|2",
" 3 call3 result file3.py:1|1|3",
" 4 main root file4.py:1|1|4",
" 5 call4 param0 file4.py:1|1|4",
" 6 call5 param1 file5.py:1|1|5",
" 7 leaf sink file6.py:1|1|6",
"",
],
)
self._clear_stdout()
self.interactive.current_trace_frame_index = 4
self.interactive._output_trace_tuples(trace_tuples)
output = self.stdout.getvalue()
self.assertEqual(
output.split("\n"),
[
" # ⎇ [callable] [port] [location]",
" 1 leaf source file1.py:1|1|1",
" 2 call2 result file2.py:1|1|2",
" 3 call3 result file3.py:1|1|3",
" 4 main root file4.py:1|1|4",
" --> 5 call4 param0 file4.py:1|1|4",
" 6 call5 param1 file5.py:1|1|5",
" 7 leaf sink file6.py:1|1|6",
"",
],
)
def testTraceFromIssue(self):
run = self.fakes.run()
self.fakes.issue()
instance = self.fakes.instance()
source = self.fakes.source()
frames = [
self.fakes.postcondition(
caller="call1",
caller_port="root",
callee="leaf",
callee_port="source",
location=(1, 1, 1),
),
self.fakes.precondition(
caller="call1",
caller_port="root",
callee="leaf",
callee_port="sink",
location=(1, 1, 2),
),
]
self.fakes.saver.add_all(
[
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[0].id, issue_instance_id=instance.id
),
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[1].id, issue_instance_id=instance.id
),
]
)
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=source.id, trace_length=0
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=source.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.trace()
stderr = self.stderr.getvalue().strip()
self.assertIn("Use 'issue ID' or 'frame ID'", stderr)
self.interactive.issue(1)
self._clear_stdout()
self.interactive.trace()
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
" # ⎇ [callable] [port] [location]",
" 1 leaf source lib/server/posts/response.py:1|1|1",
" --> 2 Foo.barMethod root /r/some/filename.py:6|7|8",
" 3 leaf sink lib/server/posts/request.py:1|1|2",
"",
],
)
def testTraceFromFrame(self):
run = self.fakes.run()
frames = self._basic_trace_frames()
sink = self.fakes.sink("sink")
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=sink.id, trace_length=1
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=sink.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.frame(int(frames[0].id))
self._clear_stdout()
self.interactive.trace()
self.assertEqual(self.interactive.sinks, {"sink"})
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
" # ⎇ [callable] [port] [location]",
" --> 1 call1 root lib/server/posts/request.py:1|1|1",
" 2 call2 param0 lib/server/posts/request.py:1|1|1",
" 3 leaf sink lib/server/posts/request.py:1|2|1",
"",
],
)
def testTraceMissingFrames(self):
run = self.fakes.run()
self.fakes.issue()
instance = self.fakes.instance()
source = self.fakes.source()
frames = [
self.fakes.postcondition(
caller="call1",
caller_port="root",
callee="leaf",
callee_port="source",
location=(1, 1, 1),
),
self.fakes.precondition(
caller="call1",
caller_port="root",
callee="call2",
callee_port="param0",
location=(1, 1, 1),
),
]
self.fakes.saver.add_all(
[
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[0].id, issue_instance_id=instance.id
),
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[1].id, issue_instance_id=instance.id
),
]
)
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=source.id, trace_length=0
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=source.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.issue(1)
self.interactive.trace()
stdout = self.stdout.getvalue().strip()
self.assertIn("Missing trace frame: call2:param0", stdout)
def testTraceCursorLocation(self):
run = self.fakes.run()
self.fakes.issue()
instance = self.fakes.instance(callable="Issue callable")
source = self.fakes.source()
frames = [
self.fakes.postcondition(
caller="call1",
caller_port="root",
callee="leaf",
callee_port="source",
location=(1, 1, 1),
),
self.fakes.precondition(
caller="call1",
caller_port="root",
callee="leaf",
callee_port="sink",
location=(1, 2, 1),
),
]
self.fakes.saver.add_all(
[
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[0].id, issue_instance_id=instance.id
),
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[1].id, issue_instance_id=instance.id
),
]
)
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=source.id, trace_length=0
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=source.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.assertIsNone(self.interactive.callable())
self.interactive.issue(1)
self.assertEqual(self.interactive.callable(), "Issue callable")
self.assertEqual(self.interactive.current_trace_frame_index, 1)
self.interactive.next_cursor_location()
self.assertEqual(self.interactive.current_trace_frame_index, 2)
self.assertEqual(self.interactive.callable(), "leaf")
self.interactive.next_cursor_location()
self.assertEqual(self.interactive.current_trace_frame_index, 2)
self.interactive.prev_cursor_location()
self.assertEqual(self.interactive.current_trace_frame_index, 1)
self.interactive.prev_cursor_location()
self.assertEqual(self.interactive.current_trace_frame_index, 0)
self.interactive.prev_cursor_location()
self.assertEqual(self.interactive.current_trace_frame_index, 0)
def testJumpToLocation(self):
run = self.fakes.run()
self.fakes.issue()
instance = self.fakes.instance()
source = self.fakes.source()
frames = [
self.fakes.postcondition(
caller="call1", caller_port="root", callee="leaf", callee_port="source"
),
self.fakes.precondition(
caller="call1", caller_port="root", callee="leaf", callee_port="sink"
),
]
self.fakes.saver.add_all(
[
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[0].id, issue_instance_id=instance.id
),
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[1].id, issue_instance_id=instance.id
),
]
)
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=source.id, trace_length=0
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=source.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.issue(1)
self.assertEqual(self.interactive.current_trace_frame_index, 1)
self.interactive.jump(1)
self.assertEqual(self.interactive.current_trace_frame_index, 0)
self.interactive.jump(3)
self.assertEqual(self.interactive.current_trace_frame_index, 2)
self.interactive.jump(4)
self.assertEqual(self.interactive.current_trace_frame_index, 2)
self.interactive.jump(0)
self.assertEqual(self.interactive.current_trace_frame_index, 2)
def testTraceNoSinks(self):
run = self.fakes.run()
self.fakes.issue()
instance = self.fakes.instance()
source = self.fakes.source("source1")
frame = self.fakes.postcondition(
caller="call1", caller_port="root", callee="leaf", callee_port="source"
)
self.fakes.saver.add(
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frame.id, issue_instance_id=instance.id
)
)
self.fakes.saver.add(
TraceFrameLeafAssoc.Record(
trace_frame_id=frame.id, leaf_id=source.id, trace_length=0
)
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.sources = {"source1"}
self.interactive.issue(1)
self._clear_stdout()
self.interactive.trace()
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
" # ⎇ [callable] [port] [location]",
" 1 leaf source lib/server/posts/response.py:4|5|6",
" --> 2 Foo.barMethod root /r/some/filename.py:6|7|8",
"",
],
)
def _set_up_branched_trace(self) -> List[TraceFrame]:
run = self.fakes.run()
self.fakes.issue()
instance = self.fakes.instance()
source = self.fakes.source("source1")
sink = self.fakes.sink("sink1")
self.fakes.saver.add_all(
[
IssueInstanceSharedTextAssoc.Record(
issue_instance_id=instance.id, shared_text_id=source.id
),
IssueInstanceSharedTextAssoc.Record(
issue_instance_id=instance.id, shared_text_id=sink.id
),
]
)
frames = []
for i in range(6):
if i < 2: # 2 postconditions
frames.append(
self.fakes.postcondition(
caller="call1",
caller_port="root",
callee="leaf",
callee_port="source",
location=(i, i, i),
)
)
self.fakes.saver.add(
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[-1].id, leaf_id=source.id, trace_length=i
)
)
self.fakes.saver.add(
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[-1].id, issue_instance_id=instance.id
)
)
elif i < 4:
frames.append(
self.fakes.precondition(
caller="call1",
caller_port="root",
callee="call2",
callee_port="param2",
location=(i, i, i),
)
)
self.fakes.saver.add(
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[-1].id, leaf_id=sink.id, trace_length=i
)
)
self.fakes.saver.add(
IssueInstanceTraceFrameAssoc.Record(
trace_frame_id=frames[-1].id, issue_instance_id=instance.id
)
)
else:
frames.append(
self.fakes.precondition(
caller="call2",
caller_port="param2",
callee="leaf",
callee_port="sink",
location=(i, i, i),
)
)
self.fakes.saver.add(
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[-1].id,
leaf_id=sink.id,
trace_length=5 - i,
)
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
return frames
def testTraceBranchNumber(self):
self._set_up_branched_trace()
self.interactive.setup()
self.interactive.issue(1)
self.assertEqual(self.interactive.sources, {"source1"})
self.assertEqual(self.interactive.sinks, {"sink1"})
self._clear_stdout()
self.interactive.trace()
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
" # ⎇ [callable] [port] [location]",
" 1 +2 leaf source lib/server/posts/response.py:0|0|0",
" --> 2 Foo.barMethod root /r/some/filename.py:6|7|8",
" 3 +2 call2 param2 lib/server/posts/request.py:2|2|2",
" 4 +2 leaf sink lib/server/posts/request.py:5|5|5",
"",
],
)
def testShowBranches(self):
self._set_up_branched_trace()
self.interactive.setup()
self.interactive.issue(1)
# Parent at root
self.interactive.prev_cursor_location()
with patch("click.prompt", return_value=0):
self.interactive.branch()
output = self.stdout.getvalue().strip()
self.assertIn(
"[*] leaf : source\n"
" [0 hops: source1]\n"
" [lib/server/posts/response.py:0|0|0]",
output,
)
self.assertIn(
"[2] leaf : source\n"
" [1 hops: source1]\n"
" [lib/server/posts/response.py:1|1|1]",
output,
)
self._clear_stdout()
# Move to call2:param2
self.interactive.next_cursor_location()
self.interactive.next_cursor_location()
with patch("click.prompt", return_value=0):
self.interactive.branch()
output = self.stdout.getvalue().strip()
self.assertIn(
"[*] call2 : param2\n"
" [2 hops: sink1]\n"
" [lib/server/posts/request.py:2|2|2]",
output,
)
self.assertIn(
"[2] call2 : param2\n"
" [3 hops: sink1]\n"
" [lib/server/posts/request.py:3|3|3]",
output,
)
self._clear_stdout()
# Move to leaf:sink
self.interactive.next_cursor_location()
with patch("click.prompt", return_value=0):
self.interactive.branch()
output = self.stdout.getvalue().strip()
self.assertIn(
"[*] leaf : sink\n"
" [0 hops: sink1]\n"
" [lib/server/posts/request.py:5|5|5]",
output,
)
self.assertIn(
"[2] leaf : sink\n"
" [1 hops: sink1]\n"
" [lib/server/posts/request.py:4|4|4]",
output,
)
def testGetTraceFrameBranches(self):
frames = self._set_up_branched_trace()
self.interactive.setup()
self.interactive.issue(1)
# Parent at root
self.interactive.prev_cursor_location()
with self.db.make_session() as session:
branches = self.interactive._get_trace_frame_branches(session)
self.assertEqual(len(branches), 2)
self.assertEqual(int(branches[0].id), int(frames[0].id))
self.assertEqual(int(branches[1].id), int(frames[1].id))
# Parent is no longer root
self.interactive.next_cursor_location()
self.interactive.next_cursor_location()
self.interactive.next_cursor_location()
branches = self.interactive._get_trace_frame_branches(session)
self.assertEqual(len(branches), 2)
self.assertEqual(int(branches[0].id), int(frames[5].id))
self.assertEqual(int(branches[1].id), int(frames[4].id))
def testBranch(self):
self._set_up_branched_trace()
self.interactive.setup()
self.interactive.issue(1)
self.interactive.prev_cursor_location()
# We are testing for the source location, which differs between branches
self._clear_stdout()
self.interactive.branch(2) # location 0|0|0 -> 1|1|1
output = self.stdout.getvalue().strip()
self.assertIn(
" --> 1 +2 leaf source lib/server/posts/response.py:1|1|1", output
)
self._clear_stdout()
self.interactive.branch(1) # location 1|1|1 -> 0|0|0
output = self.stdout.getvalue().strip()
self.assertIn(
" --> 1 +2 leaf source lib/server/posts/response.py:0|0|0", output
)
self.interactive.next_cursor_location()
self.interactive.next_cursor_location()
self._clear_stdout()
self.interactive.branch(2) # location 2|2|2 -> 3|3|3
output = self.stdout.getvalue().strip()
self.assertIn(
" --> 3 +2 call2 param2 lib/server/posts/request.py:3|3|3", output
)
self.interactive.next_cursor_location()
self._clear_stdout()
self.interactive.branch(2) # location 4|4|4 -> 5|5|5
output = self.stdout.getvalue().strip()
self.assertIn(
" 3 +2 call2 param2 lib/server/posts/request.py:3|3|3", output
)
self.assertIn(
" --> 4 +2 leaf sink lib/server/posts/request.py:4|4|4", output
)
self.interactive.branch(3) # location 4|4|4 -> 5|5|5
stderr = self.stderr.getvalue().strip()
self.assertIn("Branch number invalid", stderr)
def testBranchPrefixLengthChanges(self):
run = self.fakes.run()
self.fakes.issue()
instance = self.fakes.instance()
source = self.fakes.source("source1")
sink = self.fakes.sink("sink1")
frames = [
self.fakes.postcondition(
caller="call1", caller_port="root", callee="leaf", callee_port="source"
),
self.fakes.postcondition(
caller="call1",
caller_port="root",
callee="prev_call",
callee_port="result",
),
self.fakes.postcondition(
caller="prev_call",
caller_port="result",
callee="leaf",
callee_port="source",
),
self.fakes.precondition(
caller="call1", caller_port="root", callee="leaf", callee_port="sink"
),
]
self.fakes.saver.add_all(
[
IssueInstanceSharedTextAssoc.Record(
issue_instance_id=instance.id, shared_text_id=source.id
),
IssueInstanceSharedTextAssoc.Record(
issue_instance_id=instance.id, shared_text_id=sink.id
),
]
)
self.fakes.saver.add_all(
[
IssueInstanceTraceFrameAssoc.Record(
issue_instance_id=instance.id, trace_frame_id=frames[0].id
),
IssueInstanceTraceFrameAssoc.Record(
issue_instance_id=instance.id, trace_frame_id=frames[1].id
),
IssueInstanceTraceFrameAssoc.Record(
issue_instance_id=instance.id, trace_frame_id=frames[3].id
),
]
)
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=source.id, trace_length=0
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=source.id, trace_length=1
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[2].id, leaf_id=source.id, trace_length=0
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[3].id, leaf_id=sink.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.setup()
self.interactive.issue(1)
self._clear_stdout()
self.interactive.prev_cursor_location()
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
" # ⎇ [callable] [port] [location]",
" --> 1 +2 leaf source lib/server/posts/response.py:4|5|6",
" 2 Foo.barMethod root /r/some/filename.py:6|7|8",
" 3 leaf sink lib/server/posts/request.py:4|5|6",
"",
],
)
self._clear_stdout()
self.interactive.branch(2)
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
" # ⎇ [callable] [port] [location]",
" 1 leaf source lib/server/posts/response.py:4|5|6",
" --> 2 +2 prev_call result lib/server/posts/response.py:4|5|6",
" 3 Foo.barMethod root /r/some/filename.py:6|7|8",
" 4 leaf sink lib/server/posts/request.py:4|5|6",
"",
],
)
self._clear_stdout()
with patch("click.prompt", return_value=0):
self.interactive.branch()
output = self.stdout.getvalue().strip()
self.assertIn("[*] prev_call : result", output)
self.assertIn(" [1 hops: source1]", output)
def testCurrentBranchIndex(self):
trace_frames = [TraceFrame(id=1), TraceFrame(id=2), TraceFrame(id=3)]
self.interactive.current_trace_frame_index = 0
self.interactive.trace_tuples = [TraceTuple(trace_frame=TraceFrame(id=1))]
self.assertEqual(0, self.interactive._current_branch_index(trace_frames))
self.interactive.trace_tuples[0].trace_frame.id = 2
self.assertEqual(1, self.interactive._current_branch_index(trace_frames))
self.interactive.trace_tuples[0].trace_frame.id = 3
self.assertEqual(2, self.interactive._current_branch_index(trace_frames))
self.interactive.trace_tuples[0].trace_frame.id = 4
self.assertEqual(-1, self.interactive._current_branch_index(trace_frames))
def testVerifyEntrypointSelected(self):
self.interactive.current_issue_instance_id = -1
self.interactive.current_frame_id = -1
with self.assertRaises(UserError):
self.interactive._verify_entrypoint_selected()
self.interactive.current_issue_instance_id = 1
try:
self.interactive._verify_entrypoint_selected()
except UserError:
self.fail("Unexpected UserError")
self.interactive.current_issue_instance_id = -1
self.interactive.current_frame_id = 1
try:
self.interactive._verify_entrypoint_selected()
except UserError:
self.fail("Unexpected UserError")
self.interactive.current_issue_instance_id = 1
with self.assertRaises(AssertionError):
self.interactive._verify_entrypoint_selected()
def testVerifyMultipleBranches(self):
self.interactive.current_trace_frame_index = 0
self.interactive.trace_tuples = [
TraceTuple(trace_frame=TraceFrame(id=1), branches=1),
TraceTuple(trace_frame=TraceFrame(id=2), branches=2),
]
with self.assertRaises(UserError):
self.interactive._verify_multiple_branches()
self.interactive.current_trace_frame_index = 1
try:
self.interactive._verify_multiple_branches()
except UserError:
self.fail("Unexpected UserError")
def testAddListOrElementFilterErrors(self):
with self.assertRaises(UserError):
self.interactive._add_list_or_element_filter_to_query(
"not a list", None, None, "arg0", int
)
with self.assertRaises(UserError):
self.interactive._add_list_or_element_filter_to_query(
[], None, None, "arg0", str
)
def testAddListOrStringFilterToQuery(self):
shared_texts = [
SharedText(id=1, contents="prefix"),
SharedText(id=2, contents="suffix"),
SharedText(id=3, contents="prefix_suffix"),
SharedText(id=4, contents="fix"),
]
with self.db.make_session() as session:
self._add_to_session(session, shared_texts)
session.commit()
query = session.query(SharedText.contents)
self.assertEqual(
self.interactive._add_list_or_string_filter_to_query(
["prefix", "suffix"], query, SharedText.contents, "contents"
).all(),
[("prefix",), ("suffix",)],
)
self.assertEqual(
self.interactive._add_list_or_string_filter_to_query(
["%prefix%"], query, SharedText.contents, "contents"
).all(),
[("prefix",), ("prefix_suffix",)],
)
self.assertEqual(
self.interactive._add_list_or_string_filter_to_query(
["%fix%"], query, SharedText.contents, "contents"
).all(),
[("prefix",), ("suffix",), ("prefix_suffix",), ("fix",)],
)
def testCreateIssueOutputStringNoSourcesNoSinks(self):
issue = IssueQueryResult(
id=1,
filename="module.py",
location=SourceLocation(1, 2, 3),
code=1000,
callable="module.function1",
message="root",
min_trace_length_to_sources=1,
min_trace_length_to_sinks=1,
)
sources = []
sinks = ["sink1", "sink2"]
features = []
result = self.interactive._create_issue_output_string(
issue, sources, sinks, features
)
self.assertIn("Sources: No sources", result)
self.assertIn("Sinks: sink1", result)
sources = ["source1", "source2"]
sinks = []
result = self.interactive._create_issue_output_string(
issue, sources, sinks, features
)
self.assertIn("Sources: source1", result)
self.assertIn("Sinks: No sinks", result)
def testCreateIssueOutputStringNoFeatures(self):
issue = IssueQueryResult(
id=1,
filename="module.py",
location=SourceLocation(1, 2, 3),
code=1000,
callable="module.function1",
message="root",
min_trace_length_to_sources=1,
min_trace_length_to_sinks=1,
)
sources = []
sinks = ["sink1"]
features = []
result = self.interactive._create_issue_output_string(
issue, sources, sinks, features
)
self.assertIn("Features: No features", result)
sources = []
sinks = ["sink1"]
features = ["via:feature1"]
result = self.interactive._create_issue_output_string(
issue, sources, sinks, features
)
self.assertIn("Features: via:feature1", result)
def testCreateIssueOutputStringTraceLength(self):
issue1 = IssueQueryResult(
id=1,
filename="module.py",
location=SourceLocation(1, 2, 3),
code=1000,
callable="module.function1",
message="root",
min_trace_length_to_sources=0,
min_trace_length_to_sinks=6,
)
sources = []
sinks = ["sink1", "sink2"]
features = []
result = self.interactive._create_issue_output_string(
issue1, sources, sinks, features
)
self.assertIn("Min Trace Length: Source (0) | Sink (6)", result)
issue2 = IssueQueryResult(
id=1,
filename="module.py",
location=SourceLocation(1, 2, 3),
code=1000,
callable="module.function1",
message="root",
min_trace_length_to_sources=3,
min_trace_length_to_sinks=1,
)
sources = []
sinks = ["sink1", "sink2"]
result = self.interactive._create_issue_output_string(
issue2, sources, sinks, features
)
self.assertIn("Min Trace Length: Source (3) | Sink (1)", result)
def testListSourceCode(self):
mock_data = """if this_is_true:
print("This was true")
else:
print("This was false")
"""
self.interactive.setup()
self.interactive.current_issue_instance_id = 1
self.interactive.current_trace_frame_index = 0
self.interactive.trace_tuples = [
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(0),
filename="file.py",
caller="",
caller_port="",
callee="callee",
callee_port="",
callee_location=SourceLocation(2, 10, 25),
),
placeholder=True,
)
]
with patch("builtins.open", mock_open(read_data=mock_data)) as mock_file:
self._clear_stdout()
self.interactive.list_source_code(2)
mock_file.assert_called_once_with(f"{os.getcwd()}/file.py", "r")
output = self.stdout.getvalue()
self.assertEqual(
output.split("\n"),
[
"In callee [file.py:2|10|25]",
" 1 if this_is_true:",
' --> 2 print("This was true")',
" ^^^^^^^^^^^^^^^",
" 3 else:",
' 4 print("This was false")',
"",
],
)
mock_file.reset_mock()
self._clear_stdout()
self.interactive.list_source_code(1)
mock_file.assert_called_once_with(f"{os.getcwd()}/file.py", "r")
output = self.stdout.getvalue()
self.assertEqual(
output.split("\n"),
[
"In callee [file.py:2|10|25]",
" 1 if this_is_true:",
' --> 2 print("This was true")',
" ^^^^^^^^^^^^^^^",
" 3 else:",
"",
],
)
def testListSourceCodeFileNotFound(self):
self.interactive.setup()
self.interactive.current_issue_instance_id = 1
self.interactive.current_trace_frame_index = 0
self.interactive.trace_tuples = [
TraceTuple(
trace_frame=TraceFrameQueryResult(
id=DBID(0),
caller="",
caller_port="",
callee="",
callee_port="",
filename="file.py",
callee_location=SourceLocation(2, 1, 1),
)
)
]
with patch("builtins.open", mock_open(read_data="not read")) as mock_file:
mock_file.side_effect = FileNotFoundError()
self.interactive.list_source_code()
self.assertIn("Couldn't open", self.stderr.getvalue())
self.assertNotIn("file.py", self.stdout.getvalue())
def testGroupTraceFrames(self):
trace_frames = [
TraceFrameQueryResult(
id=DBID(1),
caller="caller1",
caller_port="port1",
callee="",
callee_port="",
),
TraceFrameQueryResult(
id=DBID(2),
caller="caller1",
caller_port="port1",
callee="",
callee_port="",
),
TraceFrameQueryResult(
id=DBID(3),
caller="caller2",
caller_port="port2",
callee="",
callee_port="",
),
TraceFrameQueryResult(
id=DBID(4),
caller="caller2",
caller_port="port2",
callee="",
callee_port="",
),
TraceFrameQueryResult(
id=DBID(5),
caller="caller2",
caller_port="port3",
callee="",
callee_port="",
),
]
buckets = self.interactive._group_trace_frames(trace_frames, 5)
self.assertEqual(3, len(buckets.keys()))
self.assertIn(("caller1", "port1"), buckets.keys())
self.assertIn(("caller2", "port2"), buckets.keys())
self.assertIn(("caller2", "port3"), buckets.keys())
self.assertEqual(
[1, 2], [int(frame.id) for frame in buckets[("caller1", "port1")]]
)
self.assertEqual(
[3, 4], [int(frame.id) for frame in buckets[("caller2", "port2")]]
)
self.assertEqual(
[5], [int(frame.id) for frame in buckets[("caller2", "port3")]]
)
def testListTracesBasic(self):
self.fakes.run()
post1 = self.fakes.postcondition(
caller="caller1", caller_port="port1", callee="callee1", callee_port="port1"
)
post2 = self.fakes.postcondition(
caller="caller1", caller_port="port1", callee="callee2", callee_port="port2"
)
post3 = self.fakes.postcondition(
caller="caller2", caller_port="port2", callee="callee3", callee_port="port3"
)
post4 = self.fakes.postcondition(
caller="caller2", caller_port="port2", callee="callee4", callee_port="port4"
)
post5 = self.fakes.postcondition(
caller="caller2", caller_port="port3", callee="callee5", callee_port="port5"
)
self.fakes.save_all(self.db)
self.interactive.current_run_id = 1
self._clear_stdout()
self.interactive.frames(kind=TraceKind.POSTCONDITION)
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
"[id] [caller:caller_port -> callee:callee_port]",
"---- caller1:port1 ->",
f"{post1.id} callee1:port1",
f"{post2.id} callee2:port2",
"---- caller2:port2 ->",
f"{post3.id} callee3:port3",
f"{post4.id} callee4:port4",
"---- caller2:port3 ->",
f"{post5.id} callee5:port5",
"",
],
)
self._clear_stdout()
self.interactive.frames(kind=TraceKind.PRECONDITION)
self.assertEqual(self.stdout.getvalue().strip(), "No trace frames found.")
def testListTracesFilterCallersCallees(self):
run = self.fakes.run()
frames = self._basic_trace_frames()
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
self.interactive.current_run_id = 1
self._clear_stdout()
self.interactive.frames(callers=["call2"])
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
"[id] [caller:caller_port -> callee:callee_port]",
"---- call2:param0 ->",
f"{frames[1].id} leaf:sink",
"",
],
)
self._clear_stdout()
self.interactive.frames(callees=["call2"])
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
"[id] [caller:caller_port -> callee:callee_port]",
"---- call1:root ->",
f"{frames[0].id} call2:param0",
"",
],
)
def testListFramesWithLimit(self):
frames = self._set_up_branched_trace()
self.interactive.run(1)
self._clear_stdout()
self.interactive.frames(limit=3)
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
"[id] [caller:caller_port -> callee:callee_port]",
"---- call1:root ->",
f"{frames[3].id} call2:param2",
f"{frames[2].id} call2:param2",
f"{frames[1].id} leaf:source",
"...",
"Showing 3/6 matching frames. To see more, call 'frames' with "
"the 'limit' argument.",
"",
],
)
def testSetFrame(self):
frames = self._basic_trace_frames()
sink = self.fakes.sink("sink")
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=sink.id, trace_length=1
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=sink.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
self.interactive.setup()
self.interactive.frame(0)
self.assertIn("Trace frame 0 doesn't exist.", self.stderr.getvalue())
self._clear_stdout()
self.interactive.frame(1)
self.assertIn("Trace frame 1", self.stdout.getvalue())
self.assertNotIn("Trace frame 2", self.stdout.getvalue())
self._clear_stdout()
self.interactive.frame(2)
self.assertNotIn("Trace frame 1", self.stdout.getvalue())
self.assertIn("Trace frame 2", self.stdout.getvalue())
def testSetFrameUpdatesRun(self):
run1 = self.fakes.run()
frames = [
self.fakes.precondition(
caller="call1",
caller_port="root",
callee="call2",
callee_port="param0",
location=(1, 1, 1),
),
self.fakes.precondition(
caller="call2",
caller_port="param1",
callee="call3",
callee_port="param2",
location=(1, 1, 1),
),
]
run2 = self.fakes.run()
sink = self.fakes.sink("sink1")
self.fakes.saver.add_all(
[
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[0].id, leaf_id=sink.id, trace_length=1
),
TraceFrameLeafAssoc.Record(
trace_frame_id=frames[1].id, leaf_id=sink.id, trace_length=0
),
]
)
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run1)
session.add(run2)
session.commit()
self.interactive.setup()
self.assertEqual(int(self.interactive.current_run_id), 2)
self.interactive.frame(int(frames[0].id))
self.assertEqual(int(self.interactive.current_run_id), 1)
def testIsBeforeRoot(self):
self.interactive.trace_tuples = [
TraceTuple(trace_frame=TraceFrame(kind=TraceKind.POSTCONDITION)),
TraceTuple(trace_frame=TraceFrame(kind=TraceKind.PRECONDITION)),
]
self.interactive.current_trace_frame_index = 0
self.assertTrue(self.interactive._is_before_root())
self.interactive.current_trace_frame_index = 1
self.assertFalse(self.interactive._is_before_root())
def testIsRootTraceTuple(self):
trace_tuple = TraceTuple(trace_frame=TraceFrame(callee_port="root"))
self.assertTrue(self.interactive._is_root_trace_tuple(trace_tuple))
trace_tuple = TraceTuple(trace_frame=TraceFrame(callee_port="not_root"))
self.assertFalse(self.interactive._is_root_trace_tuple(trace_tuple))
def testParents(self):
self._set_up_branched_trace()
self.interactive.setup()
self.interactive.frame(3)
self.interactive.current_trace_frame_index = 1
self._clear_stdout()
with patch("click.prompt", return_value=0):
self.interactive.parents()
self.assertEqual(
self.stdout.getvalue().split("\n"),
["[1] call1 : root", "[2] call1 : root", ""],
)
self._clear_stdout()
self.interactive.current_trace_frame_index = 0
self.interactive.parents()
self.assertIn("No parents calling", self.stdout.getvalue())
self.interactive.current_trace_frame_index = 2
self.interactive.parents()
self.assertIn("Try running from a non-leaf node", self.stderr.getvalue())
def testParentsSelectParent(self):
self._set_up_branched_trace()
self.interactive.setup()
self.interactive.frame(3)
self.interactive.current_trace_frame_index = 1
self._clear_stdout()
with patch("click.prompt", return_value=1):
self.interactive.parents()
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
"[1] call1 : root",
"[2] call1 : root",
"",
" # ⎇ [callable] [port] [location]",
" --> 1 call1 root lib/server/posts/request.py:2|2|2",
" 2 call2 param2 lib/server/posts/request.py:2|2|2",
" 3 +2 leaf sink lib/server/posts/request.py:5|5|5",
"",
],
)
def testUpdateTraceTuplesNewParent(self):
frames = [
self.fakes.postcondition(callee="A"),
self.fakes.postcondition(callee="B"),
self.fakes.postcondition(callee="C"),
self.fakes.postcondition(callee="D"),
self.fakes.postcondition(callee="E"),
]
self.fakes.save_all(self.db)
self.interactive.setup()
# Test postcondition
self.interactive.current_trace_frame_index = 2
with self.db.make_session() as session:
self.interactive.trace_tuples = [
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[0])),
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[1])),
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[2])),
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[3])),
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[4])),
]
trace_frame = TraceFrameQueryResult(
id=DBID(0),
caller="caller",
caller_port="caller_port",
callee="F",
callee_port="callee_port",
filename="file.py",
callee_location=SourceLocation(1, 1, 1),
kind=TraceKind.POSTCONDITION,
)
self.interactive._update_trace_tuples_new_parent(trace_frame)
self.assertEqual(self.interactive.current_trace_frame_index, 3)
self.assertEqual(
[
self.interactive._get_callable_from_trace_tuple(trace_tuple)[0]
for trace_tuple in self.interactive.trace_tuples
],
["A", "B", "F", "caller"],
)
self.assertTrue(self.interactive.trace_tuples[-1].placeholder)
# Test precondition
self.interactive.current_trace_frame_index = 2
with self.db.make_session() as session:
self.interactive.trace_tuples = [
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[0])),
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[1])),
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[2])),
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[3])),
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[4])),
]
trace_frame = TraceFrameQueryResult(
id=DBID(0),
caller="caller",
caller_port="caller_port",
callee="F",
callee_port="callee_port",
filename="file.py",
callee_location=SourceLocation(1, 1, 1),
kind=TraceKind.PRECONDITION,
)
self.interactive._update_trace_tuples_new_parent(trace_frame)
self.assertEqual(self.interactive.current_trace_frame_index, 0)
self.assertEqual(
[
self.interactive._get_callable_from_trace_tuple(trace_tuple)[0]
for trace_tuple in self.interactive.trace_tuples
],
["caller", "F", "D", "E"],
)
self.assertTrue(self.interactive.trace_tuples[0].placeholder)
def testAllLeavesByKind(self):
shared_texts = [
SharedText(id=1, contents="source1", kind=SharedTextKind.SOURCE),
SharedText(id=2, contents="source2", kind=SharedTextKind.SOURCE),
SharedText(id=3, contents="source3", kind=SharedTextKind.SOURCE),
SharedText(id=4, contents="sink4", kind=SharedTextKind.SINK),
SharedText(id=5, contents="sink5", kind=SharedTextKind.SINK),
]
with self.db.make_session() as session:
self._add_to_session(session, shared_texts)
session.commit()
self.assertEqual(
self.interactive._all_leaves_by_kind(session, SharedTextKind.SOURCE),
{1: "source1", 2: "source2", 3: "source3"},
)
self.assertEqual(
self.interactive._all_leaves_by_kind(session, SharedTextKind.SINK),
{4: "sink4", 5: "sink5"},
)
def testDetails(self):
run = self.fakes.run()
frames = [
self.fakes.precondition(
caller="call1",
caller_port="root",
callee="call2",
callee_port="param0",
location=(1, 1, 1),
),
self.fakes.precondition(
caller="call2",
caller_port="param1",
callee="call3",
callee_port="param2",
location=(1, 1, 1),
),
]
issues = [self.fakes.issue(), self.fakes.issue(), self.fakes.issue()]
self.fakes.instance(issue_id=issues[0].id, callable="call2"),
self.fakes.instance(issue_id=issues[1].id, callable="call3"),
self.fakes.instance(issue_id=issues[2].id, callable="call2"),
self.fakes.save_all(self.db)
with self.db.make_session(expire_on_commit=False) as session:
session.add(run)
session.commit()
self.interactive.setup()
with self.db.make_session() as session:
self.interactive.trace_tuples = [
TraceTuple(trace_frame=self._frame_to_query_result(session, frames[0]))
]
self.interactive.current_issue_instance_id = 1
self.interactive.current_trace_frame_index = 0
self._clear_stdout()
self.interactive.details()
self.assertEqual(
self.stdout.getvalue().split("\n"),
[
f"Trace frame {frames[0].id}",
" Caller: call1 : root",
" Callee: call2 : param0",
" Kind: TraceKind.precondition",
" Sinks: ",
" Location: lib/server/posts/request.py:1|1|1",
"",
"Issues in callable (call2): 2",
"",
"Postconditions with caller (call2):",
"No trace frames found.",
"",
"Preconditions with caller (call2):",
"[id] [caller:caller_port -> callee:callee_port]",
"---- call2:param1 ->",
f"{frames[1].id} call3:param2",
"",
],
)
def mock_pager(self, output_string):
self.pager_calls += 1
def testPager(self):
run = self.fakes.run()
self.fakes.issue()
self.fakes.instance()
self.fakes.save_all(self.db)
with self.db.make_session() as session:
session.add(run)
session.commit()
# Default is no pager in tests
self.pager_calls = 0
with patch("IPython.core.page.page", self.mock_pager):
self.interactive.setup()
self.interactive.issues(use_pager=False)
self.interactive.runs(use_pager=False)
self.assertEqual(self.pager_calls, 0)
self.pager_calls = 0
with patch("IPython.core.page.page", self.mock_pager):
self.interactive.setup()
self.interactive.issues(use_pager=True)
self.interactive.runs(use_pager=True)
self.assertEqual(self.pager_calls, 2)
| [
"[email protected]"
] | |
82b94aa0ddd70df563d846434a596b315ad4d8a1 | 84341d15f4b8d13b09c7dabe2b7286705ee86b7b | /scripts/multi_rc/topk_evidence_self_training/roberta_predict_sentence3.0.py | e9e34a6953dcd9a10999bf35f331f6ab6d91b805 | [] | no_license | UMP-Healthcare-AI/Self-Training-MRC | 7a0ef0c52f0064cfc32a2bedb433608ed10328a7 | 0601158085bb11e454aee1ebaa987f5aa741ab3f | refs/heads/master | 2022-12-01T17:46:28.463777 | 2020-08-14T01:56:25 | 2020-08-14T01:56:25 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,094 | py | import subprocess
import time
import logging
import os
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
def run_cmd(command: str):
logger.info(command)
subprocess.check_call(command, shell=True)
def wait_for_file(file: str):
if not os.path.exists(file):
logger.info(f'Could not find file {file}. Waiting...')
minute_cnt = 0
while not os.path.exists(file):
print(f'The {minute_cnt}th minute...')
time.sleep(60)
minute_cnt += 1
time.sleep(60)
logger.info(f'Find file {file} after waiting for {minute_cnt} minutes')
# model
roberta_large_model_dir = "/home/jiaofangkai/roberta-large"
train_file = '/home/jiaofangkai/multi-rc/splitv2/train.json'
dev_file = '/home/jiaofangkai/multi-rc/splitv2/dev.json'
task_name = 'topk-rc-roberta'
reader_name = 'topk-multi-rc-roberta'
bert_name = 'hie-topk-roberta'
k = 2000
label_threshold = 0.8
weight_threshold = 0.5
recurrent_times = 10
num_train_epochs = [8] * 10
sentence_id_file = None
num_evidence = 3
root_dir = f'experiments/multi-rc/topk-evidence/roberta-self-training/v1.0_acc_top{k}'
os.makedirs(root_dir, exist_ok=True)
f_handler = logging.FileHandler(os.path.join(root_dir, f'output.log'))
f_handler.setLevel(logging.INFO)
f_handler.setFormatter(logging.Formatter(fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt='%m/%d/%Y %H:%M:%S'))
logger.addHandler(f_handler)
logger.info('Self-training parameters:')
logger.info(f'k: {k}')
logger.info(f'label_threshold: {label_threshold}')
logger.info(f'weight_threshold: {weight_threshold}')
logger.info(f'recurrent_times: {recurrent_times}')
logger.info(f'num_evidence: {num_evidence}')
learning_rate = 1e-5
for i in range(recurrent_times):
logger.info(f'Start running for the {i}th times.')
output_dir = f'{root_dir}/recurrent{i}'
if i == 0:
evidence_lambda = 0.0
else:
evidence_lambda = 0.8
cmd = f'python main2_0.6.2_topk_predict_sentences.py --bert_model roberta-large ' \
f'--vocab_file {roberta_large_model_dir} --model_file {roberta_large_model_dir} ' \
f'--output_dir {output_dir} --predict_dir {output_dir} ' \
f'--train_file {train_file} --predict_file {dev_file} ' \
f'--max_seq_length 512 --train_batch_size 32 --predict_batch_size 1 ' \
f'--learning_rate {learning_rate} --num_train_epochs {num_train_epochs[i]} ' \
f'--fp16 --fp16_opt_level O2 --gradient_accumulation_steps 32 --per_eval_step 100 ' \
f'--bert_name {bert_name} --task_name {task_name} --reader_name {reader_name} ' \
f'--evidence_lambda {evidence_lambda} ' \
f'--do_label --only_correct --label_threshold {label_threshold} --weight_threshold {weight_threshold} ' \
f'--num_evidence {num_evidence} --max_grad_norm 5.0 --adam_epsilon 1e-6 '
run_cmd(cmd)
logger.info('=' * 50)
| [
"[email protected]"
] | |
9d1075dd4cf3c10e3855309c10cd54598f9bbd9e | 8d0f35341ad14f1f1edd71481e5fa870a4cb6ed5 | /setup.py | acbda8936e4d0aeefd6b89aa075f25c298c7c64f | [
"MIT"
] | permissive | gitter-badger/django-static-precompiler | f22b2414ef73c8ad0c0e448f1d821dfc73868ee4 | aad008df68dc7194ed9d6e78ab4358f94e456894 | refs/heads/master | 2021-01-12T21:45:20.258776 | 2014-11-19T05:04:15 | 2014-11-19T05:04:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,264 | py | from setuptools import setup, find_packages
import os
import sys
def read(fname):
path = os.path.join(os.path.dirname(__file__), fname)
if sys.version < '3':
return open(path).read()
return open(path, encoding="utf-8").read()
README = read('README.rst')
CHANGES = read('CHANGES.rst')
setup(
name="django-static-precompiler",
packages=find_packages(),
version="0.7",
author="Andrey Fedoseev",
author_email="[email protected]",
url="https://github.com/andreyfedoseev/django-static-precompiler",
description="Django template tags to compile all kinds of static files "
"(SASS, LESS, CoffeeScript).",
long_description="\n\n".join([README, CHANGES]),
classifiers=[
'Development Status :: 4 - Beta',
'Framework :: Django',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Topic :: Internet :: WWW/HTTP',
],
keywords=["sass", "scss", "less", "css", "coffeescript", "javascript"],
tests_require=[
"mock",
],
test_suite="static_precompiler.tests.suite",
)
| [
"[email protected]"
] | |
61c96eaafdcde160af8110b4322db04ba28832ed | 15f321878face2af9317363c5f6de1e5ddd9b749 | /solutions_python/Problem_118/1006.py | f920c1947a495d32a85e4a572c45898ba6ebdea5 | [] | no_license | dr-dos-ok/Code_Jam_Webscraper | c06fd59870842664cd79c41eb460a09553e1c80a | 26a35bf114a3aa30fc4c677ef069d95f41665cc0 | refs/heads/master | 2020-04-06T08:17:40.938460 | 2018-10-14T10:12:47 | 2018-10-14T10:12:47 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 880 | py | import numpy as np
from math import ceil, sqrt
fin = open('C-small-attempt0.in')
fout = open('out_small.txt', 'w')
def isqrt(x):
n = int(x)
if n == 0:
return 0
a, b = divmod(n.bit_length(), 2)
x = 2**(a+b)
while True:
y = (x + n//x)//2
if y >= x:
return x
x = y
T = int(fin.readline().rstrip('\n'))
def IsPalindrome(x):
return(str(x)[::-1] == str(x))
for iter in range(T):
num_palindromes = 0
num_lims = np.array(fin.readline().rstrip('\n').split(), dtype=long)
min_val = num_lims[0]
max_val = num_lims[1]
start_val = int(ceil(sqrt(min_val)))
for i in range(start_val, isqrt(max_val)+1,1):
if IsPalindrome(i) and IsPalindrome(pow(i,2)):
num_palindromes = num_palindromes + 1
case_num = iter+1
fout.write('Case #%d: ' %case_num + '%d\n' %num_palindromes)
fin.close()
fout.close() | [
"[email protected]"
] | |
63ef114161ba9d387165bf9cd0c4eee914e502f5 | 60e57860bc819d83cd628b5179ba7f4d655562b8 | /implementation/lps/lps/tests.py | 17794922910430e7844501e766a65b1be823652f | [] | no_license | Schwenger/TinBots | 9a3075c9e84762f34e33f15970eaadc305eed003 | ce7554f357b291b6ff7ae0214d4f8bbf5bd0c48e | refs/heads/master | 2021-01-19T21:15:31.948534 | 2017-08-31T21:34:09 | 2017-08-31T21:34:09 | 63,550,617 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,989 | py | # -*- coding: utf-8 -*-
# Copyright (C) 2016, Maximilian Köhl <[email protected]>
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License version 3 as published by
# the Free Software Foundation.
#
# 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 Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
import threading
import time
from lps.commands import Commands
from lps.constants import Modes
from lps.debugger import Debugger, INFO
class VictimDirectionTest:
def __init__(self, tinbot, debugger=None):
self.debugger = debugger or Debugger.current
self.tinbot = tinbot
self.thread = None
self.iterations = None
self.result = None
self.done = threading.Event()
def start(self, iterations=50):
self.iterations = iterations
self.result = []
self.thread = threading.Thread(target=self.run)
self.thread.start()
def run(self):
self.tinbot.package_event += self.on_package
self.debugger.print_message('Starting Victim Direction Test', INFO)
self.tinbot.set_mode(Modes.VICDIR)
for iteration in range(self.iterations):
time.sleep(0.5)
self.done.clear()
self.tinbot.start()
self.done.wait()
self.tinbot.reset()
self.tinbot.package_event -= self.on_package
def on_package(self, device, source, target, command, payload):
if command != Commands.VICTIM_PHI:
return
x, y, phi = Commands.VICTIM_PHI.decode(payload)
self.result.append((phi, self.tinbot.victim_phi))
self.done.set()
| [
"[email protected]"
] | |
960efe5c81f0c4d2490e1618d848cb089fb10f97 | 62e45255088abb536e9ea6fcbe497e83bad171a0 | /ippython/multiplo.py | 9dce2387b353d86c8dd7be1608c2d034bf585dc5 | [] | no_license | jmery24/python | a24f562c8d893a97a5d9011e9283eba948b8b6dc | 3e35ac9c9efbac4ff20374e1dfa75a7af6003ab9 | refs/heads/master | 2020-12-25T21:56:17.063767 | 2015-06-18T04:59:05 | 2015-06-18T04:59:05 | 36,337,473 | 0 | 0 | null | 2015-05-27T02:26:54 | 2015-05-27T02:26:54 | null | UTF-8 | Python | false | false | 503 | py | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 22 07:18:42 2013
@author: daniel
"""
#programa:multiplo.py
#calcula los multiplos de numero en el intervalo [numero, limite]
#data input
num = 1
repeticiones = int(raw_input('valor limite <entero positivo>: '))
multiplo = int(raw_input('multiplos del numero: '))
#proccesing
if repeticiones < 0:
print 'no es posible la operacion'
else:
while num <= repeticiones / multiplo:
print multiplo * num
num += 1
print 'Hecho' | [
"[email protected]"
] | |
08668a50432d86ff9e4f7c7c779e74597a487dd9 | c9f48f8a85c01ea826a6abc4fe69f1cb002c74a7 | /chapter18/learning_log11/ll_env/Scripts/django-admin.py | f311544b363b35f348d233c9a4a4f2360fd9a18d | [] | no_license | wangyu6/book1 | 67f02645aa7fe6c39bb5b5ea3f2670225274f4b5 | fd2cd7c6c8a3144624e0cc2ef9608b77416c4820 | refs/heads/master | 2020-04-16T07:16:46.052592 | 2019-04-24T06:25:37 | 2019-04-24T06:25:37 | 165,380,114 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 176 | py | #!d:\py_work\book1\chapter18\learning_log11\ll_env\scripts\python.exe
from django.core import management
if __name__ == "__main__":
management.execute_from_command_line()
| [
"[email protected]"
] | |
f1d9996b93014b4210b992142021b15cb3c21578 | 16c8fdf291430475f40d578b0d64552eb64046e9 | /colour/plotting/tm3018/tests/test_components.py | f11dce989d2eabcc845c3554a2ee35f1ac115482 | [
"BSD-3-Clause"
] | permissive | nodefeet/colour | 4c1bfed87ce173ff878bdf288fd9828bb68022e3 | 319dd5b1c45aef6983eff1830f918c1e593fb530 | refs/heads/develop | 2022-02-19T17:39:36.657993 | 2022-02-15T08:38:26 | 2022-02-15T08:38:26 | 460,456,444 | 0 | 0 | BSD-3-Clause | 2022-02-17T13:53:37 | 2022-02-17T13:53:36 | null | UTF-8 | Python | false | false | 5,064 | py | """Defines the unit tests for the :mod:`colour.plotting.tm3018.components` module."""
from __future__ import annotations
import unittest
from matplotlib.pyplot import Axes, Figure
from colour.colorimetry import SDS_ILLUMINANTS
from colour.hints import cast
from colour.quality import (
ColourQuality_Specification_ANSIIESTM3018,
colour_fidelity_index_ANSIIESTM3018,
)
from colour.plotting.tm3018.components import (
plot_spectra_ANSIIESTM3018,
plot_colour_vector_graphic,
plot_16_bin_bars,
plot_local_chroma_shifts,
plot_local_hue_shifts,
plot_local_colour_fidelities,
plot_colour_fidelity_indexes,
)
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "New BSD License - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "[email protected]"
__status__ = "Production"
__all__ = [
"TestPlotSpectraANSIIESTM3018",
"TestPlotColourVectorGraphic",
"TestPlot16BinBars",
"TestPlotLocalChromaShifts",
"TestPlotLocalHueShifts",
"TestPlotLocalColourFidelities",
"TestPlotColourFidelityIndexes",
]
SPECIFICATION_ANSIIESTM3018: ColourQuality_Specification_ANSIIESTM3018 = cast(
ColourQuality_Specification_ANSIIESTM3018,
colour_fidelity_index_ANSIIESTM3018(SDS_ILLUMINANTS["FL2"], True),
)
class TestPlotSpectraANSIIESTM3018(unittest.TestCase):
"""
Define :func:`colour.plotting.tm3018.components.
plot_spectra_ANSIIESTM3018` definition unit tests methods.
"""
def test_plot_spectra_ANSIIESTM3018(self):
"""
Test :func:`colour.plotting.tm3018.components.\
plot_spectra_ANSIIESTM3018` definition.
"""
figure, axes = plot_spectra_ANSIIESTM3018(SPECIFICATION_ANSIIESTM3018)
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
class TestPlotColourVectorGraphic(unittest.TestCase):
"""
Define :func:`colour.plotting.tm3018.components.\
plot_colour_vector_graphic` definition unit tests methods.
"""
def test_plot_colour_vector_graphic(self):
"""
Test :func:`colour.plotting.tm3018.components.\
plot_colour_vector_graphic` definition.
"""
figure, axes = plot_colour_vector_graphic(SPECIFICATION_ANSIIESTM3018)
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
class TestPlot16BinBars(unittest.TestCase):
"""
Define :func:`colour.plotting.tm3018.components.plot_16_bin_bars`
definition unit tests methods.
"""
def test_plot_16_bin_bars(self):
"""
Test :func:`colour.plotting.tm3018.components.plot_16_bin_bars`
definition.
"""
figure, axes = plot_16_bin_bars(range(16), "{0}")
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
class TestPlotLocalChromaShifts(unittest.TestCase):
"""
Define :func:`colour.plotting.tm3018.components.plot_local_chroma_shifts`
definition unit tests methods.
"""
def test_plot_local_chroma_shifts(self):
"""
Test :func:`colour.plotting.tm3018.components.\
plot_local_chroma_shifts` definition.
"""
figure, axes = plot_local_chroma_shifts(SPECIFICATION_ANSIIESTM3018)
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
class TestPlotLocalHueShifts(unittest.TestCase):
"""
Define :func:`colour.plotting.tm3018.components.plot_local_hue_shifts`
definition unit tests methods.
"""
def test_plot_local_hue_shifts(self):
"""
Test :func:`colour.plotting.tm3018.components.\
plot_local_hue_shifts` definition.
"""
figure, axes = plot_local_hue_shifts(SPECIFICATION_ANSIIESTM3018)
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
class TestPlotLocalColourFidelities(unittest.TestCase):
"""
Define :func:`colour.plotting.tm3018.components.
plot_local_colour_fidelities` definition unit tests methods.
"""
def test_plot_local_colour_fidelities(self):
"""
Test :func:`colour.plotting.tm3018.components.\
plot_local_colour_fidelities` definition.
"""
figure, axes = plot_local_colour_fidelities(
SPECIFICATION_ANSIIESTM3018
)
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
class TestPlotColourFidelityIndexes(unittest.TestCase):
"""
Define :func:`colour.plotting.tm3018.components.\
plot_colour_fidelity_indexes` definition unit tests methods.
"""
def test_plot_colour_fidelity_indexes(self):
"""
Test :func:`colour.plotting.tm3018.components.\
plot_colour_fidelity_indexes` definition.
"""
figure, axes = plot_colour_fidelity_indexes(
SPECIFICATION_ANSIIESTM3018
)
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
if __name__ == "__main__":
unittest.main()
| [
"[email protected]"
] | |
ca29d96325ee8f61ab5f0947f4388cd848582dd5 | e71b6d14fbdbc57c7234ca45a47329d7d02fc6f7 | /flask_api/venv/lib/python3.7/site-packages/vsts/release/v4_1/models/task_input_definition_base.py | 7d9e81d65f08fc44f1f9696cf00be3bf1a173c64 | [] | no_license | u-blavins/secret_sasquatch_society | c36993c738ab29a6a4879bfbeb78a5803f4f2a57 | 0214eadcdfa9b40254e331a6617c50b422212f4c | refs/heads/master | 2020-08-14T00:39:52.948272 | 2020-01-22T13:54:58 | 2020-01-22T13:54:58 | 215,058,646 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,850 | py | # --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
# Generated file, DO NOT EDIT
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------------------------
from msrest.serialization import Model
class TaskInputDefinitionBase(Model):
"""TaskInputDefinitionBase.
:param aliases:
:type aliases: list of str
:param default_value:
:type default_value: str
:param group_name:
:type group_name: str
:param help_mark_down:
:type help_mark_down: str
:param label:
:type label: str
:param name:
:type name: str
:param options:
:type options: dict
:param properties:
:type properties: dict
:param required:
:type required: bool
:param type:
:type type: str
:param validation:
:type validation: :class:`TaskInputValidation <microsoft.-team-foundation.-distributed-task.-common.-contracts.v4_1.models.TaskInputValidation>`
:param visible_rule:
:type visible_rule: str
"""
_attribute_map = {
'aliases': {'key': 'aliases', 'type': '[str]'},
'default_value': {'key': 'defaultValue', 'type': 'str'},
'group_name': {'key': 'groupName', 'type': 'str'},
'help_mark_down': {'key': 'helpMarkDown', 'type': 'str'},
'label': {'key': 'label', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'options': {'key': 'options', 'type': '{str}'},
'properties': {'key': 'properties', 'type': '{str}'},
'required': {'key': 'required', 'type': 'bool'},
'type': {'key': 'type', 'type': 'str'},
'validation': {'key': 'validation', 'type': 'TaskInputValidation'},
'visible_rule': {'key': 'visibleRule', 'type': 'str'}
}
def __init__(self, aliases=None, default_value=None, group_name=None, help_mark_down=None, label=None, name=None, options=None, properties=None, required=None, type=None, validation=None, visible_rule=None):
super(TaskInputDefinitionBase, self).__init__()
self.aliases = aliases
self.default_value = default_value
self.group_name = group_name
self.help_mark_down = help_mark_down
self.label = label
self.name = name
self.options = options
self.properties = properties
self.required = required
self.type = type
self.validation = validation
self.visible_rule = visible_rule
| [
"[email protected]"
] | |
2a05690aed829799730463e6bd5fdd67a501eab8 | 70c32e104ab814d21c37fc27fd025e82ad3d2119 | /Fujitsu/benchmarks/deepcam/implementations/implementation_abci_fj/fused_lamb.py | e6e7ca211a2901293388cee5c6e3cd584eb50047 | [
"Apache-2.0"
] | permissive | cecevu/hpc_results_v0.7 | f340452daa7fe8f80f0242f3918ae330a38c12bd | 2895e2956e363ce0e9ddbbcbd8503eec1a285e5a | refs/heads/main | 2023-03-05T23:29:52.236214 | 2021-02-15T22:47:04 | 2021-02-15T22:47:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 14,994 | py | import torch
import os
import ast
from apex.multi_tensor_apply import multi_tensor_applier
num_step = 1
def UpdateIndex2LayerIndex(input):
layer_num = 301
flag = 0
conv = [1,4,10,14,18,23,27,31,36,40,44,46,50,54,58,62,66,70,74,78,82,86,90,94,98,102\
,106,110,114,118,122,126,130,134,138,142,146,150,154,158,162,166,170,174,178,182,186,190\
,194,198,202,206,210,214,218,222,226,230,234,241,245,249,251,252,255,256,259,260,263,266\
,269,272,278,281,290,291,293,294,296]
if type(input) is not int:
raise Exception("input value error!")
if input > layer_num:
return layer_num - 1
if input < 1:
return -1
for num in conv:
if input == num:
return input - 1
elif input > num:
flag = num
continue
else:
return flag - 1
return flag - 1
class FusedLAMB(torch.optim.Optimizer):
"""Implements LAMB algorithm.
Currently GPU-only. Requires Apex to be installed via
``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``.
This version of fused LAMB implements 2 fusions.
* Fusion of the LAMB update's elementwise operations
* A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.
:class:`apex.optimizers.FusedLAMB`'s usage is identical to any ordinary Pytorch optimizer::
opt = apex.optimizers.FusedLAMB(model.parameters(), lr = ....)
...
opt.step()
:class:`apex.optimizers.FusedLAMB` may be used with or without Amp. If you wish to use :class:`FusedLAMB` with Amp,
you may choose any ``opt_level``::
opt = apex.optimizers.FusedLAMB(model.parameters(), lr = ....)
model, opt = amp.initialize(model, opt, opt_level="O0" or "O1 or "O2")
...
opt.step()
In general, ``opt_level="O1"`` is recommended.
LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups.
lr (float, optional): learning rate. (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its norm. (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability. (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
NOT SUPPORTED now! (default: False)
adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
True for decoupled weight decay(also known as AdamW) (default: True)
grad_averaging (bool, optional): whether apply (1-beta2) to grad when
calculating running averages of gradient. (default: True)
set_grad_none (bool, optional): whether set grad to None when zero_grad()
method is called. (default: True)
max_grad_norm (float, optional): value used to clip global grad norm
(default: 1.0)
use_nvlamb (boolean, optional): Apply adaptive learning rate to 0.0
weight decay parameter (default: False)
.. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes:
https://arxiv.org/abs/1904.00962
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params, lr=1e-3, bias_correction=True,
betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01,
amsgrad=False, adam_w_mode=True,
grad_averaging=True, set_grad_none=True,
max_grad_norm=1.0, use_nvlamb=False):
if amsgrad:
raise RuntimeError('FusedLAMB does not support the AMSGrad variant.')
defaults = dict(lr=lr, bias_correction=bias_correction,
betas=betas, eps=eps, weight_decay=weight_decay,
grad_averaging=grad_averaging,
max_grad_norm=max_grad_norm)
super(FusedLAMB, self).__init__(params, defaults)
if multi_tensor_applier.available:
import amp_C
self.multi_tensor_l2norm=amp_C.multi_tensor_l2norm
# Skip buffer
self._dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device=self.param_groups[0]["params"][0].device)
self.multi_tensor_lamb = amp_C.multi_tensor_lamb
else:
raise RuntimeError('apex.optimizers.FusedLAMB requires cuda extensions')
self.adam_w_mode = 1 if adam_w_mode else 0
self.set_grad_none = set_grad_none
self.use_nvlamb = use_nvlamb
set_skip_layer_num_str ="{" + os.getenv("SET_STOP_LAYER_NUM") + "}"
self.skip_layer_num = ast.literal_eval(set_skip_layer_num_str)
set_skip_layer_thr_str ="{" + os.getenv("SET_STOP_LAYER_THR") + "}"
self.skip_layer_thr = ast.literal_eval(set_skip_layer_thr_str)
self.skip_layer_count = 1
self.Average_num = 10
self.layer_w_sum = 0
self.layer_w_sum_count = 0
self.BD_end_num = 0
def zero_grad(self):
if self.set_grad_none:
for group in self.param_groups:
for i, p in enumerate(group['params']):
#for p in group['params']:
p.grad = None
else:
super(FusedLAMB, self).zero_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
global num_step
global layer_num
global cac_bd_layer_num
global cac_bd_end_flag
global t_lr
global End_lr
global End_ratio
if num_step == 1:
layer_num = 301
cac_bd_layer_num = -1
cac_bd_end_flag = {}
self.braking_distance = int(os.getenv('CAC_BRAKING_DISTANCE',"-1"))
self.grad_norms = {}
ratio = 1.000
self.skipped_idx = 0
self.old_skipped_idx = 0
t_lr = {}
Layer = 0
End_lr = float(os.getenv('CAC_FINISH_LR',"0.000003"))
self.N_factor = int(os.getenv('CAC_BRAKING_FACTOR',"-1"))
self.braking_count = {}
mpi_rank = os.environ.get("OMPI_COMM_WORLD_RANK", -1)
if mpi_rank == "0":
print("Braking_Distance:", self.braking_distance)
for Layer in range(layer_num):
t_lr[Layer] = 1.0
cac_bd_end_flag[Layer] = 0
if self.braking_distance > 0:
self.braking_count[Layer] = self.braking_distance
loss = None
if closure is not None:
loss = closure()
# create separate grad lists for fp32 and fp16 params
g_all_32, g_all_16 = [], []
for group in self.param_groups:
if self.skip_layer_count <= len(self.skip_layer_num):
p_skip_layer_count = self.skip_layer_count
p_skip = self.skip_layer_num[p_skip_layer_count]
p_thr = self.skip_layer_thr[p_skip_layer_count]
else:
p_skip = 99999
p_thr = 99999
# skip judge & set to
for i, p in enumerate(group['params']):
if p.grad is None:
continue
if i == p_skip:
self.layer_w_sum += torch.norm(p)
self.layer_w_sum_count += 1
if self.layer_w_sum_count == self.Average_num:
self.layer_w_sum = self.layer_w_sum / self.Average_num
if self.layer_w_sum < p_thr:
Skip_index = UpdateIndex2LayerIndex(p_skip)
cac_bd_layer_num = Skip_index
mpi_rank = os.environ.get("OMPI_COMM_WORLD_RANK", -1)
if mpi_rank == "0":
print("******Skip Layer: {}, {}".format(str(Skip_index), self.layer_w_sum))
self.skip_layer_count += 1
self.layer_w_sum_count = 0
self.layer_w_sum = 0
else:
self.layer_w_sum_count = 0
self.layer_w_sum = 0
# mpi_rank = os.environ.get("OMPI_COMM_WORLD_RANK", -1)
# if mpi_rank == "0":
# with open("param.csv", "a") as f:
# print("Dump, {}, {}, {}, {}, {}, {}, {}, {}".format(num_step, i, str(t_lr[i]), p_w, p_g, p_e, p_skip, p_thr), file=f)
if p.dtype == torch.float32:
g_all_32.append(p.grad.data)
elif p.dtype == torch.float16:
g_all_16.append(p.grad.data)
else:
raise RuntimeError('FusedLAMB only support fp16 and fp32.')
num_step += 1
device = self.param_groups[0]["params"][0].device
g_norm_32, g_norm_16 = torch.zeros(1, device=device), torch.zeros(1, device=device)
# compute grad norm for two lists
if len(g_all_32) > 0:
g_norm_32 = multi_tensor_applier(self.multi_tensor_l2norm,
self._dummy_overflow_buf,
[g_all_32], False)[0]
if len(g_all_16) > 0:
g_norm_16 = multi_tensor_applier(self.multi_tensor_l2norm,
self._dummy_overflow_buf,
[g_all_16], False)[0]
# blend two grad norms to get global grad norm
global_grad_norm = multi_tensor_applier(self.multi_tensor_l2norm,
self._dummy_overflow_buf,
[[g_norm_32, g_norm_16]],
False)[0]
max_grad_norm = self.defaults['max_grad_norm']
for group in self.param_groups:
bias_correction = 1 if group['bias_correction'] else 0
beta1, beta2 = group['betas']
grad_averaging = 1 if group['grad_averaging'] else 0
# assume same step across group now to simplify things
# per parameter step can be easily support by making it tensor, or pass list into kernel
if 'step' in group:
group['step'] += 1
else:
group['step'] = 1
# create lists for multi-tensor apply
g_16, p_16, m_16, v_16 = [], [], [], []
g_32, p_32, m_32, v_32 = [], [], [], []
for i, p in enumerate(group['params']):
#for p in group['params']:
if p.grad is None:
continue
# if self.grad_norms[i] == 0.0:
# continue
if p.grad.data.is_sparse:
raise RuntimeError('FusedLAMB does not support sparse gradients, please consider SparseAdam instead')
state = self.state[p]
# State initialization
if len(state) == 0:
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if p.dtype == torch.float16:
g_16.append(p.grad.data)
p_16.append(p.data)
m_16.append(state['exp_avg'])
v_16.append(state['exp_avg_sq'])
elif p.dtype == torch.float32:
g_32.append(p.grad.data)
p_32.append(p.data)
m_32.append(state['exp_avg'])
v_32.append(state['exp_avg_sq'])
else:
raise RuntimeError('FusedLAMB only support fp16 and fp32.')
self.skipped_idx = layer_num - len(m_32)
if cac_bd_layer_num != -1:
for Layer in range(cac_bd_layer_num + 1):
if cac_bd_end_flag[Layer] == 0:
if self.braking_count[Layer] > 0:
self.braking_count[Layer] -= 1
if self.braking_count[Layer] >= 0:
ratio = (self.braking_distance - self.N_factor)/ self.braking_distance
t_lr[Layer] = ratio
m_32[Layer - self.skipped_idx] *= t_lr[Layer]
if self.braking_count[Layer] == 0:
cac_bd_end_flag[Layer] = 1
os.environ['CAC_STOP_LAYER_NUM'] = str(Layer + 1)
if(len(g_16) > 0):
multi_tensor_applier(self.multi_tensor_lamb,
self._dummy_overflow_buf,
[g_16, p_16, m_16, v_16],
group['lr'],
beta1,
beta2,
group['eps'],
group['step'],
bias_correction,
group['weight_decay'],
grad_averaging,
self.adam_w_mode,
global_grad_norm,
max_grad_norm,
self.use_nvlamb)
if(len(g_32) > 0):
multi_tensor_applier(self.multi_tensor_lamb,
self._dummy_overflow_buf,
[g_32, p_32, m_32, v_32],
group['lr'],
beta1,
beta2,
group['eps'],
group['step'],
bias_correction,
group['weight_decay'],
grad_averaging,
self.adam_w_mode,
global_grad_norm,
max_grad_norm,
self.use_nvlamb)
return loss
| [
"[email protected]"
] | |
73a3eee3fd2e1f1a0057bd58a22f2e7a32c4130b | 87aeb666b3e0d1e2d75fbb11c6e1bcc71402d098 | /task_manager/taskapp/migrations/0001_initial.py | 0a993da519f68d801dab37465e113bc760814c2b | [] | no_license | Alexey-Krapivnitsky/Task-Manager | 25934aff3c56c442e2080da7d910705fbbe27f4c | 62ee195bd79f86915e2c4dd5af9cc65797230f42 | refs/heads/master | 2022-12-17T13:27:53.660378 | 2020-10-01T19:23:54 | 2020-10-01T19:23:54 | 299,638,525 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,174 | py | # Generated by Django 3.1.1 on 2020-09-30 13:57
import datetime
from django.db import migrations, models
import django.db.models.deletion
from django.utils.timezone import utc
class Migration(migrations.Migration):
initial = True
dependencies = [
('authapp', '0003_auto_20200930_1857'),
]
operations = [
migrations.CreateModel(
name='Task',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('task_name', models.CharField(max_length=25, null=True)),
('task_description', models.CharField(max_length=250, null=True)),
('created_at', models.DateTimeField(default=datetime.datetime(2020, 9, 30, 13, 57, 2, 549223, tzinfo=utc))),
('task_status', models.CharField(choices=[('N', 'New'), ('P', 'Planned'), ('W', 'Working'), ('F', 'Finished')], max_length=1)),
('task_finished_date', models.DateTimeField(null=True)),
('task_owner', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='authapp.user')),
],
),
]
| [
"[email protected]"
] | |
6cccd938961ceaf9dc6a7234ea27deaebf7670bc | e1b52ce8238e7fe0d1c2cd8627535279edeb4bcb | /app/__init__.py | b65f106af4139090033d0b02ef36fab7a1dbcff2 | [
"MIT"
] | permissive | nevooronni/Stackoverflow-lite-APIs | 5cfb9048f56156c88f7f5c35fcba50b99b0d1111 | eb0af8588628e174822be947b8a914ed64e97236 | refs/heads/master | 2020-04-14T12:22:21.658955 | 2019-01-02T15:28:23 | 2019-01-02T15:28:23 | 163,838,121 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 620 | py | """creating app"""
import os
from flask import Flask
from instance.config import app_config
"""
import the configurations from the .confifrom dotenv import load_dotenv, find_dotenv
load_dotenv(find_dotenv()) #pass override=True to override current system environment variables
g file which is in the instance folder
"""
def create_app(config_name):
"""
creating the app using the configurations in the directory created in the .config file
"""
app = Flask(__name__, instance_relative_config=True)
app.config.from_object(app_config[config_name])
app.config.from_pyfile('config.py')
return app
| [
"[email protected]"
] | |
7dc43320f08fd0538e450d05265783dd856dc117 | a1d30d667cbf814db1809c31cf68ba75c01f819c | /Google/2. medium/362. Design Hit Counter.py | 579742997805aaf7ccf5cd8362f9bcbc212660fb | [] | no_license | yemao616/summer18 | adb5f0e04e6f1e1da6894b0b99a61da3c5cba8ee | 8bb17099be02d997d554519be360ef4aa1c028e3 | refs/heads/master | 2021-06-02T04:32:07.703198 | 2020-01-09T17:45:29 | 2020-01-09T17:45:29 | 110,744,323 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,159 | py | # Design a hit counter which counts the number of hits received in the past 5 minutes.
# Each function accepts a timestamp parameter (in seconds granularity) and you may assume that calls are being made to the system in chronological order (ie, the timestamp is monotonically increasing). You may assume that the earliest timestamp starts at 1.
# It is possible that several hits arrive roughly at the same time.
# Example:
# HitCounter counter = new HitCounter();
# // hit at timestamp 1.
# counter.hit(1);
# // hit at timestamp 2.
# counter.hit(2);
# // hit at timestamp 3.
# counter.hit(3);
# // get hits at timestamp 4, should return 3.
# counter.getHits(4);
# // hit at timestamp 300.
# counter.hit(300);
# // get hits at timestamp 300, should return 4.
# counter.getHits(300);
# // get hits at timestamp 301, should return 3.
# counter.getHits(301);
# Follow up:
# What if the number of hits per second could be very large? Does your design scale?
class HitCounter(object):
def __init__(self):
"""
Initialize your data structure here.
"""
from collections import deque
self.num_of_hits = 0
self.time_hits = deque()
def hit(self, timestamp):
"""
Record a hit.
@param timestamp - The current timestamp (in seconds granularity).
:type timestamp: int
:rtype: void
"""
if not self.time_hits or self.time_hits[-1][0] != timestamp:
self.time_hits.append([timestamp, 1])
else:
self.time_hits[-1][1] += 1
self.num_of_hits += 1
def getHits(self, timestamp):
"""
Return the number of hits in the past 5 minutes.
@param timestamp - The current timestamp (in seconds granularity).
:type timestamp: int
:rtype: int
"""
while self.time_hits and self.time_hits[0][0] <= timestamp - 300:
self.num_of_hits -= self.time_hits.popleft()[1]
return self.num_of_hits
# Your HitCounter object will be instantiated and called as such:
# obj = HitCounter()
# obj.hit(timestamp)
# param_2 = obj.getHits(timestamp) | [
"[email protected]"
] | |
a447848b01fb067427ac2e26e86ee507529529fc | a81c07a5663d967c432a61d0b4a09de5187be87b | /build/android/pylib/base/environment_factory.py | 4d3727444f43ce92292cd4e4ca2f5e68dedb9799 | [
"BSD-3-Clause"
] | permissive | junxuezheng/chromium | c401dec07f19878501801c9e9205a703e8643031 | 381ce9d478b684e0df5d149f59350e3bc634dad3 | refs/heads/master | 2023-02-28T17:07:31.342118 | 2019-09-03T01:42:42 | 2019-09-03T01:42:42 | 205,967,014 | 2 | 0 | BSD-3-Clause | 2019-09-03T01:48:23 | 2019-09-03T01:48:23 | null | UTF-8 | Python | false | false | 947 | py | # Copyright 2014 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from pylib import constants
from pylib.local.device import local_device_environment
from pylib.local.emulator import local_emulator_environment
from pylib.local.machine import local_machine_environment
def CreateEnvironment(args, output_manager, error_func):
if args.environment == 'local':
if args.command not in constants.LOCAL_MACHINE_TESTS:
if args.avd_name:
return local_emulator_environment.LocalEmulatorEnvironment(
args, output_manager, error_func)
return local_device_environment.LocalDeviceEnvironment(
args, output_manager, error_func)
else:
return local_machine_environment.LocalMachineEnvironment(
args, output_manager, error_func)
error_func('Unable to create %s environment.' % args.environment)
| [
"[email protected]"
] | |
97a6be8339a60d63037f8b82f678e9d303761f4b | c9b1e04ba65ba3e0af2a8ae86b88187b72bcaa0b | /branches/Release0_70_Branch/tests/unittests/persistenceTests/HTMLWriterTest.py | 84743b68f664018b2bfc3847f13b0d62eebaf424 | [] | no_license | feitianyiren/TaskCoach | 7762a89d5b521cfba0827323a9e8a91d1579810b | 0b7427562074845ac771e59e24a750aa5b432589 | refs/heads/master | 2020-04-08T04:56:35.491490 | 2016-01-12T13:29:03 | 2016-01-12T13:29:03 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,514 | py | '''
Task Coach - Your friendly task manager
Copyright (C) 2004-2008 Frank Niessink <[email protected]>
Task Coach is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
Task Coach is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
import wx, StringIO
import test
from unittests import dummy
from taskcoachlib import persistence, gui, config, widgets
from taskcoachlib.domain import task, category, effort, date, note
class HTMLWriterTestCase(test.wxTestCase):
def setUp(self):
super(HTMLWriterTestCase, self).setUp()
self.fd = StringIO.StringIO()
self.writer = persistence.HTMLWriter(self.fd)
self.task = task.Task('Task subject')
self.taskList = task.TaskList([self.task])
self.effortList = effort.EffortList(self.taskList)
self.categories = category.CategoryList()
self.notes = note.NoteContainer()
self.settings = config.Settings(load=False)
self.viewerContainer = gui.viewercontainer.ViewerContainer(\
widgets.Notebook(self.frame), self.settings, 'mainviewer')
self.createViewer()
def __writeAndRead(self, selectionOnly):
self.writer.write(self.viewer, selectionOnly)
return self.fd.getvalue()
def expectInHTML(self, htmlFragment, selectionOnly=False):
html = self.__writeAndRead(selectionOnly)
self.failUnless(htmlFragment in html,
'%s not in %s'%(htmlFragment, html))
def expectNotInHTML(self, htmlFragment, selectionOnly=False):
html = self.__writeAndRead(selectionOnly)
self.failIf(htmlFragment in html, '%s in %s'%(htmlFragment, html))
class TaskTests(object):
def testTaskSubject(self):
self.expectInHTML('>Task subject<')
def testWriteSelectionOnly(self):
self.expectNotInHTML('>Task subject<', selectionOnly=True)
def testWriteSelectionOnly_SelectedChild(self):
child = task.Task('Child')
self.task.addChild(child)
self.taskList.append(child)
self.selectItem(1)
self.expectInHTML('>Task subject<')
def testSubjectColumnAlignment(self):
self.expectInHTML('<td align="left">Task subject</td>')
def testOverdueTask(self):
self.task.setDueDate(date.Yesterday())
self.expectInHTML('<font color="#FF0000">Task subject</font>')
def testCompletedTask(self):
self.task.setCompletionDate()
self.expectInHTML('<font color="#00FF00">Task subject</font>')
def testTaskDueToday(self):
self.task.setDueDate(date.Today())
expectedColor = '%02X%02X%02X'%eval(self.settings.get('color', 'duetodaytasks'))[:3]
self.expectInHTML('<font color="#%s">Task subject</font>'%expectedColor)
def testInactiveTask(self):
self.task.setStartDate(date.Tomorrow())
expectedColor = '%02X%02X%02X'%eval(self.settings.get('color', 'inactivetasks'))[:3]
self.expectInHTML('<font color="#%s">Task subject</font>'%expectedColor)
def testCategoryColor(self):
cat = category.Category('cat', color=wx.RED)
self.task.addCategory(cat)
self.expectInHTML('<tr bgcolor="#FF0000">')
def testCategoryColorAsTuple(self):
cat = category.Category('cat', color=(255, 0, 0, 0))
self.task.addCategory(cat)
self.expectInHTML('<tr bgcolor="#FF0000">')
class HTMLListWriterTest(TaskTests, HTMLWriterTestCase):
def createViewer(self):
self.viewer = gui.viewer.TaskListViewer(self.frame, self.taskList,
self.settings, categories=self.categories, efforts=self.effortList)
def selectItem(self, index):
self.viewer.widget.SelectItem(index)
def testTaskDescription(self):
self.task.setDescription('Task description')
self.viewer.showColumnByName('description')
self.expectInHTML('>Task description<')
def testTaskDescriptionWithNewLine(self):
self.task.setDescription('Line1\nLine2')
self.viewer.showColumnByName('description')
self.expectInHTML('>Line1<br>Line2<')
class HTMLTreeWriterTest(TaskTests, HTMLWriterTestCase):
def createViewer(self):
self.viewer = gui.viewer.TaskTreeViewer(self.frame, self.taskList,
self.settings, categories=self.categories, efforts=self.effortList)
def selectItem(self, index):
item, cookie = self.viewer.widget.GetFirstChild(self.viewer.widget.GetRootItem())
self.viewer.widget.SelectItem(item)
class EffortWriterTest(HTMLWriterTestCase):
def setUp(self):
super(EffortWriterTest, self).setUp()
self.task.addEffort(effort.Effort(self.task))
def createViewer(self):
self.viewer = gui.viewer.EffortListViewer(self.frame, self.taskList,
self.settings)
def testTaskSubject(self):
self.expectInHTML('>Task subject<')
def testEffortDuration(self):
self.expectInHTML('>0:00:00<')
| [
"[email protected]"
] | |
4cf43299d54bb37595d33106ca7efa886f45fa1f | a9b67cdcdd09d90669e87ececc0830419ad999c6 | /blog/urls.py | 2f4b41b1bd42ea09787542f648536b9799b3d907 | [] | no_license | ver0nika4ka/my-first-blog | c05d30d21900e8706200c9971307faaa603a6220 | bf81939ca502ab5b7c5b4577b01ede1c518838d9 | refs/heads/master | 2021-06-25T18:25:24.703987 | 2021-01-05T06:59:36 | 2021-01-05T06:59:36 | 193,432,440 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,019 | py | # Так мы импортировали функцию path Django и все views (представления) из приложения blog
from django.urls import path
from . import views
urlpatterns = [
path('', views.post_list, name='post_list'),
# Фрагмент post/<int:pk>/ определяет шаблон URL-адреса:
# post/ значит, что после начала строки URL должен содержать слово post и косую черту /.
# <int:pk> — эта часть означает, что Django ожидает целочисленное значение и преобразует его в представление — переменную pk.
# / — затем нам нужен еще один символ / перед тем, как адрес закончится.
path('post/<int:pk>/', views.post_detail, name='post_detail'),
path('post/new/', views.post_new, name='post_new'),
path('post/<int:pk>/edit/', views.post_edit, name='post_edit'),
] | [
"[email protected]"
] | |
f2c7c34ff3fe71ccce7318a2d04bdd2e37ca1372 | 09e57dd1374713f06b70d7b37a580130d9bbab0d | /benchmark/startCirq858.py | 8d4b711a55f0b71384de14f529d3ccf647d1d1f6 | [
"BSD-3-Clause"
] | permissive | UCLA-SEAL/QDiff | ad53650034897abb5941e74539e3aee8edb600ab | d968cbc47fe926b7f88b4adf10490f1edd6f8819 | refs/heads/main | 2023-08-05T04:52:24.961998 | 2021-09-19T02:56:16 | 2021-09-19T02:56:16 | 405,159,939 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,432 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 5/15/20 4:49 PM
# @File : grover.py
# qubit number=5
# total number=42
import cirq
import cirq.google as cg
from typing import Optional
import sys
from math import log2
import numpy as np
#thatsNoCode
from cirq.contrib.svg import SVGCircuit
# Symbols for the rotation angles in the QAOA circuit.
def make_circuit(n: int, input_qubit):
c = cirq.Circuit() # circuit begin
c.append(cirq.H.on(input_qubit[0])) # number=3
c.append(cirq.H.on(input_qubit[1])) # number=4
c.append(cirq.H.on(input_qubit[2])) # number=5
c.append(cirq.H.on(input_qubit[3])) # number=6
c.append(cirq.H.on(input_qubit[4])) # number=21
for i in range(2):
c.append(cirq.H.on(input_qubit[0])) # number=1
c.append(cirq.H.on(input_qubit[1])) # number=2
c.append(cirq.H.on(input_qubit[2])) # number=7
c.append(cirq.H.on(input_qubit[3])) # number=8
c.append(cirq.H.on(input_qubit[0])) # number=17
c.append(cirq.H.on(input_qubit[1])) # number=18
c.append(cirq.H.on(input_qubit[2])) # number=19
c.append(cirq.H.on(input_qubit[3])) # number=20
c.append(cirq.H.on(input_qubit[0])) # number=36
c.append(cirq.CZ.on(input_qubit[1],input_qubit[0])) # number=37
c.append(cirq.H.on(input_qubit[0])) # number=38
c.append(cirq.X.on(input_qubit[0])) # number=29
c.append(cirq.CNOT.on(input_qubit[1],input_qubit[0])) # number=30
c.append(cirq.CNOT.on(input_qubit[0],input_qubit[1])) # number=32
c.append(cirq.CNOT.on(input_qubit[0],input_qubit[1])) # number=39
c.append(cirq.X.on(input_qubit[1])) # number=40
c.append(cirq.CNOT.on(input_qubit[0],input_qubit[1])) # number=41
c.append(cirq.CNOT.on(input_qubit[0],input_qubit[1])) # number=34
c.append(cirq.H.on(input_qubit[2])) # number=25
c.append(cirq.CZ.on(input_qubit[0],input_qubit[2])) # number=26
c.append(cirq.H.on(input_qubit[2])) # number=35
c.append(cirq.H.on(input_qubit[2])) # number=27
c.append(cirq.X.on(input_qubit[2])) # number=23
c.append(cirq.CNOT.on(input_qubit[0],input_qubit[2])) # number=24
c.append(cirq.X.on(input_qubit[3])) # number=12
c.append(cirq.X.on(input_qubit[0])) # number=13
c.append(cirq.X.on(input_qubit[1])) # number=14
c.append(cirq.X.on(input_qubit[2])) # number=15
c.append(cirq.X.on(input_qubit[3])) # number=16
c.append(cirq.Z.on(input_qubit[1])) # number=31
# circuit end
c.append(cirq.measure(*input_qubit, key='result'))
return c
def bitstring(bits):
return ''.join(str(int(b)) for b in bits)
if __name__ == '__main__':
qubit_count = 5
input_qubits = [cirq.GridQubit(i, 0) for i in range(qubit_count)]
circuit = make_circuit(qubit_count,input_qubits)
circuit = cg.optimized_for_sycamore(circuit, optimizer_type='sqrt_iswap')
circuit_sample_count =2000
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=circuit_sample_count)
frequencies = result.histogram(key='result', fold_func=bitstring)
writefile = open("../data/startCirq858.csv","w+")
print(format(frequencies),file=writefile)
print("results end", file=writefile)
print(circuit.__len__(), file=writefile)
print(circuit,file=writefile)
writefile.close() | [
"[email protected]"
] | |
c5a07c81fbe718191d7e3692de37bdb34ffea009 | 7628bfc96a7248461d3f2071992d682c0ad18272 | /test/test_runners.py | ec4a2e80525cd0e2c6011733b05cf1da14bac0ec | [
"Apache-2.0"
] | permissive | war3gu/tensorforce | d0c72b26364368d873b1e2717595718669b7fa2d | 70d551f8f92b694aae51262663e77c25576ecfad | refs/heads/master | 2020-12-11T14:51:21.916658 | 2020-01-13T20:43:25 | 2020-01-13T20:43:25 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 6,105 | py | # Copyright 2018 Tensorforce Team. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import copy
import time
import unittest
from tensorforce import ParallelRunner, Runner
from test.unittest_base import UnittestBase
class TestRunners(UnittestBase, unittest.TestCase):
min_timesteps = 3
require_observe = True
def test_runner(self):
self.start_tests(name='runner')
agent, environment = self.prepare()
runner = Runner(agent=agent, environment=environment)
runner.run(num_episodes=10, use_tqdm=False)
runner.close()
self.finished_test()
# callback
agent, environment = self.prepare()
runner = Runner(agent=agent, environment=environment)
callback_episode_frequency = 2
self.num_callbacks = 0
def callback(r):
self.num_callbacks += 1
self.assertEqual(r.episodes, self.num_callbacks * callback_episode_frequency)
runner.run(
num_episodes=5, callback=callback,
callback_episode_frequency=callback_episode_frequency, use_tqdm=False
)
callback_timestep_frequency = 3
self.num_callbacks = 0
def callback(r):
self.num_callbacks += 1
self.assertEqual(r.episode_timestep, self.num_callbacks * callback_timestep_frequency)
runner.run(
num_episodes=1, callback=callback,
callback_timestep_frequency=callback_timestep_frequency, use_tqdm=False
)
self.is_callback1 = False
self.is_callback2 = False
def callback1(r):
self.is_callback1 = True
def callback2(r):
self.is_callback2 = True
runner.run(
num_episodes=1, callback=[callback1, callback2],
callback_timestep_frequency=callback_timestep_frequency, use_tqdm=False
)
runner.close()
self.finished_test(assertion=(self.is_callback1 and self.is_callback2))
# evaluation
agent, environment = self.prepare()
runner = Runner(agent=agent, environment=environment)
self.num_evaluations = 0
evaluation_frequency = 3
num_evaluation_iterations = 2
def evaluation_callback(r):
self.num_evaluations += 1
self.assertEqual(r.episodes, self.num_evaluations * evaluation_frequency)
self.assertEqual(len(r.evaluation_timesteps), num_evaluation_iterations)
runner.run(
num_episodes=6, use_tqdm=False, evaluation_callback=evaluation_callback,
evaluation_frequency=evaluation_frequency,
num_evaluation_iterations=num_evaluation_iterations
)
runner.close()
self.finished_test()
def test_parallel_runner(self):
self.start_tests(name='parallel-runner')
agent, environment1 = self.prepare(
update=dict(unit='episodes', batch_size=1), parallel_interactions=2
)
environment2 = copy.deepcopy(environment1)
runner = ParallelRunner(agent=agent, environments=[environment1, environment2])
runner.run(num_episodes=5, use_tqdm=False)
runner.close()
self.finished_test()
# callback
agent, environment1 = self.prepare(
update=dict(unit='episodes', batch_size=1), parallel_interactions=2
)
environment2 = copy.deepcopy(environment1)
runner = ParallelRunner(agent=agent, environments=[environment1, environment2])
callback_episode_frequency = 2
self.num_callbacks = 0
def callback(r, parallel):
self.num_callbacks += 1
self.assertEqual(r.episodes, self.num_callbacks * callback_episode_frequency)
runner.run(
num_episodes=5, callback=callback,
callback_episode_frequency=callback_episode_frequency, use_tqdm=False
)
time.sleep(1)
callback_timestep_frequency = 3
def callback(r, parallel):
self.assertEqual(r.episode_timestep[parallel] % callback_timestep_frequency, 0)
runner.run(
num_episodes=1, callback=callback,
callback_timestep_frequency=callback_timestep_frequency, use_tqdm=False
)
self.is_callback1 = False
self.is_callback2 = False
def callback1(r, parallel):
self.is_callback1 = True
def callback2(r, parallel):
self.is_callback2 = True
runner.run(
num_episodes=1, callback=[callback1, callback2],
callback_timestep_frequency=callback_timestep_frequency, use_tqdm=False
)
runner.close()
self.finished_test(assertion=(self.is_callback1 and self.is_callback2))
# evaluation
agent, environment1 = self.prepare(
update=dict(unit='episodes', batch_size=1), parallel_interactions=2
)
environment2 = copy.deepcopy(environment1)
evaluation_environment = copy.deepcopy(environment1)
runner = ParallelRunner(
agent=agent, environments=[environment1, environment2],
evaluation_environment=evaluation_environment
)
self.num_evaluations = 0
def evaluation_callback(r):
self.num_evaluations += 1
runner.run(num_episodes=5, use_tqdm=False, evaluation_callback=evaluation_callback)
runner.close()
self.assertGreaterEqual(self.num_evaluations, 1)
self.finished_test()
| [
"[email protected]"
] | |
b2edd05a4a829eae2b86d8543f76dd6e0c7cea91 | 9d7f0f91e51083039013f792d6f5d0067a663844 | /ventura-sanic/db.py | 430ccc5732f0299e749ca7e7f4a3d27d869b9300 | [] | no_license | ventura-open-source/non-blocking-service-example | 88b98325cfe5db8af0155e8b4b7590c56d6f0884 | d5bec25e986981004a198d74aecc11f32f70eff4 | refs/heads/master | 2020-03-22T05:51:07.927025 | 2018-07-03T14:24:20 | 2018-07-03T14:24:20 | 139,594,427 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 332 | py | import logging
import peewee_async
# Setup mysql connection
database = peewee_async.MySQLDatabase(
'ventura_laravel',
user='root',
password='root',
host='localhost',
port=3306,
)
# No need for sync anymore!
database.set_allow_sync(False)
# Create async models manager
objects = peewee_async.Manager(database)
| [
"[email protected]"
] | |
3af3448c062fe48f3b47b009218c66d850913b87 | f2604386f503a06de34f0e6c23993dd0d1ce093d | /wallet/urls.py | ac851ec7356c586163db22b20c9502ca89aeb8f0 | [] | no_license | kicha-05/wallet | 6c64ebbf2849d084b68ea13b60a6ac675a958c78 | ae6af4479ab3b7211265c943ccb1791965b2fa66 | refs/heads/master | 2022-12-25T11:31:15.926443 | 2020-10-08T07:48:12 | 2020-10-08T07:48:12 | 302,228,970 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 419 | py | from django.contrib import admin
from django.urls import path
from django.conf.urls import url
from .views import *
urlpatterns = [
path('init', InitializeAccount.as_view(), name='init'),
path('wallet', WalletView.as_view(), name='wallet'),
path('wallet/deposit', DepositWithdrawVirtualMoney.as_view(), name='deposit'),
path('wallet/withdraw', DepositWithdrawVirtualMoney.as_view(), name='withdraw')
]
| [
"[email protected]"
] | |
b6b415e6bf267ac3378d6b864303165d97c05cdc | 3f48e3308674212408c3b6ca972eb4f793bf142b | /f0_data_process/chip_seq/final_chipseq/sicer_df/py1_write_run_sicer_df_slurm.py | d366ec6d7bdea0661c665daf34df7addf7f051d8 | [] | no_license | zanglab/utx_code | 8497840ace81e0337f92f04fafbb691f0ed24865 | 32fc7851207f650b3cc78974ab798f8606099e56 | refs/heads/main | 2023-06-28T17:38:33.231877 | 2021-07-27T01:31:00 | 2021-07-27T01:31:00 | 388,648,753 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,039 | py | import sys,argparse
import os,glob
import numpy as np
import pandas as pd
from scipy import stats
import re,bisect
slurm_dir = 'slurm_files'
os.makedirs(slurm_dir,exist_ok=True)
project_dir='/nv/vol190/zanglab/zw5j/since2019_projects/UTX_HaoJiang'
outdir='sicer_out'
sub_dirs=['re_1st_submission_H3K4me3_MLL4SC_trim','re_202012_H3K4me2_trim','202102_H3K27ac_H3K4me1_trim','202011_UTX_trim','202102_UTX_H3K27me3_trim']
# celltypes = ['Vector','WT','DEL','EIF','MT2','TPR']
factors= ['UTX','UTXFEB','H3K4me1','H3K4me2','H3K4me3','H3K27ac','H3K27me3','MLL4']
compr_pairs = [['Vector','WT'],['WT','DEL'],['DEL','EIF']]
for sub_dir in sub_dirs:
for compr_pair in compr_pairs:
for factor in factors:
sicer_outdir='{}/{}_over_{}_{}'.format(outdir,compr_pair[1],compr_pair[0],factor)
os.makedirs(sicer_outdir,exist_ok=True)
basename_treatment = '{}_{}'.format(compr_pair[1],factor)
basename_control = '{}_{}'.format(compr_pair[0],factor)
bam_control ='{}/f0_data_process/chip_seq/final_chipseq/{}/process_qc_out/{}/{}_treat.bam'.format(project_dir,sub_dir,basename_control,basename_control)
bam_treatment ='{}/f0_data_process/chip_seq/final_chipseq/{}/process_qc_out/{}/{}_treat.bam'.format(project_dir,sub_dir,basename_treatment,basename_treatment)
if os.path.isfile(bam_control):
#print(sub_dir,celltype,factor)
slurmfile = '{}/{}_over_{}_{}.slurm'.format(slurm_dir,compr_pair[1],compr_pair[0],factor)
with open(slurmfile,'w') as slurmout:
slurmout.write('''#!/bin/bash
#SBATCH -n 1
#SBATCH --mem=20000
#SBATCH -t 24:00:00
#SBATCH -p standard
#SBATCH -A cphg_cz3d
''')
slurmout.write('#SBATCH -o {}/slurm_{}_over_{}_{}.out\n\n'.format(slurm_dir,compr_pair[1],compr_pair[0],factor))
slurmout.write('time sicer_df -t \\\n{} \\\n{} \\\n-s hg38 --output_directory {}\n'.format(bam_treatment,bam_control,sicer_outdir))
| [
"[email protected]"
] | |
a618ed18a38138e126c6e7cf13a613ff0eeac304 | 5f6425e9d83b57b864e48f227e1dc58356a555c0 | /utils/palettes/archive/selenized_medium_0_4.py | 94959c43f26c4b355583da48508d798782d438af | [
"MIT"
] | permissive | jan-warchol/selenized | b374fa7822f281b16aa8b52e34bd1e585db75904 | df1c7f1f94f22e2c717f8224158f6f4097c5ecbe | refs/heads/master | 2023-06-22T09:37:02.962677 | 2022-09-12T20:24:40 | 2022-09-12T20:24:40 | 45,570,283 | 663 | 58 | MIT | 2023-04-18T09:33:22 | 2015-11-04T22:00:52 | Emacs Lisp | UTF-8 | Python | false | false | 590 | py | name = 'Selenized medium v0.4 (adapted monotones)'
palette = {
"bg_0": "#154053",
"fg_0": "#a8bcc3",
"bg_1": "#245971",
"red": "#fc5851",
"green": "#78b93e",
"yellow": "#d8b033",
"blue": "#4e97f5",
"magenta": "#f16dc5",
"cyan": "#41c7b9",
"dim_0": "#7c95a0",
"bg_2": "#245971",
"br_red": "#ff675d",
"br_green": "#85c74c",
"br_yellow": "#e7be42",
"br_blue": "#5ea4ff",
"br_magenta": "#ff7bd3",
"br_cyan": "#52d5c7",
"fg_1": "#c4d8df",
}
| [
"[email protected]"
] | |
0ad20010a16169575984c8d5f5c526a6e6379811 | 7675abbb6a30fdb56cb534cbd198571f75cc926e | /dazubi_fan_site/dazubi_fan_site/settings.py | 240dda22c91a32799aaaab32a9a20c13a4781f3d | [] | no_license | moreal/dazbee_fan_site | 4b2077635118987eb2731b36030c47cd181cc25a | dfb0e02a25087917968be6f4ee54049e9cca1b07 | refs/heads/master | 2020-04-09T02:37:04.173566 | 2018-12-01T13:56:51 | 2018-12-01T13:56:51 | 159,947,801 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,115 | py | """
Django settings for dazubi_fan_site project.
Generated by 'django-admin startproject' using Django 2.1.3.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'abelz*+9_9_^&u$s5=zefr!*-z#t9gp$3)in%^v6er82end3di'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'dazubi_fan_site.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'dazubi_fan_site.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.1/howto/static-files/
STATIC_URL = '/static/'
| [
"[email protected]"
] | |
f05a887b3080d00cd4f30b2eda7596f2a6d01e91 | 89dedd7f3c7acc81d12e2bcb2e716f9af9e5fa04 | /third_party/WebKit/Source/bindings/modules/v8/generated.gypi | 360698ad1d396054e3e70c5bd4bcde09947b4c2e | [
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MIT",
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer",
"LGPL-2.1-only",
"GPL-2.0-only",
"LGPL-2.0-only",
"BSD-2-Clause",
"LicenseRef-scancode-other-copyleft",
"BSD-3-Clause"
] | permissive | bino7/chromium | 8d26f84a1b6e38a73d1b97fea6057c634eff68cb | 4666a6bb6fdcb1114afecf77bdaa239d9787b752 | refs/heads/master | 2022-12-22T14:31:53.913081 | 2016-09-06T10:05:11 | 2016-09-06T10:05:11 | 67,410,510 | 1 | 3 | BSD-3-Clause | 2022-12-17T03:08:52 | 2016-09-05T10:11:59 | null | UTF-8 | Python | false | false | 11,037 | gypi | # Copyright 2014 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'variables': {
'bindings_modules_v8_output_dir': '<(SHARED_INTERMEDIATE_DIR)/blink/bindings/modules/v8',
'bindings_modules_v8_generated_union_type_files': [
'<(bindings_modules_v8_output_dir)/ArrayBufferOrArrayBufferViewOrDictionary.cpp',
'<(bindings_modules_v8_output_dir)/ArrayBufferOrArrayBufferViewOrDictionary.h',
'<(bindings_modules_v8_output_dir)/ArrayBufferOrArrayBufferViewOrUSVString.cpp',
'<(bindings_modules_v8_output_dir)/ArrayBufferOrArrayBufferViewOrUSVString.h',
'<(bindings_modules_v8_output_dir)/ArrayBufferViewOrBlobOrStringOrFormData.cpp',
'<(bindings_modules_v8_output_dir)/ArrayBufferViewOrBlobOrStringOrFormData.h',
'<(bindings_modules_v8_output_dir)/BooleanOrConstrainBooleanParameters.cpp',
'<(bindings_modules_v8_output_dir)/BooleanOrConstrainBooleanParameters.h',
'<(bindings_modules_v8_output_dir)/BooleanOrMediaTrackConstraints.cpp',
'<(bindings_modules_v8_output_dir)/BooleanOrMediaTrackConstraints.h',
'<(bindings_modules_v8_output_dir)/ClientOrServiceWorkerOrMessagePort.cpp',
'<(bindings_modules_v8_output_dir)/ClientOrServiceWorkerOrMessagePort.h',
'<(bindings_modules_v8_output_dir)/DictionaryOrString.cpp',
'<(bindings_modules_v8_output_dir)/DictionaryOrString.h',
'<(bindings_modules_v8_output_dir)/DoubleOrConstrainDoubleRange.cpp',
'<(bindings_modules_v8_output_dir)/DoubleOrConstrainDoubleRange.h',
'<(bindings_modules_v8_output_dir)/FormDataOrURLSearchParams.cpp',
'<(bindings_modules_v8_output_dir)/FormDataOrURLSearchParams.h',
'<(bindings_modules_v8_output_dir)/HTMLImageElementOrHTMLVideoElementOrHTMLCanvasElementOrImageBitmap.cpp',
'<(bindings_modules_v8_output_dir)/HTMLImageElementOrHTMLVideoElementOrHTMLCanvasElementOrImageBitmap.h',
'<(bindings_modules_v8_output_dir)/LongOrConstrainLongRange.cpp',
'<(bindings_modules_v8_output_dir)/LongOrConstrainLongRange.h',
'<(bindings_modules_v8_output_dir)/OffscreenCanvasRenderingContext2DOrWebGLRenderingContextOrWebGL2RenderingContext.cpp',
'<(bindings_modules_v8_output_dir)/OffscreenCanvasRenderingContext2DOrWebGLRenderingContextOrWebGL2RenderingContext.h',
'<(bindings_modules_v8_output_dir)/RTCIceCandidateInitOrRTCIceCandidate.cpp',
'<(bindings_modules_v8_output_dir)/RTCIceCandidateInitOrRTCIceCandidate.h',
'<(bindings_modules_v8_output_dir)/RenderingContext.cpp',
'<(bindings_modules_v8_output_dir)/RenderingContext.h',
'<(bindings_modules_v8_output_dir)/RequestOrUSVString.cpp',
'<(bindings_modules_v8_output_dir)/RequestOrUSVString.h',
'<(bindings_modules_v8_output_dir)/ServiceWorkerOrMessagePort.cpp',
'<(bindings_modules_v8_output_dir)/ServiceWorkerOrMessagePort.h',
'<(bindings_modules_v8_output_dir)/StringOrArrayBufferOrNFCMessage.cpp',
'<(bindings_modules_v8_output_dir)/StringOrArrayBufferOrNFCMessage.h',
'<(bindings_modules_v8_output_dir)/StringOrCanvasGradientOrCanvasPattern.cpp',
'<(bindings_modules_v8_output_dir)/StringOrCanvasGradientOrCanvasPattern.h',
'<(bindings_modules_v8_output_dir)/StringOrStringSequence.cpp',
'<(bindings_modules_v8_output_dir)/StringOrStringSequence.h',
'<(bindings_modules_v8_output_dir)/StringOrStringSequenceOrConstrainDOMStringParameters.cpp',
'<(bindings_modules_v8_output_dir)/StringOrStringSequenceOrConstrainDOMStringParameters.h',
'<(bindings_modules_v8_output_dir)/StringOrStringSequenceOrDOMStringList.cpp',
'<(bindings_modules_v8_output_dir)/StringOrStringSequenceOrDOMStringList.h',
'<(bindings_modules_v8_output_dir)/StringOrUnsignedLong.cpp',
'<(bindings_modules_v8_output_dir)/StringOrUnsignedLong.h',
'<(bindings_modules_v8_output_dir)/UnsignedLongOrUnsignedLongSequence.cpp',
'<(bindings_modules_v8_output_dir)/UnsignedLongOrUnsignedLongSequence.h',
],
'conditions': [
['OS=="win" and buildtype=="Official"', {
# On Windows Official release builds, we try to preserve symbol
# space.
'bindings_modules_v8_generated_aggregate_files': [
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings.cpp',
],
}, {
'bindings_modules_v8_generated_aggregate_files': [
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings00.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings01.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings02.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings03.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings04.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings05.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings06.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings07.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings08.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings09.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings10.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings11.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings12.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings13.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings14.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings15.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings16.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings17.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings18.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings19.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings20.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings21.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings22.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings23.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings24.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings25.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings26.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings27.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings28.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings29.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings30.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings31.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings32.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings33.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings34.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings35.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings36.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings37.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings38.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings39.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings40.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings41.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings42.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings43.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings44.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings45.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings46.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings47.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings48.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings49.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings50.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings51.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings52.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings53.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings54.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings55.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings56.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings57.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings58.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings59.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings60.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings61.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings62.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings63.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings64.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings65.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings66.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings67.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings68.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings69.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings70.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings71.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings72.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings73.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings74.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings75.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings76.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings77.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings78.cpp',
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindings79.cpp',
],
}],
],
'bindings_modules_v8_generated_partial_aggregate_files': [
'<(bindings_modules_v8_output_dir)/V8GeneratedModulesBindingsPartial.cpp',
],
},
}
| [
"[email protected]"
] |
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