repo_name stringlengths 5 100 | ref stringlengths 12 67 | path stringlengths 4 244 | copies stringlengths 1 8 | content stringlengths 0 1.05M ⌀ |
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csferrie/python-qinfer | refs/heads/master | src/qinfer/parallel.py | 3 | #!/usr/bin/python
# -*- coding: utf-8 -*-
##
# parallel.py: Tools for distributing computation.
##
# © 2017, Chris Ferrie (csferrie@gmail.com) and
# Christopher Granade (cgranade@cgranade.com).
#
# 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.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 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.
##
## FEATURES ##################################################################
from __future__ import absolute_import
from __future__ import division # Ensures that a/b is always a float.
## EXPORTS ###################################################################
__all__ = ['DirectViewParallelizedModel']
## IMPORTS ###################################################################
import numpy as np
from qinfer.derived_models import DerivedModel
import warnings
try:
import ipyparallel as ipp
interactive = ipp.interactive
except ImportError:
try:
import IPython.parallel as ipp
interactive = ipp.interactive
except (ImportError, AttributeError):
import warnings
warnings.warn(
"Could not import IPython parallel. "
"Parallelization support will be disabled."
)
ipp = None
interactive = lambda fn: fn
## LOGGING ###################################################################
import logging
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
## CLASSES ###################################################################
class DirectViewParallelizedModel(DerivedModel):
r"""
Given an instance of a :class:`Model`, parallelizes execution of that model's
likelihood by breaking the ``modelparams`` array into segments and
executing a segment on each member of a :class:`~ipyparallel.DirectView`.
This :class:`Model` assumes that it has ownership over the DirectView, such
that no other processes will send tasks during the lifetime of the Model.
If you are having trouble pickling your model, consider switching to
``dill`` by calling ``direct_view.use_dill()``. This mode gives more support
for closures.
:param qinfer.Model serial_model: Model to be parallelized. This
model will be distributed to the engines in the direct view, such that
the model must support pickling.
:param ipyparallel.DirectView direct_view: Direct view onto the engines
that will be used to parallelize evaluation of the model's likelihood
function.
:param bool purge_client: If ``True``, then this model will purge results
and metadata from the IPython client whenever the model cache is cleared.
This is useful for solving memory leaks caused by very large numbers of
calls to ``likelihood``. By default, this is disabled, since enabling
this option can cause data loss if the client is being sent other tasks
during the operation of this model.
:param int serial_threshold: Sets the number of model vectors below which
the serial model is to be preferred. By default, this is set to ``10 *
n_engines``, where ``n_engines`` is the number of engines exposed by
``direct_view``.
"""
## INITIALIZER ##
def __init__(self, serial_model, direct_view, purge_client=False, serial_threshold=None):
if ipp is None:
raise RuntimeError(
"This model requires IPython parallelization support, "
"but an error was raised importing IPython.parallel."
)
self._dv = direct_view
self._purge_client = purge_client
self._serial_threshold = (
10 * self.n_engines
if serial_threshold is None else int(serial_threshold)
)
super(DirectViewParallelizedModel, self).__init__(serial_model)
## SPECIAL METHODS ##
def __getstate__(self):
# Since instances of this class will be pickled as they are passed to
# remote engines, we need to be careful not to include _dv
return {
'_underlying_model': self._underlying_model,
'_dv': None,
'_call_count': self._call_count,
'_sim_count': self._sim_count,
'_serial_threshold': self._serial_threshold
}
## PROPERTIES ##
# Provide _serial_model as a back-compat.
@property
def _serial_model(self):
warnings.warn("_serial_model is deprecated in favor of _underlying_model.",
DeprecationWarning
)
return self._underlying_model
@_serial_model.setter
def _serial_model(self, value):
warnings.warn("_serial_model is deprecated in favor of _underlying_model.",
DeprecationWarning
)
self._underlying_model = value
@property
def n_engines(self):
"""
The number of engines seen by the direct view owned by this parallelized
model.
:rtype: int
"""
return len(self._dv) if self._dv is not None else 0
## METHODS ##
def clear_cache(self):
"""
Clears any cache associated with the serial model and the engines
seen by the direct view.
"""
self.underlying_model.clear_cache()
try:
logger.info('DirectView results has {} items. Clearing.'.format(
len(self._dv.results)
))
self._dv.purge_results('all')
if self._purge_client:
self._dv.client.purge_everything()
except:
pass
def likelihood(self, outcomes, modelparams, expparams):
"""
Returns the likelihood for the underlying (serial) model, distributing
the model parameter array across the engines controlled by this
parallelized model. Returns what the serial model would return, see
:attr:`~Model.likelihood`
"""
# By calling the superclass implementation, we can consolidate
# call counting there.
super(DirectViewParallelizedModel, self).likelihood(outcomes, modelparams, expparams)
# If there's less models than some threshold, just use the serial model.
# By default, we'll set that threshold to be the number of engines * 10.
if modelparams.shape[0] <= self._serial_threshold:
return self.underlying_model.likelihood(outcomes, modelparams, expparams)
if self._dv is None:
raise RuntimeError(
"No direct view provided; this may be because the instance was "
"loaded from a pickle or NumPy saved array without providing a "
"new direct view."
)
# Need to decorate with interactive to overcome namespace issues with
# remote engines.
@interactive
def serial_likelihood(mps, sm, os, eps):
return sm.likelihood(os, mps, eps)
# TODO: check whether there's a better way to pass the extra parameters
# that doesn't use so much memory.
# The trick is that serial_likelihood will be pickled, so we need to be
# careful about closures.
L = self._dv.map_sync(
serial_likelihood,
np.array_split(modelparams, self.n_engines, axis=0),
[self.underlying_model] * self.n_engines,
[outcomes] * self.n_engines,
[expparams] * self.n_engines
)
return np.concatenate(L, axis=1)
def simulate_experiment(self, modelparams, expparams, repeat=1, split_by_modelparams=True):
"""
Simulates the underlying (serial) model using the parallel
engines. Returns what the serial model would return, see
:attr:`~Simulatable.simulate_experiment`
:param bool split_by_modelparams: If ``True``, splits up
``modelparams`` into `n_engines` chunks and distributes
across engines. If ``False``, splits up ``expparams``.
"""
# By calling the superclass implementation, we can consolidate
# simulation counting there.
super(DirectViewParallelizedModel, self).simulate_experiment(modelparams, expparams, repeat=repeat)
if self._dv is None:
raise RuntimeError(
"No direct view provided; this may be because the instance was "
"loaded from a pickle or NumPy saved array without providing a "
"new direct view."
)
# Need to decorate with interactive to overcome namespace issues with
# remote engines.
@interactive
def serial_simulator(sm, mps, eps, r):
return sm.simulate_experiment(mps, eps, repeat=r)
if split_by_modelparams:
# If there's less models than some threshold, just use the serial model.
# By default, we'll set that threshold to be the number of engines * 10.
if modelparams.shape[0] <= self._serial_threshold:
return self.underlying_model.simulate_experiment(modelparams, expparams, repeat=repeat)
# The trick is that serial_likelihood will be pickled, so we need to be
# careful about closures.
os = self._dv.map_sync(
serial_simulator,
[self.underlying_model] * self.n_engines,
np.array_split(modelparams, self.n_engines, axis=0),
[expparams] * self.n_engines,
[repeat] * self.n_engines
)
return np.concatenate(os, axis=0)
else:
# If there's less models than some threshold, just use the serial model.
# By default, we'll set that threshold to be the number of engines * 10.
if expparams.shape[0] <= self._serial_threshold:
return self.underlying_model.simulate_experiment(modelparams, expparams, repeat=repeat)
# The trick is that serial_likelihood will be pickled, so we need to be
# careful about closures.
os = self._dv.map_sync(
serial_simulator,
[self.underlying_model] * self.n_engines,
[modelparams] * self.n_engines,
np.array_split(expparams, self.n_engines, axis=0),
[repeat] * self.n_engines
)
return np.concatenate(os, axis=1)
|
ToonTownInfiniteRepo/ToontownInfinite | refs/heads/master | toontown/dna/ply/ctokens.py | 363 | # ----------------------------------------------------------------------
# ctokens.py
#
# Token specifications for symbols in ANSI C and C++. This file is
# meant to be used as a library in other tokenizers.
# ----------------------------------------------------------------------
# Reserved words
tokens = [
# Literals (identifier, integer constant, float constant, string constant, char const)
'ID', 'TYPEID', 'ICONST', 'FCONST', 'SCONST', 'CCONST',
# Operators (+,-,*,/,%,|,&,~,^,<<,>>, ||, &&, !, <, <=, >, >=, ==, !=)
'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'MOD',
'OR', 'AND', 'NOT', 'XOR', 'LSHIFT', 'RSHIFT',
'LOR', 'LAND', 'LNOT',
'LT', 'LE', 'GT', 'GE', 'EQ', 'NE',
# Assignment (=, *=, /=, %=, +=, -=, <<=, >>=, &=, ^=, |=)
'EQUALS', 'TIMESEQUAL', 'DIVEQUAL', 'MODEQUAL', 'PLUSEQUAL', 'MINUSEQUAL',
'LSHIFTEQUAL','RSHIFTEQUAL', 'ANDEQUAL', 'XOREQUAL', 'OREQUAL',
# Increment/decrement (++,--)
'PLUSPLUS', 'MINUSMINUS',
# Structure dereference (->)
'ARROW',
# Ternary operator (?)
'TERNARY',
# Delimeters ( ) [ ] { } , . ; :
'LPAREN', 'RPAREN',
'LBRACKET', 'RBRACKET',
'LBRACE', 'RBRACE',
'COMMA', 'PERIOD', 'SEMI', 'COLON',
# Ellipsis (...)
'ELLIPSIS',
]
# Operators
t_PLUS = r'\+'
t_MINUS = r'-'
t_TIMES = r'\*'
t_DIVIDE = r'/'
t_MODULO = r'%'
t_OR = r'\|'
t_AND = r'&'
t_NOT = r'~'
t_XOR = r'\^'
t_LSHIFT = r'<<'
t_RSHIFT = r'>>'
t_LOR = r'\|\|'
t_LAND = r'&&'
t_LNOT = r'!'
t_LT = r'<'
t_GT = r'>'
t_LE = r'<='
t_GE = r'>='
t_EQ = r'=='
t_NE = r'!='
# Assignment operators
t_EQUALS = r'='
t_TIMESEQUAL = r'\*='
t_DIVEQUAL = r'/='
t_MODEQUAL = r'%='
t_PLUSEQUAL = r'\+='
t_MINUSEQUAL = r'-='
t_LSHIFTEQUAL = r'<<='
t_RSHIFTEQUAL = r'>>='
t_ANDEQUAL = r'&='
t_OREQUAL = r'\|='
t_XOREQUAL = r'^='
# Increment/decrement
t_INCREMENT = r'\+\+'
t_DECREMENT = r'--'
# ->
t_ARROW = r'->'
# ?
t_TERNARY = r'\?'
# Delimeters
t_LPAREN = r'\('
t_RPAREN = r'\)'
t_LBRACKET = r'\['
t_RBRACKET = r'\]'
t_LBRACE = r'\{'
t_RBRACE = r'\}'
t_COMMA = r','
t_PERIOD = r'\.'
t_SEMI = r';'
t_COLON = r':'
t_ELLIPSIS = r'\.\.\.'
# Identifiers
t_ID = r'[A-Za-z_][A-Za-z0-9_]*'
# Integer literal
t_INTEGER = r'\d+([uU]|[lL]|[uU][lL]|[lL][uU])?'
# Floating literal
t_FLOAT = r'((\d+)(\.\d+)(e(\+|-)?(\d+))? | (\d+)e(\+|-)?(\d+))([lL]|[fF])?'
# String literal
t_STRING = r'\"([^\\\n]|(\\.))*?\"'
# Character constant 'c' or L'c'
t_CHARACTER = r'(L)?\'([^\\\n]|(\\.))*?\''
# Comment (C-Style)
def t_COMMENT(t):
r'/\*(.|\n)*?\*/'
t.lexer.lineno += t.value.count('\n')
return t
# Comment (C++-Style)
def t_CPPCOMMENT(t):
r'//.*\n'
t.lexer.lineno += 1
return t
|
georgewhr/dbwrt | refs/heads/master | tools/perf/scripts/python/net_dropmonitor.py | 2669 | # Monitor the system for dropped packets and proudce a report of drop locations and counts
import os
import sys
sys.path.append(os.environ['PERF_EXEC_PATH'] + \
'/scripts/python/Perf-Trace-Util/lib/Perf/Trace')
from perf_trace_context import *
from Core import *
from Util import *
drop_log = {}
kallsyms = []
def get_kallsyms_table():
global kallsyms
try:
f = open("/proc/kallsyms", "r")
except:
return
for line in f:
loc = int(line.split()[0], 16)
name = line.split()[2]
kallsyms.append((loc, name))
kallsyms.sort()
def get_sym(sloc):
loc = int(sloc)
# Invariant: kallsyms[i][0] <= loc for all 0 <= i <= start
# kallsyms[i][0] > loc for all end <= i < len(kallsyms)
start, end = -1, len(kallsyms)
while end != start + 1:
pivot = (start + end) // 2
if loc < kallsyms[pivot][0]:
end = pivot
else:
start = pivot
# Now (start == -1 or kallsyms[start][0] <= loc)
# and (start == len(kallsyms) - 1 or loc < kallsyms[start + 1][0])
if start >= 0:
symloc, name = kallsyms[start]
return (name, loc - symloc)
else:
return (None, 0)
def print_drop_table():
print "%25s %25s %25s" % ("LOCATION", "OFFSET", "COUNT")
for i in drop_log.keys():
(sym, off) = get_sym(i)
if sym == None:
sym = i
print "%25s %25s %25s" % (sym, off, drop_log[i])
def trace_begin():
print "Starting trace (Ctrl-C to dump results)"
def trace_end():
print "Gathering kallsyms data"
get_kallsyms_table()
print_drop_table()
# called from perf, when it finds a correspoinding event
def skb__kfree_skb(name, context, cpu, sec, nsec, pid, comm,
skbaddr, location, protocol):
slocation = str(location)
try:
drop_log[slocation] = drop_log[slocation] + 1
except:
drop_log[slocation] = 1
|
andyzsf/django | refs/heads/master | tests/shortcuts/urls.py | 11 | from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^render_to_response/$', views.render_to_response_view),
url(r'^render_to_response/request_context/$', views.render_to_response_view_with_request_context),
url(r'^render_to_response/content_type/$', views.render_to_response_view_with_content_type),
url(r'^render_to_response/dirs/$', views.render_to_response_view_with_dirs),
url(r'^render_to_response/context_instance_misuse/$', views.render_to_response_with_context_instance_misuse),
url(r'^render/$', views.render_view),
url(r'^render/base_context/$', views.render_view_with_base_context),
url(r'^render/content_type/$', views.render_view_with_content_type),
url(r'^render/dirs/$', views.render_with_dirs),
url(r'^render/status/$', views.render_view_with_status),
url(r'^render/current_app/$', views.render_view_with_current_app),
url(r'^render/current_app_conflict/$', views.render_view_with_current_app_conflict),
]
|
michael-donat/ansible | refs/heads/devel | lib/ansible/utils/__init__.py | 2520 | # (c) 2012-2014, Michael DeHaan <michael.dehaan@gmail.com>
#
# This file is part of Ansible
#
# Ansible 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.
#
# Ansible 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 Ansible. If not, see <http://www.gnu.org/licenses/>.
# Make coding more python3-ish
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
|
sekikn/ambari | refs/heads/trunk | ambari-server/src/test/python/TestServerUpgrade.py | 2 | '''
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
os.environ["ROOT"] = ""
import StringIO
import sys
from ambari_commons.exceptions import FatalException
from unittest import TestCase
from mock.mock import patch, MagicMock
from ambari_commons import os_utils
import platform
import shutil
project_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)),os.path.normpath("../../../../"))
shutil.copyfile(project_dir+"/ambari-server/conf/unix/ambari.properties", "/tmp/ambari.properties")
_search_file = os_utils.search_file
os_utils.search_file = MagicMock(return_value="/tmp/ambari.properties")
with patch.object(platform, "linux_distribution", return_value = MagicMock(return_value=('Redhat', '6.4', 'Final'))):
with patch("os.path.isdir", return_value = MagicMock(return_value=True)):
with patch("os.access", return_value = MagicMock(return_value=True)):
with patch.object(os_utils, "parse_log4j_file", return_value={'ambari.log.dir': '/var/log/ambari-server'}):
from ambari_server.serverUpgrade import set_current, SetCurrentVersionOptions
import ambari_server
os_utils.search_file = _search_file
@patch.object(platform, "linux_distribution", new = MagicMock(return_value=('Redhat', '6.4', 'Final')))
@patch("os.path.isdir", new = MagicMock(return_value=True))
@patch("os.access", new = MagicMock(return_value=True))
class TestServerUpgrade(TestCase):
@patch("ambari_server.serverUpgrade.is_server_runing")
@patch('ambari_server.serverUpgrade.SetCurrentVersionOptions.no_finalize_options_set')
@patch('ambari_server.serverUpgrade.get_validated_string_input')
@patch('ambari_server.serverUpgrade.get_ambari_properties')
@patch('ambari_server.serverUtils.get_ambari_server_api_base')
@patch('ambari_commons.logging_utils.get_verbose')
@patch('urllib2.urlopen')
def test_set_current(self, urlopen_mock, get_verbose_mock, get_ambari_server_api_base_mock,
get_ambari_properties_mock, get_validated_string_input_mock,
no_finalize_options_set_mock, is_server_runing_mock):
options = MagicMock()
options.cluster_name = 'cc'
options.desired_repo_version = 'HDP-2.2.2.0-2561'
options.force_repo_version = None
# Case when server is not running
is_server_runing_mock.return_value = False, None
try:
set_current(options)
self.fail("Server is not running - should error out")
except FatalException:
pass # expected
is_server_runing_mock.return_value = True, 11111
# Test insufficient options case
no_finalize_options_set_mock.return_value = True
try:
set_current(options)
self.fail("Should error out")
except FatalException:
pass # expected
no_finalize_options_set_mock.return_value = False
# Test normal flow
get_validated_string_input_mock.return_value = 'dummy_string'
p = get_ambari_properties_mock.return_value
p.get_property.side_effect = ["8080", "false", "false"]
get_ambari_server_api_base_mock.return_value = 'http://127.0.0.1:8080/api/v1/'
get_verbose_mock.retun_value = False
set_current(options)
self.assertTrue(urlopen_mock.called)
request = urlopen_mock.call_args_list[0][0][0]
self.assertEquals(request._Request__original, 'http://127.0.0.1:8080/api/v1/clusters/cc/stack_versions')
self.assertEquals(request.data, '{"ClusterStackVersions": {"state": "CURRENT", "repository_version": "HDP-2.2.2.0-2561", "force": false}}')
self.assertEquals(request.origin_req_host, '127.0.0.1')
self.assertEquals(request.headers, {'X-requested-by': 'ambari', 'Authorization': 'Basic ZHVtbXlfc3RyaW5nOmR1bW15X3N0cmluZw=='})
@patch("ambari_server.serverUpgrade.is_server_runing")
@patch('ambari_server.serverUpgrade.SetCurrentVersionOptions.no_finalize_options_set')
@patch('ambari_server.serverUpgrade.get_validated_string_input')
@patch('ambari_server.serverUpgrade.get_ambari_properties')
@patch('ambari_server.serverUtils.get_ambari_server_api_base')
@patch('ambari_commons.logging_utils.get_verbose')
@patch('urllib2.urlopen')
def test_set_current_with_force(self, urlopen_mock, get_verbose_mock, get_ambari_server_api_base_mock,
get_ambari_properties_mock, get_validated_string_input_mock,
no_finalize_options_set_mock, is_server_runing_mock):
options = MagicMock()
options.cluster_name = 'cc'
options.desired_repo_version = 'HDP-2.2.2.0-2561'
options.force_repo_version = True
# Case when server is not running
is_server_runing_mock.return_value = False, None
try:
set_current(options)
self.fail("Server is not running - should error out")
except FatalException:
pass # expected
is_server_runing_mock.return_value = True, 11111
# Test insufficient options case
no_finalize_options_set_mock.return_value = True
try:
set_current(options)
self.fail("Should error out")
except FatalException:
pass # expected
no_finalize_options_set_mock.return_value = False
# Test normal flow
get_validated_string_input_mock.return_value = 'dummy_string'
p = get_ambari_properties_mock.return_value
p.get_property.side_effect = ["8080", "false", "false"]
get_ambari_server_api_base_mock.return_value = 'http://127.0.0.1:8080/api/v1/'
get_verbose_mock.retun_value = False
set_current(options)
self.assertTrue(urlopen_mock.called)
request = urlopen_mock.call_args_list[0][0][0]
self.assertEquals(request._Request__original, 'http://127.0.0.1:8080/api/v1/clusters/cc/stack_versions')
self.assertEquals(request.data, '{"ClusterStackVersions": {"state": "CURRENT", "repository_version": "HDP-2.2.2.0-2561", "force": true}}')
self.assertEquals(request.origin_req_host, '127.0.0.1')
self.assertEquals(request.headers, {'X-requested-by': 'ambari', 'Authorization': 'Basic ZHVtbXlfc3RyaW5nOmR1bW15X3N0cmluZw=='})
def testCurrentVersionOptions(self):
# Negative test cases
options = MagicMock()
options.cluster_name = None
options.desired_repo_version = 'HDP-2.2.2.0-2561'
cvo = SetCurrentVersionOptions(options)
self.assertTrue(cvo.no_finalize_options_set())
options = MagicMock()
options.cluster_name = 'cc'
options.desired_repo_version = None
cvo = SetCurrentVersionOptions(options)
self.assertTrue(cvo.no_finalize_options_set())
# Positive test case
options = MagicMock()
options.cluster_name = 'cc'
options.desired_repo_version = 'HDP-2.2.2.0-2561'
cvo = SetCurrentVersionOptions(options)
self.assertFalse(cvo.no_finalize_options_set())
|
bgris/ODL_bgris | refs/heads/master | lib/python3.5/site-packages/matplotlib/spines.py | 10 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import matplotlib
import matplotlib.artist as martist
from matplotlib.artist import allow_rasterization
from matplotlib import docstring
import matplotlib.transforms as mtransforms
import matplotlib.lines as mlines
import matplotlib.patches as mpatches
import matplotlib.path as mpath
import matplotlib.cbook as cbook
import numpy as np
import warnings
rcParams = matplotlib.rcParams
class Spine(mpatches.Patch):
"""an axis spine -- the line noting the data area boundaries
Spines are the lines connecting the axis tick marks and noting the
boundaries of the data area. They can be placed at arbitrary
positions. See function:`~matplotlib.spines.Spine.set_position`
for more information.
The default position is ``('outward',0)``.
Spines are subclasses of class:`~matplotlib.patches.Patch`, and
inherit much of their behavior.
Spines draw a line or a circle, depending if
function:`~matplotlib.spines.Spine.set_patch_line` or
function:`~matplotlib.spines.Spine.set_patch_circle` has been
called. Line-like is the default.
"""
def __str__(self):
return "Spine"
@docstring.dedent_interpd
def __init__(self, axes, spine_type, path, **kwargs):
"""
- *axes* : the Axes instance containing the spine
- *spine_type* : a string specifying the spine type
- *path* : the path instance used to draw the spine
Valid kwargs are:
%(Patch)s
"""
super(Spine, self).__init__(**kwargs)
self.axes = axes
self.set_figure(self.axes.figure)
self.spine_type = spine_type
self.set_facecolor('none')
self.set_edgecolor(rcParams['axes.edgecolor'])
self.set_linewidth(rcParams['axes.linewidth'])
self.set_capstyle('projecting')
self.axis = None
self.set_zorder(2.5)
self.set_transform(self.axes.transData) # default transform
self._bounds = None # default bounds
self._smart_bounds = False
# Defer initial position determination. (Not much support for
# non-rectangular axes is currently implemented, and this lets
# them pass through the spines machinery without errors.)
self._position = None
if not isinstance(path, matplotlib.path.Path):
msg = "'path' must be an instance of 'matplotlib.path.Path'"
raise ValueError(msg)
self._path = path
# To support drawing both linear and circular spines, this
# class implements Patch behavior two ways. If
# self._patch_type == 'line', behave like a mpatches.PathPatch
# instance. If self._patch_type == 'circle', behave like a
# mpatches.Ellipse instance.
self._patch_type = 'line'
# Behavior copied from mpatches.Ellipse:
# Note: This cannot be calculated until this is added to an Axes
self._patch_transform = mtransforms.IdentityTransform()
def set_smart_bounds(self, value):
"""set the spine and associated axis to have smart bounds"""
self._smart_bounds = value
# also set the axis if possible
if self.spine_type in ('left', 'right'):
self.axes.yaxis.set_smart_bounds(value)
elif self.spine_type in ('top', 'bottom'):
self.axes.xaxis.set_smart_bounds(value)
self.stale = True
def get_smart_bounds(self):
"""get whether the spine has smart bounds"""
return self._smart_bounds
def set_patch_circle(self, center, radius):
"""set the spine to be circular"""
self._patch_type = 'circle'
self._center = center
self._width = radius * 2
self._height = radius * 2
self._angle = 0
# circle drawn on axes transform
self.set_transform(self.axes.transAxes)
self.stale = True
def set_patch_line(self):
"""set the spine to be linear"""
self._patch_type = 'line'
self.stale = True
# Behavior copied from mpatches.Ellipse:
def _recompute_transform(self):
"""NOTE: This cannot be called until after this has been added
to an Axes, otherwise unit conversion will fail. This
maxes it very important to call the accessor method and
not directly access the transformation member variable.
"""
assert self._patch_type == 'circle'
center = (self.convert_xunits(self._center[0]),
self.convert_yunits(self._center[1]))
width = self.convert_xunits(self._width)
height = self.convert_yunits(self._height)
self._patch_transform = mtransforms.Affine2D() \
.scale(width * 0.5, height * 0.5) \
.rotate_deg(self._angle) \
.translate(*center)
def get_patch_transform(self):
if self._patch_type == 'circle':
self._recompute_transform()
return self._patch_transform
else:
return super(Spine, self).get_patch_transform()
def get_path(self):
return self._path
def _ensure_position_is_set(self):
if self._position is None:
# default position
self._position = ('outward', 0.0) # in points
self.set_position(self._position)
def register_axis(self, axis):
"""register an axis
An axis should be registered with its corresponding spine from
the Axes instance. This allows the spine to clear any axis
properties when needed.
"""
self.axis = axis
if self.axis is not None:
self.axis.cla()
self.stale = True
def cla(self):
"""Clear the current spine"""
self._position = None # clear position
if self.axis is not None:
self.axis.cla()
def is_frame_like(self):
"""return True if directly on axes frame
This is useful for determining if a spine is the edge of an
old style MPL plot. If so, this function will return True.
"""
self._ensure_position_is_set()
position = self._position
if cbook.is_string_like(position):
if position == 'center':
position = ('axes', 0.5)
elif position == 'zero':
position = ('data', 0)
if len(position) != 2:
raise ValueError("position should be 2-tuple")
position_type, amount = position
if position_type == 'outward' and amount == 0:
return True
else:
return False
def _adjust_location(self):
"""automatically set spine bounds to the view interval"""
if self.spine_type == 'circle':
return
if self._bounds is None:
if self.spine_type in ('left', 'right'):
low, high = self.axes.viewLim.intervaly
elif self.spine_type in ('top', 'bottom'):
low, high = self.axes.viewLim.intervalx
else:
raise ValueError('unknown spine spine_type: %s' %
self.spine_type)
if self._smart_bounds:
# attempt to set bounds in sophisticated way
if low > high:
# handle inverted limits
low, high = high, low
viewlim_low = low
viewlim_high = high
del low, high
if self.spine_type in ('left', 'right'):
datalim_low, datalim_high = self.axes.dataLim.intervaly
ticks = self.axes.get_yticks()
elif self.spine_type in ('top', 'bottom'):
datalim_low, datalim_high = self.axes.dataLim.intervalx
ticks = self.axes.get_xticks()
# handle inverted limits
ticks = list(ticks)
ticks.sort()
ticks = np.array(ticks)
if datalim_low > datalim_high:
datalim_low, datalim_high = datalim_high, datalim_low
if datalim_low < viewlim_low:
# Data extends past view. Clip line to view.
low = viewlim_low
else:
# Data ends before view ends.
cond = (ticks <= datalim_low) & (ticks >= viewlim_low)
tickvals = ticks[cond]
if len(tickvals):
# A tick is less than or equal to lowest data point.
low = tickvals[-1]
else:
# No tick is available
low = datalim_low
low = max(low, viewlim_low)
if datalim_high > viewlim_high:
# Data extends past view. Clip line to view.
high = viewlim_high
else:
# Data ends before view ends.
cond = (ticks >= datalim_high) & (ticks <= viewlim_high)
tickvals = ticks[cond]
if len(tickvals):
# A tick is greater than or equal to highest data
# point.
high = tickvals[0]
else:
# No tick is available
high = datalim_high
high = min(high, viewlim_high)
else:
low, high = self._bounds
v1 = self._path.vertices
assert v1.shape == (2, 2), 'unexpected vertices shape'
if self.spine_type in ['left', 'right']:
v1[0, 1] = low
v1[1, 1] = high
elif self.spine_type in ['bottom', 'top']:
v1[0, 0] = low
v1[1, 0] = high
else:
raise ValueError('unable to set bounds for spine "%s"' %
self.spine_type)
@allow_rasterization
def draw(self, renderer):
self._adjust_location()
ret = super(Spine, self).draw(renderer)
self.stale = False
return ret
def _calc_offset_transform(self):
"""calculate the offset transform performed by the spine"""
self._ensure_position_is_set()
position = self._position
if cbook.is_string_like(position):
if position == 'center':
position = ('axes', 0.5)
elif position == 'zero':
position = ('data', 0)
assert len(position) == 2, "position should be 2-tuple"
position_type, amount = position
assert position_type in ('axes', 'outward', 'data')
if position_type == 'outward':
if amount == 0:
# short circuit commonest case
self._spine_transform = ('identity',
mtransforms.IdentityTransform())
elif self.spine_type in ['left', 'right', 'top', 'bottom']:
offset_vec = {'left': (-1, 0),
'right': (1, 0),
'bottom': (0, -1),
'top': (0, 1),
}[self.spine_type]
# calculate x and y offset in dots
offset_x = amount * offset_vec[0] / 72.0
offset_y = amount * offset_vec[1] / 72.0
self._spine_transform = ('post',
mtransforms.ScaledTranslation(
offset_x,
offset_y,
self.figure.dpi_scale_trans))
else:
warnings.warn('unknown spine type "%s": no spine '
'offset performed' % self.spine_type)
self._spine_transform = ('identity',
mtransforms.IdentityTransform())
elif position_type == 'axes':
if self.spine_type in ('left', 'right'):
self._spine_transform = ('pre',
mtransforms.Affine2D.from_values(
# keep y unchanged, fix x at
# amount
0, 0, 0, 1, amount, 0))
elif self.spine_type in ('bottom', 'top'):
self._spine_transform = ('pre',
mtransforms.Affine2D.from_values(
# keep x unchanged, fix y at
# amount
1, 0, 0, 0, 0, amount))
else:
warnings.warn('unknown spine type "%s": no spine '
'offset performed' % self.spine_type)
self._spine_transform = ('identity',
mtransforms.IdentityTransform())
elif position_type == 'data':
if self.spine_type in ('right', 'top'):
# The right and top spines have a default position of 1 in
# axes coordinates. When specifying the position in data
# coordinates, we need to calculate the position relative to 0.
amount -= 1
if self.spine_type in ('left', 'right'):
self._spine_transform = ('data',
mtransforms.Affine2D().translate(
amount, 0))
elif self.spine_type in ('bottom', 'top'):
self._spine_transform = ('data',
mtransforms.Affine2D().translate(
0, amount))
else:
warnings.warn('unknown spine type "%s": no spine '
'offset performed' % self.spine_type)
self._spine_transform = ('identity',
mtransforms.IdentityTransform())
def set_position(self, position):
"""set the position of the spine
Spine position is specified by a 2 tuple of (position type,
amount). The position types are:
* 'outward' : place the spine out from the data area by the
specified number of points. (Negative values specify placing the
spine inward.)
* 'axes' : place the spine at the specified Axes coordinate (from
0.0-1.0).
* 'data' : place the spine at the specified data coordinate.
Additionally, shorthand notations define a special positions:
* 'center' -> ('axes',0.5)
* 'zero' -> ('data', 0.0)
"""
if position in ('center', 'zero'):
# special positions
pass
else:
if len(position) != 2:
raise ValueError("position should be 'center' or 2-tuple")
if position[0] not in ['outward', 'axes', 'data']:
msg = ("position[0] should be in [ 'outward' | 'axes' |"
" 'data' ]")
raise ValueError(msg)
self._position = position
self._calc_offset_transform()
self.set_transform(self.get_spine_transform())
if self.axis is not None:
self.axis.reset_ticks()
self.stale = True
def get_position(self):
"""get the spine position"""
self._ensure_position_is_set()
return self._position
def get_spine_transform(self):
"""get the spine transform"""
self._ensure_position_is_set()
what, how = self._spine_transform
if what == 'data':
# special case data based spine locations
data_xform = self.axes.transScale + \
(how + self.axes.transLimits + self.axes.transAxes)
if self.spine_type in ['left', 'right']:
result = mtransforms.blended_transform_factory(
data_xform, self.axes.transData)
elif self.spine_type in ['top', 'bottom']:
result = mtransforms.blended_transform_factory(
self.axes.transData, data_xform)
else:
raise ValueError('unknown spine spine_type: %s' %
self.spine_type)
return result
if self.spine_type in ['left', 'right']:
base_transform = self.axes.get_yaxis_transform(which='grid')
elif self.spine_type in ['top', 'bottom']:
base_transform = self.axes.get_xaxis_transform(which='grid')
else:
raise ValueError('unknown spine spine_type: %s' %
self.spine_type)
if what == 'identity':
return base_transform
elif what == 'post':
return base_transform + how
elif what == 'pre':
return how + base_transform
else:
raise ValueError("unknown spine_transform type: %s" % what)
def set_bounds(self, low, high):
"""Set the bounds of the spine."""
if self.spine_type == 'circle':
raise ValueError(
'set_bounds() method incompatible with circular spines')
self._bounds = (low, high)
self.stale = True
def get_bounds(self):
"""Get the bounds of the spine."""
return self._bounds
@classmethod
def linear_spine(cls, axes, spine_type, **kwargs):
"""
(staticmethod) Returns a linear :class:`Spine`.
"""
# all values of 13 get replaced upon call to set_bounds()
if spine_type == 'left':
path = mpath.Path([(0.0, 13), (0.0, 13)])
elif spine_type == 'right':
path = mpath.Path([(1.0, 13), (1.0, 13)])
elif spine_type == 'bottom':
path = mpath.Path([(13, 0.0), (13, 0.0)])
elif spine_type == 'top':
path = mpath.Path([(13, 1.0), (13, 1.0)])
else:
raise ValueError('unable to make path for spine "%s"' % spine_type)
result = cls(axes, spine_type, path, **kwargs)
result.set_visible(rcParams['axes.spines.{0}'.format(spine_type)])
return result
@classmethod
def circular_spine(cls, axes, center, radius, **kwargs):
"""
(staticmethod) Returns a circular :class:`Spine`.
"""
path = mpath.Path.unit_circle()
spine_type = 'circle'
result = cls(axes, spine_type, path, **kwargs)
result.set_patch_circle(center, radius)
return result
def set_color(self, c):
"""
Set the edgecolor.
ACCEPTS: matplotlib color arg or sequence of rgba tuples
.. seealso::
:meth:`set_facecolor`, :meth:`set_edgecolor`
For setting the edge or face color individually.
"""
# The facecolor of a spine is always 'none' by default -- let
# the user change it manually if desired.
self.set_edgecolor(c)
self.stale = True
|
invitu/odoomrp-wip | refs/heads/8.0 | purchase_pricelist_rules/__openerp__.py | 11 | # -*- encoding: utf-8 -*-
##############################################################################
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see http://www.gnu.org/licenses/.
#
##############################################################################
{
"name": "Pricelist Rules - Purchase extension",
"version": "1.0",
"depends": [
"purchase",
"account",
"purchase_discount",
"product",
"product_pricelist_rules",
],
"author": "OdooMRP team,"
"AvanzOSC,"
"Serv. Tecnol. Avanzados - Pedro M. Baeza",
"contributors": [
"Oihane Crucelaegui <oihanecrucelaegi@avanzosc.es>",
"Pedro M. Baeza <pedro.baeza@serviciosbaeza.com>",
"Ana Juaristi <ajuaristio@gmail.com>",
],
"category": "Hidden/Dependency",
"website": "http://www.odoomrp.com",
"summary": "",
"data": [
"views/purchase_pricelist_view.xml",
"views/purchase_view.xml",
"views/pricelist_view.xml",
"security/ir.model.access.csv",
"security/purchase_pricelist_rules_security.xml",
],
"installable": True,
"auto_install": True,
}
|
fanhero/thumbor | refs/heads/master | thumbor/storages/mixed_storage.py | 1 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# thumbor imaging service
# https://github.com/thumbor/thumbor/wiki
# Licensed under the MIT license:
# http://www.opensource.org/licenses/mit-license
# Copyright (c) 2011 globo.com thumbor@googlegroups.com
from thumbor.storages import BaseStorage
from tornado import gen
class Storage(BaseStorage):
def __init__(self, context, file_storage=None, crypto_storage=None, detector_storage=None):
BaseStorage.__init__(self, context)
self.file_storage = file_storage
self.crypto_storage = crypto_storage
self.detector_storage = detector_storage
def _init_file_storage(self):
if self.file_storage is None:
self.context.modules.importer.import_item(
config_key='file_storage',
item_value=self.context.config.MIXED_STORAGE_FILE_STORAGE,
class_name='Storage'
)
self.file_storage = self.context.modules.file_storage = self.context.modules.importer.file_storage(self.context)
def _init_crypto_storage(self):
if self.crypto_storage is None:
self.context.modules.importer.import_item(
config_key='crypto_storage',
item_value=self.context.config.MIXED_STORAGE_CRYPTO_STORAGE,
class_name='Storage'
)
self.crypto_storage = self.context.modules.crypto_storage = self.context.modules.importer.crypto_storage(self.context)
def _init_detector_storage(self):
if self.detector_storage is None:
self.context.modules.importer.import_item(
config_key='detector_storage',
item_value=self.context.config.MIXED_STORAGE_DETECTOR_STORAGE,
class_name='Storage'
)
self.detector_storage = self.context.modules.detector_storage = \
self.context.modules.importer.detector_storage(self.context)
def put(self, path, bytes):
self._init_file_storage()
self.file_storage.put(path, bytes)
def put_detector_data(self, path, data):
self._init_detector_storage()
self.detector_storage.put_detector_data(path, data)
def put_crypto(self, path):
self._init_crypto_storage()
self.crypto_storage.put_crypto(path)
@gen.coroutine
def get_crypto(self, path):
self._init_crypto_storage()
result = yield gen.maybe_future(self.crypto_storage.get_crypto(path))
raise gen.Return(result)
@gen.coroutine
def get_detector_data(self, path):
self._init_detector_storage()
result = yield gen.maybe_future(self.detector_storage.get_detector_data(path))
raise gen.Return(result)
@gen.coroutine
def get(self, path):
self._init_file_storage()
result = yield gen.maybe_future(self.file_storage.get(path))
raise gen.Return(result)
@gen.coroutine
def exists(self, path):
print(path)
self._init_file_storage()
if self.context.config.VARIABLE_RESULTS_ENABLED:
self.file_storage.set_bucket(path.split('/')[0])
result = yield gen.maybe_future(self.file_storage.exists(path))
raise gen.Return(result)
def resolve_original_photo_path(self, request, filename):
return self.file_storage.resolve_original_photo_path(request, filename)
|
dougfelt/nototools | refs/heads/master | nototools/extra_locale_data.py | 1 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
#
# Copyright 2014 Google Inc. 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.
"""Extra locale data that's still missing from CLDR."""
__author__ = 'roozbeh@google.com (Roozbeh Pournader)'
LIKELY_SUBTAGS = {
'abr': ('abr', 'Latn', 'GH'), # Abron
'abq': ('abq', 'Cyrl', 'RU'), # Abaza
'ada': ('ada', 'Latn', 'GH'), # Adangme
'ae': ('ae', 'Avst', 'ZZ'), # Avestan
'aeb': ('aeb', 'Arab', 'TN'), # Tunisian Arabic
'aii': ('aii', 'Syrc', 'IQ'), # Assyrian Neo-Aramaic
'ain': ('ain', 'Kana', 'JP'), # Ainu
'akk': ('akk', 'Xsux', 'ZZ'), # Akkadian
'akz': ('akz', 'Latn', 'US'), # Alabama
'ale': ('ale', 'Latn', 'US'), # Aleut
'aln': ('aln', 'Latn', 'XK'), # Gheg Albanian
'an': ('an', 'Latn', 'ES'), # Aragonese
'anp': ('anp', 'Deva', 'IN'), # Angika
'arc': ('arc', 'Armi', 'ZZ'), # Imperial Aramaic
'aro': ('aro', 'Latn', 'BO'), # Araona
'arp': ('arp', 'Latn', 'US'), # Arapaho
'arq': ('arq', 'Arab', 'DZ'), # Algerian Arabic
'arw': ('arw', 'Latn', 'GY'), # Arawak
'ary': ('ary', 'Arab', 'MA'), # Moroccan Arabic
'arz': ('arz', 'Arab', 'EG'), # Egyptian Arabic
'avk': ('avk', 'Latn', '001'), # Kotava
'azb': ('azb', 'Arab', 'IR'), # Southern Azerbaijani
'bar': ('bar', 'Latn', 'AT'), # Bavarian
'ber': ('ber', 'Arab', 'MA'), # Berber
'bej': ('bej', 'Arab', 'SD'), # Beja
'bci': ('bci', 'Latn', 'CI'), # Baoulé
'bgc': ('bgc', 'Deva', 'IN'), # Haryanvi
'bhi': ('bhi', 'Deva', 'IN'), # Bhilali
'bhk': ('bhk', 'Latn', 'PH'), # Albay Bikol
'bla': ('bla', 'Latn', 'CA'), # Blackfoot
'blt': ('blt', 'Tavt', 'VN'), # Tai Dam
'bpy': ('bpy', 'Beng', 'IN'), # Bishnupriya
'bqi': ('bqi', 'Arab', 'IR'), # Bakhtiari
'bsq': ('bsq', 'Bass', 'LR'), # Bassa
'bzx': ('bzx', 'Latn', 'ML'), # Kelengaxo Bozo
'cad': ('cad', 'Latn', 'US'), # Caddo
'car': ('car', 'Latn', 'VE'), # Galibi Carib
'cay': ('cay', 'Latn', 'CA'), # Cayuga
'chn': ('chn', 'Latn', 'US'), # Chinook Jargon
'cho': ('cho', 'Latn', 'US'), # Choctaw
'chy': ('chy', 'Latn', 'US'), # Cheyenne
'cjs': ('cjs', 'Cyrl', 'RU'), # Shor
'ckt': ('ckt', 'Cyrl', 'RU'), # Chukchi
'cop': ('cop', 'Copt', 'EG'), # Coptic
'cpf': ('cpf', 'Latn', 'HT'), # Creoles, French
'cps': ('cps', 'Latn', 'PH'), # Capiznon
'crh': ('crh', 'Latn', 'UA'), # Crimean Tatar
'crs': ('crs', 'Latn', 'SC'), # Seselwa Creole French
'ctd': ('ctd', 'Latn', 'MM'), # Tedim Chin
'dak': ('dak', 'Latn', 'US'), # Dakota
'dcc': ('dcc', 'Arab', 'IN'), # Deccan
'del': ('del', 'Latn', 'US'), # Delaware
'din': ('din', 'Latn', 'SS'), # Dinka
'dng': ('dng', 'Cyrl', 'KG'), # Dungan
'dtp': ('dtp', 'Latn', 'MY'), # Central Dusun
'egl': ('egl', 'Latn', 'IT'), # Emilian
'egy': ('egy', 'Egyp', 'ZZ'), # Ancient Egyptian
'eka': ('eka', 'Latn', 'NG'), # Ekajuk
'eky': ('eky', 'Kali', 'TH'), # Eastern Kayah
'esu': ('esu', 'Latn', 'US'), # Central Yupik
'ett': ('ett', 'Ital', 'IT'), # Etruscan
'evn': ('evn', 'Latn', 'CN'), # Evenki
'ext': ('ext', 'Latn', 'ES'), # Extremaduran
'ffm': ('ffm', 'Latn', 'ML'), # Maasina Fulfulde
'frc': ('frc', 'Latn', 'US'), # Cajun French
'frr': ('frr', 'Latn', 'DE'), # Northern Frisian
'frs': ('frs', 'Latn', 'DE'), # Eastern Frisian
'fud': ('fud', 'Latn', 'WF'), # East Futuna
'fuq': ('fuq', 'Latn', 'NE'), # Central-Eastern Niger Fulfulde
'fuv': ('fuv', 'Latn', 'NG'), # Nigerian Fulfulde
'gan': ('gan', 'Hans', 'CN'), # Gan Chinese
'gay': ('gay', 'Latn', 'ID'), # Gayo
'gba': ('gba', 'Latn', 'CF'), # Gbaya
'gbz': ('gbz', 'Arab', 'IR'), # Zoroastrian Dari
'gld': ('gld', 'Cyrl', 'RU'), # Nanai
'gom': ('gom', 'Deva', 'IN'), # Goan Konkani
'got': ('got', 'Goth', 'ZZ'), # Gothic
'grb': ('grb', 'Latn', 'LR'), # Grebo
'grc': ('grc', 'Grek', 'ZZ'), # Ancient Greek
'guc': ('guc', 'Latn', 'CO'), # Wayuu
'gur': ('gur', 'Latn', 'GH'), # Frafra
'hai': ('hai', 'Latn', 'CA'), # Haida
'hak': ('hak', 'Hant', 'CN'), # Hakka Chinese
'haz': ('haz', 'Arab', 'AF'), # Hazaragi
'hif': ('hif', 'Deva', 'FJ'), # Fiji Hindi
'hit': ('hit', 'Xsux', 'ZZ'), # Hittite
'hmd': ('hmd', 'Plrd', 'CN'), # A-Hmao
'hmn': ('hmn', 'Latn', 'CN'), # Hmong
'hnj': ('hnj', 'Latn', 'LA'), # Hmong Njua
'hno': ('hno', 'Arab', 'PK'), # Northern Hindko
'hop': ('hop', 'Latn', 'US'), # Hopi
'hsn': ('hsn', 'Hans', 'CN'), # Xiang Chinese
'hup': ('hup', 'Latn', 'US'), # Hupa
'hz': ('hz', 'Latn', 'NA'), # Herero
'iba': ('iba', 'Latn', 'MY'), # Iban
'ikt': ('ikt', 'Latn', 'CA'), # Inuinnaqtun
'izh': ('izh', 'Latn', 'RU'), # Ingrian
'jam': ('jam', 'Latn', 'JM'), # Jamaican Creole English
'jpr': ('jpr', 'Hebr', 'IL'), # Judeo-Persian
'jrb': ('jrb', 'Hebr', 'IL'), # Jedeo-Arabic
'jut': ('jut', 'Latn', 'DK'), # Jutish
'kac': ('kac', 'Latn', 'MM'), # Kachin
'kca': ('kca', 'Cyrl', 'RU'), # Khanty
'kfy': ('kfy', 'Deva', 'IN'), # Kumaoni
'kjh': ('kjh', 'Cyrl', 'RU'), # Khakas
'kkh': ('kkh', 'Lana', 'MM'), # Khün
'khn': ('khn', 'Deva', 'IN'), # Khandesi
'kiu': ('kiu', 'Latn', 'TR'), # Kirmanjki
'kpy': ('kpy', 'Cyrl', 'RU'), # Koryak
'kr': ('kr', 'Arab', 'NG'), # Kanuri
'krj': ('krj', 'Latn', 'PH'), # Kinaray-a
'kut': ('kut', 'Latn', 'CA'), # Kutenai
'kxm': ('kxm', 'Thai', 'TH'), # Northern Khmer
'kyu': ('kyu', 'Kali', 'MM'), # Western Kayah
'lab': ('lab', 'Lina', 'ZZ'), # Linear A
'lad': ('lad', 'Latn', 'IL'), # Ladino
'lam': ('lam', 'Latn', 'ZM'), # Lamba
'laj': ('laj', 'Latn', 'UG'), # Lango
'lfn': ('lfn', 'Latn', '001'), # Lingua Franca Nova
'lij': ('lij', 'Latn', 'IT'), # Ligurian
'liv': ('liv', 'Latn', 'LV'), # Livonian
'ljp': ('ljp', 'Latn', 'ID'), # Lampung Api
'lrc': ('lrc', 'Arab', 'IR'), # Northern Luri
'ltg': ('ltg', 'Latn', 'LV'), # Latgalian
'lui': ('lui', 'Latn', 'US'), # Luiseno
'lun': ('lun', 'Latn', 'ZM'), # Lunda
'lus': ('lus', 'Latn', 'IN'), # Mizo
'lut': ('lut', 'Latn', 'US'), # Lushootseed
'lzh': ('lzh', 'Hant', 'CN'), # Literary Chinese
'lzz': ('lzz', 'Latn', 'TR'), # Laz
'mdt': ('mdt', 'Latn', 'CG'), # Mbere
'mfa': ('mfa', 'Arab', 'TH'), # Pattani Malay
'mic': ('mic', 'Latn', 'CA'), # Micmac
'mnc': ('mnc', 'Mong', 'CN'), # Manchu
'mns': ('mns', 'Cyrl', 'RU'), # Mansi
'mro': ('mro', 'Mroo', 'BD'), # Mru (dlf, also Latn?)
'mtr': ('mtr', 'Deva', 'IN'), # Mewari
'mus': ('mus', 'Latn', 'US'), # Creek
'mwl': ('mwl', 'Latn', 'PT'), # Mirandese
'mwv': ('mwv', 'Latn', 'ID'), # Mentawai
'myx': ('myx', 'Latn', 'UG'), # Masaaba
'myz': ('myz', 'Mand', 'ZZ'), # Classical Mandaic
'mzn': ('mzn', 'Arab', 'IR'), # Mazanderani
'nan': ('nan', 'Latn', 'CN'), # Min Nan Chinese
'ndc': ('ndc', 'Latn', 'MZ'), # Ndau
'ngl': ('ngl', 'Latn', 'MZ'), # Lomwe
'nia': ('nia', 'Latn', 'ID'), # Nias
'njo': ('njo', 'Latn', 'IN'), # Ao Naga
'noe': ('noe', 'Deva', 'IN'), # Nimadi
'nog': ('nog', 'Cyrl', 'RU'), # Nogai
'non': ('non', 'Runr', 'ZZ'), # Old Norse
'nov': ('nov', 'Latn', '001'), # Novial
'nyo': ('nyo', 'Latn', 'UG'), # Nyoro
'nzi': ('nzi', 'Latn', 'GH'), # Nzima
'ohu': ('ohu', 'Hung', 'HR'), # Old Hungarian
'oj': ('oj', 'Latn', 'CA'), # Ojibwa
'osa': ('osa', 'Latn', 'US'), # Osage
'osc': ('osc', 'Ital', 'ZZ'), # Oscan
'otk': ('otk', 'Orkh', 'ZZ'), # Old Turkish
'pal': ('pal', 'Phli', 'ZZ'), # Pahlavi FIXME: should really be 'Phlv'
'pcd': ('pcd', 'Latn', 'FR'), # Picard
'pdc': ('pdc', 'Latn', 'US'), # Pennsylvania German
'pdt': ('pdt', 'Latn', 'CA'), # Plautdietsch
'peo': ('peo', 'Xpeo', 'ZZ'), # Old Persian
'pfl': ('pfl', 'Latn', 'DE'), # Palatine German
'phn': ('phn', 'Phnx', 'ZZ'), # Phoenician
'pi': ('pi', 'Brah', 'ZZ'), # Pali
'pms': ('pms', 'Latn', 'IT'), # Piedmontese
'pnt': ('pnt', 'Grek', 'GR'), # Pontic
'prs': ('prs', 'Arab', 'AF'), # Dari
'qug': ('qug', 'Latn', 'EC'), # Chimborazo Highland Quichua
'rom': ('rom', 'Latn', 'RO'), # Romany
'sck': ('sck', 'Deva', 'IN'), # Sadri
'skr': ('skr', 'Arab', 'PK'), # Seraiki
'sou': ('sou', 'Thai', 'TH'), # Southern Thai
'swv': ('swv', 'Deva', 'IN'), # Shekhawati
'tab': ('tab', 'Cyrl', 'RU'), # Tabassaran (dlf)
'ude': ('ude', 'Cyrl', 'RU'), # Udihe (dlf)
'uga': ('uga', 'Ugar', 'ZZ'), # Ugaritic
'vep': ('vep', 'Latn', 'RU'), # Veps
'vmw': ('vmw', 'Latn', 'MZ'), # Makhuwa
'wbr': ('wbr', 'Deva', 'IN'), # Wagdi
'wbq': ('wbq', 'Telu', 'IN'), # Waddar
'wls': ('wls', 'Latn', 'WF'), # Wallisian
'wtm': ('wtm', 'Deva', 'IN'), # Mewati
'yrk': ('yrk', 'Cyrl', 'RU'), # Nenets (dlf)
'xnr': ('xnr', 'Deva', 'IN'), # Kangri
'xum': ('xum', 'Ital', 'ZZ'), # Umbrian (dlf)
'zdj': ('zdj', 'Arab', 'KM'), # Ngazidja Comorian
'und-Mult': ('skr', 'Mult', 'ZZ'), # ancient writing system for Saraiki,
# Arabic now used
'und-Hung': ('ohu', 'Hung', 'ZZ'), # Old Hungarian, Carpathian basin
'und-Hluw': ('hlu', 'Hluw', 'ZZ'), # Hieroglyphic Luwian
'und-Ahom': ('aho', 'Ahom', 'ZZ'), # Ahom
}
ENGLISH_SCRIPT_NAMES = {
'Cans': u'Canadian Aboriginal', # shorten name for display purposes,
#match Noto font name
}
ENGLISH_LANGUAGE_NAMES = {
'abr': u'Abron',
'abq': u'Abaza',
'aho': u'Ahom',
'aii': u'Assyrian Neo-Aramaic',
'akz': u'Alabama',
'amo': u'Amo',
'aoz': u'Uab Meto',
'atj': u'Atikamekw',
'bap': u'Bantawa',
'bci': u'Baoulé',
'ber': u'Berber',
'bft': u'Balti',
'bfy': u'Bagheli',
'bgc': u'Haryanvi',
'bgx': u'Balkan Gagauz Turkish',
'bh': u'Bihari',
'bhb': u'Bhili',
'bhi': u'Bhilali',
'bhk': u'Albay Bikol',
'bjj': u'Kanauji',
'bku': u'Buhid',
'blt': u'Tai Dam',
'bmq': u'Bomu',
'bqi': u'Bakhtiari',
'bqv': u'Koro Wachi',
'bsq': u'Bassa',
'bto': u'Rinconada Bikol',
'btv': u'Bateri',
'buc': u'Bushi',
'bvb': u'Bube',
'bya': u'Batak',
'bze': u'Jenaama Bozo',
'bzx': u'Kelengaxo Bozo',
'ccp': u'Chakma',
'cja': u'Western Cham',
'cjs': u'Shor',
'cjm': u'Eastern Cham',
'ckt': u'Chukchi',
'cpf': u'French-based Creoles',
'crj': u'Southern East Cree',
'crk': u'Plains Cree',
'crl': u'Northern East Cree',
'crm': u'Moose Cree',
'crs': u'Seselwa Creole French',
'csw': u'Swampy Cree',
'ctd': u'Tedim Chin',
'dcc': u'Deccan',
'dng': u'Dungan',
'dnj': u'Dan',
'dtm': u'Tomo Kan Dogon',
'eky': u'Eastern Kayah',
'ett': u'Etruscan',
'evn': u'Evenki',
'ffm': u'Maasina Fulfulde',
'fud': u'East Futuna',
'fuq': u'Central-Eastern Niger Fulfulde',
'fuv': u'Nigerian Fulfulde',
'gbm': u'Garhwali',
'gcr': u'Guianese Creole French',
'ggn': u'Eastern Gurung',
'gjk': u'Kachi Koli',
'gju': u'Gujari',
'gld': u'Nanai',
'gos': u'Gronings',
'grt': u'Garo',
'gub': u'Guajajára',
'gvr': u'Western Gurung',
'haz': u'Hazaragi',
'hlu': u'Hieroglyphic Luwian',
'hmd': u'A-Hmao',
'hnd': u'Southern Hindko',
'hne': u'Chhattisgarhi',
'hnj': u'Hmong Njua',
'hnn': u'Hanunoo',
'hno': u'Northern Hindko',
'hoc': u'Ho',
'hoj': u'Haroti',
'hop': u'Hopi',
'ikt': u'Inuinnaqtun',
'jml': u'Jumli',
'kao': u'Xaasongaxango',
'kca': u'Khanty',
'kck': u'Kalanga',
'kdt': u'Kuy',
'kfr': u'Kachchi',
'kfy': u'Kumaoni',
'kge': u'Komering',
'khb': u'Lü',
'khn': u'Khandesi',
'kht': u'Khamti',
'kjg': u'Khmu',
'kjh': u'Khakas',
'kkh': u'Khün',
'kpy': u'Koryak',
'kvr': u'Kerinci',
'kvx': u'Parkari Koli',
'kxm': u'Northern Khmer',
'kxp': u'Wadiyara Koli',
'kyu': u'Western Kayah',
'lab': u'Linear A',
'laj': u'Lango',
'lbe': u'Lak',
'lbw': u'Tolaki',
'lcp': u'Western Lawa',
'lep': u'Lepcha',
'lif': u'Limbu',
'lis': u'Lisu',
'ljp': u'Lampung Api',
'lki': u'Laki',
'lmn': u'Lambadi',
'lrc': u'Northern Luri',
'lut': u'Lushootseed',
'luz': u'Southern Luri',
'lwl': u'Eastern Lawa',
'maz': u'Central Mazahua',
'mdh': u'Maguindanaon',
'mdt': u'Mbere',
'mfa': u'Pattani Malay',
'mgp': u'Eastern Magar',
'mgy': u'Mbunga',
'mns': u'Mansi',
'mnw': u'Mon',
'moe': u'Montagnais',
'mrd': u'Western Magar',
'mro': u'Mru',
'mru': u'Cameroon Mono',
'mtr': u'Mewari',
'mvy': u'Indus Kohistani',
'mwk': u'Kita Maninkakan',
'mxc': u'Manyika',
'myx': u'Masaaba',
'myz': u'Classical Mandaic',
'nch': u'Central Huasteca Nahuatl',
'ndc': u'Ndau',
'ngl': u'Lomwe',
'nhe': u'Eastern Huasteca Nahuatl',
'nhw': u'Western Huasteca Nahuatl',
'nij': u'Ngaju',
'nod': u'Northern Thai',
'noe': u'Nimadi',
'nsk': u'Naskapi',
'nxq': u'Naxi',
'ohu': u'Old Hungarian',
'osc': u'Oscan',
'otk': u'Old Turkish',
'pcm': u'Nigerian Pidgin',
'pka': u'Ardhamāgadhī Prākrit',
'pko': u'Pökoot',
'pra': u'Prakrit', # language family name
'prd': u'Parsi-Dari',
'prs': u'Dari',
'puu': u'Punu',
'rcf': u'Réunion Creole French',
'rej': u'Rejang',
'ria': u'Riang', # (India)
'rjs': u'Rajbanshi',
'rkt': u'Rangpuri',
'rmf': u'Kalo Finnish Romani',
'rmo': u'Sinte Romani',
'rmt': u'Domari',
'rmu': u'Tavringer Romani',
'rng': u'Ronga',
'rob': u'Tae’',
'ryu': u'Central Okinawan',
'saf': u'Safaliba',
'sck': u'Sadri',
'scs': u'North Slavey',
'sdh': u'Southern Kurdish',
'sef': u'Cebaara Senoufo',
'skr': u'Seraiki',
'smp': u'Samaritan',
'sou': u'Southern Thai',
'srb': u'Sora',
'srx': u'Sirmauri',
'swv': u'Shekhawati',
'sxn': u'Sangir',
'syi': u'Seki',
'syl': u'Sylheti',
'tab': u'Tabassaran',
'taj': u'Eastern Tamang',
'tbw': u'Tagbanwa',
'tdd': u'Tai Nüa',
'tdg': u'Western Tamang',
'tdh': u'Thulung',
'thl': u'Dangaura Tharu',
'thq': u'Kochila Tharu',
'thr': u'Rana Tharu',
'tkt': u'Kathoriya Tharu',
'tli': u'Tlingit',
'tsf': u'Southwestern Tamang',
'tsg': u'Tausug',
'tsj': u'Tshangla',
'ttj': u'Tooro',
'tts': u'Northeastern Thai',
'ude': u'Udihe',
'uli': u'Ulithian',
'unr': u'Mundari',
'unx': u'Munda',
'vic': u'Virgin Islands Creole English',
'vmw': u'Makhu',
'wbr': u'Wagdi',
'wbq': u'Waddar',
'wls': u'Wallisian',
'wtm': u'Mewati',
'xav': u'Xavánte',
'xcr': u'Carian',
'xlc': u'Lycian',
'xld': u'Lydian',
'xmn': u'Manichaean Middle Persian',
'xmr': u'Meroitic',
'xna': u'Ancient North Arabian',
'xnr': u'Kangri',
'xpr': u'Parthian',
'xsa': u'Sabaean',
'xsr': u'Sherpa',
'xum': u'Umbrian',
'yrk': u'Nenets',
'yua': u'Yucatec Maya',
'zdj': u'Ngazidja Comorian',
'zmi': u'Negeri Sembilan Malay',
}
# Supplement mapping of languages to scripts
LANG_TO_SCRIPTS = {
'ber': ['Arab', 'Latn', 'Tfng'],
'hak': ['Hans', 'Hant', 'Latn'],
'nan': ['Hans', 'Hant', 'Latn'],
'yue': ['Hant'],
}
# Supplement mapping of regions to lang_scripts
REGION_TO_LANG_SCRIPTS = {
'CN': ['hak-Hans', 'hak-Latn', 'nan-Hans', 'nan-Latn', 'yue-Hans'],
'HK': ['yue-Hant'],
'MN': ['mn-Mong'],
'MY': ['zh-Hans'],
'TW': ['hak-Hant', 'hak-Latn', 'nan-Hant', 'nan-Latn'],
}
PARENT_LOCALES = {
'ky-Latn': 'root',
'sd-Deva': 'root',
'tg-Arab': 'root',
'ug-Cyrl': 'root',
}
NATIVE_NAMES = {
'mn-Mong': u'ᠮᠣᠨᠭᠭᠣᠯ ᠬᠡᠯᠡ',
}
EXEMPLARS = {
'und-Avst': r'[\U010b00-\U010b35]',
'und-Bali': r'[\u1b05-\u1b33]',
'und-Bamu': r'[\ua6a0-\ua6ef]',
'und-Cham': r'[\uaa00-\uaa28 \uaa50-\uaa59]',
'und-Copt': r'[\u2c80-\u2cb1]',
'und-Egyp': r'[\U013000-\U01303f]',
'und-Hira': r'[\u3041-\u3096\u3099-\u309f\U01b000-\U01b001]',
'und-Java': r'[\ua984-\ua9b2]',
'und-Kali': r'[\ua90a-\ua925 \ua900-\ua909]',
'und-Kana': r'[\u30a0-\u30ff \u31f0-\u31ff]',
'und-Khar': r'[\U010a10-\U010a13\U010a15-\U010a17\U010a19-\U010a33'
r'\U010A38-\U010a3a]',
'und-Kthi': r'[\U11080-\U110C1]',
'und-Lana': r'[\u1a20-\u1a4c]',
'und-Lepc': r'[\u1c00-\u1c23]',
'und-Linb': r'[\U010000-\U01000b \U010080-\U01009f]',
'und-Mand': r'[\u0840-\u0858]',
'und-Mtei': r'[\uabc0-\uabe2]',
'und-Orkh': r'[\U010c00-\U010c48]',
'und-Phag': r'[\ua840-\ua877]',
'und-Saur': r'[\ua882-\ua8b3]',
'und-Sund': r'[\u1b83-\u1ba0]',
'und-Sylo': r'[\ua800-\ua82b]',
'und-Tavt': r'[\uaa80-\uaaaf \uaadb-\uaadf]',
'und-Tglg': r'[\u1700-\u170c \u170e-\u1711]',
'und-Ugar': r'[\U010380-\U01039d \U01039f]',
'und-Xsux': r'[\U012000-\U01202f]',
'und-Zmth': r'[\U010AC0-\U010AE6 \U010AEB-\U010AF6]',
'und-Zsye': r'[\u2049\u231a\u231b\u2600\u260e\u2614\u2615\u26fa\u2708\u2709'
r'\u270f\u3297\U01f004\U01f170\U01f193\U01f197\U01f30d\U01f318'
r'\U01f332\U01f334\U01f335\U01f344\U01f346\U01f352\U01f381'
r'\U01f393\U01f3a7\U01f3b8\U01f3e1\U01f402\U01f40a\U01f418'
r'\U01f419\U01f41b\U01f41f\U01f422\U01f424\U01f427\U01f44c'
r'\U01f44d\U01f453\U01f463\U01f4bb\U01f4ce\U01f4d3\U01f4d6'
r'\U01f4e1\U01f4fb\U01f511\U01f525\U01f565\U01F63a\U01f680'
r'\U01f681\U01f683\U01f686\U01f68c\U01f6a2\U01f6a3\U01f6b4]',
'und-Zsym': r'[\u20ac\u20b9\u2103\u2109\u2115\u2116\u211a\u211e\u2122'
r'\u21d0-\u21d3\u2203\u2205\u2207\u2208\u220f\u221e\u2248\u2284'
r'\u231a\u23e3\u23f3\u2400\u2460\u24b6\u2523\u2533\u2602\u260e'
r'\u2615\u261c\u2637\u263a\u264f\u2656\u2663\u266b'
r'\u267b\u267f\u26f9\u2708\u2740\u2762\u2a6b\u2a93\u2a64\u2e19'
r'\u4dc3\U010137\U01017B\U0101ef\U01d122\U01d15e\U01d161]'
}
|
rawrgulmuffins/flask | refs/heads/master | flask/exthook.py | 142 | # -*- coding: utf-8 -*-
"""
flask.exthook
~~~~~~~~~~~~~
Redirect imports for extensions. This module basically makes it possible
for us to transition from flaskext.foo to flask_foo without having to
force all extensions to upgrade at the same time.
When a user does ``from flask.ext.foo import bar`` it will attempt to
import ``from flask_foo import bar`` first and when that fails it will
try to import ``from flaskext.foo import bar``.
We're switching from namespace packages because it was just too painful for
everybody involved.
This is used by `flask.ext`.
:copyright: (c) 2015 by Armin Ronacher.
:license: BSD, see LICENSE for more details.
"""
import sys
import os
from ._compat import reraise
class ExtensionImporter(object):
"""This importer redirects imports from this submodule to other locations.
This makes it possible to transition from the old flaskext.name to the
newer flask_name without people having a hard time.
"""
def __init__(self, module_choices, wrapper_module):
self.module_choices = module_choices
self.wrapper_module = wrapper_module
self.prefix = wrapper_module + '.'
self.prefix_cutoff = wrapper_module.count('.') + 1
def __eq__(self, other):
return self.__class__.__module__ == other.__class__.__module__ and \
self.__class__.__name__ == other.__class__.__name__ and \
self.wrapper_module == other.wrapper_module and \
self.module_choices == other.module_choices
def __ne__(self, other):
return not self.__eq__(other)
def install(self):
sys.meta_path[:] = [x for x in sys.meta_path if self != x] + [self]
def find_module(self, fullname, path=None):
if fullname.startswith(self.prefix):
return self
def load_module(self, fullname):
if fullname in sys.modules:
return sys.modules[fullname]
modname = fullname.split('.', self.prefix_cutoff)[self.prefix_cutoff]
for path in self.module_choices:
realname = path % modname
try:
__import__(realname)
except ImportError:
exc_type, exc_value, tb = sys.exc_info()
# since we only establish the entry in sys.modules at the
# very this seems to be redundant, but if recursive imports
# happen we will call into the move import a second time.
# On the second invocation we still don't have an entry for
# fullname in sys.modules, but we will end up with the same
# fake module name and that import will succeed since this
# one already has a temporary entry in the modules dict.
# Since this one "succeeded" temporarily that second
# invocation now will have created a fullname entry in
# sys.modules which we have to kill.
sys.modules.pop(fullname, None)
# If it's an important traceback we reraise it, otherwise
# we swallow it and try the next choice. The skipped frame
# is the one from __import__ above which we don't care about
if self.is_important_traceback(realname, tb):
reraise(exc_type, exc_value, tb.tb_next)
continue
module = sys.modules[fullname] = sys.modules[realname]
if '.' not in modname:
setattr(sys.modules[self.wrapper_module], modname, module)
return module
raise ImportError('No module named %s' % fullname)
def is_important_traceback(self, important_module, tb):
"""Walks a traceback's frames and checks if any of the frames
originated in the given important module. If that is the case then we
were able to import the module itself but apparently something went
wrong when the module was imported. (Eg: import of an import failed).
"""
while tb is not None:
if self.is_important_frame(important_module, tb):
return True
tb = tb.tb_next
return False
def is_important_frame(self, important_module, tb):
"""Checks a single frame if it's important."""
g = tb.tb_frame.f_globals
if '__name__' not in g:
return False
module_name = g['__name__']
# Python 2.7 Behavior. Modules are cleaned up late so the
# name shows up properly here. Success!
if module_name == important_module:
return True
# Some python versions will will clean up modules so early that the
# module name at that point is no longer set. Try guessing from
# the filename then.
filename = os.path.abspath(tb.tb_frame.f_code.co_filename)
test_string = os.path.sep + important_module.replace('.', os.path.sep)
return test_string + '.py' in filename or \
test_string + os.path.sep + '__init__.py' in filename
|
prataprc/tayra | refs/heads/master | tayra/runtime.py | 1 | # -*- coding: utf-8 -*-
# This file is subject to the terms and conditions defined in
# file 'LICENSE', which is part of this source code package.
# Copyright (c) 2011 R Pratap Chakravarthy
"""
Run-time engine, a stack machine interpreting instructions generated by
:class:`tayra.codegen.InstrGen`. Instructions are nothing but methods on the
stack machine object.
References automatically made available in a template script.
-------------------------------------------------------------
``_m``,
Reference to :class:`StackMachine` instance used to generate the final
HTML text.
``this``,
Every template script can be viewed as an object instance which can be
referenced using ``this``. In case of template scripts making use of
inheritance feature, ``this`` will always refer to the template script
at the end of the inheritance chain.
``local``,
For non-inheriting template scripts ``this`` and ``local`` refer to the
same object. In case of template scripts using inheritance feature,
unlike ``this`` symbol which refers to the template script at the end of
the inheritance chain, ``local`` will always refer to the template script
object in which it is used.
``parent``,
In case of inheriting scripts, ``parent`` will refer to the base template
from which ``local`` template script derives.
``next``,
In case of inheriting scripts, ``next`` will refer to the deriving
template script.
All names ``this``, ``local``, ``parent``, ``next`` refer to the same type of
object, template-module. Having a refence to template-module allows developers
to access global variables and functions defined in the module.
In case if a template script is part of an inheritance chain, then, attribute
references on the template-module will propogate towards the top of the chain
until an attribute is found in one of the base module.
``_compiler``,
Refers to :class:`tayra.compiler.TTLCompiler` plugin instance.
``_context``,
Refers to the context dictionary.
``_ttlhash``,
Refers to the hash value generated from template text.
``_ttlfile``,
File name of template script.
``__compiler``,
Temporary variable that is optionally created for compiling and importing
library templates. Refers to a :class:`tayra.compiler.TTLCompiler` plugin
instance.
``__ttlcode``,
Temporary variable that is optionally created while importing library
templates.
``<Implemented-interfaces>``,
When a template script implements an interface, interface name and plugin
name will also be made available in the global context.
Other than this every template script will have the following standard imports
.. code-block :: python
:linenos:
import imp
from io import StringIO
from pluggdapps.plugin import Plugin, implements
import pluggdapps.utils as h
from tayra import BaseTTLPlugin
"""
import re
import pluggdapps.utils as h
from tayra.lexer import TTLLexer
from tayra.interfaces import ITayraTags, ITayraExpression, ITayraFilterBlock
__traceback_hide__ = True
class StackMachine( object ) :
"""Stack machine instruction interpreterr. Intructions are method calls on
this object. Supported instructions are,::
indent, append, extend, pushbuf, popbuf, popbuftext, popobject,
handletag, evalexprs, inherit
Initializing a stack machine can be a costly operation, so to avoid
creating a stack machine object everytime, create it once and call
:meth:`_init` before interpreting a new template-module.
"""
def __init__( self, compiler ):
self.compiler = compiler
self.encoding = compiler.encoding
self.tagplugins = [ compiler.qp( ITayraTags, name )
for name in compiler['tag.plugins'] ]
self.tagplugins.append( compiler.qp( ITayraTags, 'tayra.Tags' ))
self.exprplugins = { p.caname.split('.', 1)[1] : p
for p in compiler.qps(ITayraExpression) }
self.exprdefault = compiler.qp(
ITayraExpression, compiler['expression.default'] )
def _init( self, ifile ):
self.bufstack = [ [] ]
self.ifile = ifile
self.htmlindent = ''
#---- Stack machine instructions
def indent( self ) :
"""Add indentation to output HTML text."""
return self.append( self.htmlindent )
def upindent( self, up='' ) :
"""Increase the indentation level for subsequent HTML text by ``up``
spaces."""
self.htmlindent += up
return self.htmlindent
def downindent( self, down='' ) :
"""Decrease the indentation level for subsequent HTML text by ``down``
spaces."""
self.htmlindent = self.htmlindent[:-len(down)]
return self.htmlindent
def append( self, value ) :
"""Append ``value`` string to recently pushed buffer in the buffer
stack."""
self.bufstack[-1].append( value )
return value
def extend( self, value ) :
"""Extend recently pushed buffer in the buffer stack using ``value``
list."""
if isinstance(value, list) :
self.bufstack[-1].extend( value )
else :
raise Exception( 'Unable to extend context stack' )
def pushbuf( self, buf=None ) :
"""Push an empty buffer into the buffer stack."""
buf = []
self.bufstack.append( buf )
return buf
def popbuf( self ) :
"""Pop the recently pushed buffer in the buffer stack and return the
same. Return type is a list of strings."""
return self.bufstack.pop(-1)
def popbuftext( self ) :
"""Same as :meth:`popbuf` and additionaly join all the string elements
in the buffer and return the same. Return type is string."""
x = ''.join( self.popbuf() )
return x
def popobject( self ):
"""Return the first element from recently pushed buffer in the buffer
stack. Discard the remaining buffer. If recently pushed buffer is
empty, return None."""
lst = self.popbuf()
return lst[0] if lst else None
regex_tag = re.compile(
r'(\{[^\}]*\})|(%s=%s)|(%s)|([^ \t\r\n]+)' % (
TTLLexer.attrname, TTLLexer.attrvalue, TTLLexer.attrvalue ))
def handletag( self, contents, tagbegin, **kwargs ):
"""Parse ``tagbegin`` for tokens, style and attribute. Along with them
add ``content`` to pass them to tag-plugin handlers. :meth:`append`
the html text returned by the handler into the buffer stack.
There are two types of pruning, inner-prunning (specified by <... !>)
and outer-prunning (specified by <! ...>). Pruning modifiers are
detected by the AST and passed as part of modifier dictionary.
optional key-word arguments,
``indent``,
Boolean to keep indentation in output html.
``nl``,
String.
``iprune``,
Boolean, if True, should remove all whitespaces before and after
the content enclosed by this tag.
``oprune``,
Boolean, if True, should remove all leading and trailining
whitespaces around this tag element.
"""
indent = kwargs.get('indent', False)
nl = kwargs.get('nl', '')
if kwargs.get('iprune',False) :
contents = self.iprune(contents)
if kwargs.get('oprune', False) :
contents = contents
tagbegin = tagbegin.replace('\n', ' ')[1:-1] # remove < and >
if tagbegin[-1] == '/' : # Self closing tag
if contents.strip('\n \t\r') :
self.compiler.pa.logwarn(
"skipping content after self closing tag %r" % contents )
contents = ''
tagbegin = tagbegin[:-1]
try : tagname, tagbegin = tagbegin.split(' ', 1)
except : tagname, tagbegin = tagbegin, ''
styles, attributes, tokens = [], [], []
for m in self.regex_tag.finditer( tagbegin ) :
if not m : continue
parts = m.groups()
parts[0] and styles.append( parts[0][1:-1].strip() )
parts[1] and attributes.append( parts[1] )
tokens.extend( parts[2:] )
tokens = list( filter( None, tokens ))
for plugin in self.tagplugins :
html = plugin.handle( self, tagname, tokens, styles, attributes,
contents )
if html == None : continue
self.append( html )
break
else :
raise Exception("Unable to handle tag %r" % tagname)
def evalexprs( self, name, text, filters, globals_, locals_ ) :
"""Evaluate expression ``text`` in ``globals_`` and ``locals_``
context using plugin prefix in ``text`` or using the
TTLCompiler['expression.default'] plugin. Convert the resulting
object to string, pipe them to the list of filters specified by
``filters``. ``filters`` is comma separated value of escape filters
to be applied on the output string.
"""
exprp=self.exprplugins['expression'+name] if name else self.exprdefault
out = exprp.eval( self, text, globals_, locals_ )
if not filters : return out
filters = h.parsecsv( filters )
if 'n' in filters : return out
# TODO : if exprdefault and exprp are same plugin, we might end up
# calling it twice. Try to improve the performance.
for f in filters :
out1 = exprp.filter( self, f, out )
if out1 == None :
out1 = self.exprdefault.filter( self, f, out )
if out1 != None :
out = out1
break
return out
def inherit( self, ttlloc, childglobals ):
"""Special instruction used by @inherit directive."""
compiler = self.compiler()
code = compiler.compilettl( file=ttlloc )
# inherit module
parent_context = childglobals['_context']
parent_context.update({
'this' : childglobals['this'],
'parent' : None,
'next' : childglobals['local'],
})
module = compiler.load( code, context=parent_context )
childglobals['this']._linkparent( Namespace( None, module ))
childglobals['local'].parent = module
return module
def hitch( self, obj, cls, interfacefunc, *args, **kwargs ) :
def fnhitched( self, *a, **kw ) :
kwargs.update( kw )
return interfacefunc( self, *(args+a), **kwargs )
return fnhitched.__get__( obj, cls )
def iprune(self, contents):
return contents.strip(' \n\r\t\f')
class Namespace( object ):
"""Namespace wrapper for template-modules to traverse the interitance
chain for referred attributes."""
def __init__( self, parentnm, localmod ):
self._parentnm = parentnm
self._localmod = localmod
def __getattr__( self, name ):
if self._parentnm :
return getattr(
self._localmod, name, getattr( self._parentnm, name, None )
)
else :
return getattr( self._localmod, name, None )
def __setattr__( self, name, value ):
if name in [ '_parentnm', '_localmod' ] :
self.__dict__[name] = value
else :
setattr( self._localmod, name, value )
return value
def _linkparent( self, parentnm ):
nm, parnm = self, self._parentnm
while parnm : nm, parnm = parnm, parnm._parentnm
nm._parentnm = parentnm
return parentnm
|
alphafoobar/intellij-community | refs/heads/master | python/helpers/pydev/pydevd_plugins/django_debug.py | 51 | from pydevd_comm import CMD_SET_BREAK, CMD_ADD_EXCEPTION_BREAK
import inspect
from pydevd_constants import STATE_SUSPEND, GetThreadId, DictContains, DictIterItems
from pydevd_file_utils import NormFileToServer, GetFileNameAndBaseFromFile
from pydevd_breakpoints import LineBreakpoint, get_exception_name
import pydevd_vars
import traceback
import pydev_log
from pydevd_frame_utils import add_exception_to_frame, FCode, cached_call, just_raised
DJANGO_SUSPEND = 2
class DjangoLineBreakpoint(LineBreakpoint):
def __init__(self, file, line, condition, func_name, expression):
self.file = file
LineBreakpoint.__init__(self, line, condition, func_name, expression)
def is_triggered(self, template_frame_file, template_frame_line):
return self.file == template_frame_file and self.line == template_frame_line
def __str__(self):
return "DjangoLineBreakpoint: %s-%d" %(self.file, self.line)
def add_line_breakpoint(plugin, pydb, type, file, line, condition, expression, func_name):
if type == 'django-line':
breakpoint = DjangoLineBreakpoint(file, line, condition, func_name, expression)
if not hasattr(pydb, 'django_breakpoints'):
_init_plugin_breaks(pydb)
return breakpoint, pydb.django_breakpoints
return None
def add_exception_breakpoint(plugin, pydb, type, exception):
if type == 'django':
if not hasattr(pydb, 'django_exception_break'):
_init_plugin_breaks(pydb)
pydb.django_exception_break[exception] = True
pydb.setTracingForUntracedContexts()
return True
return False
def _init_plugin_breaks(pydb):
pydb.django_exception_break = {}
pydb.django_breakpoints = {}
def remove_exception_breakpoint(plugin, pydb, type, exception):
if type == 'django':
try:
del pydb.django_exception_break[exception]
return True
except:
pass
return False
def get_breakpoints(plugin, pydb, type):
if type == 'django-line':
return pydb.django_breakpoints
return None
def _inherits(cls, *names):
if cls.__name__ in names:
return True
inherits_node = False
for base in inspect.getmro(cls):
if base.__name__ in names:
inherits_node = True
break
return inherits_node
def _is_django_render_call(frame):
try:
name = frame.f_code.co_name
if name != 'render':
return False
if not DictContains(frame.f_locals, 'self'):
return False
cls = frame.f_locals['self'].__class__
inherits_node = _inherits(cls, 'Node')
if not inherits_node:
return False
clsname = cls.__name__
return clsname != 'TextNode' and clsname != 'NodeList'
except:
traceback.print_exc()
return False
def _is_django_context_get_call(frame):
try:
if not DictContains(frame.f_locals, 'self'):
return False
cls = frame.f_locals['self'].__class__
return _inherits(cls, 'BaseContext')
except:
traceback.print_exc()
return False
def _is_django_resolve_call(frame):
try:
name = frame.f_code.co_name
if name != '_resolve_lookup':
return False
if not DictContains(frame.f_locals, 'self'):
return False
cls = frame.f_locals['self'].__class__
clsname = cls.__name__
return clsname == 'Variable'
except:
traceback.print_exc()
return False
def _is_django_suspended(thread):
return thread.additionalInfo.suspend_type == DJANGO_SUSPEND
def suspend_django(mainDebugger, thread, frame, cmd=CMD_SET_BREAK):
frame = DjangoTemplateFrame(frame)
if frame.f_lineno is None:
return None
#try:
# if thread.additionalInfo.filename == frame.f_code.co_filename and thread.additionalInfo.line == frame.f_lineno:
# return None # don't stay twice on the same line
#except AttributeError:
# pass
pydevd_vars.addAdditionalFrameById(GetThreadId(thread), {id(frame): frame})
mainDebugger.setSuspend(thread, cmd)
thread.additionalInfo.suspend_type = DJANGO_SUSPEND
thread.additionalInfo.filename = frame.f_code.co_filename
thread.additionalInfo.line = frame.f_lineno
return frame
def _find_django_render_frame(frame):
while frame is not None and not _is_django_render_call(frame):
frame = frame.f_back
return frame
#=======================================================================================================================
# Django Frame
#=======================================================================================================================
def _read_file(filename):
f = open(filename, "r")
s = f.read()
f.close()
return s
def _offset_to_line_number(text, offset):
curLine = 1
curOffset = 0
while curOffset < offset:
if curOffset == len(text):
return -1
c = text[curOffset]
if c == '\n':
curLine += 1
elif c == '\r':
curLine += 1
if curOffset < len(text) and text[curOffset + 1] == '\n':
curOffset += 1
curOffset += 1
return curLine
def _get_source(frame):
try:
node = frame.f_locals['self']
if hasattr(node, 'source'):
return node.source
else:
pydev_log.error_once("WARNING: Template path is not available. Please set TEMPLATE_DEBUG=True in your settings.py to make "
" django template breakpoints working")
return None
except:
pydev_log.debug(traceback.format_exc())
return None
def _get_template_file_name(frame):
try:
source = _get_source(frame)
if source is None:
pydev_log.debug("Source is None\n")
return None
fname = source[0].name
if fname == '<unknown source>':
pydev_log.debug("Source name is %s\n" % fname)
return None
else:
filename, base = GetFileNameAndBaseFromFile(fname)
return filename
except:
pydev_log.debug(traceback.format_exc())
return None
def _get_template_line(frame):
source = _get_source(frame)
file_name = _get_template_file_name(frame)
try:
return _offset_to_line_number(_read_file(file_name), source[1][0])
except:
return None
class DjangoTemplateFrame:
def __init__(self, frame):
file_name = _get_template_file_name(frame)
self.back_context = frame.f_locals['context']
self.f_code = FCode('Django Template', file_name)
self.f_lineno = _get_template_line(frame)
self.f_back = frame
self.f_globals = {}
self.f_locals = self.collect_context(self.back_context)
self.f_trace = None
def collect_context(self, context):
res = {}
try:
for d in context.dicts:
for k, v in d.items():
res[k] = v
except AttributeError:
pass
return res
def changeVariable(self, name, value):
for d in self.back_context.dicts:
for k, v in d.items():
if k == name:
d[k] = value
def change_variable(plugin, frame, attr, expression):
if isinstance(frame, DjangoTemplateFrame):
result = eval(expression, frame.f_globals, frame.f_locals)
frame.changeVariable(attr, result)
return result
return False
def _is_django_exception_break_context(frame):
try:
name = frame.f_code.co_name
except:
name = None
return name in ['_resolve_lookup', 'find_template']
#=======================================================================================================================
# Django Step Commands
#=======================================================================================================================
def can_not_skip(plugin, mainDebugger, pydb_frame, frame):
if mainDebugger.django_breakpoints and _is_django_render_call(frame):
filename = _get_template_file_name(frame)
django_breakpoints_for_file = mainDebugger.django_breakpoints.get(filename)
if django_breakpoints_for_file:
return True
return False
def has_exception_breaks(plugin):
if len(plugin.main_debugger.django_exception_break) > 0:
return True
return False
def has_line_breaks(plugin):
for file, breakpoints in DictIterItems(plugin.main_debugger.django_breakpoints):
if len(breakpoints) > 0:
return True
return False
def cmd_step_into(plugin, mainDebugger, frame, event, args, stop_info, stop):
mainDebugger, filename, info, thread = args
plugin_stop = False
if _is_django_suspended(thread):
stop_info['django_stop'] = event == 'call' and _is_django_render_call(frame)
plugin_stop = stop_info['django_stop']
stop = stop and _is_django_resolve_call(frame.f_back) and not _is_django_context_get_call(frame)
if stop:
info.pydev_django_resolve_frame = 1 #we remember that we've go into python code from django rendering frame
return stop, plugin_stop
def cmd_step_over(plugin, mainDebugger, frame, event, args, stop_info, stop):
mainDebugger, filename, info, thread = args
plugin_stop = False
if _is_django_suspended(thread):
stop_info['django_stop'] = event == 'call' and _is_django_render_call(frame)
plugin_stop = stop_info['django_stop']
stop = False
return stop, plugin_stop
else:
if event == 'return' and info.pydev_django_resolve_frame is not None and _is_django_resolve_call(frame.f_back):
#we return to Django suspend mode and should not stop before django rendering frame
info.pydev_step_stop = info.pydev_django_resolve_frame
info.pydev_django_resolve_frame = None
thread.additionalInfo.suspend_type = DJANGO_SUSPEND
stop = info.pydev_step_stop is frame and event in ('line', 'return')
return stop, plugin_stop
def stop(plugin, mainDebugger, frame, event, args, stop_info, arg, step_cmd):
mainDebugger, filename, info, thread = args
if DictContains(stop_info, 'django_stop') and stop_info['django_stop']:
frame = suspend_django(mainDebugger, thread, frame, step_cmd)
if frame:
mainDebugger.doWaitSuspend(thread, frame, event, arg)
return True
return False
def get_breakpoint(plugin, mainDebugger, pydb_frame, frame, event, args):
mainDebugger, filename, info, thread = args
flag = False
django_breakpoint = None
new_frame = None
type = 'django'
if event == 'call' and info.pydev_state != STATE_SUSPEND and \
mainDebugger.django_breakpoints and _is_django_render_call(frame):
filename = _get_template_file_name(frame)
pydev_log.debug("Django is rendering a template: %s\n" % filename)
django_breakpoints_for_file = mainDebugger.django_breakpoints.get(filename)
if django_breakpoints_for_file:
pydev_log.debug("Breakpoints for that file: %s\n" % django_breakpoints_for_file)
template_line = _get_template_line(frame)
pydev_log.debug("Tracing template line: %d\n" % template_line)
if DictContains(django_breakpoints_for_file, template_line):
django_breakpoint = django_breakpoints_for_file[template_line]
flag = True
new_frame = DjangoTemplateFrame(frame)
return flag, django_breakpoint, new_frame, type
def suspend(plugin, mainDebugger, thread, frame, bp_type):
if bp_type == 'django':
return suspend_django(mainDebugger, thread, frame)
return None
def exception_break(plugin, mainDebugger, pydb_frame, frame, args, arg):
mainDebugger, filename, info, thread = args
exception, value, trace = arg
if mainDebugger.django_exception_break and \
get_exception_name(exception) in ['VariableDoesNotExist', 'TemplateDoesNotExist', 'TemplateSyntaxError'] and \
just_raised(trace) and _is_django_exception_break_context(frame):
render_frame = _find_django_render_frame(frame)
if render_frame:
suspend_frame = suspend_django(mainDebugger, thread, render_frame, CMD_ADD_EXCEPTION_BREAK)
if suspend_frame:
add_exception_to_frame(suspend_frame, (exception, value, trace))
flag = True
thread.additionalInfo.message = 'VariableDoesNotExist'
suspend_frame.f_back = frame
frame = suspend_frame
return (flag, frame)
return None |
Darkmoth/python-django-4 | refs/heads/master | Thing/env/Lib/site-packages/django/utils/dateparse.py | 63 | """Functions to parse datetime objects."""
# We're using regular expressions rather than time.strptime because:
# - They provide both validation and parsing.
# - They're more flexible for datetimes.
# - The date/datetime/time constructors produce friendlier error messages.
import datetime
import re
from django.utils import six
from django.utils.timezone import get_fixed_timezone, utc
date_re = re.compile(
r'(?P<year>\d{4})-(?P<month>\d{1,2})-(?P<day>\d{1,2})$'
)
time_re = re.compile(
r'(?P<hour>\d{1,2}):(?P<minute>\d{1,2})'
r'(?::(?P<second>\d{1,2})(?:\.(?P<microsecond>\d{1,6})\d{0,6})?)?'
)
datetime_re = re.compile(
r'(?P<year>\d{4})-(?P<month>\d{1,2})-(?P<day>\d{1,2})'
r'[T ](?P<hour>\d{1,2}):(?P<minute>\d{1,2})'
r'(?::(?P<second>\d{1,2})(?:\.(?P<microsecond>\d{1,6})\d{0,6})?)?'
r'(?P<tzinfo>Z|[+-]\d{2}(?::?\d{2})?)?$'
)
standard_duration_re = re.compile(
r'^'
r'(?:(?P<days>-?\d+) )?'
r'((?:(?P<hours>\d+):)(?=\d+:\d+))?'
r'(?:(?P<minutes>\d+):)?'
r'(?P<seconds>\d+)'
r'(?:\.(?P<microseconds>\d{1,6})\d{0,6})?'
r'$'
)
# Support the sections of ISO 8601 date representation that are accepted by
# timedelta
iso8601_duration_re = re.compile(
r'^P'
r'(?:(?P<days>\d+(.\d+)?)D)?'
r'(?:T'
r'(?:(?P<hours>\d+(.\d+)?)H)?'
r'(?:(?P<minutes>\d+(.\d+)?)M)?'
r'(?:(?P<seconds>\d+(.\d+)?)S)?'
r')?'
r'$'
)
def parse_date(value):
"""Parses a string and return a datetime.date.
Raises ValueError if the input is well formatted but not a valid date.
Returns None if the input isn't well formatted.
"""
match = date_re.match(value)
if match:
kw = {k: int(v) for k, v in six.iteritems(match.groupdict())}
return datetime.date(**kw)
def parse_time(value):
"""Parses a string and return a datetime.time.
This function doesn't support time zone offsets.
Raises ValueError if the input is well formatted but not a valid time.
Returns None if the input isn't well formatted, in particular if it
contains an offset.
"""
match = time_re.match(value)
if match:
kw = match.groupdict()
if kw['microsecond']:
kw['microsecond'] = kw['microsecond'].ljust(6, '0')
kw = {k: int(v) for k, v in six.iteritems(kw) if v is not None}
return datetime.time(**kw)
def parse_datetime(value):
"""Parses a string and return a datetime.datetime.
This function supports time zone offsets. When the input contains one,
the output uses a timezone with a fixed offset from UTC.
Raises ValueError if the input is well formatted but not a valid datetime.
Returns None if the input isn't well formatted.
"""
match = datetime_re.match(value)
if match:
kw = match.groupdict()
if kw['microsecond']:
kw['microsecond'] = kw['microsecond'].ljust(6, '0')
tzinfo = kw.pop('tzinfo')
if tzinfo == 'Z':
tzinfo = utc
elif tzinfo is not None:
offset_mins = int(tzinfo[-2:]) if len(tzinfo) > 3 else 0
offset = 60 * int(tzinfo[1:3]) + offset_mins
if tzinfo[0] == '-':
offset = -offset
tzinfo = get_fixed_timezone(offset)
kw = {k: int(v) for k, v in six.iteritems(kw) if v is not None}
kw['tzinfo'] = tzinfo
return datetime.datetime(**kw)
def parse_duration(value):
"""Parses a duration string and returns a datetime.timedelta.
The preferred format for durations in Django is '%d %H:%M:%S.%f'.
Also supports ISO 8601 representation.
"""
match = standard_duration_re.match(value)
if not match:
match = iso8601_duration_re.match(value)
if match:
kw = match.groupdict()
if kw.get('microseconds'):
kw['microseconds'] = kw['microseconds'].ljust(6, '0')
kw = {k: float(v) for k, v in six.iteritems(kw) if v is not None}
return datetime.timedelta(**kw)
|
pybel/pybel | refs/heads/master | src/pybel/struct/summary/__init__.py | 1 | # -*- coding: utf-8 -*-
"""Summary functions for BEL graphs."""
from . import edge_summary, errors, node_summary, provenance
from .edge_summary import *
from .errors import *
from .node_summary import *
from .provenance import *
__all__ = [k for k in locals() if not k.startswith('_')]
|
isandlaTech/cohorte-demos | refs/heads/dev | led/dump/led-demo-yun/cohorte/dist/cohorte-1.0.0-20141216.234517-57-python-distribution/repo/sleekxmpp/plugins/xep_0020/__init__.py | 12 | """
SleekXMPP: The Sleek XMPP Library
Copyright (C) 2013 Nathanael C. Fritz, Lance J.T. Stout
This file is part of SleekXMPP.
See the file LICENSE for copying permission.
"""
from sleekxmpp.plugins.base import register_plugin
from sleekxmpp.plugins.xep_0020 import stanza
from sleekxmpp.plugins.xep_0020.stanza import FeatureNegotiation
from sleekxmpp.plugins.xep_0020.feature_negotiation import XEP_0020
register_plugin(XEP_0020)
|
rastala/mmlspark | refs/heads/master | tools/hdi/update_livy.py | 1 | #!/usr/bin/env python
# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.
import sys, json
if len(sys.argv) != 3:
raise Exception(("Not enough" if len(sys.argv) < 3 else "Too many")
+ " arguments.")
_, config_file, maven_pkg = sys.argv
with open(config_file) as conf_file:
conf=json.load(conf_file)
conf["session_configs"]["conf"] = {}
conf["session_configs"]["conf"]["spark.jars.packages"] = maven_pkg
with open(config_file, "w") as outfile:
json.dump(conf, outfile, indent=2, sort_keys=True)
|
SDSG-Invenio/invenio | refs/heads/invenio-inis | invenio/legacy/websubmit/webinterface.py | 6 | # This file is part of Invenio.
# Copyright (C) 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014 CERN.
#
# Invenio 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 2 of the
# License, or (at your option) any later version.
#
# Invenio 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 Invenio; if not, write to the Free Software Foundation, Inc.,
# 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA.
__lastupdated__ = """$Date$"""
__revision__ = "$Id$"
import os
import errno
import time
import cgi
import sys
import shutil
from urllib import urlencode
from collections import defaultdict
from invenio.legacy.bibrecord import create_record
from invenio.config import \
CFG_ACCESS_CONTROL_LEVEL_SITE, \
CFG_SITE_LANG, \
CFG_SITE_NAME, \
CFG_SITE_URL, \
CFG_SITE_SECURE_URL, \
CFG_WEBSUBMIT_STORAGEDIR, \
CFG_PREFIX, \
CFG_CERN_SITE
from invenio.utils.crossref import get_marcxml_for_doi, CrossrefError
from invenio.utils import apache
from invenio.legacy.dbquery import run_sql
from invenio.modules.access.engine import acc_authorize_action
from invenio.modules.access.control import acc_is_role
from invenio.legacy.webpage import warning_page
from invenio.legacy.webuser import getUid, page_not_authorized, collect_user_info, \
isGuestUser
from invenio.legacy.search_engine import is_user_owner_of_record
from invenio.ext.legacy.handler import wash_urlargd, WebInterfaceDirectory
from invenio.utils.url import make_canonical_urlargd, redirect_to_url
from invenio.base.i18n import gettext_set_language
from invenio.legacy.bibdocfile.api import stream_file, \
decompose_file, propose_next_docname
from invenio.ext.logging import register_exception
from invenio.utils.html import is_html_text_editor_installed
from invenio.legacy.websubmit.icon_creator import create_icon, InvenioWebSubmitIconCreatorError
from invenio.legacy.ckeditor.connector import process_CKEditor_upload, send_response
import invenio.legacy.template
websubmit_templates = invenio.legacy.template.load('websubmit')
from invenio.legacy.websearch.adminlib import get_detailed_page_tabs
from invenio.utils.json import json, CFG_JSON_AVAILABLE
import invenio.legacy.template
from flask import session
webstyle_templates = invenio.legacy.template.load('webstyle')
websearch_templates = invenio.legacy.template.load('websearch')
from invenio.legacy.websubmit.engine import home, action, interface, endaction, makeCataloguesTable
class WebInterfaceSubmitPages(WebInterfaceDirectory):
_exports = ['summary', 'sub', 'direct', '', 'attachfile', 'uploadfile', \
'getuploadedfile', 'upload_video', ('continue', 'continue_'), \
'doilookup']
def uploadfile(self, req, form):
"""
Similar to /submit, but only consider files. Nice for
asynchronous Javascript uploads. Should be used to upload a
single file.
Also try to create an icon, and return URL to file(s) + icon(s)
Authentication is performed based on session ID passed as
parameter instead of cookie-based authentication, due to the
use of this URL by the Flash plugin (to upload multiple files
at once), which does not route cookies.
FIXME: consider adding /deletefile and /modifyfile functions +
parsing of additional parameters to rename files, add
comments, restrictions, etc.
"""
argd = wash_urlargd(form, {
'doctype': (str, ''),
'access': (str, ''),
'indir': (str, ''),
'session_id': (str, ''),
'rename': (str, ''),
})
curdir = None
if "indir" not in form or \
"doctype" not in form or \
"access" not in form:
raise apache.SERVER_RETURN(apache.HTTP_BAD_REQUEST)
else:
curdir = os.path.join(CFG_WEBSUBMIT_STORAGEDIR,
argd['indir'],
argd['doctype'],
argd['access'])
user_info = collect_user_info(req)
if "session_id" in form:
# Are we uploading using Flash, which does not transmit
# cookie? The expect to receive session_id as a form
# parameter. First check that IP addresses do not
# mismatch.
uid = session.uid
user_info = collect_user_info(uid)
try:
act_fd = file(os.path.join(curdir, 'act'))
action = act_fd.read()
act_fd.close()
except:
action = ""
try:
recid_fd = file(os.path.join(curdir, 'SN'))
recid = recid_fd.read()
recid_fd.close()
except:
recid = ''
user_is_owner = False
if recid:
user_is_owner = is_user_owner_of_record(user_info, recid)
try:
categ_fd = file(os.path.join(curdir, 'combo%s' %argd['doctype']))
categ = categ_fd.read()
categ_fd.close()
except IOError:
categ = '*'
# Is user authorized to perform this action?
(auth_code, auth_message) = acc_authorize_action(uid, "submit",
authorized_if_no_roles=not isGuestUser(uid),
verbose=0,
doctype=argd['doctype'],
act=action,
categ=categ)
if acc_is_role("submit", doctype=argd['doctype'], act=action) and auth_code != 0 and not user_is_owner:
# User cannot submit
raise apache.SERVER_RETURN(apache.HTTP_UNAUTHORIZED)
else:
# Process the upload and get the response
added_files = {}
for key, formfields in form.items():
filename = key.replace("[]", "")
file_to_open = os.path.join(curdir, filename)
if hasattr(formfields, "filename") and formfields.filename:
dir_to_open = os.path.abspath(os.path.join(curdir,
'files',
str(user_info['uid']),
key))
try:
assert(dir_to_open.startswith(CFG_WEBSUBMIT_STORAGEDIR))
except AssertionError:
register_exception(req=req, prefix='curdir="%s", key="%s"' % (curdir, key))
raise apache.SERVER_RETURN(apache.HTTP_FORBIDDEN)
if not os.path.exists(dir_to_open):
try:
os.makedirs(dir_to_open)
except OSError as e:
if e.errno != errno.EEXIST:
# If the issue is only that directory
# already exists, then continue, else
# report
register_exception(req=req, alert_admin=True)
raise apache.SERVER_RETURN(apache.HTTP_FORBIDDEN)
filename = formfields.filename
## Before saving the file to disc, wash the filename (in particular
## washing away UNIX and Windows (e.g. DFS) paths):
filename = os.path.basename(filename.split('\\')[-1])
filename = filename.strip()
if filename != "":
# Check that file does not already exist
n = 1
while os.path.exists(os.path.join(dir_to_open, filename)):
#dirname, basename, extension = decompose_file(new_destination_path)
basedir, name, extension = decompose_file(filename)
new_name = propose_next_docname(name)
filename = new_name + extension
# This may be dangerous if the file size is bigger than the available memory
fp = open(os.path.join(dir_to_open, filename), "w")
fp.write(formfields.file.read())
fp.close()
fp = open(os.path.join(curdir, "lastuploadedfile"), "w")
fp.write(filename)
fp.close()
fp = open(file_to_open, "w")
fp.write(filename)
fp.close()
try:
# Create icon
(icon_path, icon_name) = create_icon(
{ 'input-file' : os.path.join(dir_to_open, filename),
'icon-name' : filename, # extension stripped automatically
'icon-file-format' : 'gif',
'multipage-icon' : False,
'multipage-icon-delay' : 100,
'icon-scale' : "300>", # Resize only if width > 300
'verbosity' : 0,
})
icons_dir = os.path.join(os.path.join(curdir,
'icons',
str(user_info['uid']),
key))
if not os.path.exists(icons_dir):
# Create uid/icons dir if needed
try:
os.makedirs(icons_dir)
except OSError as e:
if e.errno != errno.EEXIST:
# If the issue is only that
# directory already exists,
# then continue, else report
register_exception(req=req, alert_admin=True)
raise apache.SERVER_RETURN(apache.HTTP_FORBIDDEN)
os.rename(os.path.join(icon_path, icon_name),
os.path.join(icons_dir, icon_name))
added_files[key] = {'name': filename,
'iconName': icon_name}
except InvenioWebSubmitIconCreatorError as e:
# We could not create the icon
added_files[key] = {'name': filename}
continue
else:
raise apache.SERVER_RETURN(apache.HTTP_BAD_REQUEST)
# Send our response
if CFG_JSON_AVAILABLE:
return json.dumps(added_files)
def upload_video(self, req, form):
"""
A clone of uploadfile but for (large) videos.
Does not copy the uploaded file to the websubmit directory.
Instead, the path to the file is stored inside the submission directory.
"""
def gcd(a, b):
""" the euclidean algorithm """
while a:
a, b = b % a, a
return b
from invenio.modules.encoder.extract import extract_frames
from invenio.modules.encoder.config import CFG_BIBENCODE_WEBSUBMIT_ASPECT_SAMPLE_DIR, CFG_BIBENCODE_WEBSUBMIT_ASPECT_SAMPLE_FNAME
from invenio.modules.encoder.encode import determine_aspect
from invenio.modules.encoder.utils import probe
from invenio.modules.encoder.metadata import ffprobe_metadata
from invenio.legacy.websubmit.config import CFG_WEBSUBMIT_TMP_VIDEO_PREFIX
argd = wash_urlargd(form, {
'doctype': (str, ''),
'access': (str, ''),
'indir': (str, ''),
'session_id': (str, ''),
'rename': (str, ''),
})
curdir = None
if "indir" not in form or \
"doctype" not in form or \
"access" not in form:
raise apache.SERVER_RETURN(apache.HTTP_BAD_REQUEST)
else:
curdir = os.path.join(CFG_WEBSUBMIT_STORAGEDIR,
argd['indir'],
argd['doctype'],
argd['access'])
user_info = collect_user_info(req)
if "session_id" in form:
# Are we uploading using Flash, which does not transmit
# cookie? The expect to receive session_id as a form
# parameter. First check that IP addresses do not
# mismatch.
uid = session.uid
user_info = collect_user_info(uid)
try:
act_fd = file(os.path.join(curdir, 'act'))
action = act_fd.read()
act_fd.close()
except:
act = ""
# Is user authorized to perform this action?
(auth_code, auth_message) = acc_authorize_action(uid, "submit",
authorized_if_no_roles=not isGuestUser(uid),
verbose=0,
doctype=argd['doctype'],
act=action)
if acc_is_role("submit", doctype=argd['doctype'], act=action) and auth_code != 0:
# User cannot submit
raise apache.SERVER_RETURN(apache.HTTP_UNAUTHORIZED)
else:
# Process the upload and get the response
json_response = {}
for key, formfields in form.items():
filename = key.replace("[]", "")
if hasattr(formfields, "filename") and formfields.filename:
dir_to_open = os.path.abspath(os.path.join(curdir,
'files',
str(user_info['uid']),
key))
try:
assert(dir_to_open.startswith(CFG_WEBSUBMIT_STORAGEDIR))
except AssertionError:
register_exception(req=req, prefix='curdir="%s", key="%s"' % (curdir, key))
raise apache.SERVER_RETURN(apache.HTTP_FORBIDDEN)
if not os.path.exists(dir_to_open):
try:
os.makedirs(dir_to_open)
except OSError as e:
if e.errno != errno.EEXIST:
# If the issue is only that directory
# already exists, then continue, else
# report
register_exception(req=req, alert_admin=True)
raise apache.SERVER_RETURN(apache.HTTP_FORBIDDEN)
filename = formfields.filename
## Before saving the file to disc, wash the filename (in particular
## washing away UNIX and Windows (e.g. DFS) paths):
filename = os.path.basename(filename.split('\\')[-1])
filename = filename.strip()
if filename != "":
# Check that file does not already exist
while os.path.exists(os.path.join(dir_to_open, filename)):
#dirname, basename, extension = decompose_file(new_destination_path)
basedir, name, extension = decompose_file(filename)
new_name = propose_next_docname(name)
filename = new_name + extension
#-------------#
# VIDEO STUFF #
#-------------#
## Remove all previous uploads
filelist = os.listdir(os.path.split(formfields.file.name)[0])
for afile in filelist:
if argd['access'] in afile:
os.remove(os.path.join(os.path.split(formfields.file.name)[0], afile))
## Check if the file is a readable video
## We must exclude all image and audio formats that are readable by ffprobe
if (os.path.splitext(filename)[1] in ['jpg', 'jpeg', 'gif', 'tiff', 'bmp', 'png', 'tga',
'jp2', 'j2k', 'jpf', 'jpm', 'mj2', 'biff', 'cgm',
'exif', 'img', 'mng', 'pic', 'pict', 'raw', 'wmf', 'jpe', 'jif',
'jfif', 'jfi', 'tif', 'webp', 'svg', 'ai', 'ps', 'psd',
'wav', 'mp3', 'pcm', 'aiff', 'au', 'flac', 'wma', 'm4a', 'wv', 'oga',
'm4a', 'm4b', 'm4p', 'm4r', 'aac', 'mp4', 'vox', 'amr', 'snd']
or not probe(formfields.file.name)):
formfields.file.close()
raise apache.SERVER_RETURN(apache.HTTP_FORBIDDEN)
## We have no "delete" attribute in Python 2.4
if sys.hexversion < 0x2050000:
## We need to rename first and create a dummy file
## Rename the temporary file for the garbage collector
new_tmp_fullpath = os.path.split(formfields.file.name)[0] + "/" + CFG_WEBSUBMIT_TMP_VIDEO_PREFIX + argd['access'] + "_" + os.path.split(formfields.file.name)[1]
os.rename(formfields.file.name, new_tmp_fullpath)
dummy = open(formfields.file.name, "w")
dummy.close()
formfields.file.close()
else:
# Mark the NamedTemporatyFile as not to be deleted
formfields.file.delete = False
formfields.file.close()
## Rename the temporary file for the garbage collector
new_tmp_fullpath = os.path.split(formfields.file.name)[0] + "/" + CFG_WEBSUBMIT_TMP_VIDEO_PREFIX + argd['access'] + "_" + os.path.split(formfields.file.name)[1]
os.rename(formfields.file.name, new_tmp_fullpath)
# Write the path to the temp file to a file in STORAGEDIR
fp = open(os.path.join(dir_to_open, "filepath"), "w")
fp.write(new_tmp_fullpath)
fp.close()
fp = open(os.path.join(dir_to_open, "filename"), "w")
fp.write(filename)
fp.close()
## We are going to extract some thumbnails for websubmit ##
sample_dir = os.path.join(curdir, 'files', str(user_info['uid']), CFG_BIBENCODE_WEBSUBMIT_ASPECT_SAMPLE_DIR)
try:
## Remove old thumbnails
shutil.rmtree(sample_dir)
except OSError:
register_exception(req=req, alert_admin=False)
try:
os.makedirs(os.path.join(curdir, 'files', str(user_info['uid']), sample_dir))
except OSError:
register_exception(req=req, alert_admin=False)
try:
extract_frames(input_file=new_tmp_fullpath,
output_file=os.path.join(sample_dir, CFG_BIBENCODE_WEBSUBMIT_ASPECT_SAMPLE_FNAME),
size="600x600",
numberof=5)
json_response['frames'] = []
for extracted_frame in os.listdir(sample_dir):
json_response['frames'].append(extracted_frame)
except:
## If the frame extraction fails, something was bad with the video
os.remove(new_tmp_fullpath)
register_exception(req=req, alert_admin=False)
raise apache.SERVER_RETURN(apache.HTTP_FORBIDDEN)
## Try to detect the aspect. if this fails, the video is not readable
## or a wrong file might have been uploaded
try:
(aspect, width, height) = determine_aspect(new_tmp_fullpath)
if aspect:
aspx, aspy = aspect.split(':')
else:
the_gcd = gcd(width, height)
aspx = str(width / the_gcd)
aspy = str(height / the_gcd)
json_response['aspx'] = aspx
json_response['aspy'] = aspy
except TypeError:
## If the aspect detection completely fails
os.remove(new_tmp_fullpath)
register_exception(req=req, alert_admin=False)
raise apache.SERVER_RETURN(apache.HTTP_FORBIDDEN)
## Try to extract some metadata from the video container
metadata = ffprobe_metadata(new_tmp_fullpath)
json_response['meta_title'] = metadata['format'].get('TAG:title')
json_response['meta_description'] = metadata['format'].get('TAG:description')
json_response['meta_year'] = metadata['format'].get('TAG:year')
json_response['meta_author'] = metadata['format'].get('TAG:author')
## Empty file name
else:
raise apache.SERVER_RETURN(apache.HTTP_BAD_REQUEST)
## We found our file, we can break the loop
break;
# Send our response
if CFG_JSON_AVAILABLE:
dumped_response = json.dumps(json_response)
# store the response in the websubmit directory
# this is needed if the submission is not finished and continued later
response_dir = os.path.join(curdir, 'files', str(user_info['uid']), "response")
try:
os.makedirs(response_dir)
except OSError:
# register_exception(req=req, alert_admin=False)
pass
fp = open(os.path.join(response_dir, "response"), "w")
fp.write(dumped_response)
fp.close()
return dumped_response
def getuploadedfile(self, req, form):
"""
Stream uploaded files.
For the moment, restrict to files in ./curdir/files/uid or
./curdir/icons/uid directory, so that we are sure we stream
files only to the user who uploaded them.
"""
argd = wash_urlargd(form, {'indir': (str, None),
'doctype': (str, None),
'access': (str, None),
'icon': (int, 0),
'key': (str, None),
'filename': (str, None),
'nowait': (int, 0)})
if None in argd.values():
raise apache.SERVER_RETURN(apache.HTTP_BAD_REQUEST)
uid = getUid(req)
if argd['icon']:
file_path = os.path.join(CFG_WEBSUBMIT_STORAGEDIR,
argd['indir'],
argd['doctype'],
argd['access'],
'icons',
str(uid),
argd['key'],
argd['filename']
)
else:
file_path = os.path.join(CFG_WEBSUBMIT_STORAGEDIR,
argd['indir'],
argd['doctype'],
argd['access'],
'files',
str(uid),
argd['key'],
argd['filename']
)
abs_file_path = os.path.abspath(file_path)
if abs_file_path.startswith(CFG_WEBSUBMIT_STORAGEDIR):
# Check if file exist. Note that icon might not yet have
# been created.
if not argd['nowait']:
for i in range(5):
if os.path.exists(abs_file_path):
return stream_file(req, abs_file_path)
time.sleep(1)
else:
if os.path.exists(abs_file_path):
return stream_file(req, abs_file_path)
# Send error 404 in all other cases
raise apache.SERVER_RETURN(apache.HTTP_NOT_FOUND)
def attachfile(self, req, form):
"""
Process requests received from CKEditor to upload files.
If the uploaded file is an image, create an icon version
"""
if not is_html_text_editor_installed():
return apache.HTTP_NOT_FOUND
if 'type' not in form:
form['type'] = 'File'
if 'upload' not in form or \
not form['type'] in \
['File', 'Image', 'Flash', 'Media']:
#return apache.HTTP_NOT_FOUND
pass
filetype = form['type'].lower()
uid = getUid(req)
# URL where the file can be fetched after upload
user_files_path = '%(CFG_SITE_URL)s/submit/getattachedfile/%(uid)s' % \
{'uid': uid,
'CFG_SITE_URL': CFG_SITE_URL,
'filetype': filetype}
# Path to directory where uploaded files are saved
user_files_absolute_path = '%(CFG_PREFIX)s/var/tmp/attachfile/%(uid)s/%(filetype)s' % \
{'uid': uid,
'CFG_PREFIX': CFG_PREFIX,
'filetype': filetype}
try:
os.makedirs(user_files_absolute_path)
except:
pass
user_info = collect_user_info(req)
(auth_code, auth_message) = acc_authorize_action(user_info, 'attachsubmissionfile')
msg = ""
if user_info['email'] == 'guest':
# User is guest: must login prior to upload
msg = 'Please login before uploading file.'
elif auth_code:
# User cannot submit
msg = 'Sorry, you are not allowed to submit files.'
## elif len(form['upload']) != 1:
## msg = 'Sorry, you must upload one single file'
else:
# Process the upload and get the response
(msg, uploaded_file_path, uploaded_file_name, uploaded_file_url, callback_function) = \
process_CKEditor_upload(form, uid, user_files_path, user_files_absolute_path)
if uploaded_file_path:
# Create an icon
if form.get('type','') == 'Image':
try:
(icon_path, icon_name) = create_icon(
{ 'input-file' : uploaded_file_path,
'icon-name' : os.path.splitext(uploaded_file_name)[0],
'icon-file-format' : os.path.splitext(uploaded_file_name)[1][1:] or 'gif',
'multipage-icon' : False,
'multipage-icon-delay' : 100,
'icon-scale' : "700>", # Resize only if width > 700
'verbosity' : 0,
})
# Move original file to /original dir, and replace it with icon file
original_user_files_absolute_path = os.path.join(user_files_absolute_path,
'original')
if not os.path.exists(original_user_files_absolute_path):
# Create /original dir if needed
os.mkdir(original_user_files_absolute_path)
os.rename(uploaded_file_path,
original_user_files_absolute_path + os.sep + uploaded_file_name)
os.rename(icon_path + os.sep + icon_name,
uploaded_file_path)
except InvenioWebSubmitIconCreatorError as e:
pass
user_files_path += '/' + filetype + '/' + uploaded_file_name
else:
user_files_path = ''
if not msg:
msg = 'No valid file found'
# Send our response
send_response(req, msg, user_files_path, callback_function)
def _lookup(self, component, path):
""" This handler is invoked for the dynamic URLs (for getting
and putting attachments) Eg:
/submit/getattachedfile/41336978/image/myfigure.png
/submit/attachfile/41336978/image/myfigure.png
"""
if component == 'getattachedfile' and len(path) > 2:
uid = path[0] # uid of the submitter
file_type = path[1] # file, image, flash or media (as
# defined by CKEditor)
if file_type in ['file', 'image', 'flash', 'media']:
file_name = '/'.join(path[2:]) # the filename
def answer_get(req, form):
"""Accessing files attached to submission."""
form['file'] = file_name
form['type'] = file_type
form['uid'] = uid
return self.getattachedfile(req, form)
return answer_get, []
# All other cases: file not found
return None, []
def getattachedfile(self, req, form):
"""
Returns a file uploaded to the submission 'drop box' by the
CKEditor.
"""
argd = wash_urlargd(form, {'file': (str, None),
'type': (str, None),
'uid': (int, 0)})
# Can user view this record, i.e. can user access its
# attachments?
uid = getUid(req)
user_info = collect_user_info(req)
if not argd['file'] is None:
# Prepare path to file on disk. Normalize the path so that
# ../ and other dangerous components are removed.
path = os.path.abspath(CFG_PREFIX + '/var/tmp/attachfile/' + \
'/' + str(argd['uid']) + \
'/' + argd['type'] + '/' + argd['file'])
# Check that we are really accessing attachements
# directory, for the declared record.
if path.startswith(CFG_PREFIX + '/var/tmp/attachfile/') and os.path.exists(path):
return stream_file(req, path)
# Send error 404 in all other cases
return(apache.HTTP_NOT_FOUND)
def continue_(self, req, form):
"""
Continue an interrupted submission.
"""
args = wash_urlargd(form, {'access': (str, ''), 'doctype': (str, '')})
ln = args['ln']
_ = gettext_set_language(ln)
access = args['access']
doctype = args['doctype']
if not access or not doctype:
return warning_page(_("Sorry, invalid arguments"), req=req, ln=ln)
user_info = collect_user_info(req)
email = user_info['email']
res = run_sql("SELECT action, status FROM sbmSUBMISSIONS WHERE id=%s AND email=%s and doctype=%s", (access, email, doctype))
if res:
action, status = res[0]
if status == 'finished':
return warning_page(_("Note: the requested submission has already been completed"), req=req, ln=ln)
redirect_to_url(req, CFG_SITE_SECURE_URL + '/submit/direct?' + urlencode({
'sub': action + doctype,
'access': access}))
return warning_page(_("Sorry, you don't seem to have initiated a submission with the provided access number"), req=req, ln=ln)
def direct(self, req, form):
"""Directly redirected to an initialized submission."""
args = wash_urlargd(form, {'sub': (str, ''),
'access' : (str, '')})
sub = args['sub']
access = args['access']
ln = args['ln']
_ = gettext_set_language(ln)
uid = getUid(req)
if CFG_ACCESS_CONTROL_LEVEL_SITE >= 1:
return page_not_authorized(req, "direct",
navmenuid='submit',
text=_("Submissions are not available"))
myQuery = req.args
if not sub:
return warning_page(_("Sorry, 'sub' parameter missing..."), req, ln=ln)
res = run_sql("SELECT docname,actname FROM sbmIMPLEMENT WHERE subname=%s", (sub,))
if not res:
return warning_page(_("Sorry. Cannot analyse parameter"), req, ln=ln)
else:
# get document type
doctype = res[0][0]
# get action name
action = res[0][1]
# get category
categ = req.form.get('combo%s' % doctype, '*')
# retrieve other parameter values
params = dict(form)
# Check if user is authorized, based on doctype/action/categ,
# in order to give guest users a chance to log in if needed:
(auth_code, auth_message) = acc_authorize_action(req, 'submit',
authorized_if_no_roles=not isGuestUser(uid),
verbose=0,
doctype=doctype,
act=action,
categ=categ)
if not auth_code == 0 and isGuestUser(uid):
# Propose to login
redirection_params = params
redirection_params['referer'] = CFG_SITE_SECURE_URL + req.unparsed_uri
return redirect_to_url(req, "%s/youraccount/login%s" % (
CFG_SITE_SECURE_URL,
make_canonical_urlargd(redirection_params, {})),
norobot=True)
# else: continue, and let main interface control the access
# find existing access number
if not access:
# create 'unique' access number
pid = os.getpid()
now = time.time()
access = "%i_%s" % (now, pid)
# retrieve 'dir' value
res = run_sql ("SELECT dir FROM sbmACTION WHERE sactname=%s", (action,))
dir = res[0][0]
mainmenu = req.headers_in.get('referer')
params['access'] = access
params['act'] = action
params['doctype'] = doctype
params['startPg'] = '1'
params['mainmenu'] = mainmenu
params['ln'] = ln
params['indir'] = dir
url = "%s/submit?%s" % (CFG_SITE_SECURE_URL, urlencode(params))
redirect_to_url(req, url)
def sub(self, req, form):
"""DEPRECATED: /submit/sub is deprecated now, so raise email to the admin (but allow submission to continue anyway)"""
args = wash_urlargd(form, {'password': (str, '')})
uid = getUid(req)
if uid == -1 or CFG_ACCESS_CONTROL_LEVEL_SITE >= 1:
return page_not_authorized(req, "../sub/",
navmenuid='submit')
try:
raise DeprecationWarning, 'submit/sub handler has been used. Please use submit/direct. e.g. "submit/sub?RN=123@SBIFOO" -> "submit/direct?RN=123&sub=SBIFOO"'
except DeprecationWarning:
register_exception(req=req, alert_admin=True)
ln = args['ln']
_ = gettext_set_language(ln)
#DEMOBOO_RN=DEMO-BOOK-2008-001&ln=en&password=1223993532.26572%40APPDEMOBOO
params = dict(form)
password = args['password']
if password:
del params['password']
if "@" in password:
params['access'], params['sub'] = password.split('@', 1)
else:
params['sub'] = password
else:
args = str(req.args).split('@')
if len(args) > 1:
params = {'sub' : args[-1]}
args = '@'.join(args[:-1])
params.update(cgi.parse_qs(args))
else:
return warning_page(_("Sorry, invalid URL..."), req, ln=ln)
url = "%s/submit/direct?%s" % (CFG_SITE_SECURE_URL, urlencode(params, doseq=True))
redirect_to_url(req, url)
def summary(self, req, form):
args = wash_urlargd(form, {
'doctype': (str, ''),
'act': (str, ''),
'access': (str, ''),
'indir': (str, '')})
ln = args['ln']
uid = getUid(req)
if uid == -1 or CFG_ACCESS_CONTROL_LEVEL_SITE >= 1:
return page_not_authorized(req, "../summary",
navmenuid='submit')
t = ""
curdir = os.path.join(CFG_WEBSUBMIT_STORAGEDIR, args['indir'], args['doctype'], args['access'])
try:
assert(curdir == os.path.abspath(curdir))
except AssertionError:
register_exception(req=req, alert_admin=True, prefix='Possible cracking tentative: indir="%s", doctype="%s", access="%s"' % (args['indir'], args['doctype'], args['access']))
return warning_page("Invalid parameters", req, ln)
subname = "%s%s" % (args['act'], args['doctype'])
res = run_sql("select sdesc,fidesc,pagenb,level from sbmFIELD where subname=%s "
"order by pagenb,fieldnb", (subname,))
nbFields = 0
values = []
for arr in res:
if arr[0] != "":
val = {
'mandatory' : (arr[3] == 'M'),
'value' : '',
'page' : arr[2],
'name' : arr[0],
}
if os.path.exists(os.path.join(curdir, curdir, arr[1])):
fd = open(os.path.join(curdir, arr[1]),"r")
value = fd.read()
fd.close()
value = value.replace("\n"," ")
value = value.replace("Select:","")
else:
value = ""
val['value'] = value
values.append(val)
return websubmit_templates.tmpl_submit_summary(
ln = args['ln'],
values = values,
)
def doilookup(self, req, form):
"""
Returns the metadata from the crossref website based on the DOI.
"""
args = wash_urlargd(form, {
'doi': (str, '')})
response = defaultdict(list)
if args['doi']:
doi = args['doi']
try:
marcxml_template = get_marcxml_for_doi(doi)
except CrossrefError:
# Just ignore Crossref errors
pass
else:
record = create_record(marcxml_template)[0]
if record:
# We need to convert this record structure to a simple dictionary
for key, value in record.items(): # key, value = (773, [([('0', 'PER:64142'), ...], ' ', ' ', '', 47)])
for val in value: # val = ([('0', 'PER:64142'), ...], ' ', ' ', '', 47)
ind1 = val[1].replace(" ", "_")
ind2 = val[2].replace(" ", "_")
for (k, v) in val[0]: # k, v = ('0', 'PER:5409')
response[key+ind1+ind2+k].append(v)
# The output dictionary is something like:
# {"100__a": ['Smith, J.'],
# "700__a": ['Anderson, J.', 'Someoneelse, E.'],
# "700__u": ['University1', 'University2']}
# return dictionary as JSON
return json.dumps(response)
def index(self, req, form):
args = wash_urlargd(form, {
'c': (str, CFG_SITE_NAME),
'doctype': (str, ''),
'act': (str, ''),
'startPg': (str, "1"),
'access': (str, ''),
'mainmenu': (str, ''),
'fromdir': (str, ''),
'nextPg': (str, ''),
'nbPg': (str, ''),
'curpage': (str, '1'),
'step': (str, '0'),
'mode': (str, 'U'),
})
## Strip whitespace from beginning and end of doctype and action:
args["doctype"] = args["doctype"].strip()
args["act"] = args["act"].strip()
def _index(req, c, ln, doctype, act, startPg, access,
mainmenu, fromdir, nextPg, nbPg, curpage, step,
mode):
auth_args = {}
if doctype:
auth_args['doctype'] = doctype
if act:
auth_args['act'] = act
uid = getUid(req)
if uid == -1 or CFG_ACCESS_CONTROL_LEVEL_SITE >= 1:
return page_not_authorized(req, "direct",
navmenuid='submit')
if CFG_CERN_SITE:
## HACK BEGIN: this is a hack for CMS and ATLAS draft
user_info = collect_user_info(req)
if doctype == 'CMSPUB' and act == "" and 'cds-admin [CERN]' not in user_info['group'] and not user_info['email'].lower() == 'cds.support@cern.ch':
if isGuestUser(uid):
return redirect_to_url(req, "%s/youraccount/login%s" % (
CFG_SITE_SECURE_URL,
make_canonical_urlargd({'referer' : CFG_SITE_SECURE_URL + req.unparsed_uri, 'ln' : args['ln']}, {}))
, norobot=True)
if 'cms-publication-committee-chair [CERN]' not in user_info['group']:
return page_not_authorized(req, "../submit", text="In order to access this submission interface you need to be member of the CMS Publication Committee Chair.",
navmenuid='submit')
elif doctype == 'ATLPUB' and 'cds-admin [CERN]' not in user_info['group'] and not user_info['email'].lower() == 'cds.support@cern.ch':
if isGuestUser(uid):
return redirect_to_url(req, "%s/youraccount/login%s" % (
CFG_SITE_SECURE_URL,
make_canonical_urlargd({'referer' : CFG_SITE_SECURE_URL + req.unparsed_uri, 'ln' : args['ln']}, {}))
, norobot=True)
if 'atlas-gen [CERN]' not in user_info['group']:
return page_not_authorized(req, "../submit", text="In order to access this submission interface you need to be member of ATLAS.",
navmenuid='submit')
## HACK END
if doctype == "":
catalogues_text, at_least_one_submission_authorized, submission_exists = makeCataloguesTable(req, ln=CFG_SITE_LANG)
if not at_least_one_submission_authorized and submission_exists:
if isGuestUser(uid):
return redirect_to_url(req, "%s/youraccount/login%s" % (
CFG_SITE_SECURE_URL,
make_canonical_urlargd({'referer' : CFG_SITE_SECURE_URL + req.unparsed_uri, 'ln' : args['ln']}, {}))
, norobot=True)
else:
return page_not_authorized(req, "../submit",
uid=uid,
navmenuid='submit')
return home(req, catalogues_text, c, ln)
elif act == "":
return action(req, c, ln, doctype)
elif int(step)==0:
return interface(req, c, ln, doctype, act, startPg, access, mainmenu, fromdir, nextPg, nbPg, curpage)
else:
return endaction(req, c, ln, doctype, act, startPg, access, mainmenu, fromdir, nextPg, nbPg, curpage, step, mode)
return _index(req, **args)
# Answer to both /submit/ and /submit
__call__ = index
# def retrieve_most_recent_attached_file(file_path):
# """
# Retrieve the latest file that has been uploaded with the
# CKEditor. This is the only way to retrieve files that the
# CKEditor has renamed after the upload.
# Eg: 'prefix/image.jpg' was uploaded but did already
# exist. CKEditor silently renamed it to 'prefix/image(1).jpg':
# >>> retrieve_most_recent_attached_file('prefix/image.jpg')
# 'prefix/image(1).jpg'
# """
# (base_path, filename) = os.path.split(file_path)
# base_name = os.path.splitext(filename)[0]
# file_ext = os.path.splitext(filename)[1][1:]
# most_recent_filename = filename
# i = 0
# while True:
# i += 1
# possible_filename = "%s(%d).%s" % \
# (base_name, i, file_ext)
# if os.path.exists(base_path + os.sep + possible_filename):
# most_recent_filename = possible_filename
# else:
# break
# return os.path.join(base_path, most_recent_filename)
|
kawamuray/ganeti | refs/heads/build | test/py/testutils.py | 4 | #
#
# Copyright (C) 2006, 2007, 2008 Google Inc.
#
# This program 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 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
# 02110-1301, USA.
"""Utilities for unit testing"""
import os
import sys
import stat
import tempfile
import unittest
import logging
from ganeti import utils
def GetSourceDir():
return os.environ.get("TOP_SRCDIR", ".")
def TestDataFilename(name):
"""Returns the filename of a given test data file.
@type name: str
@param name: the 'base' of the file name, as present in
the test/data directory
@rtype: str
@return: the full path to the filename, such that it can
be used in 'make distcheck' rules
"""
return "%s/test/data/%s" % (GetSourceDir(), name)
def ReadTestData(name):
"""Returns the content of a test data file.
This is just a very simple wrapper over utils.ReadFile with the
proper test file name.
"""
return utils.ReadFile(TestDataFilename(name))
def _SetupLogging(verbose):
"""Setupup logging infrastructure.
"""
fmt = logging.Formatter("%(asctime)s: %(threadName)s"
" %(levelname)s %(message)s")
if verbose:
handler = logging.StreamHandler()
else:
handler = logging.FileHandler(os.devnull, "a")
handler.setLevel(logging.NOTSET)
handler.setFormatter(fmt)
root_logger = logging.getLogger("")
root_logger.setLevel(logging.NOTSET)
root_logger.addHandler(handler)
class GanetiTestProgram(unittest.TestProgram):
def runTests(self):
"""Runs all tests.
"""
_SetupLogging("LOGTOSTDERR" in os.environ)
sys.stderr.write("Running %s\n" % self.progName)
sys.stderr.flush()
# Ensure assertions will be evaluated
if not __debug__:
raise Exception("Not running in debug mode, assertions would not be"
" evaluated")
# Check again, this time with a real assertion
try:
assert False
except AssertionError:
pass
else:
raise Exception("Assertion not evaluated")
return unittest.TestProgram.runTests(self)
class GanetiTestCase(unittest.TestCase):
"""Helper class for unittesting.
This class defines a few utility functions that help in building
unittests. Child classes must call the parent setup and cleanup.
"""
def setUp(self):
self._temp_files = []
def tearDown(self):
while self._temp_files:
try:
utils.RemoveFile(self._temp_files.pop())
except EnvironmentError:
pass
def assertFileContent(self, file_name, expected_content):
"""Checks that the content of a file is what we expect.
@type file_name: str
@param file_name: the file whose contents we should check
@type expected_content: str
@param expected_content: the content we expect
"""
actual_content = utils.ReadFile(file_name)
self.assertEqual(actual_content, expected_content)
def assertFileMode(self, file_name, expected_mode):
"""Checks that the mode of a file is what we expect.
@type file_name: str
@param file_name: the file whose contents we should check
@type expected_mode: int
@param expected_mode: the mode we expect
"""
st = os.stat(file_name)
actual_mode = stat.S_IMODE(st.st_mode)
self.assertEqual(actual_mode, expected_mode)
def assertFileUid(self, file_name, expected_uid):
"""Checks that the user id of a file is what we expect.
@type file_name: str
@param file_name: the file whose contents we should check
@type expected_uid: int
@param expected_uid: the user id we expect
"""
st = os.stat(file_name)
actual_uid = st.st_uid
self.assertEqual(actual_uid, expected_uid)
def assertFileGid(self, file_name, expected_gid):
"""Checks that the group id of a file is what we expect.
@type file_name: str
@param file_name: the file whose contents we should check
@type expected_gid: int
@param expected_gid: the group id we expect
"""
st = os.stat(file_name)
actual_gid = st.st_gid
self.assertEqual(actual_gid, expected_gid)
def assertEqualValues(self, first, second, msg=None):
"""Compares two values whether they're equal.
Tuples are automatically converted to lists before comparing.
"""
return self.assertEqual(UnifyValueType(first),
UnifyValueType(second),
msg=msg)
def _CreateTempFile(self):
"""Creates a temporary file and adds it to the internal cleanup list.
This method simplifies the creation and cleanup of temporary files
during tests.
"""
fh, fname = tempfile.mkstemp(prefix="ganeti-test", suffix=".tmp")
os.close(fh)
self._temp_files.append(fname)
return fname
def patch_object(*args, **kwargs):
"""Unified patch_object for various versions of Python Mock.
Different Python Mock versions provide incompatible versions of patching an
object. More recent versions use _patch_object, older ones used patch_object.
This function unifies the different variations.
"""
import mock
try:
# pylint: disable=W0212
return mock._patch_object(*args, **kwargs)
except AttributeError:
# pylint: disable=E1101
return mock.patch_object(*args, **kwargs)
def UnifyValueType(data):
"""Converts all tuples into lists.
This is useful for unittests where an external library doesn't keep types.
"""
if isinstance(data, (tuple, list)):
return [UnifyValueType(i) for i in data]
elif isinstance(data, dict):
return dict([(UnifyValueType(key), UnifyValueType(value))
for (key, value) in data.iteritems()])
return data
class CallCounter(object):
"""Utility class to count number of calls to a function/method.
"""
def __init__(self, fn):
"""Initializes this class.
@type fn: Callable
"""
self._fn = fn
self._count = 0
def __call__(self, *args, **kwargs):
"""Calls wrapped function with given parameters.
"""
self._count += 1
return self._fn(*args, **kwargs)
def Count(self):
"""Returns number of calls.
@rtype: number
"""
return self._count
|
MikkelSchubert/paleomix | refs/heads/master | tests/common_tests/formats_tests/bed_test.py | 1 | #!/usr/bin/python3
#
# Copyright (c) 2012 Mikkel Schubert <MikkelSch@gmail.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.
#
import copy
from pathlib import Path
from typing import Any, List
from unittest.mock import Mock
import pytest
from paleomix.common.formats.bed import (
BEDError,
BEDRecord,
merge_bed_records,
pad_bed_records,
read_bed_file,
sort_bed_by_bamfile,
)
########################################################################################
# BEDRecord constructor
def test_bedrecord__constructor_3():
record = BEDRecord("my_contig", 12, 345)
assert record.contig == "my_contig"
assert record.start == 12
assert record.end == 345
assert record.name is None
assert record.score is None
assert record.strand is None
assert str(record) == "my_contig\t12\t345"
assert repr(record) == "BEDRecord(contig='my_contig', start=12, end=345)"
def test_bedrecord__constructor_6():
record = BEDRecord("my_contig", 12, 345, "my_name", -3, "-")
assert record.contig == "my_contig"
assert record.start == 12
assert record.end == 345
assert record.name == "my_name"
assert record.score == -3
assert record.strand == "-"
assert str(record) == "my_contig\t12\t345\tmy_name\t-3\t-"
assert (
repr(record) == "BEDRecord(contig='my_contig', start=12, "
"end=345, name='my_name', score=-3, strand='-')"
)
@pytest.mark.parametrize("contig", (None, ""))
def test_bedrecord__constructor__empty_contig(contig: Any):
with pytest.raises(ValueError, match="contig is blank"):
BEDRecord(contig, 12, 345, "my_name", -3, "-")
@pytest.mark.parametrize("strand", ("", "?", "foo"))
def test_bedrecord__constructor__invalid_strand(strand: Any):
with pytest.raises(ValueError, match="invalid strand"):
BEDRecord("contig", 12, 345, "my_name", -3, strand)
########################################################################################
# BEDRecord.parse
@pytest.mark.parametrize("text", ("", "contig", "contig\t0"))
def test_bedrecord__parse__0_to_2_fields(text: str):
with pytest.raises(BEDError, match="not enough columns"):
BEDRecord.parse(text)
def test_bedrecord__parse__3_fields():
text = "my_contig\t12\t345"
record = BEDRecord.parse(text)
assert str(record) == text
assert repr(record) == "BEDRecord(contig='my_contig', start=12, end=345)"
_6_COLUMN_LINES = [
"my_contig\t12\t345\tmy_name\t-3\t-",
"my_contig\t12\t345\tmy_name\t-3\t-\tfoo\tbar",
]
@pytest.mark.parametrize("text", _6_COLUMN_LINES)
def test_bedrecord__parse__6_fields(text: str):
record = BEDRecord.parse(text)
assert record.contig == "my_contig"
assert record.start == 12
assert record.end == 345
assert record.name == "my_name"
assert record.score == -3
assert record.strand == "-"
assert str(record) == "\t".join(text.split("\t")[:6])
assert (
repr(record) == "BEDRecord(contig='my_contig', start=12, "
"end=345, name='my_name', score=-3, strand='-')"
)
def test_bedrecord__parse__invalid_values_1():
with pytest.raises(BEDError, match="contig is blank"):
BEDRecord.parse("\t123\t456")
def test_bedrecord__parse__invalid_values_2():
with pytest.raises(BEDError, match="Expected int in column 2"):
BEDRecord.parse("contig\tsix\t456")
def test_bedrecord__parse__invalid_values_3():
with pytest.raises(BEDError, match="Expected int in column 3"):
BEDRecord.parse("contig\t123\tlots")
def test_bedrecord__parse__invalid_values_5():
with pytest.raises(BEDError, match="Expected int in column 5"):
BEDRecord.parse("contig\t123\t456\tfoo\tbar")
def test_bedrecord__parse__invalid_values_6():
with pytest.raises(BEDError, match="strand must be \\+ or -"):
BEDRecord.parse("contig\t123\t456\tfoo\t0\t?")
def test_bedrecord__parse__invalid_values_1_():
tmpl = "contig\t0\t%s\t\t0\t-"
BEDRecord.parse(tmpl % (1,)) # check template
with pytest.raises(BEDError, match=""):
BEDRecord.parse(tmpl % ("not a number",))
def test_bedrecord__setters__unset_fields__after_end():
record = BEDRecord.parse("my_name\t17\t258")
record.strand = "-"
assert str(record) == "my_name\t17\t258\t\t0\t-"
record = BEDRecord.parse("my_name\t17\t258\tregion")
record.strand = "-"
assert str(record) == "my_name\t17\t258\tregion\t0\t-"
record = BEDRecord.parse("my_name\t17\t258\tregion\t33")
record.strand = "-"
assert str(record) == "my_name\t17\t258\tregion\t33\t-"
record = BEDRecord.parse("my_name\t17\t258\tregion\t33\t+")
record.strand = "-"
assert str(record) == "my_name\t17\t258\tregion\t33\t-"
###############################################################################
# BEDRecord comparisons
def test_sorting__compared_with_non_bed_record():
with pytest.raises(TypeError):
BEDRecord("chr2", 1, 20) < "foo"
def test_bedrecord__cmp():
record_1 = BEDRecord("my_contig", 12, 345, "my_name", -3, "-")
record_2 = BEDRecord("other", 565, 684, "name2", 0, "+")
for key in ("contig", "start", "end", "name", "score", "strand"):
record_tmp = copy.copy(record_1)
assert record_1 == record_tmp
setattr(record_tmp, key, getattr(record_2, key))
assert record_1 != record_tmp
setattr(record_tmp, key, getattr(record_1, key))
assert record_1 == record_tmp
###############################################################################
# pad_bed_records
def test_pad_bed_records__empty_sequences():
pad_bed_records([], 10)
pad_bed_records((), 10)
def test_pad_bed_records():
records = [
BEDRecord("chr1", 10, 90),
BEDRecord("chr2", 100, 200),
]
assert pad_bed_records(records, 20) == [
BEDRecord("chr1", 0, 110),
BEDRecord("chr2", 80, 220),
]
def test_pad_bed_records__with_max_lengths():
max_sizes = {"chr1": 100, "chr2": 200}
records = [
BEDRecord("chr1", 10, 90),
BEDRecord("chr2", 10, 90),
BEDRecord("chr2", 100, 190),
]
assert pad_bed_records(records, 20, max_sizes) == [
BEDRecord("chr1", 0, 100),
BEDRecord("chr2", 0, 110),
BEDRecord("chr2", 80, 200),
]
def test_pad_bed_records__negative_padding():
records = [
BEDRecord("chr1", 10, 90),
BEDRecord("chr2", 100, 200),
]
assert pad_bed_records(records, -15) == [
BEDRecord("chr1", 25, 75),
BEDRecord("chr2", 115, 185),
]
def test_pad_bed_records__negative_padding__near_empty_records():
assert pad_bed_records([BEDRecord("chr1", 10, 90)], -38) == [
BEDRecord("chr1", 48, 52)
]
assert pad_bed_records([BEDRecord("chr1", 10, 90)], -39) == [
BEDRecord("chr1", 49, 51)
]
assert pad_bed_records([BEDRecord("chr1", 10, 91)], -40) == [
BEDRecord("chr1", 50, 51)
]
def test_pad_bed_records__negative_padding__empty_records():
assert pad_bed_records([BEDRecord("chr1", 10, 90)], -40) == []
assert pad_bed_records([BEDRecord("chr1", 10, 90)], -41) == []
assert pad_bed_records([BEDRecord("chr1", 10, 91)], -41) == []
###############################################################################
# merge_bed_records
def test_merge_records__empty_sequences():
assert merge_bed_records(()) == []
assert merge_bed_records([]) == []
def test_merge_records__single_record():
assert merge_bed_records([BEDRecord("chr1", 1234, 5678)]) == [
BEDRecord("chr1", 1234, 5678)
]
def test_merge_records__minimal_fields_only():
assert merge_bed_records([BEDRecord("chr1", 1234, 5678, "foo", 1, "-")]) == [
BEDRecord("chr1", 1234, 5678)
]
def test_merge_records__overlapping_records_1():
assert merge_bed_records(
[BEDRecord("chr1", 1234, 5678), BEDRecord("chr1", 5677, 9012)]
) == [BEDRecord("chr1", 1234, 9012)]
def test_merge_records__overlapping_records_2():
assert merge_bed_records(
[BEDRecord("chr1", 1234, 5678), BEDRecord("chr1", 5678, 9012)]
) == [BEDRecord("chr1", 1234, 9012)]
def test_merge_records__non_overlapping_records_1():
assert merge_bed_records(
[BEDRecord("chr1", 1234, 5678), BEDRecord("chr1", 5679, 9012)]
) == [BEDRecord("chr1", 1234, 5678), BEDRecord("chr1", 5679, 9012)]
def test_merge_records__non_overlapping_records_2():
assert merge_bed_records(
[BEDRecord("chr1", 1234, 5678), BEDRecord("chr1", 5680, 9012)]
) == [BEDRecord("chr1", 1234, 5678), BEDRecord("chr1", 5680, 9012)]
def test_merge_records__complex_example():
assert merge_bed_records(
[
BEDRecord("chr1", 1234, 5678),
BEDRecord("chr1", 5678, 9012),
BEDRecord("chr2", 1, 20),
BEDRecord("chr2", 100, 200),
BEDRecord("chr2", 150, 250),
]
) == [
BEDRecord("chr1", 1234, 9012),
BEDRecord("chr2", 1, 20),
BEDRecord("chr2", 100, 250),
]
def test_merge_records__complex_example__unsorted():
assert merge_bed_records(
[
BEDRecord("chr2", 100, 200),
BEDRecord("chr1", 1234, 5678),
BEDRecord("chr2", 150, 250),
BEDRecord("chr2", 1, 20),
BEDRecord("chr1", 5678, 9012),
]
) == [
BEDRecord("chr1", 1234, 9012),
BEDRecord("chr2", 1, 20),
BEDRecord("chr2", 100, 250),
]
###############################################################################
# read_bed_file
_SIMPLE_BED = """chr1\t7\t9
chr3\t123\t45123\tFoo\t0
chr9\t777\t999
"""
_SIMPLE_BED_WITH_SKIPPED_LINES = """
chr1\t7\t9
# chr3\t123\t45123\tFoo\t0
chr9\t777\t999
"""
def _write_bed(tmp_path: Path, data: str) -> str:
filename = tmp_path / "tmp.bed"
with filename.open("wt") as handle:
handle.write(data)
return str(filename)
def test_read_bed_file__empty_file(tmp_path: Path):
filename = _write_bed(tmp_path, "")
assert list(read_bed_file(filename)) == []
def test_read_bed_file__simple_records(tmp_path: Path):
filename = _write_bed(tmp_path, _SIMPLE_BED)
assert list(read_bed_file(filename)) == [
BEDRecord("chr1", 7, 9),
BEDRecord("chr3", 123, 45123, "Foo", 0),
BEDRecord("chr9", 777, 999),
]
def test_read_bed_file__simple_records_and_skipped_lines(tmp_path: Path):
filename = _write_bed(tmp_path, _SIMPLE_BED_WITH_SKIPPED_LINES)
assert list(read_bed_file(filename)) == [
BEDRecord("chr1", 7, 9),
BEDRecord("chr9", 777, 999),
]
def test_read_bed_file__parse_error(tmp_path: Path):
filename = _write_bed(tmp_path, "chr1\t0\tabc")
with pytest.raises(BEDError, match=":1: Expected int in column 3 but found 'abc'"):
next(read_bed_file(filename))
_INVALID_START_END = ["chr1\t0\t0", "chr1\t-1\t100", "chr1\t-100\t-1", "chr1\t100\t10"]
_POSITIONS_INSIDE_CONTIG = ["chr1\t0\t50", "chr1\t50\t99", "chr1\t99\t100"]
_POSITIONS_OUTSIDE_CONTIG = ["chr1\t0\t101", "chr1\t99\t102", "chr1\t200\t300"]
@pytest.mark.parametrize("text", _INVALID_START_END)
def test_read_bed_file__invalid_coordinates(tmp_path: Path, text: str):
filename = _write_bed(tmp_path, text)
with pytest.raises(BEDError, match="invalid start/end coordinates"):
list(read_bed_file(filename))
@pytest.mark.parametrize("text", _POSITIONS_INSIDE_CONTIG)
def test_read_bed_file__inside_contig(tmp_path: Path, text: str):
filename = _write_bed(tmp_path, text)
list(read_bed_file(filename, {"chr1": 100}))
@pytest.mark.parametrize("text", _POSITIONS_OUTSIDE_CONTIG)
def test_read_bed_file__outside_contig(tmp_path: Path, text: str):
filename = _write_bed(tmp_path, text)
with pytest.raises(BEDError, match="coordinates outside contig"):
list(read_bed_file(filename, {"chr1": 100}))
def test_read_bed_file__unknown_contigs(tmp_path: Path):
filename = _write_bed(tmp_path, "chr2\t0\t100")
with pytest.raises(BEDError, match="unknown contig"):
list(read_bed_file(filename, {"chr1": 100}))
########################################################################################
# sort_bed_by_bamfile
_UNSORTED_RECORDS = [
BEDRecord("chr3", 100, 200),
BEDRecord("chr2", 500, 10010),
BEDRecord("chr3", 1, 10),
BEDRecord("chr1", 0, 1234),
]
def test_sort_bed_by_bamfile__empty_list():
handle: Any = Mock()
handle.references = []
regions: List[BEDRecord] = []
sort_bed_by_bamfile(handle, regions)
assert regions == []
def test_sort_bed_by_bamfile__known_contigs_1():
handle: Any = Mock()
handle.references = ["chr1", "chr2", "chr3"]
regions = list(_UNSORTED_RECORDS)
sort_bed_by_bamfile(handle, regions)
assert regions == [
BEDRecord("chr1", 0, 1234),
BEDRecord("chr2", 500, 10010),
BEDRecord("chr3", 1, 10),
BEDRecord("chr3", 100, 200),
]
def test_sort_bed_by_bamfile__known_contigs_2():
handle: Any = Mock()
handle.references = ["chr3", "chr2", "chr1"]
regions = list(_UNSORTED_RECORDS)
sort_bed_by_bamfile(handle, regions)
assert regions == [
BEDRecord("chr3", 1, 10),
BEDRecord("chr3", 100, 200),
BEDRecord("chr2", 500, 10010),
BEDRecord("chr1", 0, 1234),
]
def test_sort_bed_by_bamfile__unknown_contigs_1():
handle: Any = Mock()
handle.references = ["chr1", "chr2"]
regions = list(_UNSORTED_RECORDS)
sort_bed_by_bamfile(handle, regions)
assert regions == [
BEDRecord("chr1", 0, 1234),
BEDRecord("chr2", 500, 10010),
BEDRecord("chr3", 1, 10),
BEDRecord("chr3", 100, 200),
]
def test_sort_bed_by_bamfile__unknown_contigs_2():
handle: Any = Mock()
handle.references = []
regions = list(_UNSORTED_RECORDS)
sort_bed_by_bamfile(handle, regions)
assert regions == [
BEDRecord("chr1", 0, 1234),
BEDRecord("chr2", 500, 10010),
BEDRecord("chr3", 1, 10),
BEDRecord("chr3", 100, 200),
]
|
matthappens/taskqueue | refs/heads/master | taskqueue/venv_tq/lib/python2.7/site-packages/werkzeug/testsuite/__init__.py | 99 | # -*- coding: utf-8 -*-
"""
werkzeug.testsuite
~~~~~~~~~~~~~~~~~~
Contains all test Werkzeug tests.
:copyright: (c) 2013 by Armin Ronacher.
:license: BSD, see LICENSE for more details.
"""
from __future__ import with_statement
import re
import sys
import unittest
import shutil
import tempfile
import atexit
from werkzeug.utils import find_modules
from werkzeug._compat import text_type, integer_types, reraise
def get_temporary_directory():
directory = tempfile.mkdtemp()
@atexit.register
def remove_directory():
try:
shutil.rmtree(directory)
except EnvironmentError:
pass
return directory
def iter_suites(package):
"""Yields all testsuites."""
for module in find_modules(package, include_packages=True):
mod = __import__(module, fromlist=['*'])
if hasattr(mod, 'suite'):
yield mod.suite()
def find_all_tests(suite):
"""Yields all the tests and their names from a given suite."""
suites = [suite]
while suites:
s = suites.pop()
try:
suites.extend(s)
except TypeError:
yield s, '%s.%s.%s' % (
s.__class__.__module__,
s.__class__.__name__,
s._testMethodName
)
class WerkzeugTestCase(unittest.TestCase):
"""Baseclass for all the tests that Werkzeug uses. Use these
methods for testing instead of the camelcased ones in the
baseclass for consistency.
"""
def setup(self):
pass
def teardown(self):
pass
def setUp(self):
self.setup()
def tearDown(self):
unittest.TestCase.tearDown(self)
self.teardown()
def assert_line_equal(self, x, y):
assert x == y, "lines not equal\n a = %r\n b = %r" % (x, y)
def assert_equal(self, x, y, msg=None):
return self.assertEqual(x, y, msg)
def assert_not_equal(self, x, y):
return self.assertNotEqual(x, y)
def assert_raises(self, exc_type, callable=None, *args, **kwargs):
catcher = _ExceptionCatcher(self, exc_type)
if callable is None:
return catcher
with catcher:
callable(*args, **kwargs)
if sys.version_info[:2] == (2, 6):
def assertIsNone(self, x):
assert x is None, "%r is not None" % (x,)
def assertIsNotNone(self, x):
assert x is not None, "%r is None" % (x, )
def assertIn(self, x, y):
assert x in y, "%r not in %r" % (x, y)
def assertNotIn(self, x, y):
assert x not in y, "%r in %r" % (x, y)
def assertIsInstance(self, x, y):
assert isinstance(x, y), "not isinstance(%r, %r)" % (x, y)
def assertIs(self, x, y):
assert x is y, "%r is not %r" % (x, y)
def assertIsNot(self, x, y):
assert x is not y, "%r is %r" % (x, y)
def assertSequenceEqual(self, x, y):
self.assertEqual(x, y)
def assertRaisesRegex(self, exc_type, regex, *args, **kwargs):
catcher = _ExceptionCatcher(self, exc_type)
if not args:
return catcher
elif callable(args[0]):
with catcher:
args[0](*args[1:], **kwargs)
if args[0] is not None:
assert re.search(args[0], catcher.exc_value[0])
else:
raise NotImplementedError()
elif sys.version_info[0] == 2:
def assertRaisesRegex(self, *args, **kwargs):
return self.assertRaisesRegexp(*args, **kwargs)
def assert_is_none(self, x):
self.assertIsNone(x)
def assert_is_not_none(self, x):
self.assertIsNotNone(x)
def assert_in(self, x, y):
self.assertIn(x, y)
def assert_is_instance(self, x, y):
self.assertIsInstance(x, y)
def assert_not_in(self, x, y):
self.assertNotIn(x, y)
def assert_is(self, x, y):
self.assertIs(x, y)
def assert_is_not(self, x, y):
self.assertIsNot(x, y)
def assert_true(self, x):
self.assertTrue(x)
def assert_false(self, x):
self.assertFalse(x)
def assert_raises_regex(self, *args, **kwargs):
return self.assertRaisesRegex(*args, **kwargs)
def assert_sequence_equal(self, x, y):
self.assertSequenceEqual(x, y)
def assert_strict_equal(self, x, y):
'''Stricter version of assert_equal that doesn't do implicit conversion
between unicode and strings'''
self.assert_equal(x, y)
assert issubclass(type(x), type(y)) or issubclass(type(y), type(x)), \
'%s != %s' % (type(x), type(y))
if isinstance(x, (bytes, text_type, integer_types)) or x is None:
return
elif isinstance(x, dict) or isinstance(y, dict):
x = sorted(x.items())
y = sorted(y.items())
elif isinstance(x, set) or isinstance(y, set):
x = sorted(x)
y = sorted(y)
rx, ry = repr(x), repr(y)
if rx != ry:
rx = rx[:200] + (rx[200:] and '...')
ry = ry[:200] + (ry[200:] and '...')
raise AssertionError(rx, ry)
assert repr(x) == repr(y), repr((x, y))[:200]
class _ExceptionCatcher(object):
def __init__(self, test_case, exc_type):
self.test_case = test_case
self.exc_type = exc_type
self.exc_value = None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, tb):
exception_name = self.exc_type.__name__
if exc_type is None:
self.test_case.fail('Expected exception of type %r' %
exception_name)
elif not issubclass(exc_type, self.exc_type):
reraise(exc_type, exc_value, tb)
self.exc_value = exc_value
return True
class BetterLoader(unittest.TestLoader):
"""A nicer loader that solves two problems. First of all we are setting
up tests from different sources and we're doing this programmatically
which breaks the default loading logic so this is required anyways.
Secondly this loader has a nicer interpolation for test names than the
default one so you can just do ``run-tests.py ViewTestCase`` and it
will work.
"""
def getRootSuite(self):
return suite()
def loadTestsFromName(self, name, module=None):
root = self.getRootSuite()
if name == 'suite':
return root
all_tests = []
for testcase, testname in find_all_tests(root):
if testname == name or \
testname.endswith('.' + name) or \
('.' + name + '.') in testname or \
testname.startswith(name + '.'):
all_tests.append(testcase)
if not all_tests:
raise LookupError('could not find test case for "%s"' % name)
if len(all_tests) == 1:
return all_tests[0]
rv = unittest.TestSuite()
for test in all_tests:
rv.addTest(test)
return rv
def suite():
"""A testsuite that has all the Flask tests. You can use this
function to integrate the Flask tests into your own testsuite
in case you want to test that monkeypatches to Flask do not
break it.
"""
suite = unittest.TestSuite()
for other_suite in iter_suites(__name__):
suite.addTest(other_suite)
return suite
def main():
"""Runs the testsuite as command line application."""
try:
unittest.main(testLoader=BetterLoader(), defaultTest='suite')
except Exception:
import sys
import traceback
traceback.print_exc()
sys.exit(1)
|
ahmadRagheb/goldenHR | refs/heads/master | erpnext/hr/doctype/salary_detail/salary_detail.py | 56 | # -*- coding: utf-8 -*-
# Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
from frappe.model.document import Document
class SalaryDetail(Document):
pass
|
RUB-SysSec/kAFL | refs/heads/master | kAFL-Fuzzer/fuzzer/protocol.py | 1 | """
Copyright (C) 2017 Sergej Schumilo
This file is part of kAFL Fuzzer (kAFL).
QEMU-PT 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 2 of the License, or
(at your option) any later version.
QEMU-PT 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 QEMU-PT. If not, see <http://www.gnu.org/licenses/>.
"""
class FuzzingResult:
def __init__(self, pos, crash, timeout, kasan, affected_bytes, slave_id, performance, reloaded=False, new_bits=True, qid=0):
self.pos = pos
self.crash = crash
self.timeout = timeout
self.kasan = kasan
self.affected_bytes = affected_bytes
self.slave_id = slave_id
self.reloaded = reloaded
self.performance = performance
self.new_bits = new_bits
self.qid = qid
KAFL_TAG_REQ = 0
KAFL_TAG_JOB = 1
KAFL_TAG_OUTPUT = 2
KAFL_TAG_START = 3
KAFL_TAG_RESULT = 4
KAFL_TAG_MAP_INFO = 5
KAFL_TAG_NXT_FIN = 6
KAFL_TAG_NXT_UNFIN = 7
KAFL_TAG_UNTOUCHED_NODES = 8
KAFL_TAG_REQ_BITMAP = 9
KAFL_TAG_REQ_EFFECTOR = 10
KAFL_TAG_GET_EFFECTOR = 11
KAFL_INIT_BITMAP = 12
KAFL_TAG_REQ_SAMPLING = 13
KAFL_TAG_REQ_BENCHMARK = 14
KAFL_TAG_ABORT_REQ = 15
|
deshipu/micropython | refs/heads/master | tests/basics/set_difference.py | 55 | l = [1, 2, 3, 4]
s = set(l)
outs = [s.difference(),
s.difference({1}),
s.difference({1}, [1, 2]),
s.difference({1}, {1, 2}, {2, 3})]
for out in outs:
print(sorted(out))
s = set(l)
print(s.difference_update())
print(sorted(s))
print(s.difference_update({1}))
print(sorted(s))
print(s.difference_update({1}, [2]))
print(sorted(s))
s.difference_update(s)
print(s)
|
kevinmel2000/brython | refs/heads/master | www/src/Lib/test/unittests/test_xml_dom_minicompat.py | 100 | # Tests for xml.dom.minicompat
import pickle
import unittest
import xml.dom
from xml.dom.minicompat import *
class EmptyNodeListTestCase(unittest.TestCase):
"""Tests for the EmptyNodeList class."""
def test_emptynodelist_item(self):
# Test item access on an EmptyNodeList.
node_list = EmptyNodeList()
self.assertIsNone(node_list.item(0))
self.assertIsNone(node_list.item(-1)) # invalid item
with self.assertRaises(IndexError):
node_list[0]
with self.assertRaises(IndexError):
node_list[-1]
def test_emptynodelist_length(self):
node_list = EmptyNodeList()
# Reading
self.assertEqual(node_list.length, 0)
# Writing
with self.assertRaises(xml.dom.NoModificationAllowedErr):
node_list.length = 111
def test_emptynodelist___add__(self):
node_list = EmptyNodeList() + NodeList()
self.assertEqual(node_list, NodeList())
def test_emptynodelist___radd__(self):
node_list = [1,2] + EmptyNodeList()
self.assertEqual(node_list, [1,2])
class NodeListTestCase(unittest.TestCase):
"""Tests for the NodeList class."""
def test_nodelist_item(self):
# Test items access on a NodeList.
# First, use an empty NodeList.
node_list = NodeList()
self.assertIsNone(node_list.item(0))
self.assertIsNone(node_list.item(-1))
with self.assertRaises(IndexError):
node_list[0]
with self.assertRaises(IndexError):
node_list[-1]
# Now, use a NodeList with items.
node_list.append(111)
node_list.append(999)
self.assertEqual(node_list.item(0), 111)
self.assertIsNone(node_list.item(-1)) # invalid item
self.assertEqual(node_list[0], 111)
self.assertEqual(node_list[-1], 999)
def test_nodelist_length(self):
node_list = NodeList([1, 2])
# Reading
self.assertEqual(node_list.length, 2)
# Writing
with self.assertRaises(xml.dom.NoModificationAllowedErr):
node_list.length = 111
def test_nodelist___add__(self):
node_list = NodeList([3, 4]) + [1, 2]
self.assertEqual(node_list, NodeList([3, 4, 1, 2]))
def test_nodelist___radd__(self):
node_list = [1, 2] + NodeList([3, 4])
self.assertEqual(node_list, NodeList([1, 2, 3, 4]))
def test_nodelist_pickle_roundtrip(self):
# Test pickling and unpickling of a NodeList.
# Empty NodeList.
node_list = NodeList()
pickled = pickle.dumps(node_list)
unpickled = pickle.loads(pickled)
self.assertEqual(unpickled, node_list)
# Non-empty NodeList.
node_list.append(1)
node_list.append(2)
pickled = pickle.dumps(node_list)
unpickled = pickle.loads(pickled)
self.assertEqual(unpickled, node_list)
if __name__ == '__main__':
unittest.main()
|
sbidoul/odoo | refs/heads/8.0 | addons/l10n_fr/wizard/fr_report_bilan.py | 374 | # -*- coding: utf-8 -*-
##############################################################################
#
# Copyright (c) 2008 JAILLET Simon - CrysaLEAD - www.crysalead.fr
#
# WARNING: This program as such is intended to be used by professional
# programmers who take the whole responsability of assessing all potential
# consequences resulting from its eventual inadequacies and bugs
# End users who are looking for a ready-to-use solution with commercial
# garantees and support are strongly adviced to contract a Free Software
# Service Company
#
# This program 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 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#
##############################################################################
from openerp.osv import fields, osv
class account_bilan_report(osv.osv_memory):
_name = 'account.bilan.report'
_description = 'Account Bilan Report'
def _get_default_fiscalyear(self, cr, uid, context=None):
fiscalyear_id = self.pool.get('account.fiscalyear').find(cr, uid)
return fiscalyear_id
_columns = {
'fiscalyear_id': fields.many2one('account.fiscalyear', 'Fiscal Year', required=True),
}
_defaults = {
'fiscalyear_id': _get_default_fiscalyear
}
def print_bilan_report(self, cr, uid, ids, context=None):
active_ids = context.get('active_ids', [])
data = {}
data['form'] = {}
data['ids'] = active_ids
data['form']['fiscalyear_id'] = self.browse(cr, uid, ids)[0].fiscalyear_id.id
return self.pool['report'].get_action(
cr, uid, ids, 'l10n_fr.report_l10nfrbilan', data=data, context=context
)
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
|
AmineChikhaoui/nixops | refs/heads/master | nixops/resources/azure_network_security_group.py | 6 | # -*- coding: utf-8 -*-
# Automatic provisioning of Azure network security groups.
import os
import azure
from nixops.util import attr_property
from nixops.azure_common import ResourceDefinition, ResourceState, ResId, normalize_location
from azure.mgmt.network import *
class AzureNetworkSecurityGroupDefinition(ResourceDefinition):
"""Definition of an Azure Network Security Group"""
@classmethod
def get_type(cls):
return "azure-network-security-group"
@classmethod
def get_resource_type(cls):
return "azureSecurityGroups"
def __init__(self, xml):
ResourceDefinition.__init__(self, xml)
self.nsg_name = self.get_option_value(xml, 'name', str)
self.copy_option(xml, 'resourceGroup', 'resource')
self.copy_location(xml)
self.copy_tags(xml)
self.security_rules = {
_r.get("name"): self._parse_security_rule(_r)
for _r in xml.findall("attrs/attr[@name='securityRules']/attrs/attr")
}
def _parse_security_rule(self, xml):
return {
'description': self.get_option_value(xml, 'description', str),
'protocol': self.get_option_value(xml, 'protocol', str),
'source_port_range': self.get_option_value(xml, 'sourcePortRange', str),
'destination_port_range': self.get_option_value(xml, 'destinationPortRange', str),
'source_address_prefix': self.get_option_value(xml, 'sourceAddressPrefix', str),
'destination_address_prefix': self.get_option_value(xml, 'destinationAddressPrefix', str),
'access': self.get_option_value(xml, 'access', str),
'priority': self.get_option_value(xml, 'priority', int),
'direction': self.get_option_value(xml, 'direction', str),
}
def show_type(self):
return "{0} [{1}]".format(self.get_type(), self.location)
class AzureNetworkSecurityGroupState(ResourceState):
"""State of an Azure Network Security Group"""
nsg_name = attr_property("azure.name", None)
resource_group = attr_property("azure.resourceGroup", None)
location = attr_property("azure.location", None)
tags = attr_property("azure.tags", {}, 'json')
security_rules = attr_property("azure.securityRules", {}, 'json')
@classmethod
def get_type(cls):
return "azure-network-security-group"
def show_type(self):
s = super(AzureNetworkSecurityGroupState, self).show_type()
if self.state == self.UP: s = "{0} [{1}]".format(s, self.location)
return s
@property
def resource_id(self):
return self.nsg_name
@property
def full_name(self):
return "Azure network security group '{0}'".format(self.resource_id)
def get_resource(self):
try:
return self.nrpc().network_security_groups.get(
self.resource_group, self.resource_id).network_security_group
except azure.common.AzureMissingResourceHttpError:
return None
def destroy_resource(self):
self.nrpc().network_security_groups.delete(
self.resource_group, self.resource_id)
defn_properties = [ 'location', 'tags', 'security_rules' ]
def _create_or_update(self, defn):
self.nrpc().network_security_groups.create_or_update(
defn.resource_group, defn.nsg_name,
NetworkSecurityGroup(
location = defn.location,
security_rules = [
SecurityRule(
name = _name,
description = _r['description'],
protocol = _r['protocol'],
source_port_range = _r['source_port_range'],
destination_port_range = _r['destination_port_range'],
source_address_prefix = _r['source_address_prefix'],
destination_address_prefix = _r['destination_address_prefix'],
access = _r['access'],
priority = _r['priority'],
direction = _r['direction'],
) for _name, _r in defn.security_rules.iteritems()
],
tags = defn.tags))
self.state = self.UP
self.copy_properties(defn)
def handle_changed_security_rules(self, rules):
def update_rules(k, v):
x = self.security_rules
if v == None:
x.pop(k, None)
else:
x[k] = v
self.security_rules = x
for _rule in rules:
_s_name = next((_n for _n, _x in self.security_rules.iteritems() if _n == _rule.name), None)
if _s_name is None:
self.warn("found unexpected security rule {0}".format(_rule.name))
update_rules(_rule.name, {"dummy": True})
for _name, _s_rule in self.security_rules.iteritems():
if _s_rule.get("dummy", False): continue
rule_res_name = "security rule {0}".format(_name)
rule = next((_r for _r in rules if _r.name == _name), None)
if rule is None:
self.warn("{0} has been deleted behind our back".format(rule_res_name))
update_rules(_name, None)
continue
self.handle_changed_dict(_s_rule, 'description',
rule.description,
resource_name = rule_res_name)
self.handle_changed_dict(_s_rule, 'protocol',
rule.protocol,
resource_name = rule_res_name)
self.handle_changed_dict(_s_rule, 'source_port_range',
rule.source_port_range,
resource_name = rule_res_name)
self.handle_changed_dict(_s_rule, 'destination_port_range',
rule.destination_port_range,
resource_name = rule_res_name)
self.handle_changed_dict(_s_rule, 'source_address_prefix',
rule.source_address_prefix,
resource_name = rule_res_name)
self.handle_changed_dict(_s_rule, 'destination_address_prefix',
rule.destination_address_prefix,
resource_name = rule_res_name)
self.handle_changed_dict(_s_rule, 'access',
rule.access,
resource_name = rule_res_name)
self.handle_changed_dict(_s_rule, 'priority',
rule.priority,
resource_name = rule_res_name)
self.handle_changed_dict(_s_rule, 'direction',
rule.direction,
resource_name = rule_res_name)
update_rules(_name, _s_rule)
def create(self, defn, check, allow_reboot, allow_recreate):
self.no_subscription_id_change(defn)
self.no_location_change(defn)
self.no_property_change(defn, 'resource_group')
self.copy_mgmt_credentials(defn)
self.nsg_name = defn.nsg_name
self.resource_group = defn.resource_group
if check:
nsg = self.get_settled_resource()
if not nsg:
self.warn_missing_resource()
elif self.state == self.UP:
self.warn_if_failed(nsg)
self.handle_changed_property('location', normalize_location(nsg.location),
can_fix = False)
self.handle_changed_property('tags', nsg.tags)
self.handle_changed_security_rules(nsg.security_rules)
else:
self.warn_not_supposed_to_exist()
self.confirm_destroy()
if self.state != self.UP:
if self.get_settled_resource():
raise Exception("tried creating a network security group that already exists; "
"please run 'deploy --check' to fix this")
self.log("creating {0} in {1}...".format(self.full_name, defn.location))
self._create_or_update(defn)
if self.properties_changed(defn):
self.log("updating properties of {0}...".format(self.full_name))
self.get_settled_resource_assert_exists()
self._create_or_update(defn)
def create_after(self, resources, defn):
from nixops.resources.azure_resource_group import AzureResourceGroupState
return {r for r in resources
if isinstance(r, AzureResourceGroupState) }
|
youdonghai/intellij-community | refs/heads/master | python/testData/quickFixes/AddFieldQuickFixTest/addFieldFromMethod_after.py | 80 | class A:
def __init__(self):
self.y = None
self.x = 1
def foo(self):
a = self.y
|
servo/servo | refs/heads/master | tests/wpt/web-platform-tests/tools/third_party/h2/examples/tornado/tornado-server.py | 39 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
tornado-server.py
~~~~~~~~~~~~~~~~~
A fully-functional HTTP/2 server written for Tornado.
"""
import collections
import json
import ssl
import tornado.gen
import tornado.ioloop
import tornado.iostream
import tornado.tcpserver
from h2.config import H2Configuration
from h2.connection import H2Connection
from h2.events import RequestReceived, DataReceived
def create_ssl_context(certfile, keyfile):
ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
ssl_context.options |= (
ssl.OP_NO_TLSv1 | ssl.OP_NO_TLSv1_1 | ssl.OP_NO_COMPRESSION
)
ssl_context.set_ciphers("ECDHE+AESGCM")
ssl_context.load_cert_chain(certfile=certfile, keyfile=keyfile)
ssl_context.set_alpn_protocols(["h2"])
return ssl_context
class H2Server(tornado.tcpserver.TCPServer):
@tornado.gen.coroutine
def handle_stream(self, stream, address):
handler = EchoHeadersHandler(stream)
yield handler.handle()
class EchoHeadersHandler(object):
def __init__(self, stream):
self.stream = stream
config = H2Configuration(client_side=False)
self.conn = H2Connection(config=config)
@tornado.gen.coroutine
def handle(self):
self.conn.initiate_connection()
yield self.stream.write(self.conn.data_to_send())
while True:
try:
data = yield self.stream.read_bytes(65535, partial=True)
if not data:
break
events = self.conn.receive_data(data)
for event in events:
if isinstance(event, RequestReceived):
self.request_received(event.headers, event.stream_id)
elif isinstance(event, DataReceived):
self.conn.reset_stream(event.stream_id)
yield self.stream.write(self.conn.data_to_send())
except tornado.iostream.StreamClosedError:
break
def request_received(self, headers, stream_id):
headers = collections.OrderedDict(headers)
data = json.dumps({'headers': headers}, indent=4).encode('utf-8')
response_headers = (
(':status', '200'),
('content-type', 'application/json'),
('content-length', str(len(data))),
('server', 'tornado-h2'),
)
self.conn.send_headers(stream_id, response_headers)
self.conn.send_data(stream_id, data, end_stream=True)
if __name__ == '__main__':
ssl_context = create_ssl_context('server.crt', 'server.key')
server = H2Server(ssl_options=ssl_context)
server.listen(8888)
io_loop = tornado.ioloop.IOLoop.current()
io_loop.start()
|
beni55/edx-platform | refs/heads/master | common/test/acceptance/pages/studio/textbooks.py | 103 | """
Course Textbooks page.
"""
from .course_page import CoursePage
class TextbooksPage(CoursePage):
"""
Course Textbooks page.
"""
url_path = "textbooks"
def is_browser_on_page(self):
return self.q(css='body.view-textbooks').present
|
petemoore/build-funsize | refs/heads/master | configs/gunicorn-dev-conf.py | 1 | import multiprocessing
bind = ":5000"
workers = multiprocessing.cpu_count() * 2
accesslog = "/var/log/funsize/frontend_access_log.log"
user = "daemon"
reload = True
|
Antiun/partner-contact | refs/heads/8.0 | partner_contact_nationality/__init__.py | 63 | # -*- coding: utf-8 -*-
# Odoo, Open Source Management Solution
# Copyright (C) 2014-2015 Grupo ESOC <www.grupoesoc.es>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# 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 Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from . import models
|
devs1991/test_edx_docmode | refs/heads/master | venv/lib/python2.7/site-packages/django/conf/locale/sr/formats.py | 394 | # -*- encoding: utf-8 -*-
# This file is distributed under the same license as the Django package.
#
from __future__ import unicode_literals
# The *_FORMAT strings use the Django date format syntax,
# see http://docs.djangoproject.com/en/dev/ref/templates/builtins/#date
DATE_FORMAT = 'j. F Y.'
TIME_FORMAT = 'H:i'
DATETIME_FORMAT = 'j. F Y. H:i'
YEAR_MONTH_FORMAT = 'F Y.'
MONTH_DAY_FORMAT = 'j. F'
SHORT_DATE_FORMAT = 'j.m.Y.'
SHORT_DATETIME_FORMAT = 'j.m.Y. H:i'
FIRST_DAY_OF_WEEK = 1
# The *_INPUT_FORMATS strings use the Python strftime format syntax,
# see http://docs.python.org/library/datetime.html#strftime-strptime-behavior
DATE_INPUT_FORMATS = (
'%d.%m.%Y.', '%d.%m.%y.', # '25.10.2006.', '25.10.06.'
'%d. %m. %Y.', '%d. %m. %y.', # '25. 10. 2006.', '25. 10. 06.'
# '%d. %b %y.', '%d. %B %y.', # '25. Oct 06.', '25. October 06.'
# '%d. %b \'%y.', '%d. %B \'%y.', # '25. Oct '06.', '25. October '06.'
# '%d. %b %Y.', '%d. %B %Y.', # '25. Oct 2006.', '25. October 2006.'
)
DATETIME_INPUT_FORMATS = (
'%d.%m.%Y. %H:%M:%S', # '25.10.2006. 14:30:59'
'%d.%m.%Y. %H:%M:%S.%f', # '25.10.2006. 14:30:59.000200'
'%d.%m.%Y. %H:%M', # '25.10.2006. 14:30'
'%d.%m.%Y.', # '25.10.2006.'
'%d.%m.%y. %H:%M:%S', # '25.10.06. 14:30:59'
'%d.%m.%y. %H:%M:%S.%f', # '25.10.06. 14:30:59.000200'
'%d.%m.%y. %H:%M', # '25.10.06. 14:30'
'%d.%m.%y.', # '25.10.06.'
'%d. %m. %Y. %H:%M:%S', # '25. 10. 2006. 14:30:59'
'%d. %m. %Y. %H:%M:%S.%f', # '25. 10. 2006. 14:30:59.000200'
'%d. %m. %Y. %H:%M', # '25. 10. 2006. 14:30'
'%d. %m. %Y.', # '25. 10. 2006.'
'%d. %m. %y. %H:%M:%S', # '25. 10. 06. 14:30:59'
'%d. %m. %y. %H:%M:%S.%f', # '25. 10. 06. 14:30:59.000200'
'%d. %m. %y. %H:%M', # '25. 10. 06. 14:30'
'%d. %m. %y.', # '25. 10. 06.'
)
DECIMAL_SEPARATOR = ','
THOUSAND_SEPARATOR = '.'
NUMBER_GROUPING = 3
|
brendandahl/servo | refs/heads/master | tests/wpt/web-platform-tests/websockets/handlers/echo-cookie_wsh.py | 265 | #!/usr/bin/python
from mod_pywebsocket import msgutil
def web_socket_do_extra_handshake(request):
request.ws_cookie = request.headers_in.get('Cookie')
def web_socket_transfer_data(request):
if request.ws_cookie is not None:
msgutil.send_message(request, request.ws_cookie)
else:
msgutil.send_message(request, '(none)')
|
gwpy/gwpy.github.io | refs/heads/master | docs/2.0.1/examples/spectrogram/ratio-2.py | 32 | specgram = data.spectrogram(2, fftlength=1, overlap=.5) ** (1/2.) |
DataDog/integrations-core | refs/heads/master | envoy/datadog_checks/envoy/parser.py | 1 | import re
from math import isnan
from typing import Any, Dict, List, Tuple
from six.moves import range, zip
from .errors import UnknownMetric, UnknownTags
from .metrics import METRIC_PREFIX, METRIC_TREE, METRICS
HISTOGRAM = re.compile(r'([P0-9.]+)\(([^,]+)')
PERCENTILE_SUFFIX = {
'P0': '.0percentile',
'P25': '.25percentile',
'P50': '.50percentile',
'P75': '.75percentile',
'P90': '.90percentile',
'P95': '.95percentile',
'P99': '.99percentile',
'P99.9': '.99_9percentile',
'P100': '.100percentile',
}
def _parse_metric(metric, metric_mapping, skip_part=None):
metric_parts = []
tag_names = []
tag_values = []
tag_value_builder = []
unknown_tags = []
tags_to_build = 0
minimum_tag_length = 0
# From the split metric name, any part that is not in the mapping it will become part of the tag value
for metric_part in metric.split('.'):
if metric_part in metric_mapping and metric_part != skip_part and tags_to_build >= minimum_tag_length:
# Rebuild any built up tags whenever we encounter a known metric part.
if tag_value_builder:
# Edge case where we hit a known metric part after a sequence of all unknown parts
if '|_tags_|' not in metric_mapping:
raise UnknownMetric
tags = next(
(mapped_tags for mapped_tags in metric_mapping['|_tags_|'] if tags_to_build >= len(mapped_tags)),
tuple(),
)
constructed_tag_values = construct_tag_values(tag_value_builder, len(tags))
# Once the builder has been used, clear its contents.
tag_value_builder = []
if tags:
tag_names.extend(tags)
tag_values.extend(constructed_tag_values)
else:
unknown_tags.extend(constructed_tag_values)
tags_to_build = 0
metric_parts.append(metric_part)
metric_mapping = metric_mapping[metric_part]
minimum_tag_length = len(metric_mapping['|_tags_|'][-1])
else:
tag_value_builder.append(metric_part)
tags_to_build += 1
return metric_parts, tag_value_builder, tag_names, tag_values, unknown_tags, tags_to_build, metric_mapping
def parse_metric(metric, retry=False, metric_mapping=METRIC_TREE, disable_legacy_cluster_tag=False):
# type: (str, Dict[str, Any]) -> Tuple[str, List[str], str]
"""Takes a metric formatted by Envoy and splits it into a unique
metric name. Returns the unique metric name, a list of tags, and
the name of the submission method.
Example:
'listener.0.0.0.0_80.downstream_cx_total' ->
('listener.downstream_cx_total', ['address:0.0.0.0_80'], 'count')
"""
metric_parts, tag_value_builder, tag_names, tag_values, unknown_tags, tags_to_build, last_mapping = _parse_metric(
metric, metric_mapping
)
parsed_metric = '.'.join(metric_parts)
if parsed_metric not in METRICS:
if retry:
skip_parts = []
# Retry parsing for metrics by skipping the last matched metric part
while len(metric_parts) > 1:
skip_part = metric_parts.pop()
if skip_part in skip_parts:
raise UnknownMetric
else:
skip_parts.append(skip_part)
(
metric_parts,
tag_value_builder,
tag_names,
tag_values,
unknown_tags,
tags_to_build,
last_mapping,
) = _parse_metric(metric, metric_mapping, skip_part)
parsed_metric = '.'.join(metric_parts)
if parsed_metric in METRICS:
break
else:
raise UnknownMetric
else:
raise UnknownMetric
# Rebuild any trailing tags
if tag_value_builder:
tags = next(
(mapped_tags for mapped_tags in last_mapping['|_tags_|'] if tags_to_build >= len(mapped_tags)), tuple()
)
constructed_tag_values = construct_tag_values(tag_value_builder, len(tags))
if tags:
tag_names.extend(tags)
tag_values.extend(constructed_tag_values)
else:
unknown_tags.extend(constructed_tag_values)
if unknown_tags:
raise UnknownTags('{}'.format('|||'.join(unknown_tags)))
if not disable_legacy_cluster_tag:
for name, legacy_name in [('envoy_cluster', 'cluster_name'), ('virtual_envoy_cluster', 'virtual_cluster_name')]:
try:
pos = tag_names.index(name)
tag_names.append(legacy_name)
tag_values.append(tag_values[pos])
except ValueError:
pass
tags = ['{}:{}'.format(tag_name, tag_value) for tag_name, tag_value in zip(tag_names, tag_values)]
return METRIC_PREFIX + parsed_metric, tags, METRICS[parsed_metric]['method']
def construct_tag_values(tag_builder, num_tags):
# type: (List[str], int) -> List[str]
# First fill in all trailing slots with one tag.
tags = [tag_builder.pop() for _ in range(num_tags - 1)]
# Merge any excess tag parts.
if tag_builder:
tags.append('.'.join(tag_builder))
# Return an iterator in the original order.
return reversed(tags)
def parse_histogram(metric, histogram):
"""Iterates over histogram data, yielding metric-value pairs."""
for match in HISTOGRAM.finditer(histogram):
percentile, value = match.groups()
value = float(value)
if not isnan(value):
try:
yield metric + PERCENTILE_SUFFIX[percentile], value
# In case Envoy adds more
except KeyError:
yield '{}.{}percentile'.format(metric, percentile[1:].replace('.', '_')), value
|
paplorinc/intellij-community | refs/heads/master | python/testData/intentions/PyConvertToFStringIntentionTest/percentOperatorWidthAndPrecision_after.py | 31 | f'{1:.5d} {2:3.5d} {3:3d} {"spam":>20} {4:<#d}'
|
GauravSahu/odoo | refs/heads/8.0 | addons/base_geolocalize/models/__init__.py | 1273 | import res_partner
|
Orav/kbengine | refs/heads/master | kbe/src/lib/python/Tools/scripts/finddiv.py | 1 | #! /usr/bin/env python3
"""finddiv - a grep-like tool that looks for division operators.
Usage: finddiv [-l] file_or_directory ...
For directory arguments, all files in the directory whose name ends in
.py are processed, and subdirectories are processed recursively.
This actually tokenizes the files to avoid false hits in comments or
strings literals.
By default, this prints all lines containing a / or /= operator, in
grep -n style. With the -l option specified, it prints the filename
of files that contain at least one / or /= operator.
"""
import os
import sys
import getopt
import tokenize
def main():
try:
opts, args = getopt.getopt(sys.argv[1:], "lh")
except getopt.error as msg:
usage(msg)
return 2
if not args:
usage("at least one file argument is required")
return 2
listnames = 0
for o, a in opts:
if o == "-h":
print(__doc__)
return
if o == "-l":
listnames = 1
exit = None
for filename in args:
x = process(filename, listnames)
exit = exit or x
return exit
def usage(msg):
sys.stderr.write("%s: %s\n" % (sys.argv[0], msg))
sys.stderr.write("Usage: %s [-l] file ...\n" % sys.argv[0])
sys.stderr.write("Try `%s -h' for more information.\n" % sys.argv[0])
def process(filename, listnames):
if os.path.isdir(filename):
return processdir(filename, listnames)
try:
fp = open(filename)
except IOError as msg:
sys.stderr.write("Can't open: %s\n" % msg)
return 1
g = tokenize.generate_tokens(fp.readline)
lastrow = None
for type, token, (row, col), end, line in g:
if token in ("/", "/="):
if listnames:
print(filename)
break
if row != lastrow:
lastrow = row
print("%s:%d:%s" % (filename, row, line), end=' ')
fp.close()
def processdir(dir, listnames):
try:
names = os.listdir(dir)
except OSError as msg:
sys.stderr.write("Can't list directory: %s\n" % dir)
return 1
files = []
for name in names:
fn = os.path.join(dir, name)
if os.path.normcase(fn).endswith(".py") or os.path.isdir(fn):
files.append(fn)
files.sort(key=os.path.normcase)
exit = None
for fn in files:
x = process(fn, listnames)
exit = exit or x
return exit
if __name__ == "__main__":
sys.exit(main())
|
BenTheElder/test-infra | refs/heads/master | gubernator/third_party/defusedxml/common.py | 55 | # defusedxml
#
# Copyright (c) 2013 by Christian Heimes <christian@python.org>
# Licensed to PSF under a Contributor Agreement.
# See http://www.python.org/psf/license for licensing details.
"""Common constants, exceptions and helpe functions
"""
import sys
from types import MethodType
PY3 = sys.version_info[0] == 3
PY26 = sys.version_info[:2] == (2, 6)
PY31 = sys.version_info[:2] == (3, 1)
class DefusedXmlException(ValueError):
"""Base exception
"""
def __repr__(self):
return str(self)
class DTDForbidden(DefusedXmlException):
"""Document type definition is forbidden
"""
def __init__(self, name, sysid, pubid):
super(DTDForbidden, self).__init__()
self.name = name
self.sysid = sysid
self.pubid = pubid
def __str__(self):
tpl = "DTDForbidden(name='{}', system_id={!r}, public_id={!r})"
return tpl.format(self.name, self.sysid, self.pubid)
class EntitiesForbidden(DefusedXmlException):
"""Entity definition is forbidden
"""
def __init__(self, name, value, base, sysid, pubid, notation_name):
super(EntitiesForbidden, self).__init__()
self.name = name
self.value = value
self.base = base
self.sysid = sysid
self.pubid = pubid
self.notation_name = notation_name
def __str__(self):
tpl = "EntitiesForbidden(name='{}', system_id={!r}, public_id={!r})"
return tpl.format(self.name, self.sysid, self.pubid)
class ExternalReferenceForbidden(DefusedXmlException):
"""Resolving an external reference is forbidden
"""
def __init__(self, context, base, sysid, pubid):
super(ExternalReferenceForbidden, self).__init__()
self.context = context
self.base = base
self.sysid = sysid
self.pubid = pubid
def __str__(self):
tpl = "ExternalReferenceForbidden(system_id='{}', public_id={})"
return tpl.format(self.sysid, self.pubid)
class NotSupportedError(DefusedXmlException):
"""The operation is not supported
"""
def _apply_defusing(defused_mod):
assert defused_mod is sys.modules[defused_mod.__name__]
stdlib_name = defused_mod.__origin__
__import__(stdlib_name, {}, {}, ["*"])
stdlib_mod = sys.modules[stdlib_name]
stdlib_names = set(dir(stdlib_mod))
for name, obj in vars(defused_mod).items():
if name.startswith("_") or name not in stdlib_names:
continue
setattr(stdlib_mod, name, obj)
return stdlib_mod
def _generate_etree_functions(DefusedXMLParser, _TreeBuilder,
_IterParseIterator, _parse, _iterparse):
"""Factory for functions needed by etree, dependent on whether
cElementTree or ElementTree is used."""
def parse(source, parser=None, forbid_dtd=False, forbid_entities=True,
forbid_external=True):
if parser is None:
parser = DefusedXMLParser(target=_TreeBuilder(),
forbid_dtd=forbid_dtd,
forbid_entities=forbid_entities,
forbid_external=forbid_external)
return _parse(source, parser)
if PY26 or PY31:
def bind(xmlparser, funcname, hookname):
func = getattr(DefusedXMLParser, funcname)
if PY26:
# unbound -> function
func = func.__func__
method = MethodType(func, xmlparser, xmlparser.__class__)
else:
method = MethodType(func, xmlparser)
# set hook
setattr(xmlparser._parser, hookname, method)
def iterparse(source, events=None, forbid_dtd=False,
forbid_entities=True, forbid_external=True):
it = _iterparse(source, events)
xmlparser = it._parser
if forbid_dtd:
bind(xmlparser, "defused_start_doctype_decl",
"StartDoctypeDeclHandler")
if forbid_entities:
bind(xmlparser, "defused_entity_decl",
"EntityDeclHandler")
bind(xmlparser, "defused_unparsed_entity_decl",
"UnparsedEntityDeclHandler")
if forbid_external:
bind(xmlparser, "defused_external_entity_ref_handler",
"ExternalEntityRefHandler")
return it
elif PY3:
def iterparse(source, events=None, parser=None, forbid_dtd=False,
forbid_entities=True, forbid_external=True):
close_source = False
if not hasattr(source, "read"):
source = open(source, "rb")
close_source = True
if not parser:
parser = DefusedXMLParser(target=_TreeBuilder(),
forbid_dtd=forbid_dtd,
forbid_entities=forbid_entities,
forbid_external=forbid_external)
return _IterParseIterator(source, events, parser, close_source)
else:
# Python 2.7
def iterparse(source, events=None, parser=None, forbid_dtd=False,
forbid_entities=True, forbid_external=True):
if parser is None:
parser = DefusedXMLParser(target=_TreeBuilder(),
forbid_dtd=forbid_dtd,
forbid_entities=forbid_entities,
forbid_external=forbid_external)
return _iterparse(source, events, parser)
def fromstring(text, forbid_dtd=False, forbid_entities=True,
forbid_external=True):
parser = DefusedXMLParser(target=_TreeBuilder(),
forbid_dtd=forbid_dtd,
forbid_entities=forbid_entities,
forbid_external=forbid_external)
parser.feed(text)
return parser.close()
return parse, iterparse, fromstring
|
mathmastin/NumString | refs/heads/master | src/numcass.py | 1 | __author__ = 'Matt Mastin'
import numstring
import cass
import cassandra
class NumCass(numstring.NSPGenerator):
def __init__(self, hosts=None, stringsize=0):
"""Subclass constructor for NumCass
This class is mainly for insertion of a NumStringPile into Cassandra
Use NumKeyspace to access an existing Pile.
hosts will default to localhost, but keyspace must be provided
:type stringsize: int
"""
super(NumCass, self).__init__(stringsize)
self.controller = NumKeyspace(stringsize, hosts)
def insertpile(self):
"""Inserts the pile into keyspace with a column family for each starting digit"""
count = 1
for i in self.getgen():
self.controller.session.execute_async("INSERT INTO start%s (num_string, comp) VALUES (%s, %s)",
[i.digits[0], str(i.digits), count])
count += 1
def create(self):
"""Create the Keyspace"""
self.controller.createnumkeyspace()
def delete(self):
"""Delete the Keyspace"""
self.controller.deletenumkeyspace()
#def attachkeyspace(self):
# self.controller.usekeyspace(self.controller.keyspace)
class NumKeyspace(cass.CassController):
"""Subcalss of CassController for use by NumCass
Used for interacting with a NumStringPile that lives in a Cassandra keyspace
"""
def __init__(self, stringsize=1, hosts=None):
super(NumKeyspace, self).__init__(hosts)
self.stringsize = stringsize
self.keyspace = 'numstring' + str(stringsize)
def createnumkeyspace(self):
"""Creates a keyspace using the NumString data model"""
self.session.execute(
"CREATE KEYSPACE IF NOT EXISTS numstring%s WITH REPLICATION = "
"{ 'class' : 'SimpleStrategy', 'replication_factor' : 1 }",
[self.stringsize])
# Set the keyspace of the session
self.session.set_keyspace(self.keyspace)
# This will pass "USE keyspace" to the cluster so we do not need
# to specify the keyspace in queries
self.usekeyspace(self.keyspace)
# Now we create the tables
for i in range(0, 10):
self.session.execute(
"CREATE TABLE IF NOT EXISTS start%s (num_string varchar PRIMARY KEY, comp int)", [i])
def deletenumkeyspace(self):
"""Deletes the keyspace associated to the NumKeyspace"""
self.deletekeyspace()
def numquery(self, cqlstatement):
"""Yields a generator into the results of the query cqlstatement
Note that we shouldn't need to explicitly include the name of the
keyspace
"""
# We assume that if a query is called then the keyspace exists and should be used
# if the keyspace doesn't exist then Cassandra will throw an InvalidRequest error
# if the generator is used.
self.usekeyspace(self.keyspace)
results = self.query(cqlstatement)
for i in results:
# The query yields a unicode version of the digits, so we must format and cast
# to a list of ints to send to the constructor of NumString
if self.stringsize > 1:
yield numstring.NumString(map(int, str(i[0]).lstrip('(').rstrip(')').split(',')))
else:
yield numstring.NumString([int(i[0][1])])
def getnhbs(self, numstring):
"""Returns generator into list of neighbors of numstring in the NumString graph
Two NumStrings are neighbors if one can be obtained from the other
by adding 1 to some integer in the string and subtracting 1 from another
"""
if numstring.stringsize == 0:
return None
for i in range(0,numstring.stringsize):
for j in range(0,numstring.stringsize):
if i != j:
nbr = numstring.digits
def dumpsubgraph(self, vertlist = None):
"""Dumps neighbor subgraph corresponding to the vertices in vertlist
to file for processing by Graphx"""
pass
def updatecomps(self):
"""Reads connected component data from Graphx generated file
and update component information in the NumString keyspace"""
pass |
dfunckt/django | refs/heads/master | django/contrib/gis/geos/point.py | 47 | import warnings
from ctypes import c_uint
from django.contrib.gis import gdal
from django.contrib.gis.geos import prototypes as capi
from django.contrib.gis.geos.error import GEOSException
from django.contrib.gis.geos.geometry import GEOSGeometry
from django.utils import six
from django.utils.deprecation import RemovedInDjango20Warning
from django.utils.six.moves import range
class Point(GEOSGeometry):
_minlength = 2
_maxlength = 3
has_cs = True
def __init__(self, x=None, y=None, z=None, srid=None):
"""
The Point object may be initialized with either a tuple, or individual
parameters.
For Example:
>>> p = Point((5, 23)) # 2D point, passed in as a tuple
>>> p = Point(5, 23, 8) # 3D point, passed in with individual parameters
"""
if x is None:
coords = []
elif isinstance(x, (tuple, list)):
# Here a tuple or list was passed in under the `x` parameter.
coords = x
elif isinstance(x, six.integer_types + (float,)) and isinstance(y, six.integer_types + (float,)):
# Here X, Y, and (optionally) Z were passed in individually, as parameters.
if isinstance(z, six.integer_types + (float,)):
coords = [x, y, z]
else:
coords = [x, y]
else:
raise TypeError('Invalid parameters given for Point initialization.')
point = self._create_point(len(coords), coords)
# Initializing using the address returned from the GEOS
# createPoint factory.
super(Point, self).__init__(point, srid=srid)
def _ogr_ptr(self):
return gdal.geometries.Point._create_empty() if self.empty else super(Point, self)._ogr_ptr()
@classmethod
def _create_empty(cls):
return cls._create_point(None, None)
@classmethod
def _create_point(cls, ndim, coords):
"""
Create a coordinate sequence, set X, Y, [Z], and create point
"""
if not ndim:
return capi.create_point(None)
if ndim < 2 or ndim > 3:
raise TypeError('Invalid point dimension: %s' % str(ndim))
cs = capi.create_cs(c_uint(1), c_uint(ndim))
i = iter(coords)
capi.cs_setx(cs, 0, next(i))
capi.cs_sety(cs, 0, next(i))
if ndim == 3:
capi.cs_setz(cs, 0, next(i))
return capi.create_point(cs)
def _set_list(self, length, items):
ptr = self._create_point(length, items)
if ptr:
capi.destroy_geom(self.ptr)
self._ptr = ptr
self._set_cs()
else:
# can this happen?
raise GEOSException('Geometry resulting from slice deletion was invalid.')
def _set_single(self, index, value):
self._cs.setOrdinate(index, 0, value)
def __iter__(self):
"Allows iteration over coordinates of this Point."
for i in range(len(self)):
yield self[i]
def __len__(self):
"Returns the number of dimensions for this Point (either 0, 2 or 3)."
if self.empty:
return 0
if self.hasz:
return 3
else:
return 2
def _get_single_external(self, index):
if index == 0:
return self.x
elif index == 1:
return self.y
elif index == 2:
return self.z
_get_single_internal = _get_single_external
@property
def x(self):
"Returns the X component of the Point."
return self._cs.getOrdinate(0, 0)
@x.setter
def x(self, value):
"Sets the X component of the Point."
self._cs.setOrdinate(0, 0, value)
@property
def y(self):
"Returns the Y component of the Point."
return self._cs.getOrdinate(1, 0)
@y.setter
def y(self, value):
"Sets the Y component of the Point."
self._cs.setOrdinate(1, 0, value)
@property
def z(self):
"Returns the Z component of the Point."
return self._cs.getOrdinate(2, 0) if self.hasz else None
@z.setter
def z(self, value):
"Sets the Z component of the Point."
if not self.hasz:
raise GEOSException('Cannot set Z on 2D Point.')
self._cs.setOrdinate(2, 0, value)
def get_x(self):
warnings.warn(
"`get_x()` is deprecated, use the `x` property instead.",
RemovedInDjango20Warning, 2
)
return self.x
def set_x(self, value):
warnings.warn(
"`set_x()` is deprecated, use the `x` property instead.",
RemovedInDjango20Warning, 2
)
self.x = value
def get_y(self):
warnings.warn(
"`get_y()` is deprecated, use the `y` property instead.",
RemovedInDjango20Warning, 2
)
return self.y
def set_y(self, value):
warnings.warn(
"`set_y()` is deprecated, use the `y` property instead.",
RemovedInDjango20Warning, 2
)
self.y = value
def get_z(self):
warnings.warn(
"`get_z()` is deprecated, use the `z` property instead.",
RemovedInDjango20Warning, 2
)
return self.z
def set_z(self, value):
warnings.warn(
"`set_z()` is deprecated, use the `z` property instead.",
RemovedInDjango20Warning, 2
)
self.z = value
# ### Tuple setting and retrieval routines. ###
@property
def tuple(self):
"Returns a tuple of the point."
return self._cs.tuple
@tuple.setter
def tuple(self, tup):
"Sets the coordinates of the point with the given tuple."
self._cs[0] = tup
def get_coords(self):
warnings.warn(
"`get_coords()` is deprecated, use the `tuple` property instead.",
RemovedInDjango20Warning, 2
)
return self.tuple
def set_coords(self, tup):
warnings.warn(
"`set_coords()` is deprecated, use the `tuple` property instead.",
RemovedInDjango20Warning, 2
)
self.tuple = tup
# The tuple and coords properties
coords = tuple
|
Sentient07/scikit-learn | refs/heads/master | sklearn/utils/extmath.py | 19 | """
Extended math utilities.
"""
# Authors: Gael Varoquaux
# Alexandre Gramfort
# Alexandre T. Passos
# Olivier Grisel
# Lars Buitinck
# Stefan van der Walt
# Kyle Kastner
# Giorgio Patrini
# License: BSD 3 clause
from __future__ import division
from functools import partial
import warnings
import numpy as np
from scipy import linalg
from scipy.sparse import issparse, csr_matrix
from . import check_random_state
from .fixes import np_version
from ._logistic_sigmoid import _log_logistic_sigmoid
from ..externals.six.moves import xrange
from .sparsefuncs_fast import csr_row_norms
from .validation import check_array
from ..exceptions import NonBLASDotWarning
def norm(x):
"""Compute the Euclidean or Frobenius norm of x.
Returns the Euclidean norm when x is a vector, the Frobenius norm when x
is a matrix (2-d array). More precise than sqrt(squared_norm(x)).
"""
x = np.asarray(x)
nrm2, = linalg.get_blas_funcs(['nrm2'], [x])
return nrm2(x)
# Newer NumPy has a ravel that needs less copying.
if np_version < (1, 7, 1):
_ravel = np.ravel
else:
_ravel = partial(np.ravel, order='K')
def squared_norm(x):
"""Squared Euclidean or Frobenius norm of x.
Returns the Euclidean norm when x is a vector, the Frobenius norm when x
is a matrix (2-d array). Faster than norm(x) ** 2.
"""
x = _ravel(x)
return np.dot(x, x)
def row_norms(X, squared=False):
"""Row-wise (squared) Euclidean norm of X.
Equivalent to np.sqrt((X * X).sum(axis=1)), but also supports sparse
matrices and does not create an X.shape-sized temporary.
Performs no input validation.
"""
if issparse(X):
if not isinstance(X, csr_matrix):
X = csr_matrix(X)
norms = csr_row_norms(X)
else:
norms = np.einsum('ij,ij->i', X, X)
if not squared:
np.sqrt(norms, norms)
return norms
def fast_logdet(A):
"""Compute log(det(A)) for A symmetric
Equivalent to : np.log(nl.det(A)) but more robust.
It returns -Inf if det(A) is non positive or is not defined.
"""
sign, ld = np.linalg.slogdet(A)
if not sign > 0:
return -np.inf
return ld
def _impose_f_order(X):
"""Helper Function"""
# important to access flags instead of calling np.isfortran,
# this catches corner cases.
if X.flags.c_contiguous:
return check_array(X.T, copy=False, order='F'), True
else:
return check_array(X, copy=False, order='F'), False
def _fast_dot(A, B):
if B.shape[0] != A.shape[A.ndim - 1]: # check adopted from '_dotblas.c'
raise ValueError
if A.dtype != B.dtype or any(x.dtype not in (np.float32, np.float64)
for x in [A, B]):
warnings.warn('Falling back to np.dot. '
'Data must be of same type of either '
'32 or 64 bit float for the BLAS function, gemm, to be '
'used for an efficient dot operation. ',
NonBLASDotWarning)
raise ValueError
if min(A.shape) == 1 or min(B.shape) == 1 or A.ndim != 2 or B.ndim != 2:
raise ValueError
# scipy 0.9 compliant API
dot = linalg.get_blas_funcs(['gemm'], (A, B))[0]
A, trans_a = _impose_f_order(A)
B, trans_b = _impose_f_order(B)
return dot(alpha=1.0, a=A, b=B, trans_a=trans_a, trans_b=trans_b)
def _have_blas_gemm():
try:
linalg.get_blas_funcs(['gemm'])
return True
except (AttributeError, ValueError):
warnings.warn('Could not import BLAS, falling back to np.dot')
return False
# Only use fast_dot for older NumPy; newer ones have tackled the speed issue.
if np_version < (1, 7, 2) and _have_blas_gemm():
def fast_dot(A, B):
"""Compute fast dot products directly calling BLAS.
This function calls BLAS directly while warranting Fortran contiguity.
This helps avoiding extra copies `np.dot` would have created.
For details see section `Linear Algebra on large Arrays`:
http://wiki.scipy.org/PerformanceTips
Parameters
----------
A, B: instance of np.ndarray
Input arrays. Arrays are supposed to be of the same dtype and to
have exactly 2 dimensions. Currently only floats are supported.
In case these requirements aren't met np.dot(A, B) is returned
instead. To activate the related warning issued in this case
execute the following lines of code:
>> import warnings
>> from sklearn.exceptions import NonBLASDotWarning
>> warnings.simplefilter('always', NonBLASDotWarning)
"""
try:
return _fast_dot(A, B)
except ValueError:
# Maltyped or malformed data.
return np.dot(A, B)
else:
fast_dot = np.dot
def density(w, **kwargs):
"""Compute density of a sparse vector
Return a value between 0 and 1
"""
if hasattr(w, "toarray"):
d = float(w.nnz) / (w.shape[0] * w.shape[1])
else:
d = 0 if w is None else float((w != 0).sum()) / w.size
return d
def safe_sparse_dot(a, b, dense_output=False):
"""Dot product that handle the sparse matrix case correctly
Uses BLAS GEMM as replacement for numpy.dot where possible
to avoid unnecessary copies.
"""
if issparse(a) or issparse(b):
ret = a * b
if dense_output and hasattr(ret, "toarray"):
ret = ret.toarray()
return ret
else:
return fast_dot(a, b)
def randomized_range_finder(A, size, n_iter,
power_iteration_normalizer='auto',
random_state=None):
"""Computes an orthonormal matrix whose range approximates the range of A.
Parameters
----------
A : 2D array
The input data matrix
size : integer
Size of the return array
n_iter : integer
Number of power iterations used to stabilize the result
power_iteration_normalizer : 'auto' (default), 'QR', 'LU', 'none'
Whether the power iterations are normalized with step-by-step
QR factorization (the slowest but most accurate), 'none'
(the fastest but numerically unstable when `n_iter` is large, e.g.
typically 5 or larger), or 'LU' factorization (numerically stable
but can lose slightly in accuracy). The 'auto' mode applies no
normalization if `n_iter`<=2 and switches to LU otherwise.
.. versionadded:: 0.18
random_state : RandomState or an int seed (0 by default)
A random number generator instance
Returns
-------
Q : 2D array
A (size x size) projection matrix, the range of which
approximates well the range of the input matrix A.
Notes
-----
Follows Algorithm 4.3 of
Finding structure with randomness: Stochastic algorithms for constructing
approximate matrix decompositions
Halko, et al., 2009 (arXiv:909) http://arxiv.org/pdf/0909.4061
An implementation of a randomized algorithm for principal component
analysis
A. Szlam et al. 2014
"""
random_state = check_random_state(random_state)
# Generating normal random vectors with shape: (A.shape[1], size)
Q = random_state.normal(size=(A.shape[1], size))
# Deal with "auto" mode
if power_iteration_normalizer == 'auto':
if n_iter <= 2:
power_iteration_normalizer = 'none'
else:
power_iteration_normalizer = 'LU'
# Perform power iterations with Q to further 'imprint' the top
# singular vectors of A in Q
for i in range(n_iter):
if power_iteration_normalizer == 'none':
Q = safe_sparse_dot(A, Q)
Q = safe_sparse_dot(A.T, Q)
elif power_iteration_normalizer == 'LU':
Q, _ = linalg.lu(safe_sparse_dot(A, Q), permute_l=True)
Q, _ = linalg.lu(safe_sparse_dot(A.T, Q), permute_l=True)
elif power_iteration_normalizer == 'QR':
Q, _ = linalg.qr(safe_sparse_dot(A, Q), mode='economic')
Q, _ = linalg.qr(safe_sparse_dot(A.T, Q), mode='economic')
# Sample the range of A using by linear projection of Q
# Extract an orthonormal basis
Q, _ = linalg.qr(safe_sparse_dot(A, Q), mode='economic')
return Q
def randomized_svd(M, n_components, n_oversamples=10, n_iter='auto',
power_iteration_normalizer='auto', transpose='auto',
flip_sign=True, random_state=0):
"""Computes a truncated randomized SVD
Parameters
----------
M : ndarray or sparse matrix
Matrix to decompose
n_components : int
Number of singular values and vectors to extract.
n_oversamples : int (default is 10)
Additional number of random vectors to sample the range of M so as
to ensure proper conditioning. The total number of random vectors
used to find the range of M is n_components + n_oversamples. Smaller
number can improve speed but can negatively impact the quality of
approximation of singular vectors and singular values.
n_iter : int or 'auto' (default is 'auto')
Number of power iterations. It can be used to deal with very noisy
problems. When 'auto', it is set to 4, unless `n_components` is small
(< .1 * min(X.shape)) `n_iter` in which case is set to 7.
This improves precision with few components.
.. versionchanged:: 0.18
power_iteration_normalizer : 'auto' (default), 'QR', 'LU', 'none'
Whether the power iterations are normalized with step-by-step
QR factorization (the slowest but most accurate), 'none'
(the fastest but numerically unstable when `n_iter` is large, e.g.
typically 5 or larger), or 'LU' factorization (numerically stable
but can lose slightly in accuracy). The 'auto' mode applies no
normalization if `n_iter`<=2 and switches to LU otherwise.
.. versionadded:: 0.18
transpose : True, False or 'auto' (default)
Whether the algorithm should be applied to M.T instead of M. The
result should approximately be the same. The 'auto' mode will
trigger the transposition if M.shape[1] > M.shape[0] since this
implementation of randomized SVD tend to be a little faster in that
case.
.. versionchanged:: 0.18
flip_sign : boolean, (True by default)
The output of a singular value decomposition is only unique up to a
permutation of the signs of the singular vectors. If `flip_sign` is
set to `True`, the sign ambiguity is resolved by making the largest
loadings for each component in the left singular vectors positive.
random_state : RandomState or an int seed (0 by default)
A random number generator instance to make behavior
Notes
-----
This algorithm finds a (usually very good) approximate truncated
singular value decomposition using randomization to speed up the
computations. It is particularly fast on large matrices on which
you wish to extract only a small number of components. In order to
obtain further speed up, `n_iter` can be set <=2 (at the cost of
loss of precision).
References
----------
* Finding structure with randomness: Stochastic algorithms for constructing
approximate matrix decompositions
Halko, et al., 2009 http://arxiv.org/abs/arXiv:0909.4061
* A randomized algorithm for the decomposition of matrices
Per-Gunnar Martinsson, Vladimir Rokhlin and Mark Tygert
* An implementation of a randomized algorithm for principal component
analysis
A. Szlam et al. 2014
"""
random_state = check_random_state(random_state)
n_random = n_components + n_oversamples
n_samples, n_features = M.shape
if n_iter == 'auto':
# Checks if the number of iterations is explicitely specified
# Adjust n_iter. 7 was found a good compromise for PCA. See #5299
n_iter = 7 if n_components < .1 * min(M.shape) else 4
if transpose == 'auto':
transpose = n_samples < n_features
if transpose:
# this implementation is a bit faster with smaller shape[1]
M = M.T
Q = randomized_range_finder(M, n_random, n_iter,
power_iteration_normalizer, random_state)
# project M to the (k + p) dimensional space using the basis vectors
B = safe_sparse_dot(Q.T, M)
# compute the SVD on the thin matrix: (k + p) wide
Uhat, s, V = linalg.svd(B, full_matrices=False)
del B
U = np.dot(Q, Uhat)
if flip_sign:
if not transpose:
U, V = svd_flip(U, V)
else:
# In case of transpose u_based_decision=false
# to actually flip based on u and not v.
U, V = svd_flip(U, V, u_based_decision=False)
if transpose:
# transpose back the results according to the input convention
return V[:n_components, :].T, s[:n_components], U[:, :n_components].T
else:
return U[:, :n_components], s[:n_components], V[:n_components, :]
def logsumexp(arr, axis=0):
"""Computes the sum of arr assuming arr is in the log domain.
Returns log(sum(exp(arr))) while minimizing the possibility of
over/underflow.
Examples
--------
>>> import numpy as np
>>> from sklearn.utils.extmath import logsumexp
>>> a = np.arange(10)
>>> np.log(np.sum(np.exp(a)))
9.4586297444267107
>>> logsumexp(a)
9.4586297444267107
"""
arr = np.rollaxis(arr, axis)
# Use the max to normalize, as with the log this is what accumulates
# the less errors
vmax = arr.max(axis=0)
out = np.log(np.sum(np.exp(arr - vmax), axis=0))
out += vmax
return out
def weighted_mode(a, w, axis=0):
"""Returns an array of the weighted modal (most common) value in a
If there is more than one such value, only the first is returned.
The bin-count for the modal bins is also returned.
This is an extension of the algorithm in scipy.stats.mode.
Parameters
----------
a : array_like
n-dimensional array of which to find mode(s).
w : array_like
n-dimensional array of weights for each value
axis : int, optional
Axis along which to operate. Default is 0, i.e. the first axis.
Returns
-------
vals : ndarray
Array of modal values.
score : ndarray
Array of weighted counts for each mode.
Examples
--------
>>> from sklearn.utils.extmath import weighted_mode
>>> x = [4, 1, 4, 2, 4, 2]
>>> weights = [1, 1, 1, 1, 1, 1]
>>> weighted_mode(x, weights)
(array([ 4.]), array([ 3.]))
The value 4 appears three times: with uniform weights, the result is
simply the mode of the distribution.
>>> weights = [1, 3, 0.5, 1.5, 1, 2] # deweight the 4's
>>> weighted_mode(x, weights)
(array([ 2.]), array([ 3.5]))
The value 2 has the highest score: it appears twice with weights of
1.5 and 2: the sum of these is 3.
See Also
--------
scipy.stats.mode
"""
if axis is None:
a = np.ravel(a)
w = np.ravel(w)
axis = 0
else:
a = np.asarray(a)
w = np.asarray(w)
axis = axis
if a.shape != w.shape:
w = np.zeros(a.shape, dtype=w.dtype) + w
scores = np.unique(np.ravel(a)) # get ALL unique values
testshape = list(a.shape)
testshape[axis] = 1
oldmostfreq = np.zeros(testshape)
oldcounts = np.zeros(testshape)
for score in scores:
template = np.zeros(a.shape)
ind = (a == score)
template[ind] = w[ind]
counts = np.expand_dims(np.sum(template, axis), axis)
mostfrequent = np.where(counts > oldcounts, score, oldmostfreq)
oldcounts = np.maximum(counts, oldcounts)
oldmostfreq = mostfrequent
return mostfrequent, oldcounts
def pinvh(a, cond=None, rcond=None, lower=True):
"""Compute the (Moore-Penrose) pseudo-inverse of a hermetian matrix.
Calculate a generalized inverse of a symmetric matrix using its
eigenvalue decomposition and including all 'large' eigenvalues.
Parameters
----------
a : array, shape (N, N)
Real symmetric or complex hermetian matrix to be pseudo-inverted
cond : float or None, default None
Cutoff for 'small' eigenvalues.
Singular values smaller than rcond * largest_eigenvalue are considered
zero.
If None or -1, suitable machine precision is used.
rcond : float or None, default None (deprecated)
Cutoff for 'small' eigenvalues.
Singular values smaller than rcond * largest_eigenvalue are considered
zero.
If None or -1, suitable machine precision is used.
lower : boolean
Whether the pertinent array data is taken from the lower or upper
triangle of a. (Default: lower)
Returns
-------
B : array, shape (N, N)
Raises
------
LinAlgError
If eigenvalue does not converge
Examples
--------
>>> import numpy as np
>>> a = np.random.randn(9, 6)
>>> a = np.dot(a, a.T)
>>> B = pinvh(a)
>>> np.allclose(a, np.dot(a, np.dot(B, a)))
True
>>> np.allclose(B, np.dot(B, np.dot(a, B)))
True
"""
a = np.asarray_chkfinite(a)
s, u = linalg.eigh(a, lower=lower)
if rcond is not None:
cond = rcond
if cond in [None, -1]:
t = u.dtype.char.lower()
factor = {'f': 1E3, 'd': 1E6}
cond = factor[t] * np.finfo(t).eps
# unlike svd case, eigh can lead to negative eigenvalues
above_cutoff = (abs(s) > cond * np.max(abs(s)))
psigma_diag = np.zeros_like(s)
psigma_diag[above_cutoff] = 1.0 / s[above_cutoff]
return np.dot(u * psigma_diag, np.conjugate(u).T)
def cartesian(arrays, out=None):
"""Generate a cartesian product of input arrays.
Parameters
----------
arrays : list of array-like
1-D arrays to form the cartesian product of.
out : ndarray
Array to place the cartesian product in.
Returns
-------
out : ndarray
2-D array of shape (M, len(arrays)) containing cartesian products
formed of input arrays.
Examples
--------
>>> cartesian(([1, 2, 3], [4, 5], [6, 7]))
array([[1, 4, 6],
[1, 4, 7],
[1, 5, 6],
[1, 5, 7],
[2, 4, 6],
[2, 4, 7],
[2, 5, 6],
[2, 5, 7],
[3, 4, 6],
[3, 4, 7],
[3, 5, 6],
[3, 5, 7]])
"""
arrays = [np.asarray(x) for x in arrays]
shape = (len(x) for x in arrays)
dtype = arrays[0].dtype
ix = np.indices(shape)
ix = ix.reshape(len(arrays), -1).T
if out is None:
out = np.empty_like(ix, dtype=dtype)
for n, arr in enumerate(arrays):
out[:, n] = arrays[n][ix[:, n]]
return out
def svd_flip(u, v, u_based_decision=True):
"""Sign correction to ensure deterministic output from SVD.
Adjusts the columns of u and the rows of v such that the loadings in the
columns in u that are largest in absolute value are always positive.
Parameters
----------
u, v : ndarray
u and v are the output of `linalg.svd` or
`sklearn.utils.extmath.randomized_svd`, with matching inner dimensions
so one can compute `np.dot(u * s, v)`.
u_based_decision : boolean, (default=True)
If True, use the columns of u as the basis for sign flipping.
Otherwise, use the rows of v. The choice of which variable to base the
decision on is generally algorithm dependent.
Returns
-------
u_adjusted, v_adjusted : arrays with the same dimensions as the input.
"""
if u_based_decision:
# columns of u, rows of v
max_abs_cols = np.argmax(np.abs(u), axis=0)
signs = np.sign(u[max_abs_cols, xrange(u.shape[1])])
u *= signs
v *= signs[:, np.newaxis]
else:
# rows of v, columns of u
max_abs_rows = np.argmax(np.abs(v), axis=1)
signs = np.sign(v[xrange(v.shape[0]), max_abs_rows])
u *= signs
v *= signs[:, np.newaxis]
return u, v
def log_logistic(X, out=None):
"""Compute the log of the logistic function, ``log(1 / (1 + e ** -x))``.
This implementation is numerically stable because it splits positive and
negative values::
-log(1 + exp(-x_i)) if x_i > 0
x_i - log(1 + exp(x_i)) if x_i <= 0
For the ordinary logistic function, use ``sklearn.utils.fixes.expit``.
Parameters
----------
X : array-like, shape (M, N) or (M, )
Argument to the logistic function
out : array-like, shape: (M, N) or (M, ), optional:
Preallocated output array.
Returns
-------
out : array, shape (M, N) or (M, )
Log of the logistic function evaluated at every point in x
Notes
-----
See the blog post describing this implementation:
http://fa.bianp.net/blog/2013/numerical-optimizers-for-logistic-regression/
"""
is_1d = X.ndim == 1
X = np.atleast_2d(X)
X = check_array(X, dtype=np.float64)
n_samples, n_features = X.shape
if out is None:
out = np.empty_like(X)
_log_logistic_sigmoid(n_samples, n_features, X, out)
if is_1d:
return np.squeeze(out)
return out
def softmax(X, copy=True):
"""
Calculate the softmax function.
The softmax function is calculated by
np.exp(X) / np.sum(np.exp(X), axis=1)
This will cause overflow when large values are exponentiated.
Hence the largest value in each row is subtracted from each data
point to prevent this.
Parameters
----------
X : array-like, shape (M, N)
Argument to the logistic function
copy : bool, optional
Copy X or not.
Returns
-------
out : array, shape (M, N)
Softmax function evaluated at every point in x
"""
if copy:
X = np.copy(X)
max_prob = np.max(X, axis=1).reshape((-1, 1))
X -= max_prob
np.exp(X, X)
sum_prob = np.sum(X, axis=1).reshape((-1, 1))
X /= sum_prob
return X
def safe_min(X):
"""Returns the minimum value of a dense or a CSR/CSC matrix.
Adapated from http://stackoverflow.com/q/13426580
"""
if issparse(X):
if len(X.data) == 0:
return 0
m = X.data.min()
return m if X.getnnz() == X.size else min(m, 0)
else:
return X.min()
def make_nonnegative(X, min_value=0):
"""Ensure `X.min()` >= `min_value`."""
min_ = safe_min(X)
if min_ < min_value:
if issparse(X):
raise ValueError("Cannot make the data matrix"
" nonnegative because it is sparse."
" Adding a value to every entry would"
" make it no longer sparse.")
X = X + (min_value - min_)
return X
def _incremental_mean_and_var(X, last_mean=.0, last_variance=None,
last_sample_count=0):
"""Calculate mean update and a Youngs and Cramer variance update.
last_mean and last_variance are statistics computed at the last step by the
function. Both must be initialized to 0.0. In case no scaling is required
last_variance can be None. The mean is always required and returned because
necessary for the calculation of the variance. last_n_samples_seen is the
number of samples encountered until now.
From the paper "Algorithms for computing the sample variance: analysis and
recommendations", by Chan, Golub, and LeVeque.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Data to use for variance update
last_mean : array-like, shape: (n_features,)
last_variance : array-like, shape: (n_features,)
last_sample_count : int
Returns
-------
updated_mean : array, shape (n_features,)
updated_variance : array, shape (n_features,)
If None, only mean is computed
updated_sample_count : int
References
----------
T. Chan, G. Golub, R. LeVeque. Algorithms for computing the sample
variance: recommendations, The American Statistician, Vol. 37, No. 3,
pp. 242-247
Also, see the sparse implementation of this in
`utils.sparsefuncs.incr_mean_variance_axis` and
`utils.sparsefuncs_fast.incr_mean_variance_axis0`
"""
# old = stats until now
# new = the current increment
# updated = the aggregated stats
last_sum = last_mean * last_sample_count
new_sum = X.sum(axis=0)
new_sample_count = X.shape[0]
updated_sample_count = last_sample_count + new_sample_count
updated_mean = (last_sum + new_sum) / updated_sample_count
if last_variance is None:
updated_variance = None
else:
new_unnormalized_variance = X.var(axis=0) * new_sample_count
if last_sample_count == 0: # Avoid division by 0
updated_unnormalized_variance = new_unnormalized_variance
else:
last_over_new_count = last_sample_count / new_sample_count
last_unnormalized_variance = last_variance * last_sample_count
updated_unnormalized_variance = (
last_unnormalized_variance +
new_unnormalized_variance +
last_over_new_count / updated_sample_count *
(last_sum / last_over_new_count - new_sum) ** 2)
updated_variance = updated_unnormalized_variance / updated_sample_count
return updated_mean, updated_variance, updated_sample_count
def _deterministic_vector_sign_flip(u):
"""Modify the sign of vectors for reproducibility
Flips the sign of elements of all the vectors (rows of u) such that
the absolute maximum element of each vector is positive.
Parameters
----------
u : ndarray
Array with vectors as its rows.
Returns
-------
u_flipped : ndarray with same shape as u
Array with the sign flipped vectors as its rows.
"""
max_abs_rows = np.argmax(np.abs(u), axis=1)
signs = np.sign(u[range(u.shape[0]), max_abs_rows])
u *= signs[:, np.newaxis]
return u
def stable_cumsum(arr, axis=None, rtol=1e-05, atol=1e-08):
"""Use high precision for cumsum and check that final value matches sum
Parameters
----------
arr : array-like
To be cumulatively summed as flat
axis : int, optional
Axis along which the cumulative sum is computed.
The default (None) is to compute the cumsum over the flattened array.
rtol : float
Relative tolerance, see ``np.allclose``
atol : float
Absolute tolerance, see ``np.allclose``
"""
# sum is as unstable as cumsum for numpy < 1.9
if np_version < (1, 9):
return np.cumsum(arr, axis=axis, dtype=np.float64)
out = np.cumsum(arr, axis=axis, dtype=np.float64)
expected = np.sum(arr, axis=axis, dtype=np.float64)
if not np.all(np.isclose(out.take(-1, axis=axis), expected, rtol=rtol,
atol=atol, equal_nan=True)):
warnings.warn('cumsum was found to be unstable: '
'its last element does not correspond to sum',
RuntimeWarning)
return out
|
luoyetx/mxnet | refs/heads/master | example/ssd/tools/caffe_converter/convert_mean.py | 79 | # 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.
"""Convert caffe mean
"""
import argparse
import mxnet as mx
import numpy as np
import caffe_parser
def convert_mean(binaryproto_fname, output=None):
"""Convert caffe mean
Parameters
----------
binaryproto_fname : str
Filename of the mean
output : str, optional
Save the mean into mxnet's format
Returns
-------
NDArray
Mean in ndarray
"""
mean_blob = caffe_parser.caffe_pb2.BlobProto()
with open(binaryproto_fname, 'rb') as f:
mean_blob.ParseFromString(f.read())
img_mean_np = np.array(mean_blob.data)
img_mean_np = img_mean_np.reshape(
mean_blob.channels, mean_blob.height, mean_blob.width
)
# swap channels from Caffe BGR to RGB
img_mean_np[[0, 2], :, :] = img_mean_np[[2, 0], :, :]
nd = mx.nd.array(img_mean_np)
if output is not None:
mx.nd.save(output, {"mean_image": nd})
return nd
def main():
parser = argparse.ArgumentParser(description='Convert caffe mean')
parser.add_argument('binaryproto_fname', help='Filename of the mean')
parser.add_argument('output', help='The name of the output file')
args = parser.parse_args()
convert_mean(args.binaryproto_fname, args.output)
if __name__ == '__main__':
main()
|
drougge/wellpapp-pyclient | refs/heads/master | wellpapp/shell/fusefs.py | 1 | from __future__ import print_function
import fuse
import stat
import errno
import os
import sys
from wellpapp import Client, Tag, raw_exts
import re
from time import time, sleep
from hashlib import md5
from struct import pack, unpack
from zlib import crc32
from xml.sax.saxutils import escape as xmlescape
from os.path import exists
from threading import Thread, RLock, Lock
from collections import namedtuple
if not hasattr(fuse, "__version__"):
raise RuntimeError("No fuse.__version__, too old?")
fuse.fuse_python_api = (0, 2)
if sys.version_info[0] == 2:
PY3 = False
else:
PY3 = True
unicode = str
unichr = chr
if fuse.__version__ < "1.0.0":
raise RuntimeError("Needs at least fuse 1.0.0 on python 3")
md5re = re.compile(r"^(?:\d{6}\.)?([0-9a-f]{32})\.(\w+)$")
shortmd5re = re.compile(r"^(?:\d{6}\.)?([0-9a-f]{32})$")
metamd5re = re.compile(r"^(?:\d{6}\.)?([0-9a-f]{32})\.(\w+)\.gq\.xmp$")
sre = re.compile(r"[ /]+")
orient = {0: 1, 90: 6, 180: 3, 270: 8}
default_range = (0, 10000)
_stat_t = namedtuple("stat_t", ["version", "size", "mtime", "dest", "jpegsize"])
_search_t = namedtuple("search_t", ["want", "dontwant", "order", "range", "clean"])
def NOTFOUND():
raise IOError(errno.ENOENT, "Not found")
class WpStat(fuse.Stat):
def __init__(self, mode, nlink, size, time):
self.st_mode = mode
self.st_ino = 0
self.st_dev = 0
self.st_nlink = nlink
self.st_uid = 0
self.st_gid = 0
self.st_size = size
self.st_atime = time
self.st_mtime = time
self.st_ctime = time
class Cache:
def __init__(self, ttl):
self._data = {}
self._time = time()
self._ttl = ttl
self._lock = RLock()
def get(self, key, getter):
with self._lock:
if self._time < time() - self._ttl:
self._clean()
if key not in self._data:
self._data[key] = (time(), getter(key))
return self._data[key][1]
def _clean(self):
self._time = time()
t = self._time - (self._ttl / 1.5)
too_old = [k for k, v in self._data.items() if v[0] < t]
for key in too_old:
del self._data[key]
_thumbpaths = ([".thumblocal", "normal"], [".thumblocal", "large"])
_cfgpath = "/.wellpapprc"
_cloudname = ".cloud"
_rawext = dict(zip(raw_exts, ("Jpg", "jPg", "jpG", "JPg", "JPG", "JpG", "jPG", "jpg")))
assert len(_rawext) == len(raw_exts)
_rawext_r = {v: k for k, v in _rawext.items()}
if PY3 and sys.getfilesystemencodeerrors() == 'surrogateescape':
def pathfix(path):
b = path.encode('utf-8', 'surrogateescape')
try:
return b.decode('utf-8')
except UnicodeDecodeError:
return b.decode('iso-8859-1')
else:
pathfix = str
def _un_chr(m):
return unichr(int(m.group(0)[2:], 16))
def unescape(s):
return re.sub(r'\\u[a-fA-F0-9]{4}', _un_chr, s)
class Wellpapp(fuse.Fuse):
def __init__(self, *a, **kw):
fuse.Fuse.__init__(self, *a, **kw)
self._raw2jpeg = False
self._default_search = None
self.parser.add_option(mountopt="raw2jpeg", action="store_true", dest="_raw2jpeg",
help="Present RAW files as JPEG")
self.parser.add_option(mountopt="default_search", dest="_default_search",
help="Default search (added to all searches)")
def _cfg2file(self):
cfg = self._client.cfg
data = []
for k, v in cfg.items():
if not k.startswith("_"):
data.append(k + "=" + v + "\n")
res = "".join(sorted(data))
if PY3:
res = res.encode("utf-8")
return res
def _cache_read(self):
self._cache_fh.seek(0, 1)
for line in self._cache_fh:
try:
v, m, size, mtime, dest = line.rstrip("\n").split(" ", 4)
if v == "1":
jz, dest = dest.split(" ", 1)
else:
jz = 0
assert v == "0"
self._stat_cache[m] = _stat_t(int(v), int(size), int(mtime), dest, int(jz))
except Exception:
print("Bad line in cache:", line)
def _cache_thread(self):
while True:
sleep(1)
self._cache_read()
def _prime_stat_cache(self):
fn = self._client.cfg.image_base + "/cache"
if not exists(fn): return
try:
print("Loading stat-cache..")
self._cache_fh = open(fn, "r", encoding="utf-8") if PY3 else open(fn, "r")
self._stat_cache = {}
self._cache_read()
self._use_cache = True
except Exception as e:
print("Failed to load cache:", e)
# Starting threads doesn't work from __init__.
def fsinit(self):
if self._use_cache:
t = Thread(target=self._cache_thread)
t.name = "cache loader"
t.daemon = True
t.start()
print("Ready")
def _stat(self, m):
if m not in self._stat_cache:
print(m, "not in cache")
p = self._client.image_path(m)
dest = os.readlink(p)
st = os.stat(dest)
self._stat_cache[m] = _stat_t(0, st.st_size, st.st_mtime, dest, 0)
return self._stat_cache[m]
def getattr(self, path):
path = pathfix(path)
spath = path.split("/")[1:]
mode = stat.S_IFDIR | 0o555
nlink = 2
size = 0
m = md5re.match(spath[-1])
metam = metamd5re.match(spath[-1])
time = 0
if spath[-3:-1] in _thumbpaths:
if not m or not m.group(2) != ".png": NOTFOUND()
search = self._path2search("/" + " ".join(spath[:-3]))
if not search: NOTFOUND()
if search.order or self._raw2jpeg: # order specified or potentially unwrapped
orgmd5 = self._resolve_thumb(search, spath[-1])
if not orgmd5: NOTFOUND()
mode = stat.S_IFREG | 0o444
tfn = self._client.thumb_path(orgmd5[0], spath[-2])
size = os.stat(tfn).st_size
if search.order:
# plus six digits and a period
size += 7
else:
mode = stat.S_IFLNK | 0o444
nlink = 1
elif m:
if self._use_cache:
mode = stat.S_IFREG | 0o444
version, size, time, dest, jpeg = self._stat(m.group(1))
if self._raw2jpeg and spath[-1][-3:] in _rawext_r: # wrapped RAW
size = jpeg
else:
mode = stat.S_IFLNK | 0o444
nlink = 1
elif metam:
mode = stat.S_IFREG | 0o444
size = len(self._generate_meta(metam.group(1)))
nlink = 1
elif path == "/" or spath[-1] in (".thumblocal", ".metadata") or \
spath[-2:] in _thumbpaths:
pass
elif path == _cfgpath:
mode = stat.S_IFREG | 0o444
nlink = 1
size = len(self._cfgfile)
elif spath[-1][:len(_cloudname)] == _cloudname:
mode = stat.S_IFREG | 0o444
nlink = 1
size = len(self._generate_cloud(spath[:-1], spath[-1]))
else:
search = self._path2search(path)
if not search: NOTFOUND()
try:
self._cache.get(search, self._search)
except Exception:
m = shortmd5re.match(spath[-1])
if m:
mode = stat.S_IFLNK | 0o444
nlink = 1
else:
NOTFOUND()
return WpStat(mode, nlink, size, time)
def _generate_cloud(self, spath, fn):
fn = fn[len(_cloudname):]
count = 20
if fn and fn[0] == ":":
try:
count = int(fn[1:])
except ValueError:
pass
if count < 1: count = 1
want, dontwant = self._path2search("/" + "/".join(spath))[:2]
with self._client_lock:
range = (0, count - 1 + len(want))
tags = self._client.find_tags("EI", "", range=range, guids=want,
excl_guids=dontwant, order="-post",
flags="-datatag")
want = [w[0][-27:] for w in want]
names = [t.name for t in tags if t.guid not in want]
res = "\n".join(names) + "\n"
return res.encode("utf-8")
def _generate_meta(self, m):
data = u"""<?xml version="1.0" encoding="UTF-8"?><x:xmpmeta xmlns:x="adobe:ns:meta/" x:xmptk="XMP Core 4.1.1-Exiv2"><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"><rdf:Description rdf:about="" xmlns:tiff="http://ns.adobe.com/tiff/1.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" """
with self._client_lock:
post = self._client.get_post(m, wanted=["tagname", "tagdata", "rotate"])
if "rotate" in post and post.rotate.value in orient:
data += u"tiff:Orientation=\"" + unicode(orient[post.rotate.value]) + u"\""
data += u"><dc:subject><rdf:Bag>"
tags = [tag.pname + ((u"=" + unicode(tag.value)) if tag.value else u"") for tag in post.tags]
data += u"".join([u"<rdf:li>" + xmlescape(tn) + u"</rdf:li>" \
for tn in sorted(tags)])
data += u"</rdf:Bag></dc:subject></rdf:Description></rdf:RDF></x:xmpmeta>"
return data.encode("utf-8")
def _resolve_thumb(self, search, thumbname):
thumbmd5 = thumbname[:32]
fns, pcache = self._cache.get(search, self._search)
if not pcache:
for fn in fns:
m = md5re.match(fn)
ext = m.group(2)
ofn = m.group(1) + "." + _rawext_r.get(ext, ext)
tmd5 = md5(fn.encode("utf-8")).hexdigest()
pcache[tmd5] = (md5(ofn.encode("utf-8")).hexdigest(), fn)
return pcache.get(thumbmd5)
def readlink(self, path):
path = pathfix(path)
path = path.split("/")[1:]
m = md5re.match(path[-1])
if m:
if path[-3:-1] in _thumbpaths:
return self._client.thumb_path(m.group(1), path[-2])
else:
return self._client.image_path(m.group(1))
if path[-3:-1] not in _thumbpaths:
m = shortmd5re.match(path[-1])
if m: return self._client.image_path(m.group(1))
NOTFOUND()
def readdir(self, path, offset):
path = pathfix(path)
list = [".", ".."]
search = self._path2search(path)
path = path.split("/")[1:]
if path[-1] == ".thumblocal":
list += ["normal", "large"]
elif path == [""] or path[-2:] in _thumbpaths:
pass
elif search:
try:
list += self._cache.get(search, self._search)[0]
except Exception:
NOTFOUND()
else:
NOTFOUND()
for e in list:
yield fuse.Direntry(e)
def _search(self, search):
order = search.order
range = search.range
if not range: range = default_range
assert None not in search.want
assert None not in search.dontwant
with self._client_lock:
s = self._client.search_post(guids=search.want,
excl_guids=search.dontwant,
wanted=["ext"],
order=order,
range=range)
r = []
idx = 0
prefix = ""
for p in s:
if order:
prefix = "%06d." % (idx,)
idx += 1
if search.clean:
r.append(prefix + p.md5)
else:
m = p.md5
ext = p.ext
if self._raw2jpeg and ext in _rawext and self._stat(m).jpegsize:
r.append(prefix + m + "." + _rawext[ext])
r.append(prefix + m + "." + ext)
if not PY3:
r = map(str, r)
return r, {}
def _escape_wrap(self, f_name, name, *a):
f = getattr(self._client, f_name)
with self._client_lock:
res = f(name, *a)
if not res and '\\' in name:
res = f(unescape(name), *a)
return res
def _path2search(self, path):
if path == "/": return None
want = set()
dontwant = set()
order = []
first = None
range = None
clean = False
nodefault = False
for e in filter(None, sre.split(path[1:])):
if e[0] == "-":
e = self._escape_wrap('parse_tag', e[1:], True)
dontwant.add(e)
elif e[:2] == "O:":
o = e[2:]
if o != "group":
t = Tag()
o = self._escape_wrap('find_tag', o, t, True)
assert t.valuetype
order.append(o)
elif e[:2] == "R:":
range = tuple(map(int, e[2:].split(":")))
elif e == "C:":
clean = True
elif e == "N:":
nodefault = True
else:
e = self._escape_wrap('parse_tag', e, True)
want.add(e)
if not first: first = e
if self._default_search and not nodefault:
def bare(tg):
if tg[0] in "~!":
return tg[1:]
return tg
allguids = {bare(t[0]) for t in want | dontwant if t}
for n in ("want", "dontwant"):
for t in getattr(self._default_search, n):
if bare(t[0]) not in allguids:
locals()[n].add(t)
if "group" in order:
want.remove(first)
want = [first] + list(want)
return _search_t(tuple(want), tuple(dontwant), tuple(order), range, clean)
def main(self, *a, **kw):
self._cache = Cache(30)
self._client = Client()
self._client_lock = RLock()
self._cfgfile = self._cfg2file()
self._use_cache = False
self._prime_stat_cache()
if self._raw2jpeg and not self._use_cache:
raise Exception("raw2jpeg only works with a stat-cache")
if self._raw2jpeg:
from wellpapp import RawWrapper
if self._default_search:
ds = "/" + self._default_search
self._default_search = None
self._default_search = self._path2search(ds)
if None in self._default_search.want or None in self._default_search.dontwant:
raise Exception("Default search broken (%r)" % (self._default_search,))
wp = self
class FakeFile:
keep_cache = False
direct_io = False
_fh = None
data = ""
def __init__(self, path, flags, *mode):
rwflags = flags & (os.O_RDONLY | os.O_WRONLY | os.O_RDWR)
if rwflags != os.O_RDONLY: NOTFOUND()
path = pathfix(path)
if path == _cfgpath:
self.data = wp._cfgfile
return
spath = path.split("/")
metam = metamd5re.match(spath[-1])
if metam:
self.data = wp._generate_meta(metam.group(1))
return
if spath[-1][:len(_cloudname)] == _cloudname:
self.data = wp._generate_cloud(spath[1:-1], spath[-1])
return
if spath[-3:-1] in _thumbpaths:
self.data = self._make_thumb(spath)
else:
fn = spath[-1].split(".")
m = fn[-2][-32:]
fh = self._open(m)
if fh:
if wp._raw2jpeg and fn[-1] in _rawext_r:
# @@ Check wp._stat(m).version
self._fh = RawWrapper(fh, True)
else:
self._fh = fh
self._lock = Lock()
def _open(self, m):
try:
dest = wp._stat(m).dest
return open(dest, "rb")
except Exception:
pass
try:
p = wp._client.image_path(m)
dest = os.readlink(p)
fh = open(dest, "rb")
wp._stat_cache[m] = wp._stat(m)._replace(dest=dest)
return fh
except Exception:
pass
# FUSE doesn't seem to like destroying these objects.
# But it does call release, so I'll do what I can.
def __del__(self):
self.release(0)
def release(self, flags):
if self._fh: self._fh.close()
self.data = self._fh = self._Lock = None
def _make_thumb(self, spath):
search = wp._path2search("/".join(spath[:-3]))
if not search: NOTFOUND()
orgmd5, fn = wp._resolve_thumb(search, spath[-1])
ext = fn.split(".")[-1]
tfn = wp._client.thumb_path(orgmd5, spath[-2])
fh = open(tfn, "rb")
data = fh.read()
fh.close()
if not (search.order or ext in _rawext_r):
return data
data = data.split(b"tEXtThumb::URI\0")
if len(data) != 2: NOTFOUND()
pre, post = data
clen, = unpack(">I", pre[-4:])
if search.order: # It's longer only of search was ordered
pre = pre[:-4] + pack(">I", clen + 7)
post = post[clen - 7:]
tEXt = b"tEXtThumb::URI\0" + fn.encode("utf-8")
crc = crc32(tEXt)
if crc < 0: crc += 0x100000000
tEXt += pack(">I", crc)
return pre + tEXt + post
def read(self, length, offset):
if self._fh:
with self._lock:
self._fh.seek(offset)
return self._fh.read(length)
else:
return self.data[offset:offset + length]
self.file_class = FakeFile
return fuse.Fuse.main(self, *a, **kw)
def main(arg0, argv):
server = Wellpapp(prog=arg0)
server.parse(argv, errex=1, values=server)
server.main()
|
daniparera/MCR | refs/heads/master | NXWND/ponderateSynsetWraped.py | 1 | #!/usr/bin/python
import os, subprocess
import textwrap, argparse
parserarg = argparse.ArgumentParser(
prog='ponderateSynsetWraped',
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent('''\
calculate ponderated vectors for specific synset
--------------------------------
example of use $python3 %(prog)s --synset synset [[--debug]]
'''))
parserarg.add_argument('--debug', action='store_false', default='TRUE', help='to show aditional information')
parserarg.add_argument('--synset', dest='synset', required=True, default='', type=str , help='input synsets (required)')
args = parserarg.parse_args()
debug = not(bool(args.debug))
if debug: deb="--debug"
else: deb=""
syn = args.synset
cmd = "python ponderateSynset.py --synset "+syn+" "+deb
result = subprocess.check_output(cmd, shell=True).strip().decode('utf-8')
print("++++++++++++++++++++++")
print(result)
|
FR4NK-W/osourced-scion | refs/heads/master | python/lib/log.py | 3 | # Copyright 2015 ETH Zurich
#
# 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.
"""
:mod:`log` --- Logging utilites
===============================
"""
# Stdlib
import logging
import logging.handlers
import traceback
from datetime import datetime, timezone
# This file should not include other SCION libraries, to prevent circular import
# errors.
#: Bytes
LOG_MAX_SIZE = 10 * 1024 * 1024
LOG_BACKUP_COUNT = 1
# Logging handlers that will log logging exceptions, and then re-raise them. The
# default behaviour of python's logging handlers is to catch logging exceptions,
# which hides the problem.
#
# We don't try to use the normal logging system at this point because we don't
# know if that's working at all. If it is (e.g. when the exception is a
# formatting error), when we re-raise the exception, it'll get handled by the
# normal process.
_dispatch_formatter = None
def _handleError(self, _):
self.stream.write("Exception in logging module:\n")
for line in traceback.format_exc().split("\n"):
self.stream.write(line+"\n")
self.flush()
raise
class _RotatingErrorHandler(logging.handlers.RotatingFileHandler):
handleError = _handleError
class _ConsoleErrorHandler(logging.StreamHandler):
handleError = _handleError
class Rfc3339Formatter(logging.Formatter):
def format(self, record): # pragma: no cover
lines = super().format(record).splitlines()
return "\n> ".join(lines)
def formatTime(self, record, _): # pragma: no cover
# Not using lib.util.iso_timestamp here, to avoid potential import
# loops.
# Also, using str on a datetime object inserts a ":" into the time zone,
# which, while legal, is inconsistent with logging in Go and Zlog. Fortunately,
# Python's strftime does the right thing.
return datetime.fromtimestamp(
record.created, tz=timezone.utc).strftime("%Y-%m-%d %H:%M:%S.%f%z")
class DispatchFormatter: # pragma: no cover
"""
A dispatching formatter that allows modules to install custom formatters for
their child loggers.
"""
def __init__(self, default_formatter, formatters=None):
self._default_formatter = default_formatter
self._formatters = formatters or {}
def add_formatter(self, key, formatter):
self._formatters[key] = formatter
def format(self, record):
formatter = self._formatters.get(record.name, self._default_formatter)
return formatter.format(record)
def add_formatter(name, formatter): # pragma: no cover
_dispatch_formatter.add_formatter(name, formatter)
def init_logging(log_base=None, file_level=logging.DEBUG,
console_level=logging.NOTSET):
"""
Configure logging for components (servers, routers, gateways).
"""
default_formatter = Rfc3339Formatter(
"%(asctime)s [%(levelname)s] (%(threadName)s) %(message)s")
global _dispatch_formatter
_dispatch_formatter = DispatchFormatter(default_formatter)
handlers = []
if log_base:
for lvl in sorted(logging._levelToName):
if lvl < file_level:
continue
log_file = "%s.%s" % (log_base, logging._levelToName[lvl])
h = _RotatingErrorHandler(
log_file, maxBytes=LOG_MAX_SIZE, backupCount=LOG_BACKUP_COUNT,
encoding="utf-8")
h.setLevel(lvl)
handlers.append(h)
if console_level:
h = _ConsoleErrorHandler()
h.setLevel(console_level)
handlers.append(h)
for h in handlers:
h.setFormatter(_dispatch_formatter)
# Use logging.DEBUG here, so that the handlers themselves can decide what to
# filter.
logging.basicConfig(level=logging.DEBUG, handlers=handlers)
def log_exception(msg, *args, level=logging.CRITICAL, **kwargs):
"""
Properly format an exception before logging.
"""
logging.log(level, msg, *args, **kwargs)
for line in traceback.format_exc().split("\n"):
logging.log(level, line)
def log_stack(level=logging.DEBUG):
logging.log(level, "".join(traceback.format_stack()))
|
mamikonyana/mamikonyana.github.io | refs/heads/flask | static/ml_afternoon/presentation_data/practical_s1/kmeans.py | 1 | import numpy as np
def random_initialize(data_array, num_clusters):
# TODO: Initialize cluster centers by sampling `num_clusters` points
# uniformly from data_array.
return list()
def plus_plus_initialize(data_array, num_clusters):
# TODO: Initialize cluster centers using k-means++ algorithm.
return list()
class KMeans(object):
def __init__(self, num_mixtures):
self.K = num_mixtures
self.means = []
def initialize(self, data):
"""
:param data: data, numpy 2-D array
"""
# TODO: Initialize cluster centers
# Hint: Use one of the function at the top of the file.
pass
def fit(self, data):
"""
:param data: data to fit, numpy 2-D array
"""
# TODO: Initialize Mixtures, then run EM algorithm until it converges.
pass
def predict(self, data):
"""
Return index of the cluster the point is most likely to belong.
:param data: data, numpy 2-D array
:return: labels, numpy 1-D array
"""
# TODO: Determine which cluster each of the points belongs to
pass
def get_centers(self):
"""
Return list of centers of the clusters, i.e. means
"""
return self.means
|
Johnzero/erp | refs/heads/fga | openerp/addons/hr/__init__.py | 9 | # -*- coding: utf-8 -*-
##############################################################################
#
# OpenERP, Open Source Management Solution
# Copyright (C) 2004-2010 Tiny SPRL (<http://tiny.be>).
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# 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 Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
import hr_department
import hr
import report
import wizard
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
|
chrisjones-brack3t/python-oauth2 | refs/heads/master | oauth2/clients/imap.py | 885 | """
The MIT License
Copyright (c) 2007-2010 Leah Culver, Joe Stump, Mark Paschal, Vic Fryzel
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
import oauth2
import imaplib
class IMAP4_SSL(imaplib.IMAP4_SSL):
"""IMAP wrapper for imaplib.IMAP4_SSL that implements XOAUTH."""
def authenticate(self, url, consumer, token):
if consumer is not None and not isinstance(consumer, oauth2.Consumer):
raise ValueError("Invalid consumer.")
if token is not None and not isinstance(token, oauth2.Token):
raise ValueError("Invalid token.")
imaplib.IMAP4_SSL.authenticate(self, 'XOAUTH',
lambda x: oauth2.build_xoauth_string(url, consumer, token))
|
hhstore/flask-annotated | refs/heads/master | asyncio/asyncio-3.4.3/examples/fetch/fetch0.py | 2 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Simplest possible HTTP client."""
import sys
from asyncio import *
# 协程:
@coroutine
def fetch():
r, w = yield from open_connection('python.org', 80)
request = 'GET / HTTP/1.0\r\n\r\n'
print('>', request, file=sys.stderr)
w.write(request.encode('latin-1'))
while True:
line = yield from r.readline() # 异步返回
line = line.decode('latin-1').rstrip()
if not line:
break
print('<', line, file=sys.stderr)
print(file=sys.stderr)
body = yield from r.read() # 异步返回
return body
#########################################
# 主函数
#
#########################################
def main():
loop = get_event_loop() # 事件循环
try:
body = loop.run_until_complete(fetch())
finally:
loop.close()
print(body.decode('latin-1'), end='')
if __name__ == '__main__':
main()
|
Southpaw-TACTIC/TACTIC | refs/heads/4.7 | src/tactic/ui/widget/gallery_wdg.py | 1 | ###########################################################
#
# Copyright (c) 2014, Southpaw Technology
# All Rights Reserved
#
# PROPRIETARY INFORMATION. This software is proprietary to
# Southpaw Technology, and is not to be reproduced, transmitted,
# or disclosed in any way without written permission.
#
#
#
__all__ = ['GalleryWdg']
import urllib
from pyasm.biz import Snapshot, File
from pyasm.search import Search
from pyasm.web import HtmlElement, DivWdg, Table
from pyasm.widget import TextWdg, IconWdg
from tactic.ui.common import BaseRefreshWdg
class GalleryWdg(BaseRefreshWdg):
def init(self):
self.curr_path = None
def get_display(self):
self.sobject_data = {}
top = self.top
top.add_style
top.add_class("spt_gallery_top")
inner = DivWdg()
top.add(inner)
# make the whole Gallery unselectable
inner.add_class('unselectable')
inner.add_style("position: fixed")
inner.add_style("top: 0px")
inner.add_style("left: 0px")
inner.add_style("width: 100%")
#inner.add_style("height: 100%")
inner.add_style("bottom: 0px")
inner.add_style("padding-bottom: 40px")
#inner.add_style("background: rgba(0,0,0,0.5)")
inner.add_style("background: rgba(0,0,0,1)")
inner.add_style("z-index: 2000")
width = self.kwargs.get("width")
height = self.kwargs.get("height")
# default to top.
align = self.kwargs.get("align")
if not align:
align = "top"
if not width:
width = 1300
else:
width = int(width)
paths = self.get_paths(file_type='main')
# icon type may be too small
thumb_paths = self.get_paths(file_type='web')
descriptions = []
for path in paths:
sobject = self.sobject_data.get(path)
if not sobject:
descriptions.append("")
else:
description = sobject.get("description")
if not description:
description = ""
descriptions.append(description)
total_width = width * len(paths)
inner.add_behavior( {
'type': 'load',
'width': width,
'total_width': total_width,
'descriptions': descriptions,
'cbjs_action': '''
spt.gallery = {};
// 1250 is defined also in the css styles
spt.gallery.portrait = window.innerWidth < 1250;
spt.gallery.portrait = false
spt.gallery.top = bvr.src_el;
spt.gallery.content = spt.gallery.top.getElement(".spt_gallery_content");
spt.gallery.content.setStyle('opacity','0.1')
spt.gallery.desc_el = spt.gallery.top.getElement(".spt_gallery_description");
//window.addEvent('domready', function() {
setTimeout(function() {
// set the img h or w directly
var items = bvr.src_el.getElements('.spt_gallery_item img');
// fade in
spt.gallery.content.set('tween', {duration: 250}).fade('in');
/*
for (var k=0; k < items.length; k++) {
var sizes = items[k].getSize();
var item_h = sizes.y;
var item_w = sizes.x;
if (item_h >= item_w){
//items[k].setStyle('width', 'auto');
//items[k].setStyle('height', '100%');
}
else {
//items[k].setStyle('width','auto');
//items[k].setStyle('height','100%');
}
}
*/
}, 50)
spt.gallery.width = bvr.width;
spt.gallery.descriptions = bvr.descriptions;
spt.gallery.index = 0;
spt.gallery.total = bvr.descriptions.length;
spt.gallery.left_arrow = bvr.src_el.getElement('.spt_left_arrow');
spt.gallery.right_arrow = bvr.src_el.getElement('.spt_right_arrow');
spt.gallery.videos = {};
spt.gallery.init = function() {
}
spt.gallery.stack = [];
spt.gallery.push_stack = function(key) {
spt.gallery.stack.push(key);
}
spt.gallery.show_next = function(src_el) {
if (!src_el)
src_el = spt.gallery.right_arrow;
if (spt.gallery.index >= spt.gallery.total-2) {
spt.hide(src_el);
}
if (spt.gallery.index == spt.gallery.total-1) {
return;
}
spt.gallery.index += 1;
spt.gallery.show_index(spt.gallery.index);
}
spt.gallery.show_prev = function(src_el) {
if (!src_el)
src_el = spt.gallery.left_arrow;
if (spt.gallery.index <= 1) {
spt.hide(src_el);
}
if (spt.gallery.index == 0) {
return;
}
spt.gallery.index -= 1;
spt.gallery.show_index(spt.gallery.index);
}
spt.gallery.show_index = function(index) {
// stop all videos
var videos = spt.gallery.top.getElements(".video-js");
for (var i = 0; i < videos.length; i++) {
try {
var video = videos[i];
var video_id = video.get("id");
var video_obj = videojs(video_id, {"nativeControlsForTouch": false});
video_obj.pause();
}
catch(e) {
}
}
// can't tween percentage with this library???
var width = spt.gallery.width;
var margin = - width * index;
var content = spt.gallery.content;
//content.setStyle("margin-left", margin + "px");
new Fx.Tween(content,{duration: 250}).start("margin-left", margin);
spt.gallery.index = index;
var total = spt.gallery.total;
if (index == 0) {
spt.hide(spt.gallery.left_arrow);
spt.show(spt.gallery.right_arrow);
}
else if (index == total - 1) {
spt.show(spt.gallery.left_arrow);
spt.hide(spt.gallery.right_arrow);
}
else {
spt.show(spt.gallery.left_arrow);
spt.show(spt.gallery.right_arrow);
}
var description = spt.gallery.descriptions[index];
if (!description) {
description = (index+1)+" of "+total;
}
else {
description = (index+1)+" of "+total+" - " + description;
}
spt.gallery.set_description(description);
}
spt.gallery.close = function() {
var content = spt.gallery.content;
var gallery_top = content.getParent(".spt_gallery_top");
var top = gallery_top.getParent(".spt_top");
spt.behavior.destroy_element(gallery_top);
// header is sometimes not in view after closing, if a header exists
// make sure it is scrolled into view
if (top) {
var index_header = top.getElement(".spt_index_header");
if (index_header) {
index_header.scrollIntoView();
}
}
}
spt.gallery.set_description = function(desc) {
var desc_el = spt.gallery.desc_el;
desc_el.innerHTML = desc;
}
'''
} )
scroll = DivWdg(css='spt_gallery_scroll')
inner.add(scroll)
scroll.set_box_shadow()
scroll.add_style("width: %s" % width)
if height:
scroll.add_style("height: %s" % height)
scroll.add_style("overflow-x: hidden")
scroll.add_style("overflow-y: hidden")
scroll.add_style("background: #000")
#scroll.add_style("position: absolute")
scroll.add_style("margin-left: auto")
scroll.add_style("margin-right: auto")
content = DivWdg()
top.add_attr('tabindex','-1')
scroll.add(content)
content.add_class("spt_gallery_content")
# make the items vertically align to bottom (flex-emd)
# on a regular monitor, align to top (flex-start) is better
if align == 'bottom':
align_items = 'flex-end'
else:
align_items = 'flex-start'
content.add_styles("display: flex; flex-flow: row nowrap; align-items: %s; justify-content: center;"%align_items)
content.add_style("width: %s" % total_width)
content.add_style("height: 100%")
top.add_behavior( {
'type': 'load',
'cbjs_action': '''
bvr.src_el.focus();
'''
} )
top.add_behavior( {
'type': 'mouseenter',
'cbjs_action': '''
bvr.src_el.focus();
'''
} )
top.add_behavior( {
'type': 'mouseleave',
'cbjs_action': '''
bvr.src_el.blur();
'''
} )
"""
input = TextWdg("keydown")
content.add(input)
input.add_style("position: absolute")
input.add_style("left: -5000px")
"""
top.add_behavior( {
'type': 'keydown',
'cbjs_action': '''
var key = evt.key;
if (key == "left") {
spt.gallery.push_stack(key);
spt.gallery.show_prev();
}
else if (key == "right") {
spt.gallery.push_stack(key);
spt.gallery.show_next();
}
else if (key == "esc" || key == "enter") {
spt.gallery.close();
}
'''
} )
curr_index = 0
for i, path in enumerate(paths):
path_div = DivWdg(css='spt_gallery_item')
content.add(path_div)
#path_div.add_style("float: left")
path_div.add_style("display: inline-block")
path_div.add_style("vertical-align: middle")
if path == self.curr_path:
curr_index = i
try:
thumb_path = thumb_paths[i]
except IndexError:
print("Cannot find the thumb_path [%s] "%i )
thumb_path = ''
#path_div.add_style("width: %s" % width)
#if height:
# path_div.add_style("height: %s" % height)
path_div.add_style("width: 100%")
path_div.add_style("height: 100%")
path_div.add_style("overflow-x: hidden")
path_div.add_style("overflow-y: hidden")
from tactic.ui.widget import EmbedWdg
embed = EmbedWdg(src=path, click=False, thumb_path=thumb_path, index=i, controls="true", layout="fit")
path_div.add(embed)
content.add_behavior({
'type': 'load',
'index': curr_index,
'cbjs_action': '''
if (!bvr.index) bvr.index = 0;
spt.gallery.show_index(bvr.index);
'''
} )
#icon = IconWdg(title="Close", icon="/plugins/remington/pos/icons/close.png")
icon = IconWdg(title="Close", icon="/context/icons/glyphs/close.png", width="40px")
inner.add(icon)
icon.add_style("position: absolute")
icon.add_style("cursor: pointer")
icon.add_style("top: 30px")
icon.add_style("right: 38px")
icon.add_style("opacity: 0.5")
icon.add_behavior( {
'type': 'click_up' ,
'cbjs_action': '''
spt.gallery.close();
'''
} )
icon.add_style("background", "rgba(48,48,48,0.7)")
icon.add_style("border-radius", "5px")
icon = IconWdg(title="Previous", icon="/context/icons/glyphs/chevron_left.png")
inner.add(icon)
icon.add_class('spt_left_arrow')
icon.add_style("cursor: pointer")
icon.add_style("position: absolute")
icon.add_style("top: 40%")
icon.add_style("left: 0px")
icon.add_style("opacity: 0.5")
icon.add_behavior( {
'type': 'click_up' ,
'cbjs_action': '''
var arrow = bvr.src_el;
spt.gallery.show_prev(arrow);
'''
} )
icon.add_style("background", "rgba(48,48,48,0.7)")
icon.add_style("border-radius", "5px")
icon = IconWdg(title="Next", icon="/context/icons/glyphs/chevron_right.png")
inner.add(icon)
icon.add_class('spt_right_arrow')
icon.add_style("position: absolute")
icon.add_style("cursor: pointer")
icon.add_style("top: 40%")
icon.add_style("right: 0px")
icon.add_style("opacity: 0.5")
icon.add_behavior( {
'type': 'click_up',
'cbjs_action': '''
var arrow = bvr.src_el;
spt.gallery.show_next(arrow);
'''
} )
icon.add_style("background", "rgba(48,48,48,0.7)")
icon.add_style("border-radius", "5px")
desc_div = DivWdg()
desc_div.add_class("spt_gallery_description")
desc_div.add_style("height: 30px")
desc_div.add_style("width: %s" % width)
desc_div.add_style("text-align: center")
desc_div.add_style("background: rgba(0,0,0,1)")
desc_div.add_style("color: #bbb")
desc_div.add_style("font-weight: bold")
desc_div.add_style("font-size: 16px")
desc_div.add_style("padding-top: 10px")
desc_div.add_style("margin-left: -%s" % (width/2))
desc_div.add_style("z-index: 1000")
desc_div.add("")
desc_outer_div = DivWdg()
inner.add(desc_outer_div)
desc_outer_div.add_style("position: fixed")
desc_outer_div.add(desc_div)
desc_outer_div.add_style("bottom: 0px")
desc_outer_div.add_style("left: 50%")
return top
def get_paths(self, file_type='main'):
# this is the selected one
search_key = self.kwargs.get("search_key")
search_keys = self.kwargs.get("search_keys")
paths = self.kwargs.get("paths")
if not paths:
paths = []
if search_keys:
sobjects = Search.get_by_search_keys(search_keys, keep_order=True)
# return_dict=True defaults to return the first of each snapshot list
# and so works well with is_latest=True
if sobjects and sobjects[0].get_base_search_type() == "sthpw/snapshot":
sobj_snapshot_dict = {}
for sobject in sobjects:
tmp_search_key = sobject.get_search_key()
sobj_snapshot_dict[tmp_search_key] = sobject
snapshots = sobjects
else:
sobj_snapshot_dict = Snapshot.get_by_sobjects(sobjects, is_latest=True, return_dict=True)
snapshots = sobj_snapshot_dict.values()
file_dict = Snapshot.get_files_dict_by_snapshots(snapshots, file_type=file_type)
for sobject in sobjects:
path = ''
snapshot = sobj_snapshot_dict.get(sobject.get_search_key())
# it is supposed to get one (latest), just a precaution
if isinstance(snapshot, list):
snapshot = snapshot[0]
if not snapshot:
continue
file_list = file_dict.get(snapshot.get_code())
if not file_list:
paths.append("")
continue
# NOTE: there should only be one file
tmp_paths = []
for file_object in file_list:
path = file_object.get_web_path()
# If the file type is not supported by web browsers, get the web version
# as a fallback.
# TODO: Note that this will disable
# the retrieval of a sequence of files as in ####.tif case, where
# the asset is a sequence of files. So it will only display the web
# version of the first file in the asset list.
extension = File.get_extension(path)
accepted_exts = ['mp4', 'mov', 'jpg', 'png', 'ogg', 'webm']
if file_type == 'main' and extension not in accepted_exts:
path= snapshot.get_web_path_by_type(type="web")
# If the asset is a sequence of files, retrieve all the file paths.
# NOTE: In this case, web versions do not exist for all of the files.
# The web version is generated only for the first one in the sequence.
if path.find("#") != -1:
expanded_paths = snapshot.get_expanded_web_paths()
path = "|".join(expanded_paths)
tmp_paths.append(path)
path = "|".join(tmp_paths)
self.sobject_data[path] = sobject
paths.append(path)
# set the current path the user clicks on
if not self.curr_path and sobject.get_search_key() == search_key and file_type=='main':
self.curr_path = path
elif paths:
return paths
else:
# TEST
paths = [
'/assets/test/store/The%20Boxter_v001.jpg',
'/assets/test/store/Another%20one_v001.jpg',
'/assets/test/store/Whatever_v001.jpg'
]
"""
for index,path in enumerate(paths):
path = urllib.pathname2url(path)
paths[index] = path
"""
return paths
|
samuel1208/scikit-learn | refs/heads/master | sklearn/covariance/graph_lasso_.py | 127 | """GraphLasso: sparse inverse covariance estimation with an l1-penalized
estimator.
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
# Copyright: INRIA
import warnings
import operator
import sys
import time
import numpy as np
from scipy import linalg
from .empirical_covariance_ import (empirical_covariance, EmpiricalCovariance,
log_likelihood)
from ..utils import ConvergenceWarning
from ..utils.extmath import pinvh
from ..utils.validation import check_random_state, check_array
from ..linear_model import lars_path
from ..linear_model import cd_fast
from ..cross_validation import check_cv, cross_val_score
from ..externals.joblib import Parallel, delayed
import collections
# Helper functions to compute the objective and dual objective functions
# of the l1-penalized estimator
def _objective(mle, precision_, alpha):
"""Evaluation of the graph-lasso objective function
the objective function is made of a shifted scaled version of the
normalized log-likelihood (i.e. its empirical mean over the samples) and a
penalisation term to promote sparsity
"""
p = precision_.shape[0]
cost = - 2. * log_likelihood(mle, precision_) + p * np.log(2 * np.pi)
cost += alpha * (np.abs(precision_).sum()
- np.abs(np.diag(precision_)).sum())
return cost
def _dual_gap(emp_cov, precision_, alpha):
"""Expression of the dual gap convergence criterion
The specific definition is given in Duchi "Projected Subgradient Methods
for Learning Sparse Gaussians".
"""
gap = np.sum(emp_cov * precision_)
gap -= precision_.shape[0]
gap += alpha * (np.abs(precision_).sum()
- np.abs(np.diag(precision_)).sum())
return gap
def alpha_max(emp_cov):
"""Find the maximum alpha for which there are some non-zeros off-diagonal.
Parameters
----------
emp_cov : 2D array, (n_features, n_features)
The sample covariance matrix
Notes
-----
This results from the bound for the all the Lasso that are solved
in GraphLasso: each time, the row of cov corresponds to Xy. As the
bound for alpha is given by `max(abs(Xy))`, the result follows.
"""
A = np.copy(emp_cov)
A.flat[::A.shape[0] + 1] = 0
return np.max(np.abs(A))
# The g-lasso algorithm
def graph_lasso(emp_cov, alpha, cov_init=None, mode='cd', tol=1e-4,
enet_tol=1e-4, max_iter=100, verbose=False,
return_costs=False, eps=np.finfo(np.float64).eps,
return_n_iter=False):
"""l1-penalized covariance estimator
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
emp_cov : 2D ndarray, shape (n_features, n_features)
Empirical covariance from which to compute the covariance estimate.
alpha : positive float
The regularization parameter: the higher alpha, the more
regularization, the sparser the inverse covariance.
cov_init : 2D array (n_features, n_features), optional
The initial guess for the covariance.
mode : {'cd', 'lars'}
The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where p > n. Elsewhere prefer cd
which is more numerically stable.
tol : positive float, optional
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter : integer, optional
The maximum number of iterations.
verbose : boolean, optional
If verbose is True, the objective function and dual gap are
printed at each iteration.
return_costs : boolean, optional
If return_costs is True, the objective function and dual gap
at each iteration are returned.
eps : float, optional
The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems.
return_n_iter : bool, optional
Whether or not to return the number of iterations.
Returns
-------
covariance : 2D ndarray, shape (n_features, n_features)
The estimated covariance matrix.
precision : 2D ndarray, shape (n_features, n_features)
The estimated (sparse) precision matrix.
costs : list of (objective, dual_gap) pairs
The list of values of the objective function and the dual gap at
each iteration. Returned only if return_costs is True.
n_iter : int
Number of iterations. Returned only if `return_n_iter` is set to True.
See Also
--------
GraphLasso, GraphLassoCV
Notes
-----
The algorithm employed to solve this problem is the GLasso algorithm,
from the Friedman 2008 Biostatistics paper. It is the same algorithm
as in the R `glasso` package.
One possible difference with the `glasso` R package is that the
diagonal coefficients are not penalized.
"""
_, n_features = emp_cov.shape
if alpha == 0:
if return_costs:
precision_ = linalg.inv(emp_cov)
cost = - 2. * log_likelihood(emp_cov, precision_)
cost += n_features * np.log(2 * np.pi)
d_gap = np.sum(emp_cov * precision_) - n_features
if return_n_iter:
return emp_cov, precision_, (cost, d_gap), 0
else:
return emp_cov, precision_, (cost, d_gap)
else:
if return_n_iter:
return emp_cov, linalg.inv(emp_cov), 0
else:
return emp_cov, linalg.inv(emp_cov)
if cov_init is None:
covariance_ = emp_cov.copy()
else:
covariance_ = cov_init.copy()
# As a trivial regularization (Tikhonov like), we scale down the
# off-diagonal coefficients of our starting point: This is needed, as
# in the cross-validation the cov_init can easily be
# ill-conditioned, and the CV loop blows. Beside, this takes
# conservative stand-point on the initial conditions, and it tends to
# make the convergence go faster.
covariance_ *= 0.95
diagonal = emp_cov.flat[::n_features + 1]
covariance_.flat[::n_features + 1] = diagonal
precision_ = pinvh(covariance_)
indices = np.arange(n_features)
costs = list()
# The different l1 regression solver have different numerical errors
if mode == 'cd':
errors = dict(over='raise', invalid='ignore')
else:
errors = dict(invalid='raise')
try:
# be robust to the max_iter=0 edge case, see:
# https://github.com/scikit-learn/scikit-learn/issues/4134
d_gap = np.inf
for i in range(max_iter):
for idx in range(n_features):
sub_covariance = covariance_[indices != idx].T[indices != idx]
row = emp_cov[idx, indices != idx]
with np.errstate(**errors):
if mode == 'cd':
# Use coordinate descent
coefs = -(precision_[indices != idx, idx]
/ (precision_[idx, idx] + 1000 * eps))
coefs, _, _, _ = cd_fast.enet_coordinate_descent_gram(
coefs, alpha, 0, sub_covariance, row, row,
max_iter, enet_tol, check_random_state(None), False)
else:
# Use LARS
_, _, coefs = lars_path(
sub_covariance, row, Xy=row, Gram=sub_covariance,
alpha_min=alpha / (n_features - 1), copy_Gram=True,
method='lars', return_path=False)
# Update the precision matrix
precision_[idx, idx] = (
1. / (covariance_[idx, idx]
- np.dot(covariance_[indices != idx, idx], coefs)))
precision_[indices != idx, idx] = (- precision_[idx, idx]
* coefs)
precision_[idx, indices != idx] = (- precision_[idx, idx]
* coefs)
coefs = np.dot(sub_covariance, coefs)
covariance_[idx, indices != idx] = coefs
covariance_[indices != idx, idx] = coefs
d_gap = _dual_gap(emp_cov, precision_, alpha)
cost = _objective(emp_cov, precision_, alpha)
if verbose:
print(
'[graph_lasso] Iteration % 3i, cost % 3.2e, dual gap %.3e'
% (i, cost, d_gap))
if return_costs:
costs.append((cost, d_gap))
if np.abs(d_gap) < tol:
break
if not np.isfinite(cost) and i > 0:
raise FloatingPointError('Non SPD result: the system is '
'too ill-conditioned for this solver')
else:
warnings.warn('graph_lasso: did not converge after %i iteration:'
' dual gap: %.3e' % (max_iter, d_gap),
ConvergenceWarning)
except FloatingPointError as e:
e.args = (e.args[0]
+ '. The system is too ill-conditioned for this solver',)
raise e
if return_costs:
if return_n_iter:
return covariance_, precision_, costs, i + 1
else:
return covariance_, precision_, costs
else:
if return_n_iter:
return covariance_, precision_, i + 1
else:
return covariance_, precision_
class GraphLasso(EmpiricalCovariance):
"""Sparse inverse covariance estimation with an l1-penalized estimator.
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
alpha : positive float, default 0.01
The regularization parameter: the higher alpha, the more
regularization, the sparser the inverse covariance.
mode : {'cd', 'lars'}, default 'cd'
The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where p > n. Elsewhere prefer cd
which is more numerically stable.
tol : positive float, default 1e-4
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter : integer, default 100
The maximum number of iterations.
verbose : boolean, default False
If verbose is True, the objective function and dual gap are
plotted at each iteration.
assume_centered : boolean, default False
If True, data are not centered before computation.
Useful when working with data whose mean is almost, but not exactly
zero.
If False, data are centered before computation.
Attributes
----------
covariance_ : array-like, shape (n_features, n_features)
Estimated covariance matrix
precision_ : array-like, shape (n_features, n_features)
Estimated pseudo inverse matrix.
n_iter_ : int
Number of iterations run.
See Also
--------
graph_lasso, GraphLassoCV
"""
def __init__(self, alpha=.01, mode='cd', tol=1e-4, enet_tol=1e-4,
max_iter=100, verbose=False, assume_centered=False):
self.alpha = alpha
self.mode = mode
self.tol = tol
self.enet_tol = enet_tol
self.max_iter = max_iter
self.verbose = verbose
self.assume_centered = assume_centered
# The base class needs this for the score method
self.store_precision = True
def fit(self, X, y=None):
X = check_array(X)
if self.assume_centered:
self.location_ = np.zeros(X.shape[1])
else:
self.location_ = X.mean(0)
emp_cov = empirical_covariance(
X, assume_centered=self.assume_centered)
self.covariance_, self.precision_, self.n_iter_ = graph_lasso(
emp_cov, alpha=self.alpha, mode=self.mode, tol=self.tol,
enet_tol=self.enet_tol, max_iter=self.max_iter,
verbose=self.verbose, return_n_iter=True)
return self
# Cross-validation with GraphLasso
def graph_lasso_path(X, alphas, cov_init=None, X_test=None, mode='cd',
tol=1e-4, enet_tol=1e-4, max_iter=100, verbose=False):
"""l1-penalized covariance estimator along a path of decreasing alphas
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
X : 2D ndarray, shape (n_samples, n_features)
Data from which to compute the covariance estimate.
alphas : list of positive floats
The list of regularization parameters, decreasing order.
X_test : 2D array, shape (n_test_samples, n_features), optional
Optional test matrix to measure generalisation error.
mode : {'cd', 'lars'}
The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where p > n. Elsewhere prefer cd
which is more numerically stable.
tol : positive float, optional
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter : integer, optional
The maximum number of iterations.
verbose : integer, optional
The higher the verbosity flag, the more information is printed
during the fitting.
Returns
-------
covariances_ : List of 2D ndarray, shape (n_features, n_features)
The estimated covariance matrices.
precisions_ : List of 2D ndarray, shape (n_features, n_features)
The estimated (sparse) precision matrices.
scores_ : List of float
The generalisation error (log-likelihood) on the test data.
Returned only if test data is passed.
"""
inner_verbose = max(0, verbose - 1)
emp_cov = empirical_covariance(X)
if cov_init is None:
covariance_ = emp_cov.copy()
else:
covariance_ = cov_init
covariances_ = list()
precisions_ = list()
scores_ = list()
if X_test is not None:
test_emp_cov = empirical_covariance(X_test)
for alpha in alphas:
try:
# Capture the errors, and move on
covariance_, precision_ = graph_lasso(
emp_cov, alpha=alpha, cov_init=covariance_, mode=mode, tol=tol,
enet_tol=enet_tol, max_iter=max_iter, verbose=inner_verbose)
covariances_.append(covariance_)
precisions_.append(precision_)
if X_test is not None:
this_score = log_likelihood(test_emp_cov, precision_)
except FloatingPointError:
this_score = -np.inf
covariances_.append(np.nan)
precisions_.append(np.nan)
if X_test is not None:
if not np.isfinite(this_score):
this_score = -np.inf
scores_.append(this_score)
if verbose == 1:
sys.stderr.write('.')
elif verbose > 1:
if X_test is not None:
print('[graph_lasso_path] alpha: %.2e, score: %.2e'
% (alpha, this_score))
else:
print('[graph_lasso_path] alpha: %.2e' % alpha)
if X_test is not None:
return covariances_, precisions_, scores_
return covariances_, precisions_
class GraphLassoCV(GraphLasso):
"""Sparse inverse covariance w/ cross-validated choice of the l1 penalty
Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
Parameters
----------
alphas : integer, or list positive float, optional
If an integer is given, it fixes the number of points on the
grids of alpha to be used. If a list is given, it gives the
grid to be used. See the notes in the class docstring for
more details.
n_refinements: strictly positive integer
The number of times the grid is refined. Not used if explicit
values of alphas are passed.
cv : cross-validation generator, optional
see sklearn.cross_validation module. If None is passed, defaults to
a 3-fold strategy
tol: positive float, optional
The tolerance to declare convergence: if the dual gap goes below
this value, iterations are stopped.
enet_tol : positive float, optional
The tolerance for the elastic net solver used to calculate the descent
direction. This parameter controls the accuracy of the search direction
for a given column update, not of the overall parameter estimate. Only
used for mode='cd'.
max_iter: integer, optional
Maximum number of iterations.
mode: {'cd', 'lars'}
The Lasso solver to use: coordinate descent or LARS. Use LARS for
very sparse underlying graphs, where number of features is greater
than number of samples. Elsewhere prefer cd which is more numerically
stable.
n_jobs: int, optional
number of jobs to run in parallel (default 1).
verbose: boolean, optional
If verbose is True, the objective function and duality gap are
printed at each iteration.
assume_centered : Boolean
If True, data are not centered before computation.
Useful when working with data whose mean is almost, but not exactly
zero.
If False, data are centered before computation.
Attributes
----------
covariance_ : numpy.ndarray, shape (n_features, n_features)
Estimated covariance matrix.
precision_ : numpy.ndarray, shape (n_features, n_features)
Estimated precision matrix (inverse covariance).
alpha_ : float
Penalization parameter selected.
cv_alphas_ : list of float
All penalization parameters explored.
`grid_scores`: 2D numpy.ndarray (n_alphas, n_folds)
Log-likelihood score on left-out data across folds.
n_iter_ : int
Number of iterations run for the optimal alpha.
See Also
--------
graph_lasso, GraphLasso
Notes
-----
The search for the optimal penalization parameter (alpha) is done on an
iteratively refined grid: first the cross-validated scores on a grid are
computed, then a new refined grid is centered around the maximum, and so
on.
One of the challenges which is faced here is that the solvers can
fail to converge to a well-conditioned estimate. The corresponding
values of alpha then come out as missing values, but the optimum may
be close to these missing values.
"""
def __init__(self, alphas=4, n_refinements=4, cv=None, tol=1e-4,
enet_tol=1e-4, max_iter=100, mode='cd', n_jobs=1,
verbose=False, assume_centered=False):
self.alphas = alphas
self.n_refinements = n_refinements
self.mode = mode
self.tol = tol
self.enet_tol = enet_tol
self.max_iter = max_iter
self.verbose = verbose
self.cv = cv
self.n_jobs = n_jobs
self.assume_centered = assume_centered
# The base class needs this for the score method
self.store_precision = True
def fit(self, X, y=None):
"""Fits the GraphLasso covariance model to X.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Data from which to compute the covariance estimate
"""
X = check_array(X)
if self.assume_centered:
self.location_ = np.zeros(X.shape[1])
else:
self.location_ = X.mean(0)
emp_cov = empirical_covariance(
X, assume_centered=self.assume_centered)
cv = check_cv(self.cv, X, y, classifier=False)
# List of (alpha, scores, covs)
path = list()
n_alphas = self.alphas
inner_verbose = max(0, self.verbose - 1)
if isinstance(n_alphas, collections.Sequence):
alphas = self.alphas
n_refinements = 1
else:
n_refinements = self.n_refinements
alpha_1 = alpha_max(emp_cov)
alpha_0 = 1e-2 * alpha_1
alphas = np.logspace(np.log10(alpha_0), np.log10(alpha_1),
n_alphas)[::-1]
t0 = time.time()
for i in range(n_refinements):
with warnings.catch_warnings():
# No need to see the convergence warnings on this grid:
# they will always be points that will not converge
# during the cross-validation
warnings.simplefilter('ignore', ConvergenceWarning)
# Compute the cross-validated loss on the current grid
# NOTE: Warm-restarting graph_lasso_path has been tried, and
# this did not allow to gain anything (same execution time with
# or without).
this_path = Parallel(
n_jobs=self.n_jobs,
verbose=self.verbose
)(
delayed(graph_lasso_path)(
X[train], alphas=alphas,
X_test=X[test], mode=self.mode,
tol=self.tol, enet_tol=self.enet_tol,
max_iter=int(.1 * self.max_iter),
verbose=inner_verbose)
for train, test in cv)
# Little danse to transform the list in what we need
covs, _, scores = zip(*this_path)
covs = zip(*covs)
scores = zip(*scores)
path.extend(zip(alphas, scores, covs))
path = sorted(path, key=operator.itemgetter(0), reverse=True)
# Find the maximum (avoid using built in 'max' function to
# have a fully-reproducible selection of the smallest alpha
# in case of equality)
best_score = -np.inf
last_finite_idx = 0
for index, (alpha, scores, _) in enumerate(path):
this_score = np.mean(scores)
if this_score >= .1 / np.finfo(np.float64).eps:
this_score = np.nan
if np.isfinite(this_score):
last_finite_idx = index
if this_score >= best_score:
best_score = this_score
best_index = index
# Refine the grid
if best_index == 0:
# We do not need to go back: we have chosen
# the highest value of alpha for which there are
# non-zero coefficients
alpha_1 = path[0][0]
alpha_0 = path[1][0]
elif (best_index == last_finite_idx
and not best_index == len(path) - 1):
# We have non-converged models on the upper bound of the
# grid, we need to refine the grid there
alpha_1 = path[best_index][0]
alpha_0 = path[best_index + 1][0]
elif best_index == len(path) - 1:
alpha_1 = path[best_index][0]
alpha_0 = 0.01 * path[best_index][0]
else:
alpha_1 = path[best_index - 1][0]
alpha_0 = path[best_index + 1][0]
if not isinstance(n_alphas, collections.Sequence):
alphas = np.logspace(np.log10(alpha_1), np.log10(alpha_0),
n_alphas + 2)
alphas = alphas[1:-1]
if self.verbose and n_refinements > 1:
print('[GraphLassoCV] Done refinement % 2i out of %i: % 3is'
% (i + 1, n_refinements, time.time() - t0))
path = list(zip(*path))
grid_scores = list(path[1])
alphas = list(path[0])
# Finally, compute the score with alpha = 0
alphas.append(0)
grid_scores.append(cross_val_score(EmpiricalCovariance(), X,
cv=cv, n_jobs=self.n_jobs,
verbose=inner_verbose))
self.grid_scores = np.array(grid_scores)
best_alpha = alphas[best_index]
self.alpha_ = best_alpha
self.cv_alphas_ = alphas
# Finally fit the model with the selected alpha
self.covariance_, self.precision_, self.n_iter_ = graph_lasso(
emp_cov, alpha=best_alpha, mode=self.mode, tol=self.tol,
enet_tol=self.enet_tol, max_iter=self.max_iter,
verbose=inner_verbose, return_n_iter=True)
return self
|
DmitryTsybin/Study | refs/heads/master | Coursera/Using_Python_to_Access_Web_Data/Getting_Started/week_1.py | 1 | print "hello from Russia!"
|
mikewiebe-ansible/ansible | refs/heads/devel | lib/ansible/modules/cloud/xenserver/xenserver_guest_powerstate.py | 12 | #!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright: (c) 2018, Bojan Vitnik <bvitnik@mainstream.rs>
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'}
DOCUMENTATION = r'''
---
module: xenserver_guest_powerstate
short_description: Manages power states of virtual machines running on Citrix Hypervisor/XenServer host or pool
description: >
This module can be used to power on, power off, restart or suspend virtual machine and gracefully reboot or shutdown guest OS of virtual machine.
version_added: '2.8'
author:
- Bojan Vitnik (@bvitnik) <bvitnik@mainstream.rs>
notes:
- Minimal supported version of XenServer is 5.6.
- Module was tested with XenServer 6.5, 7.1, 7.2, 7.6, Citrix Hypervisor 8.0, XCP-ng 7.6 and 8.0.
- 'To acquire XenAPI Python library, just run C(pip install XenAPI) on your Ansible Control Node. The library can also be found inside
Citrix Hypervisor/XenServer SDK (downloadable from Citrix website). Copy the XenAPI.py file from the SDK to your Python site-packages on your
Ansible Control Node to use it. Latest version of the library can also be acquired from GitHub:
https://raw.githubusercontent.com/xapi-project/xen-api/master/scripts/examples/python/XenAPI.py'
- 'If no scheme is specified in C(hostname), module defaults to C(http://) because C(https://) is problematic in most setups. Make sure you are
accessing XenServer host in trusted environment or use C(https://) scheme explicitly.'
- 'To use C(https://) scheme for C(hostname) you have to either import host certificate to your OS certificate store or use C(validate_certs: no)
which requires XenAPI library from XenServer 7.2 SDK or newer and Python 2.7.9 or newer.'
requirements:
- python >= 2.6
- XenAPI
options:
state:
description:
- Specify the state VM should be in.
- If C(state) is set to value other than C(present), then VM is transitioned into required state and facts are returned.
- If C(state) is set to C(present), then VM is just checked for existence and facts are returned.
type: str
default: present
choices: [ powered-on, powered-off, restarted, shutdown-guest, reboot-guest, suspended, present ]
name:
description:
- Name of the VM to manage.
- VMs running on XenServer do not necessarily have unique names. The module will fail if multiple VMs with same name are found.
- In case of multiple VMs with same name, use C(uuid) to uniquely specify VM to manage.
- This parameter is case sensitive.
type: str
required: yes
aliases: [ name_label ]
uuid:
description:
- UUID of the VM to manage if known. This is XenServer's unique identifier.
- It is required if name is not unique.
type: str
wait_for_ip_address:
description:
- Wait until XenServer detects an IP address for the VM.
- This requires XenServer Tools to be preinstalled on the VM to work properly.
type: bool
default: no
state_change_timeout:
description:
- 'By default, module will wait indefinitely for VM to change state or acquire an IP address if C(wait_for_ip_address: yes).'
- If this parameter is set to positive value, the module will instead wait specified number of seconds for the state change.
- In case of timeout, module will generate an error message.
type: int
default: 0
extends_documentation_fragment: xenserver.documentation
'''
EXAMPLES = r'''
- name: Power on VM
xenserver_guest_powerstate:
hostname: "{{ xenserver_hostname }}"
username: "{{ xenserver_username }}"
password: "{{ xenserver_password }}"
name: testvm_11
state: powered-on
delegate_to: localhost
register: facts
'''
RETURN = r'''
instance:
description: Metadata about the VM
returned: always
type: dict
sample: {
"cdrom": {
"type": "none"
},
"customization_agent": "native",
"disks": [
{
"name": "windows-template-testing-0",
"name_desc": "",
"os_device": "xvda",
"size": 42949672960,
"sr": "Local storage",
"sr_uuid": "0af1245e-bdb0-ba33-1446-57a962ec4075",
"vbd_userdevice": "0"
},
{
"name": "windows-template-testing-1",
"name_desc": "",
"os_device": "xvdb",
"size": 42949672960,
"sr": "Local storage",
"sr_uuid": "0af1245e-bdb0-ba33-1446-57a962ec4075",
"vbd_userdevice": "1"
}
],
"domid": "56",
"folder": "",
"hardware": {
"memory_mb": 8192,
"num_cpu_cores_per_socket": 2,
"num_cpus": 4
},
"home_server": "",
"is_template": false,
"name": "windows-template-testing",
"name_desc": "",
"networks": [
{
"gateway": "192.168.0.254",
"gateway6": "fc00::fffe",
"ip": "192.168.0.200",
"ip6": [
"fe80:0000:0000:0000:e9cb:625a:32c5:c291",
"fc00:0000:0000:0000:0000:0000:0000:0001"
],
"mac": "ba:91:3a:48:20:76",
"mtu": "1500",
"name": "Pool-wide network associated with eth1",
"netmask": "255.255.255.128",
"prefix": "25",
"prefix6": "64",
"vif_device": "0"
}
],
"other_config": {
"base_template_name": "Windows Server 2016 (64-bit)",
"import_task": "OpaqueRef:e43eb71c-45d6-5351-09ff-96e4fb7d0fa5",
"install-methods": "cdrom",
"instant": "true",
"mac_seed": "f83e8d8a-cfdc-b105-b054-ef5cb416b77e"
},
"platform": {
"acpi": "1",
"apic": "true",
"cores-per-socket": "2",
"device_id": "0002",
"hpet": "true",
"nx": "true",
"pae": "true",
"timeoffset": "-25200",
"vga": "std",
"videoram": "8",
"viridian": "true",
"viridian_reference_tsc": "true",
"viridian_time_ref_count": "true"
},
"state": "poweredon",
"uuid": "e3c0b2d5-5f05-424e-479c-d3df8b3e7cda",
"xenstore_data": {
"vm-data": ""
}
}
'''
import re
HAS_XENAPI = False
try:
import XenAPI
HAS_XENAPI = True
except ImportError:
pass
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils.xenserver import (xenserver_common_argument_spec, XAPI, XenServerObject, get_object_ref,
gather_vm_params, gather_vm_facts, set_vm_power_state, wait_for_vm_ip_address)
class XenServerVM(XenServerObject):
"""Class for managing XenServer VM.
Attributes:
vm_ref (str): XAPI reference to VM.
vm_params (dict): A dictionary with VM parameters as returned
by gather_vm_params() function.
"""
def __init__(self, module):
"""Inits XenServerVM using module parameters.
Args:
module: Reference to Ansible module object.
"""
super(XenServerVM, self).__init__(module)
self.vm_ref = get_object_ref(self.module, self.module.params['name'], self.module.params['uuid'], obj_type="VM", fail=True, msg_prefix="VM search: ")
self.gather_params()
def gather_params(self):
"""Gathers all VM parameters available in XAPI database."""
self.vm_params = gather_vm_params(self.module, self.vm_ref)
def gather_facts(self):
"""Gathers and returns VM facts."""
return gather_vm_facts(self.module, self.vm_params)
def set_power_state(self, power_state):
"""Controls VM power state."""
state_changed, current_state = set_vm_power_state(self.module, self.vm_ref, power_state, self.module.params['state_change_timeout'])
# If state has changed, update vm_params.
if state_changed:
self.vm_params['power_state'] = current_state.capitalize()
return state_changed
def wait_for_ip_address(self):
"""Waits for VM to acquire an IP address."""
self.vm_params['guest_metrics'] = wait_for_vm_ip_address(self.module, self.vm_ref, self.module.params['state_change_timeout'])
def main():
argument_spec = xenserver_common_argument_spec()
argument_spec.update(
state=dict(type='str', default='present',
choices=['powered-on', 'powered-off', 'restarted', 'shutdown-guest', 'reboot-guest', 'suspended', 'present']),
name=dict(type='str', aliases=['name_label']),
uuid=dict(type='str'),
wait_for_ip_address=dict(type='bool', default=False),
state_change_timeout=dict(type='int', default=0),
)
module = AnsibleModule(argument_spec=argument_spec,
supports_check_mode=True,
required_one_of=[
['name', 'uuid'],
],
)
result = {'failed': False, 'changed': False}
# Module will exit with an error message if no VM is found.
vm = XenServerVM(module)
# Set VM power state.
if module.params['state'] != "present":
result['changed'] = vm.set_power_state(module.params['state'])
if module.params['wait_for_ip_address']:
vm.wait_for_ip_address()
result['instance'] = vm.gather_facts()
if result['failed']:
module.fail_json(**result)
else:
module.exit_json(**result)
if __name__ == '__main__':
main()
|
hmen89/odoo | refs/heads/master | openerp/service/common.py | 86 | # -*- coding: utf-8 -*-
import logging
import openerp.release
import openerp.tools
import security
_logger = logging.getLogger(__name__)
RPC_VERSION_1 = {
'server_version': openerp.release.version,
'server_version_info': openerp.release.version_info,
'server_serie': openerp.release.serie,
'protocol_version': 1,
}
def dispatch(method, params):
if method in ['login', 'about', 'timezone_get',
'version', 'authenticate']:
pass
elif method in ['set_loglevel']:
passwd = params[0]
params = params[1:]
security.check_super(passwd)
else:
raise Exception("Method not found: %s" % method)
fn = globals()['exp_' + method]
return fn(*params)
def exp_login(db, login, password):
# TODO: legacy indirection through 'security', should use directly
# the res.users model
res = security.login(db, login, password)
msg = res and 'successful login' or 'bad login or password'
_logger.info("%s from '%s' using database '%s'", msg, login, db.lower())
return res or False
def exp_authenticate(db, login, password, user_agent_env):
res_users = openerp.registry(db)['res.users']
return res_users.authenticate(db, login, password, user_agent_env)
def exp_version():
return RPC_VERSION_1
def exp_about(extended=False):
"""Return information about the OpenERP Server.
@param extended: if True then return version info
@return string if extended is False else tuple
"""
info = _('See http://openerp.com')
if extended:
return info, openerp.release.version
return info
def exp_timezone_get(db, login, password):
return openerp.tools.misc.get_server_timezone()
def exp_set_loglevel(loglevel, logger=None):
# TODO Previously, the level was set on the now deprecated
# `openerp.netsvc.Logger` class.
return True
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
|
tomkralidis/QGIS | refs/heads/master | src/plugins/grass/scripts/r.external.all.py | 45 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
***************************************************************************
r.external.all.py
---------------------
Date : July 2009
Copyright : (C) 2009 by Lorenzo Masini
Email : rugginoso at develer dot com
***************************************************************************
* *
* This program 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 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************
"""
__author__ = 'Lorenzo Masini'
__date__ = 'July 2009'
__copyright__ = '(C) 2009, Lorenzo Masini'
############################################################################
#
# MODULE: qgis.r.external.all.py
# AUTHOR(S): Lorenzo Masini
#
# PURPOSE: Link all GDAL supported raster files into a directory
# to binary raster map layers.
# COPYRIGHT: (C) 2009 by Lorenzo Masini
#
# This program is free software under the GNU General Public
# License (>=v2). Read the file COPYING that comes with GRASS
# for details.
#
#############################################################################
#%Module
#% description: Link all GDAL supported raster files into a directory to binary raster map layers.
#% keywords: raster, import
#%End
#%option
#% key: input
#% type: string
#% gisprompt: input
#% key_desc : name
#% description: Directory containing raster files
#% required : yes
#%end
#%option
#% key: band
#% type: integer
#% description: Band to select
#% answer: 1
#% required : no
#%end
#%flag
#% key: o
#% description: Override projection (use location's projection)
#%end
#%flag
#% key: e
#% description: Extend location extents based on new dataset
#%end
#%flag
#% key: r
#% description: Recursively scan subdirectories
import os
try:
from grass.script import core as grass
except ImportError:
import grass
except:
raise Exception("Cannot find 'grass' Python module. Python is supported by GRASS from version >= 6.4")
def import_directory_of_rasters(directory, recursive):
for dir, dirnames, filenames in os.walk(directory):
for filename in filenames:
if grass.run_command('r.external', flags=flags_string, input=os.path.join(dir, filename), band=options['band'], output=filename[:-4], title=filename[:-4]) != 0:
grass.warning('Cannot import file' + filename)
if not recursive:
break
for dirname in dirnames:
import_directory_of_rasters(dirname, recursive)
def main():
input = options['input']
recursive = flags['r']
import_directory_of_rasters(input, recursive)
if __name__ == "__main__":
options, flags = grass.parser()
flags_string = "".join([k for k in flags.keys() if flags[k] and k != 'r'])
main()
|
easyfmxu/zulip | refs/heads/master | zerver/lib/upload.py | 121 | from __future__ import absolute_import
from django.conf import settings
from django.template.defaultfilters import slugify
from zerver.lib.avatar import user_avatar_hash
from boto.s3.key import Key
from boto.s3.connection import S3Connection
from mimetypes import guess_type, guess_extension
from zerver.models import get_user_profile_by_id
import base64
import os
from PIL import Image, ImageOps
from StringIO import StringIO
import random
# Performance Note:
#
# For writing files to S3, the file could either be stored in RAM
# (if it is less than 2.5MiB or so) or an actual temporary file on disk.
#
# Because we set FILE_UPLOAD_MAX_MEMORY_SIZE to 0, only the latter case
# should occur in practice.
#
# This is great, because passing the pseudofile object that Django gives
# you to boto would be a pain.
# To come up with a s3 key we randomly generate a "directory". The "file
# name" is the original filename provided by the user run through Django's
# slugify.
def sanitize_name(name):
split_name = name.split('.')
base = ".".join(split_name[:-1])
extension = split_name[-1]
return slugify(base) + "." + slugify(extension)
def random_name(bytes=60):
return base64.urlsafe_b64encode(os.urandom(bytes))
def resize_avatar(image_data):
AVATAR_SIZE = 100
im = Image.open(StringIO(image_data))
im = ImageOps.fit(im, (AVATAR_SIZE, AVATAR_SIZE), Image.ANTIALIAS)
out = StringIO()
im.save(out, format='png')
return out.getvalue()
### S3
def get_bucket(conn, bucket_name):
# Calling get_bucket() with validate=True can apparently lead
# to expensive S3 bills:
# http://www.appneta.com/blog/s3-list-get-bucket-default/
# The benefits of validation aren't completely clear to us, and
# we want to save on our bills, so we set the validate flag to False.
# (We think setting validate to True would cause us to fail faster
# in situations where buckets don't exist, but that shouldn't be
# an issue for us.)
bucket = conn.get_bucket(bucket_name, validate=False)
return bucket
def upload_image_to_s3(
bucket_name,
file_name,
content_type,
user_profile,
contents,
):
conn = S3Connection(settings.S3_KEY, settings.S3_SECRET_KEY)
bucket = get_bucket(conn, bucket_name)
key = Key(bucket)
key.key = file_name
key.set_metadata("user_profile_id", str(user_profile.id))
key.set_metadata("realm_id", str(user_profile.realm.id))
if content_type:
headers = {'Content-Type': content_type}
else:
headers = None
key.set_contents_from_string(contents, headers=headers)
def get_file_info(request, user_file):
uploaded_file_name = user_file.name
content_type = request.GET.get('mimetype')
if content_type is None:
content_type = guess_type(uploaded_file_name)[0]
else:
uploaded_file_name = uploaded_file_name + guess_extension(content_type)
return uploaded_file_name, content_type
def upload_message_image_s3(uploaded_file_name, content_type, file_data, user_profile, target_realm=None):
bucket_name = settings.S3_AUTH_UPLOADS_BUCKET
s3_file_name = "/".join([
str(target_realm.id if target_realm is not None else user_profile.realm.id),
random_name(18),
sanitize_name(uploaded_file_name)
])
url = "/user_uploads/%s" % (s3_file_name)
upload_image_to_s3(
bucket_name,
s3_file_name,
content_type,
user_profile,
file_data
)
return url
def get_signed_upload_url(path):
conn = S3Connection(settings.S3_KEY, settings.S3_SECRET_KEY)
return conn.generate_url(15, 'GET', bucket=settings.S3_AUTH_UPLOADS_BUCKET, key=path)
def get_realm_for_filename(path):
conn = S3Connection(settings.S3_KEY, settings.S3_SECRET_KEY)
key = get_bucket(conn, settings.S3_AUTH_UPLOADS_BUCKET).get_key(path)
if key is None:
# This happens if the key does not exist.
return None
return get_user_profile_by_id(key.metadata["user_profile_id"]).realm.id
def upload_avatar_image_s3(user_file, user_profile, email):
content_type = guess_type(user_file.name)[0]
bucket_name = settings.S3_AVATAR_BUCKET
s3_file_name = user_avatar_hash(email)
image_data = user_file.read()
upload_image_to_s3(
bucket_name,
s3_file_name + ".original",
content_type,
user_profile,
image_data,
)
resized_data = resize_avatar(image_data)
upload_image_to_s3(
bucket_name,
s3_file_name,
'image/png',
user_profile,
resized_data,
)
# See avatar_url in avatar.py for URL. (That code also handles the case
# that users use gravatar.)
### Local
def mkdirs(path):
dirname = os.path.dirname(path)
if not os.path.isdir(dirname):
os.makedirs(dirname)
def write_local_file(type, path, file_data):
file_path = os.path.join(settings.LOCAL_UPLOADS_DIR, type, path)
mkdirs(file_path)
with open(file_path, 'wb') as f:
f.write(file_data)
def upload_message_image_local(uploaded_file_name, content_type, file_data, user_profile, target_realm=None):
# Split into 256 subdirectories to prevent directories from getting too big
path = "/".join([
str(user_profile.realm.id),
format(random.randint(0, 255), 'x'),
random_name(18),
sanitize_name(uploaded_file_name)
])
write_local_file('files', path, file_data)
return '/user_uploads/' + path
def upload_avatar_image_local(user_file, user_profile, email):
email_hash = user_avatar_hash(email)
image_data = user_file.read()
write_local_file('avatars', email_hash+'.original', image_data)
resized_data = resize_avatar(image_data)
write_local_file('avatars', email_hash+'.png', resized_data)
### Common
if settings.LOCAL_UPLOADS_DIR is not None:
upload_message_image = upload_message_image_local
upload_avatar_image = upload_avatar_image_local
else:
upload_message_image = upload_message_image_s3
upload_avatar_image = upload_avatar_image_s3
def upload_message_image_through_web_client(request, user_file, user_profile):
uploaded_file_name, content_type = get_file_info(request, user_file)
return upload_message_image(uploaded_file_name, content_type, user_file.read(), user_profile)
|
Permutatrix/servo | refs/heads/master | tests/wpt/web-platform-tests/tools/webdriver/webdriver/servo.py | 11 | class ServoExtensionCommands(object):
def __init__(self, session):
self.session = session
@command
def get_prefs(self, *prefs):
body = {"prefs": list(prefs)}
return self.session.send_command("POST", "servo/prefs/get", body)
@command
def set_prefs(self, prefs):
body = {"prefs": prefs}
return self.session.send_command("POST", "servo/prefs/set", body)
@command
def reset_prefs(self, *prefs):
body = {"prefs": list(prefs)}
return self.session.send_command("POST", "servo/prefs/reset", body)
|
viewdy/phantomjs2 | refs/heads/master | src/qt/qtwebkit/Tools/Scripts/webkitpy/tool/commands/queuestest.py | 124 | # Copyright (C) 2009 Google Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following disclaimer
# in the documentation and/or other materials provided with the
# distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import unittest2 as unittest
from webkitpy.common.net.bugzilla import Attachment
from webkitpy.common.system.outputcapture import OutputCapture
from webkitpy.common.system.executive import ScriptError
from webkitpy.thirdparty.mock import Mock
from webkitpy.tool.commands.stepsequence import StepSequenceErrorHandler
from webkitpy.tool.mocktool import MockTool
class MockQueueEngine(object):
def __init__(self, name, queue, wakeup_event, seconds_to_sleep):
pass
def run(self):
pass
class QueuesTest(unittest.TestCase):
# This is _patch1 in mocktool.py
mock_work_item = MockTool().bugs.fetch_attachment(10000)
def assert_outputs(self, func, func_name, args, expected_stdout, expected_stderr, expected_exceptions, expected_logs):
exception = None
if expected_exceptions and func_name in expected_exceptions:
exception = expected_exceptions[func_name]
logs = None
if expected_logs and func_name in expected_logs:
logs = expected_logs[func_name]
OutputCapture().assert_outputs(self,
func,
args=args,
expected_stdout=expected_stdout.get(func_name, ""),
expected_stderr=expected_stderr.get(func_name, ""),
expected_exception=exception,
expected_logs=logs)
def _default_begin_work_queue_stderr(self, name):
string_replacements = {"name": name}
return "MOCK: update_status: %(name)s Starting Queue\n" % string_replacements
def _default_begin_work_queue_logs(self, name):
checkout_dir = '/mock-checkout'
string_replacements = {"name": name, 'checkout_dir': checkout_dir}
return "CAUTION: %(name)s will discard all local changes in \"%(checkout_dir)s\"\nRunning WebKit %(name)s.\nMOCK: update_status: %(name)s Starting Queue\n" % string_replacements
def assert_queue_outputs(self, queue, args=None, work_item=None, expected_stdout=None, expected_stderr=None, expected_exceptions=None, expected_logs=None, options=None, tool=None):
if not tool:
tool = MockTool()
# This is a hack to make it easy for callers to not have to setup a custom MockFileSystem just to test the commit-queue
# the cq tries to read the layout test results, and will hit a KeyError in MockFileSystem if we don't do this.
tool.filesystem.write_text_file('/mock-results/full_results.json', "")
if not expected_stdout:
expected_stdout = {}
if not expected_stderr:
expected_stderr = {}
if not args:
args = []
if not options:
options = Mock()
options.port = None
if not work_item:
work_item = self.mock_work_item
tool.user.prompt = lambda message: "yes"
queue.execute(options, args, tool, engine=MockQueueEngine)
self.assert_outputs(queue.queue_log_path, "queue_log_path", [], expected_stdout, expected_stderr, expected_exceptions, expected_logs)
self.assert_outputs(queue.work_item_log_path, "work_item_log_path", [work_item], expected_stdout, expected_stderr, expected_exceptions, expected_logs)
self.assert_outputs(queue.begin_work_queue, "begin_work_queue", [], expected_stdout, expected_stderr, expected_exceptions, expected_logs)
self.assert_outputs(queue.should_continue_work_queue, "should_continue_work_queue", [], expected_stdout, expected_stderr, expected_exceptions, expected_logs)
self.assert_outputs(queue.next_work_item, "next_work_item", [], expected_stdout, expected_stderr, expected_exceptions, expected_logs)
self.assert_outputs(queue.process_work_item, "process_work_item", [work_item], expected_stdout, expected_stderr, expected_exceptions, expected_logs)
self.assert_outputs(queue.handle_unexpected_error, "handle_unexpected_error", [work_item, "Mock error message"], expected_stdout, expected_stderr, expected_exceptions, expected_logs)
# Should we have a different function for testing StepSequenceErrorHandlers?
if isinstance(queue, StepSequenceErrorHandler):
self.assert_outputs(queue.handle_script_error, "handle_script_error", [tool, {"patch": self.mock_work_item}, ScriptError(message="ScriptError error message", script_args="MockErrorCommand", output="MOCK output")], expected_stdout, expected_stderr, expected_exceptions, expected_logs)
|
mdietrichc2c/server-tools | refs/heads/8.0 | __unported__/super_calendar/super_calendar.py | 24 | # -*- coding: utf-8 -*-
##############################################################################
#
# Copyright (C) 2012 Agile Business Group sagl (<http://www.agilebg.com>)
# Copyright (C) 2012 Domsense srl (<http://www.domsense.com>)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
from openerp.osv import fields, orm
from openerp.tools.translate import _
import logging
from mako.template import Template
from datetime import datetime
from openerp import tools
from openerp.tools.safe_eval import safe_eval
def _models_get(self, cr, uid, context=None):
obj = self.pool.get('ir.model')
ids = obj.search(cr, uid, [])
res = obj.read(cr, uid, ids, ['model', 'name'], context)
return [(r['model'], r['name']) for r in res]
class super_calendar_configurator(orm.Model):
_logger = logging.getLogger('super.calendar')
_name = 'super.calendar.configurator'
_columns = {
'name': fields.char('Name', size=64, required=True),
'line_ids': fields.one2many('super.calendar.configurator.line', 'configurator_id', 'Lines'),
}
def generate_calendar_records(self, cr, uid, ids, context=None):
configurator_ids = self.search(cr, uid, [])
super_calendar_pool = self.pool.get('super.calendar')
# removing old records
super_calendar_ids = super_calendar_pool.search(cr, uid, [], context=context)
super_calendar_pool.unlink(cr, uid, super_calendar_ids, context=context)
for configurator in self.browse(cr, uid, configurator_ids, context):
for line in configurator.line_ids:
current_pool = self.pool.get(line.name.model)
current_record_ids = current_pool.search(
cr,
uid,
line.domain and safe_eval(line.domain) or [],
context=context)
for current_record_id in current_record_ids:
current_record = current_pool.browse(cr, uid, current_record_id, context=context)
if (line.user_field_id and
current_record[line.user_field_id.name] and
current_record[line.user_field_id.name]._table_name != 'res.users'):
raise orm.except_orm(
_('Error'),
_("The 'User' field of record %s (%s) does not refer to res.users")
% (current_record[line.description_field_id.name], line.name.model))
if (((line.description_field_id and current_record[line.description_field_id.name]) or
line.description_code) and
current_record[line.date_start_field_id.name]):
duration = False
if (not line.duration_field_id and
line.date_stop_field_id and
current_record[line.date_start_field_id.name] and
current_record[line.date_stop_field_id.name]):
date_start = datetime.strptime(
current_record[line.date_start_field_id.name],
tools.DEFAULT_SERVER_DATETIME_FORMAT
)
date_stop = datetime.strptime(
current_record[line.date_stop_field_id.name],
tools.DEFAULT_SERVER_DATETIME_FORMAT
)
duration = (date_stop - date_start).total_seconds() / 3600
elif line.duration_field_id:
duration = current_record[line.duration_field_id.name]
if line.description_type != 'code':
name = current_record[line.description_field_id.name]
else:
parse_dict = {'o': current_record}
mytemplate = Template(line.description_code)
name = mytemplate.render(**parse_dict)
super_calendar_values = {
'name': name,
'model_description': line.description,
'date_start': current_record[line.date_start_field_id.name],
'duration': duration,
'user_id': (
line.user_field_id and
current_record[line.user_field_id.name] and
current_record[line.user_field_id.name].id or
False
),
'configurator_id': configurator.id,
'res_id': line.name.model+','+str(current_record['id']),
'model_id': line.name.id,
}
super_calendar_pool.create(cr, uid, super_calendar_values, context=context)
self._logger.info('Calendar generated')
return True
class super_calendar_configurator_line(orm.Model):
_name = 'super.calendar.configurator.line'
_columns = {
'name': fields.many2one('ir.model', 'Model', required=True),
'description': fields.char('Description', size=128, required=True),
'domain': fields.char('Domain', size=512),
'configurator_id': fields.many2one('super.calendar.configurator', 'Configurator'),
'description_type': fields.selection([
('field', 'Field'),
('code', 'Code'),
], string="Description Type"),
'description_field_id': fields.many2one(
'ir.model.fields', 'Description field',
domain="[('model_id', '=', name),('ttype', '=', 'char')]"),
'description_code': fields.text(
'Description field',
help="Use '${o}' to refer to the involved object. E.g.: '${o.project_id.name}'"
),
'date_start_field_id': fields.many2one(
'ir.model.fields', 'Start date field',
domain="['&','|',('ttype', '=', 'datetime'),('ttype', '=', 'date'),('model_id', '=', name)]",
required=True),
'date_stop_field_id': fields.many2one(
'ir.model.fields', 'End date field',
domain="['&',('ttype', '=', 'datetime'),('model_id', '=', name)]"
),
'duration_field_id': fields.many2one(
'ir.model.fields', 'Duration field',
domain="['&',('ttype', '=', 'float'),('model_id', '=', name)]"),
'user_field_id': fields.many2one(
'ir.model.fields', 'User field',
domain="['&',('ttype', '=', 'many2one'),('model_id', '=', name)]"),
}
class super_calendar(orm.Model):
_name = 'super.calendar'
_columns = {
'name': fields.char('Description', size=512, required=True),
'model_description': fields.char('Model Description', size=128, required=True),
'date_start': fields.datetime('Start date', required=True),
'duration': fields.float('Duration'),
'user_id': fields.many2one('res.users', 'User'),
'configurator_id': fields.many2one('super.calendar.configurator', 'Configurator'),
'res_id': fields.reference('Resource', selection=_models_get, size=128),
'model_id': fields.many2one('ir.model', 'Model'),
}
|
ax003d/openerp | refs/heads/master | openerp/addons/web/tests/test_menu.py | 65 | # -*- coding: utf-8 -*-
import collections
import mock
import unittest2
from ..controllers import main
class Placeholder(object):
def __init__(self, **kwargs):
for k, v in kwargs.iteritems():
setattr(self, k, v)
class LoadTest(unittest2.TestCase):
def setUp(self):
self.menu = main.Menu()
self.request = mock.Mock()
# Have self.request.session.model() return a different mock object for
# each model (but always the same mock for a given model name)
models = collections.defaultdict(mock.Mock)
model = self.request.session.model.side_effect = \
lambda model_name: models[model_name]
self.MockMenus = model('ir.ui.menu')
# Mock the absence of custom menu
model('res.users').read.return_value = [{
'menu_id': False
}]
def tearDown(self):
del self.request
del self.MockMenus
del self.menu
def test_empty(self):
self.MockMenus.search.return_value = []
self.MockMenus.read.return_value = []
root = self.menu.load(self.request)
self.MockMenus.search.assert_called_with(
[('parent_id','=', False)], 0, False, False,
self.request.context)
self.assertEqual(root['all_menu_ids'], [])
self.assertListEqual(
root['children'],
[])
def test_applications_sort(self):
self.MockMenus.search.return_value = [1, 2, 3]
self.MockMenus.read.side_effect = lambda *args: [
{'id': 1, 'sequence': 1, 'parent_id': False},
{'id': 3, 'sequence': 2, 'parent_id': False},
{'id': 2, 'sequence': 3, 'parent_id': False},
]
root = self.menu.load(self.request)
self.MockMenus.search.assert_called_with(
[('id','child_of', [1, 2, 3])], 0, False, False,
self.request.context)
self.MockMenus.read.assert_called_with(
[1, 2, 3], ['name', 'sequence', 'parent_id',
'action'],
self.request.context)
self.assertEqual(root['all_menu_ids'], [1, 2, 3])
self.assertEqual(
root['children'],
[{
'id': 1, 'sequence': 1,
'parent_id': False, 'children': []
}, {
'id': 3, 'sequence': 2,
'parent_id': False, 'children': []
}, {
'id': 2, 'sequence': 3,
'parent_id': False, 'children': []
}])
def test_deep(self):
self.MockMenus.search.side_effect = lambda domain, *args: (
[1] if domain == [('parent_id', '=', False)] else [1, 2, 3, 4])
root = {'id': 1, 'sequence': 1, 'parent_id': False}
self.MockMenus.read.side_effect = lambda ids, *args: (
[root] if ids == [1] else [
{'id': 1, 'sequence': 1, 'parent_id': False},
{'id': 2, 'sequence': 2, 'parent_id': [1, '']},
{'id': 3, 'sequence': 1, 'parent_id': [2, '']},
{'id': 4, 'sequence': 2, 'parent_id': [2, '']},
])
root = self.menu.load(self.request)
self.MockMenus.search.assert_called_with(
[('id','child_of', [1])], 0, False, False,
self.request.context)
self.assertEqual(root['all_menu_ids'], [1, 2, 3, 4])
self.assertEqual(
root['children'],
[{
'id': 1,
'sequence': 1,
'parent_id': False,
'children': [{
'id': 2,
'sequence': 2,
'parent_id': [1, ''],
'children': [{
'id': 3,
'sequence': 1,
'parent_id': [2, ''],
'children': []
}, {
'id': 4,
'sequence': 2,
'parent_id': [2, ''],
'children': []
}]
}]
}]
)
class ActionMungerTest(unittest2.TestCase):
def setUp(self):
self.menu = main.Menu()
def test_actual_treeview(self):
action = {
"views": [[False, "tree"], [False, "form"],
[False, "calendar"]],
"view_type": "tree",
"view_id": False,
"view_mode": "tree,form,calendar"
}
changed = action.copy()
del action['view_type']
main.fix_view_modes(changed)
self.assertEqual(changed, action)
def test_list_view(self):
action = {
"views": [[False, "tree"], [False, "form"],
[False, "calendar"]],
"view_type": "form",
"view_id": False,
"view_mode": "tree,form,calendar"
}
main.fix_view_modes(action)
self.assertEqual(action, {
"views": [[False, "list"], [False, "form"],
[False, "calendar"]],
"view_id": False,
"view_mode": "list,form,calendar"
})
def test_redundant_views(self):
action = {
"views": [[False, "tree"], [False, "form"],
[False, "calendar"], [42, "tree"]],
"view_type": "form",
"view_id": False,
"view_mode": "tree,form,calendar"
}
main.fix_view_modes(action)
self.assertEqual(action, {
"views": [[False, "list"], [False, "form"],
[False, "calendar"], [42, "list"]],
"view_id": False,
"view_mode": "list,form,calendar"
})
|
notriddle/servo | refs/heads/master | tests/wpt/web-platform-tests/common/redirect.py | 259 | def main(request, response):
"""Simple handler that causes redirection.
The request should typically have two query parameters:
status - The status to use for the redirection. Defaults to 302.
location - The resource to redirect to.
"""
status = 302
if "status" in request.GET:
try:
status = int(request.GET.first("status"))
except ValueError:
pass
response.status = status
location = request.GET.first("location")
response.headers.set("Location", location)
|
william-richard/moto | refs/heads/master | tests/test_sts/test_server.py | 2 | from __future__ import unicode_literals
import sure # noqa
import moto.server as server
"""
Test the different server responses
"""
def test_sts_get_session_token():
backend = server.create_backend_app("sts")
test_client = backend.test_client()
res = test_client.get("/?Action=GetSessionToken")
res.status_code.should.equal(200)
res.data.should.contain(b"SessionToken")
res.data.should.contain(b"AccessKeyId")
def test_sts_get_federation_token():
backend = server.create_backend_app("sts")
test_client = backend.test_client()
res = test_client.get("/?Action=GetFederationToken&Name=Bob")
res.status_code.should.equal(200)
res.data.should.contain(b"SessionToken")
res.data.should.contain(b"AccessKeyId")
def test_sts_get_caller_identity():
backend = server.create_backend_app("sts")
test_client = backend.test_client()
res = test_client.get("/?Action=GetCallerIdentity")
res.status_code.should.equal(200)
res.data.should.contain(b"Arn")
res.data.should.contain(b"UserId")
res.data.should.contain(b"Account")
|
DONIKAN/django | refs/heads/master | tests/generic_views/test_list.py | 309 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import datetime
from django.core.exceptions import ImproperlyConfigured
from django.test import TestCase, override_settings
from django.utils.encoding import force_str
from django.views.generic.base import View
from .models import Artist, Author, Book, Page
@override_settings(ROOT_URLCONF='generic_views.urls')
class ListViewTests(TestCase):
@classmethod
def setUpTestData(cls):
cls.artist1 = Artist.objects.create(name='Rene Magritte')
cls.author1 = Author.objects.create(name='Roberto Bolaño', slug='roberto-bolano')
cls.author2 = Author.objects.create(name='Scott Rosenberg', slug='scott-rosenberg')
cls.book1 = Book.objects.create(name='2066', slug='2066', pages=800, pubdate=datetime.date(2008, 10, 1))
cls.book1.authors.add(cls.author1)
cls.book2 = Book.objects.create(
name='Dreaming in Code', slug='dreaming-in-code', pages=300, pubdate=datetime.date(2006, 5, 1)
)
cls.page1 = Page.objects.create(
content='I was once bitten by a moose.', template='generic_views/page_template.html'
)
def test_items(self):
res = self.client.get('/list/dict/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/list.html')
self.assertEqual(res.context['object_list'][0]['first'], 'John')
def test_queryset(self):
res = self.client.get('/list/authors/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertIsInstance(res.context['view'], View)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertIsNone(res.context['paginator'])
self.assertIsNone(res.context['page_obj'])
self.assertFalse(res.context['is_paginated'])
def test_paginated_queryset(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(len(res.context['object_list']), 30)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertTrue(res.context['is_paginated'])
self.assertEqual(res.context['page_obj'].number, 1)
self.assertEqual(res.context['paginator'].num_pages, 4)
self.assertEqual(res.context['author_list'][0].name, 'Author 00')
self.assertEqual(list(res.context['author_list'])[-1].name, 'Author 29')
def test_paginated_queryset_shortdata(self):
# Test that short datasets ALSO result in a paginated view.
res = self.client.get('/list/authors/paginated/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['page_obj'].number, 1)
self.assertEqual(res.context['paginator'].num_pages, 1)
self.assertFalse(res.context['is_paginated'])
def test_paginated_get_page_by_query_string(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/', {'page': '2'})
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(len(res.context['object_list']), 30)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['author_list'][0].name, 'Author 30')
self.assertEqual(res.context['page_obj'].number, 2)
def test_paginated_get_last_page_by_query_string(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/', {'page': 'last'})
self.assertEqual(res.status_code, 200)
self.assertEqual(len(res.context['object_list']), 10)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['author_list'][0].name, 'Author 90')
self.assertEqual(res.context['page_obj'].number, 4)
def test_paginated_get_page_by_urlvar(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/3/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(len(res.context['object_list']), 30)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['author_list'][0].name, 'Author 60')
self.assertEqual(res.context['page_obj'].number, 3)
def test_paginated_page_out_of_range(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/42/')
self.assertEqual(res.status_code, 404)
def test_paginated_invalid_page(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/?page=frog')
self.assertEqual(res.status_code, 404)
def test_paginated_custom_paginator_class(self):
self._make_authors(7)
res = self.client.get('/list/authors/paginated/custom_class/')
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['paginator'].num_pages, 1)
# Custom pagination allows for 2 orphans on a page size of 5
self.assertEqual(len(res.context['object_list']), 7)
def test_paginated_custom_page_kwarg(self):
self._make_authors(100)
res = self.client.get('/list/authors/paginated/custom_page_kwarg/', {'pagina': '2'})
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
self.assertEqual(len(res.context['object_list']), 30)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertEqual(res.context['author_list'][0].name, 'Author 30')
self.assertEqual(res.context['page_obj'].number, 2)
def test_paginated_custom_paginator_constructor(self):
self._make_authors(7)
res = self.client.get('/list/authors/paginated/custom_constructor/')
self.assertEqual(res.status_code, 200)
# Custom pagination allows for 2 orphans on a page size of 5
self.assertEqual(len(res.context['object_list']), 7)
def test_paginated_orphaned_queryset(self):
self._make_authors(92)
res = self.client.get('/list/authors/paginated-orphaned/')
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['page_obj'].number, 1)
res = self.client.get(
'/list/authors/paginated-orphaned/', {'page': 'last'})
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['page_obj'].number, 3)
res = self.client.get(
'/list/authors/paginated-orphaned/', {'page': '3'})
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['page_obj'].number, 3)
res = self.client.get(
'/list/authors/paginated-orphaned/', {'page': '4'})
self.assertEqual(res.status_code, 404)
def test_paginated_non_queryset(self):
res = self.client.get('/list/dict/paginated/')
self.assertEqual(res.status_code, 200)
self.assertEqual(len(res.context['object_list']), 1)
def test_verbose_name(self):
res = self.client.get('/list/artists/')
self.assertEqual(res.status_code, 200)
self.assertTemplateUsed(res, 'generic_views/list.html')
self.assertEqual(list(res.context['object_list']), list(Artist.objects.all()))
self.assertIs(res.context['artist_list'], res.context['object_list'])
self.assertIsNone(res.context['paginator'])
self.assertIsNone(res.context['page_obj'])
self.assertFalse(res.context['is_paginated'])
def test_allow_empty_false(self):
res = self.client.get('/list/authors/notempty/')
self.assertEqual(res.status_code, 200)
Author.objects.all().delete()
res = self.client.get('/list/authors/notempty/')
self.assertEqual(res.status_code, 404)
def test_template_name(self):
res = self.client.get('/list/authors/template_name/')
self.assertEqual(res.status_code, 200)
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertTemplateUsed(res, 'generic_views/list.html')
def test_template_name_suffix(self):
res = self.client.get('/list/authors/template_name_suffix/')
self.assertEqual(res.status_code, 200)
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertTemplateUsed(res, 'generic_views/author_objects.html')
def test_context_object_name(self):
res = self.client.get('/list/authors/context_object_name/')
self.assertEqual(res.status_code, 200)
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertNotIn('authors', res.context)
self.assertIs(res.context['author_list'], res.context['object_list'])
self.assertTemplateUsed(res, 'generic_views/author_list.html')
def test_duplicate_context_object_name(self):
res = self.client.get('/list/authors/dupe_context_object_name/')
self.assertEqual(res.status_code, 200)
self.assertEqual(list(res.context['object_list']), list(Author.objects.all()))
self.assertNotIn('authors', res.context)
self.assertNotIn('author_list', res.context)
self.assertTemplateUsed(res, 'generic_views/author_list.html')
def test_missing_items(self):
self.assertRaises(ImproperlyConfigured, self.client.get, '/list/authors/invalid/')
def test_paginated_list_view_does_not_load_entire_table(self):
# Regression test for #17535
self._make_authors(3)
# 1 query for authors
with self.assertNumQueries(1):
self.client.get('/list/authors/notempty/')
# same as above + 1 query to test if authors exist + 1 query for pagination
with self.assertNumQueries(3):
self.client.get('/list/authors/notempty/paginated/')
def test_explicitly_ordered_list_view(self):
Book.objects.create(name="Zebras for Dummies", pages=800, pubdate=datetime.date(2006, 9, 1))
res = self.client.get('/list/books/sorted/')
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['object_list'][0].name, '2066')
self.assertEqual(res.context['object_list'][1].name, 'Dreaming in Code')
self.assertEqual(res.context['object_list'][2].name, 'Zebras for Dummies')
res = self.client.get('/list/books/sortedbypagesandnamedec/')
self.assertEqual(res.status_code, 200)
self.assertEqual(res.context['object_list'][0].name, 'Dreaming in Code')
self.assertEqual(res.context['object_list'][1].name, 'Zebras for Dummies')
self.assertEqual(res.context['object_list'][2].name, '2066')
@override_settings(DEBUG=True)
def test_paginated_list_view_returns_useful_message_on_invalid_page(self):
# test for #19240
# tests that source exception's message is included in page
self._make_authors(1)
res = self.client.get('/list/authors/paginated/2/')
self.assertEqual(res.status_code, 404)
self.assertEqual(force_str(res.context.get('reason')),
"Invalid page (2): That page contains no results")
def _make_authors(self, n):
Author.objects.all().delete()
for i in range(n):
Author.objects.create(name='Author %02i' % i, slug='a%s' % i)
|
LumaPictures/rez | refs/heads/pull_requests | src/rez/vendor/unittest2/case.py | 9 | """Test case implementation"""
import sys
import difflib
import pprint
import re
import unittest
import warnings
from rez.vendor.unittest2 import result
from rez.vendor.unittest2.util import (
safe_repr, safe_str, strclass,
unorderable_list_difference
)
from rez.vendor.unittest2.compatibility import wraps
__unittest = True
DIFF_OMITTED = ('\nDiff is %s characters long. '
'Set self.maxDiff to None to see it.')
class SkipTest(Exception):
"""
Raise this exception in a test to skip it.
Usually you can use TestResult.skip() or one of the skipping decorators
instead of raising this directly.
"""
class _ExpectedFailure(Exception):
"""
Raise this when a test is expected to fail.
This is an implementation detail.
"""
def __init__(self, exc_info):
# can't use super because Python 2.4 exceptions are old style
Exception.__init__(self)
self.exc_info = exc_info
class _UnexpectedSuccess(Exception):
"""
The test was supposed to fail, but it didn't!
"""
def _id(obj):
return obj
def skip(reason):
"""
Unconditionally skip a test.
"""
def decorator(test_item):
if not (isinstance(test_item, type) and issubclass(test_item, TestCase)):
@wraps(test_item)
def skip_wrapper(*args, **kwargs):
raise SkipTest(reason)
test_item = skip_wrapper
test_item.__unittest_skip__ = True
test_item.__unittest_skip_why__ = reason
return test_item
return decorator
def skipIf(condition, reason):
"""
Skip a test if the condition is true.
"""
if condition:
return skip(reason)
return _id
def skipUnless(condition, reason):
"""
Skip a test unless the condition is true.
"""
if not condition:
return skip(reason)
return _id
def expectedFailure(func):
@wraps(func)
def wrapper(*args, **kwargs):
try:
func(*args, **kwargs)
except Exception:
raise _ExpectedFailure(sys.exc_info())
raise _UnexpectedSuccess
return wrapper
class _AssertRaisesContext(object):
"""A context manager used to implement TestCase.assertRaises* methods."""
def __init__(self, expected, test_case, expected_regexp=None):
self.expected = expected
self.failureException = test_case.failureException
self.expected_regexp = expected_regexp
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, tb):
if exc_type is None:
try:
exc_name = self.expected.__name__
except AttributeError:
exc_name = str(self.expected)
raise self.failureException(
"%s not raised" % (exc_name,))
if not issubclass(exc_type, self.expected):
# let unexpected exceptions pass through
return False
self.exception = exc_value # store for later retrieval
if self.expected_regexp is None:
return True
expected_regexp = self.expected_regexp
if isinstance(expected_regexp, basestring):
expected_regexp = re.compile(expected_regexp)
if not expected_regexp.search(str(exc_value)):
raise self.failureException('"%s" does not match "%s"' %
(expected_regexp.pattern, str(exc_value)))
return True
class _TypeEqualityDict(object):
def __init__(self, testcase):
self.testcase = testcase
self._store = {}
def __setitem__(self, key, value):
self._store[key] = value
def __getitem__(self, key):
value = self._store[key]
if isinstance(value, basestring):
return getattr(self.testcase, value)
return value
def get(self, key, default=None):
if key in self._store:
return self[key]
return default
class TestCase(unittest.TestCase):
"""A class whose instances are single test cases.
By default, the test code itself should be placed in a method named
'runTest'.
If the fixture may be used for many test cases, create as
many test methods as are needed. When instantiating such a TestCase
subclass, specify in the constructor arguments the name of the test method
that the instance is to execute.
Test authors should subclass TestCase for their own tests. Construction
and deconstruction of the test's environment ('fixture') can be
implemented by overriding the 'setUp' and 'tearDown' methods respectively.
If it is necessary to override the __init__ method, the base class
__init__ method must always be called. It is important that subclasses
should not change the signature of their __init__ method, since instances
of the classes are instantiated automatically by parts of the framework
in order to be run.
"""
# This attribute determines which exception will be raised when
# the instance's assertion methods fail; test methods raising this
# exception will be deemed to have 'failed' rather than 'errored'
failureException = AssertionError
# This attribute sets the maximum length of a diff in failure messages
# by assert methods using difflib. It is looked up as an instance attribute
# so can be configured by individual tests if required.
maxDiff = 80*8
# This attribute determines whether long messages (including repr of
# objects used in assert methods) will be printed on failure in *addition*
# to any explicit message passed.
longMessage = True
# Attribute used by TestSuite for classSetUp
_classSetupFailed = False
def __init__(self, methodName='runTest'):
"""Create an instance of the class that will use the named test
method when executed. Raises a ValueError if the instance does
not have a method with the specified name.
"""
self._testMethodName = methodName
self._resultForDoCleanups = None
try:
testMethod = getattr(self, methodName)
except AttributeError:
raise ValueError("no such test method in %s: %s" % \
(self.__class__, methodName))
self._testMethodDoc = testMethod.__doc__
self._cleanups = []
# Map types to custom assertEqual functions that will compare
# instances of said type in more detail to generate a more useful
# error message.
self._type_equality_funcs = _TypeEqualityDict(self)
self.addTypeEqualityFunc(dict, 'assertDictEqual')
self.addTypeEqualityFunc(list, 'assertListEqual')
self.addTypeEqualityFunc(tuple, 'assertTupleEqual')
self.addTypeEqualityFunc(set, 'assertSetEqual')
self.addTypeEqualityFunc(frozenset, 'assertSetEqual')
self.addTypeEqualityFunc(unicode, 'assertMultiLineEqual')
def addTypeEqualityFunc(self, typeobj, function):
"""Add a type specific assertEqual style function to compare a type.
This method is for use by TestCase subclasses that need to register
their own type equality functions to provide nicer error messages.
Args:
typeobj: The data type to call this function on when both values
are of the same type in assertEqual().
function: The callable taking two arguments and an optional
msg= argument that raises self.failureException with a
useful error message when the two arguments are not equal.
"""
self._type_equality_funcs[typeobj] = function
def addCleanup(self, function, *args, **kwargs):
"""Add a function, with arguments, to be called when the test is
completed. Functions added are called on a LIFO basis and are
called after tearDown on test failure or success.
Cleanup items are called even if setUp fails (unlike tearDown)."""
self._cleanups.append((function, args, kwargs))
def setUp(self):
"Hook method for setting up the test fixture before exercising it."
@classmethod
def setUpClass(cls):
"Hook method for setting up class fixture before running tests in the class."
@classmethod
def tearDownClass(cls):
"Hook method for deconstructing the class fixture after running all tests in the class."
def tearDown(self):
"Hook method for deconstructing the test fixture after testing it."
def countTestCases(self):
return 1
def defaultTestResult(self):
return result.TestResult()
def shortDescription(self):
"""Returns a one-line description of the test, or None if no
description has been provided.
The default implementation of this method returns the first line of
the specified test method's docstring.
"""
doc = self._testMethodDoc
return doc and doc.split("\n")[0].strip() or None
def id(self):
return "%s.%s" % (strclass(self.__class__), self._testMethodName)
def __eq__(self, other):
if type(self) is not type(other):
return NotImplemented
return self._testMethodName == other._testMethodName
def __ne__(self, other):
return not self == other
def __hash__(self):
return hash((type(self), self._testMethodName))
def __str__(self):
return "%s (%s)" % (self._testMethodName, strclass(self.__class__))
def __repr__(self):
return "<%s testMethod=%s>" % \
(strclass(self.__class__), self._testMethodName)
def _addSkip(self, result, reason):
addSkip = getattr(result, 'addSkip', None)
if addSkip is not None:
addSkip(self, reason)
else:
warnings.warn("Use of a TestResult without an addSkip method is deprecated",
DeprecationWarning, 2)
result.addSuccess(self)
def run(self, result=None):
orig_result = result
if result is None:
result = self.defaultTestResult()
startTestRun = getattr(result, 'startTestRun', None)
if startTestRun is not None:
startTestRun()
self._resultForDoCleanups = result
result.startTest(self)
testMethod = getattr(self, self._testMethodName)
if (getattr(self.__class__, "__unittest_skip__", False) or
getattr(testMethod, "__unittest_skip__", False)):
# If the class or method was skipped.
try:
skip_why = (getattr(self.__class__, '__unittest_skip_why__', '')
or getattr(testMethod, '__unittest_skip_why__', ''))
self._addSkip(result, skip_why)
finally:
result.stopTest(self)
return
try:
success = False
try:
self.setUp()
except SkipTest, e:
self._addSkip(result, str(e))
except Exception:
result.addError(self, sys.exc_info())
else:
try:
testMethod()
except self.failureException:
result.addFailure(self, sys.exc_info())
except _ExpectedFailure, e:
addExpectedFailure = getattr(result, 'addExpectedFailure', None)
if addExpectedFailure is not None:
addExpectedFailure(self, e.exc_info)
else:
warnings.warn("Use of a TestResult without an addExpectedFailure method is deprecated",
DeprecationWarning)
result.addSuccess(self)
except _UnexpectedSuccess:
addUnexpectedSuccess = getattr(result, 'addUnexpectedSuccess', None)
if addUnexpectedSuccess is not None:
addUnexpectedSuccess(self)
else:
warnings.warn("Use of a TestResult without an addUnexpectedSuccess method is deprecated",
DeprecationWarning)
result.addFailure(self, sys.exc_info())
except SkipTest, e:
self._addSkip(result, str(e))
except Exception:
result.addError(self, sys.exc_info())
else:
success = True
try:
self.tearDown()
except Exception:
result.addError(self, sys.exc_info())
success = False
cleanUpSuccess = self.doCleanups()
success = success and cleanUpSuccess
if success:
result.addSuccess(self)
finally:
result.stopTest(self)
if orig_result is None:
stopTestRun = getattr(result, 'stopTestRun', None)
if stopTestRun is not None:
stopTestRun()
def doCleanups(self):
"""Execute all cleanup functions. Normally called for you after
tearDown."""
result = self._resultForDoCleanups
ok = True
while self._cleanups:
function, args, kwargs = self._cleanups.pop(-1)
try:
function(*args, **kwargs)
except Exception:
ok = False
result.addError(self, sys.exc_info())
return ok
def __call__(self, *args, **kwds):
return self.run(*args, **kwds)
def debug(self):
"""Run the test without collecting errors in a TestResult"""
self.setUp()
getattr(self, self._testMethodName)()
self.tearDown()
while self._cleanups:
function, args, kwargs = self._cleanups.pop(-1)
function(*args, **kwargs)
def skipTest(self, reason):
"""Skip this test."""
raise SkipTest(reason)
def fail(self, msg=None):
"""Fail immediately, with the given message."""
raise self.failureException(msg)
def assertFalse(self, expr, msg=None):
"Fail the test if the expression is true."
if expr:
msg = self._formatMessage(msg, "%s is not False" % safe_repr(expr))
raise self.failureException(msg)
def assertTrue(self, expr, msg=None):
"""Fail the test unless the expression is true."""
if not expr:
msg = self._formatMessage(msg, "%s is not True" % safe_repr(expr))
raise self.failureException(msg)
def _formatMessage(self, msg, standardMsg):
"""Honour the longMessage attribute when generating failure messages.
If longMessage is False this means:
* Use only an explicit message if it is provided
* Otherwise use the standard message for the assert
If longMessage is True:
* Use the standard message
* If an explicit message is provided, plus ' : ' and the explicit message
"""
if not self.longMessage:
return msg or standardMsg
if msg is None:
return standardMsg
try:
return '%s : %s' % (standardMsg, msg)
except UnicodeDecodeError:
return '%s : %s' % (safe_str(standardMsg), safe_str(msg))
def assertRaises(self, excClass, callableObj=None, *args, **kwargs):
"""Fail unless an exception of class excClass is thrown
by callableObj when invoked with arguments args and keyword
arguments kwargs. If a different type of exception is
thrown, it will not be caught, and the test case will be
deemed to have suffered an error, exactly as for an
unexpected exception.
If called with callableObj omitted or None, will return a
context object used like this::
with self.assertRaises(SomeException):
do_something()
The context manager keeps a reference to the exception as
the 'exception' attribute. This allows you to inspect the
exception after the assertion::
with self.assertRaises(SomeException) as cm:
do_something()
the_exception = cm.exception
self.assertEqual(the_exception.error_code, 3)
"""
if callableObj is None:
return _AssertRaisesContext(excClass, self)
try:
callableObj(*args, **kwargs)
except excClass:
return
if hasattr(excClass,'__name__'):
excName = excClass.__name__
else:
excName = str(excClass)
raise self.failureException, "%s not raised" % excName
def _getAssertEqualityFunc(self, first, second):
"""Get a detailed comparison function for the types of the two args.
Returns: A callable accepting (first, second, msg=None) that will
raise a failure exception if first != second with a useful human
readable error message for those types.
"""
#
# NOTE(gregory.p.smith): I considered isinstance(first, type(second))
# and vice versa. I opted for the conservative approach in case
# subclasses are not intended to be compared in detail to their super
# class instances using a type equality func. This means testing
# subtypes won't automagically use the detailed comparison. Callers
# should use their type specific assertSpamEqual method to compare
# subclasses if the detailed comparison is desired and appropriate.
# See the discussion in http://bugs.python.org/issue2578.
#
if type(first) is type(second):
asserter = self._type_equality_funcs.get(type(first))
if asserter is not None:
return asserter
return self._baseAssertEqual
def _baseAssertEqual(self, first, second, msg=None):
"""The default assertEqual implementation, not type specific."""
if not first == second:
standardMsg = '%s != %s' % (safe_repr(first), safe_repr(second))
msg = self._formatMessage(msg, standardMsg)
raise self.failureException(msg)
def assertEqual(self, first, second, msg=None):
"""Fail if the two objects are unequal as determined by the '=='
operator.
"""
assertion_func = self._getAssertEqualityFunc(first, second)
assertion_func(first, second, msg=msg)
def assertNotEqual(self, first, second, msg=None):
"""Fail if the two objects are equal as determined by the '=='
operator.
"""
if not first != second:
msg = self._formatMessage(msg, '%s == %s' % (safe_repr(first),
safe_repr(second)))
raise self.failureException(msg)
def assertAlmostEqual(self, first, second, places=None, msg=None, delta=None):
"""Fail if the two objects are unequal as determined by their
difference rounded to the given number of decimal places
(default 7) and comparing to zero, or by comparing that the
between the two objects is more than the given delta.
Note that decimal places (from zero) are usually not the same
as significant digits (measured from the most signficant digit).
If the two objects compare equal then they will automatically
compare almost equal.
"""
if first == second:
# shortcut
return
if delta is not None and places is not None:
raise TypeError("specify delta or places not both")
if delta is not None:
if abs(first - second) <= delta:
return
standardMsg = '%s != %s within %s delta' % (safe_repr(first),
safe_repr(second),
safe_repr(delta))
else:
if places is None:
places = 7
if round(abs(second-first), places) == 0:
return
standardMsg = '%s != %s within %r places' % (safe_repr(first),
safe_repr(second),
places)
msg = self._formatMessage(msg, standardMsg)
raise self.failureException(msg)
def assertNotAlmostEqual(self, first, second, places=None, msg=None, delta=None):
"""Fail if the two objects are equal as determined by their
difference rounded to the given number of decimal places
(default 7) and comparing to zero, or by comparing that the
between the two objects is less than the given delta.
Note that decimal places (from zero) are usually not the same
as significant digits (measured from the most signficant digit).
Objects that are equal automatically fail.
"""
if delta is not None and places is not None:
raise TypeError("specify delta or places not both")
if delta is not None:
if not (first == second) and abs(first - second) > delta:
return
standardMsg = '%s == %s within %s delta' % (safe_repr(first),
safe_repr(second),
safe_repr(delta))
else:
if places is None:
places = 7
if not (first == second) and round(abs(second-first), places) != 0:
return
standardMsg = '%s == %s within %r places' % (safe_repr(first),
safe_repr(second),
places)
msg = self._formatMessage(msg, standardMsg)
raise self.failureException(msg)
# Synonyms for assertion methods
# The plurals are undocumented. Keep them that way to discourage use.
# Do not add more. Do not remove.
# Going through a deprecation cycle on these would annoy many people.
assertEquals = assertEqual
assertNotEquals = assertNotEqual
assertAlmostEquals = assertAlmostEqual
assertNotAlmostEquals = assertNotAlmostEqual
assert_ = assertTrue
# These fail* assertion method names are pending deprecation and will
# be a DeprecationWarning in 3.2; http://bugs.python.org/issue2578
def _deprecate(original_func):
def deprecated_func(*args, **kwargs):
warnings.warn(
('Please use %s instead.' % original_func.__name__),
PendingDeprecationWarning, 2)
return original_func(*args, **kwargs)
return deprecated_func
failUnlessEqual = _deprecate(assertEqual)
failIfEqual = _deprecate(assertNotEqual)
failUnlessAlmostEqual = _deprecate(assertAlmostEqual)
failIfAlmostEqual = _deprecate(assertNotAlmostEqual)
failUnless = _deprecate(assertTrue)
failUnlessRaises = _deprecate(assertRaises)
failIf = _deprecate(assertFalse)
def assertSequenceEqual(self, seq1, seq2,
msg=None, seq_type=None, max_diff=80*8):
"""An equality assertion for ordered sequences (like lists and tuples).
For the purposes of this function, a valid ordered sequence type is one
which can be indexed, has a length, and has an equality operator.
Args:
seq1: The first sequence to compare.
seq2: The second sequence to compare.
seq_type: The expected datatype of the sequences, or None if no
datatype should be enforced.
msg: Optional message to use on failure instead of a list of
differences.
max_diff: Maximum size off the diff, larger diffs are not shown
"""
if seq_type is not None:
seq_type_name = seq_type.__name__
if not isinstance(seq1, seq_type):
raise self.failureException('First sequence is not a %s: %s'
% (seq_type_name, safe_repr(seq1)))
if not isinstance(seq2, seq_type):
raise self.failureException('Second sequence is not a %s: %s'
% (seq_type_name, safe_repr(seq2)))
else:
seq_type_name = "sequence"
differing = None
try:
len1 = len(seq1)
except (TypeError, NotImplementedError):
differing = 'First %s has no length. Non-sequence?' % (
seq_type_name)
if differing is None:
try:
len2 = len(seq2)
except (TypeError, NotImplementedError):
differing = 'Second %s has no length. Non-sequence?' % (
seq_type_name)
if differing is None:
if seq1 == seq2:
return
seq1_repr = repr(seq1)
seq2_repr = repr(seq2)
if len(seq1_repr) > 30:
seq1_repr = seq1_repr[:30] + '...'
if len(seq2_repr) > 30:
seq2_repr = seq2_repr[:30] + '...'
elements = (seq_type_name.capitalize(), seq1_repr, seq2_repr)
differing = '%ss differ: %s != %s\n' % elements
for i in xrange(min(len1, len2)):
try:
item1 = seq1[i]
except (TypeError, IndexError, NotImplementedError):
differing += ('\nUnable to index element %d of first %s\n' %
(i, seq_type_name))
break
try:
item2 = seq2[i]
except (TypeError, IndexError, NotImplementedError):
differing += ('\nUnable to index element %d of second %s\n' %
(i, seq_type_name))
break
if item1 != item2:
differing += ('\nFirst differing element %d:\n%s\n%s\n' %
(i, item1, item2))
break
else:
if (len1 == len2 and seq_type is None and
type(seq1) != type(seq2)):
# The sequences are the same, but have differing types.
return
if len1 > len2:
differing += ('\nFirst %s contains %d additional '
'elements.\n' % (seq_type_name, len1 - len2))
try:
differing += ('First extra element %d:\n%s\n' %
(len2, seq1[len2]))
except (TypeError, IndexError, NotImplementedError):
differing += ('Unable to index element %d '
'of first %s\n' % (len2, seq_type_name))
elif len1 < len2:
differing += ('\nSecond %s contains %d additional '
'elements.\n' % (seq_type_name, len2 - len1))
try:
differing += ('First extra element %d:\n%s\n' %
(len1, seq2[len1]))
except (TypeError, IndexError, NotImplementedError):
differing += ('Unable to index element %d '
'of second %s\n' % (len1, seq_type_name))
standardMsg = differing
diffMsg = '\n' + '\n'.join(
difflib.ndiff(pprint.pformat(seq1).splitlines(),
pprint.pformat(seq2).splitlines()))
standardMsg = self._truncateMessage(standardMsg, diffMsg)
msg = self._formatMessage(msg, standardMsg)
self.fail(msg)
def _truncateMessage(self, message, diff):
max_diff = self.maxDiff
if max_diff is None or len(diff) <= max_diff:
return message + diff
return message + (DIFF_OMITTED % len(diff))
def assertListEqual(self, list1, list2, msg=None):
"""A list-specific equality assertion.
Args:
list1: The first list to compare.
list2: The second list to compare.
msg: Optional message to use on failure instead of a list of
differences.
"""
self.assertSequenceEqual(list1, list2, msg, seq_type=list)
def assertTupleEqual(self, tuple1, tuple2, msg=None):
"""A tuple-specific equality assertion.
Args:
tuple1: The first tuple to compare.
tuple2: The second tuple to compare.
msg: Optional message to use on failure instead of a list of
differences.
"""
self.assertSequenceEqual(tuple1, tuple2, msg, seq_type=tuple)
def assertSetEqual(self, set1, set2, msg=None):
"""A set-specific equality assertion.
Args:
set1: The first set to compare.
set2: The second set to compare.
msg: Optional message to use on failure instead of a list of
differences.
assertSetEqual uses ducktyping to support
different types of sets, and is optimized for sets specifically
(parameters must support a difference method).
"""
try:
difference1 = set1.difference(set2)
except TypeError, e:
self.fail('invalid type when attempting set difference: %s' % e)
except AttributeError, e:
self.fail('first argument does not support set difference: %s' % e)
try:
difference2 = set2.difference(set1)
except TypeError, e:
self.fail('invalid type when attempting set difference: %s' % e)
except AttributeError, e:
self.fail('second argument does not support set difference: %s' % e)
if not (difference1 or difference2):
return
lines = []
if difference1:
lines.append('Items in the first set but not the second:')
for item in difference1:
lines.append(repr(item))
if difference2:
lines.append('Items in the second set but not the first:')
for item in difference2:
lines.append(repr(item))
standardMsg = '\n'.join(lines)
self.fail(self._formatMessage(msg, standardMsg))
def assertIn(self, member, container, msg=None):
"""Just like self.assertTrue(a in b), but with a nicer default message."""
if member not in container:
standardMsg = '%s not found in %s' % (safe_repr(member),
safe_repr(container))
self.fail(self._formatMessage(msg, standardMsg))
def assertNotIn(self, member, container, msg=None):
"""Just like self.assertTrue(a not in b), but with a nicer default message."""
if member in container:
standardMsg = '%s unexpectedly found in %s' % (safe_repr(member),
safe_repr(container))
self.fail(self._formatMessage(msg, standardMsg))
def assertIs(self, expr1, expr2, msg=None):
"""Just like self.assertTrue(a is b), but with a nicer default message."""
if expr1 is not expr2:
standardMsg = '%s is not %s' % (safe_repr(expr1), safe_repr(expr2))
self.fail(self._formatMessage(msg, standardMsg))
def assertIsNot(self, expr1, expr2, msg=None):
"""Just like self.assertTrue(a is not b), but with a nicer default message."""
if expr1 is expr2:
standardMsg = 'unexpectedly identical: %s' % (safe_repr(expr1),)
self.fail(self._formatMessage(msg, standardMsg))
def assertDictEqual(self, d1, d2, msg=None):
self.assert_(isinstance(d1, dict), 'First argument is not a dictionary')
self.assert_(isinstance(d2, dict), 'Second argument is not a dictionary')
if d1 != d2:
standardMsg = '%s != %s' % (safe_repr(d1, True), safe_repr(d2, True))
diff = ('\n' + '\n'.join(difflib.ndiff(
pprint.pformat(d1).splitlines(),
pprint.pformat(d2).splitlines())))
standardMsg = self._truncateMessage(standardMsg, diff)
self.fail(self._formatMessage(msg, standardMsg))
def assertDictContainsSubset(self, expected, actual, msg=None):
"""Checks whether actual is a superset of expected."""
missing = []
mismatched = []
for key, value in expected.iteritems():
if key not in actual:
missing.append(key)
elif value != actual[key]:
mismatched.append('%s, expected: %s, actual: %s' %
(safe_repr(key), safe_repr(value),
safe_repr(actual[key])))
if not (missing or mismatched):
return
standardMsg = ''
if missing:
standardMsg = 'Missing: %s' % ','.join(safe_repr(m) for m in
missing)
if mismatched:
if standardMsg:
standardMsg += '; '
standardMsg += 'Mismatched values: %s' % ','.join(mismatched)
self.fail(self._formatMessage(msg, standardMsg))
def assertItemsEqual(self, expected_seq, actual_seq, msg=None):
"""An unordered sequence specific comparison. It asserts that
expected_seq and actual_seq contain the same elements. It is
the equivalent of::
self.assertEqual(sorted(expected_seq), sorted(actual_seq))
Raises with an error message listing which elements of expected_seq
are missing from actual_seq and vice versa if any.
Asserts that each element has the same count in both sequences.
Example:
- [0, 1, 1] and [1, 0, 1] compare equal.
- [0, 0, 1] and [0, 1] compare unequal.
"""
try:
expected = sorted(expected_seq)
actual = sorted(actual_seq)
except TypeError:
# Unsortable items (example: set(), complex(), ...)
expected = list(expected_seq)
actual = list(actual_seq)
missing, unexpected = unorderable_list_difference(
expected, actual, ignore_duplicate=False
)
else:
return self.assertSequenceEqual(expected, actual, msg=msg)
errors = []
if missing:
errors.append('Expected, but missing:\n %s' %
safe_repr(missing))
if unexpected:
errors.append('Unexpected, but present:\n %s' %
safe_repr(unexpected))
if errors:
standardMsg = '\n'.join(errors)
self.fail(self._formatMessage(msg, standardMsg))
def assertMultiLineEqual(self, first, second, msg=None):
"""Assert that two multi-line strings are equal."""
self.assert_(isinstance(first, basestring), (
'First argument is not a string'))
self.assert_(isinstance(second, basestring), (
'Second argument is not a string'))
if first != second:
standardMsg = '%s != %s' % (safe_repr(first, True), safe_repr(second, True))
diff = '\n' + ''.join(difflib.ndiff(first.splitlines(True),
second.splitlines(True)))
standardMsg = self._truncateMessage(standardMsg, diff)
self.fail(self._formatMessage(msg, standardMsg))
def assertLess(self, a, b, msg=None):
"""Just like self.assertTrue(a < b), but with a nicer default message."""
if not a < b:
standardMsg = '%s not less than %s' % (safe_repr(a), safe_repr(b))
self.fail(self._formatMessage(msg, standardMsg))
def assertLessEqual(self, a, b, msg=None):
"""Just like self.assertTrue(a <= b), but with a nicer default message."""
if not a <= b:
standardMsg = '%s not less than or equal to %s' % (safe_repr(a), safe_repr(b))
self.fail(self._formatMessage(msg, standardMsg))
def assertGreater(self, a, b, msg=None):
"""Just like self.assertTrue(a > b), but with a nicer default message."""
if not a > b:
standardMsg = '%s not greater than %s' % (safe_repr(a), safe_repr(b))
self.fail(self._formatMessage(msg, standardMsg))
def assertGreaterEqual(self, a, b, msg=None):
"""Just like self.assertTrue(a >= b), but with a nicer default message."""
if not a >= b:
standardMsg = '%s not greater than or equal to %s' % (safe_repr(a), safe_repr(b))
self.fail(self._formatMessage(msg, standardMsg))
def assertIsNone(self, obj, msg=None):
"""Same as self.assertTrue(obj is None), with a nicer default message."""
if obj is not None:
standardMsg = '%s is not None' % (safe_repr(obj),)
self.fail(self._formatMessage(msg, standardMsg))
def assertIsNotNone(self, obj, msg=None):
"""Included for symmetry with assertIsNone."""
if obj is None:
standardMsg = 'unexpectedly None'
self.fail(self._formatMessage(msg, standardMsg))
def assertIsInstance(self, obj, cls, msg=None):
"""Same as self.assertTrue(isinstance(obj, cls)), with a nicer
default message."""
if not isinstance(obj, cls):
standardMsg = '%s is not an instance of %r' % (safe_repr(obj), cls)
self.fail(self._formatMessage(msg, standardMsg))
def assertNotIsInstance(self, obj, cls, msg=None):
"""Included for symmetry with assertIsInstance."""
if isinstance(obj, cls):
standardMsg = '%s is an instance of %r' % (safe_repr(obj), cls)
self.fail(self._formatMessage(msg, standardMsg))
def assertRaisesRegexp(self, expected_exception, expected_regexp,
callable_obj=None, *args, **kwargs):
"""Asserts that the message in a raised exception matches a regexp.
Args:
expected_exception: Exception class expected to be raised.
expected_regexp: Regexp (re pattern object or string) expected
to be found in error message.
callable_obj: Function to be called.
args: Extra args.
kwargs: Extra kwargs.
"""
if callable_obj is None:
return _AssertRaisesContext(expected_exception, self, expected_regexp)
try:
callable_obj(*args, **kwargs)
except expected_exception, exc_value:
if isinstance(expected_regexp, basestring):
expected_regexp = re.compile(expected_regexp)
if not expected_regexp.search(str(exc_value)):
raise self.failureException('"%s" does not match "%s"' %
(expected_regexp.pattern, str(exc_value)))
else:
if hasattr(expected_exception, '__name__'):
excName = expected_exception.__name__
else:
excName = str(expected_exception)
raise self.failureException, "%s not raised" % excName
def assertRegexpMatches(self, text, expected_regexp, msg=None):
"""Fail the test unless the text matches the regular expression."""
if isinstance(expected_regexp, basestring):
expected_regexp = re.compile(expected_regexp)
if not expected_regexp.search(text):
msg = msg or "Regexp didn't match"
msg = '%s: %r not found in %r' % (msg, expected_regexp.pattern, text)
raise self.failureException(msg)
def assertNotRegexpMatches(self, text, unexpected_regexp, msg=None):
"""Fail the test if the text matches the regular expression."""
if isinstance(unexpected_regexp, basestring):
unexpected_regexp = re.compile(unexpected_regexp)
match = unexpected_regexp.search(text)
if match:
msg = msg or "Regexp matched"
msg = '%s: %r matches %r in %r' % (msg,
text[match.start():match.end()],
unexpected_regexp.pattern,
text)
raise self.failureException(msg)
class FunctionTestCase(TestCase):
"""A test case that wraps a test function.
This is useful for slipping pre-existing test functions into the
unittest framework. Optionally, set-up and tidy-up functions can be
supplied. As with TestCase, the tidy-up ('tearDown') function will
always be called if the set-up ('setUp') function ran successfully.
"""
def __init__(self, testFunc, setUp=None, tearDown=None, description=None):
super(FunctionTestCase, self).__init__()
self._setUpFunc = setUp
self._tearDownFunc = tearDown
self._testFunc = testFunc
self._description = description
def setUp(self):
if self._setUpFunc is not None:
self._setUpFunc()
def tearDown(self):
if self._tearDownFunc is not None:
self._tearDownFunc()
def runTest(self):
self._testFunc()
def id(self):
return self._testFunc.__name__
def __eq__(self, other):
if not isinstance(other, self.__class__):
return NotImplemented
return self._setUpFunc == other._setUpFunc and \
self._tearDownFunc == other._tearDownFunc and \
self._testFunc == other._testFunc and \
self._description == other._description
def __ne__(self, other):
return not self == other
def __hash__(self):
return hash((type(self), self._setUpFunc, self._tearDownFunc,
self._testFunc, self._description))
def __str__(self):
return "%s (%s)" % (strclass(self.__class__),
self._testFunc.__name__)
def __repr__(self):
return "<%s testFunc=%s>" % (strclass(self.__class__),
self._testFunc)
def shortDescription(self):
if self._description is not None:
return self._description
doc = self._testFunc.__doc__
return doc and doc.split("\n")[0].strip() or None
|
cthrax/python-rtsp | refs/heads/master | capture-sdp.py | 1 | from optparse import OptionParser
from twisted.internet import reactor
from rtsp import RTSPClient, RTSPClientFactory
from twisted.python import failure, log
import signal
import sys
# http://blogmag.net/blog/read/38/Print_human_readable_file_size
def sizeof_fmt(num):
for x in ['bytes','KB','MB','GB','TB']:
if num < 1024.0:
return "%3.1f%s" % (num, x)
num /= 1024.0
def success(result):
print "success"
reactor.stop()
def error(failure):
print("Failure: %s" % failure.getErrorMessage())
reactor.stop()
def progress(factory):
print('Downloaded %s' % sizeof_fmt(factory.data_received))
reactor.callLater(1, progress, factory)
class SigHandler():
factory = None
def setFactory(self, factory):
self.factory = factory
def sighandler(self, signum, frame):
if (signum == signal.SIGINT):
print("Received sigint, terminating stream.")
self.factory.client.sendNextMessage()
reactor.stop()
if __name__ == '__main__':
parser = OptionParser()
parser.add_option('-u', '', dest='url', help='url to download',
metavar='URL')
parser.add_option('-f', '', dest='file', help='file to save to',
metavar='FILENAME')
options, args = parser.parse_args()
if options.url is None:
print('You must enter a url to download\n')
parser.print_help()
exit()
if not options.file:
options.file = re.search('[^/]*$', options.url).group(0)
if not options.file or len(options.file) < 1:
print('Invalid file name specified\n')
parser.print_help()
exit()
sighandler = SigHandler()
signal.signal(signal.SIGHUP | signal.SIGINT, sighandler.sighandler)
log.startLogging(sys.stdout)
factory = RTSPClientFactory(options.url, options.file)
sighandler.setFactory(factory)
factory.bandwidth = 99999999999
factory.deferred.addCallback(success).addErrback(error)
reactor.connectTCP(factory.host, factory.port, factory)
reactor.callLater(1, progress, factory)
reactor.run()
|
raccoongang/edx-platform | refs/heads/ginkgo-rg | lms/djangoapps/django_comment_client/base/views.py | 2 | import functools
import json
import logging
import random
import time
import urlparse
from django.contrib.auth.decorators import login_required
from django.contrib.auth.models import User
from django.core import exceptions
from django.http import Http404, HttpResponse, HttpResponseServerError
from django.utils.translation import ugettext as _
from django.views.decorators import csrf
from django.views.decorators.http import require_GET, require_POST
from opaque_keys.edx.keys import CourseKey
import django_comment_client.settings as cc_settings
import lms.lib.comment_client as cc
from courseware.access import has_access
from courseware.courses import get_course_by_id, get_course_overview_with_access, get_course_with_access
from django_comment_client.permissions import check_permissions_by_view, get_team, has_permission
from django_comment_client.utils import (
JsonError,
JsonResponse,
add_courseware_context,
discussion_category_id_access,
get_ability,
get_annotated_content_info,
get_cached_discussion_id_map,
get_group_id_for_comments_service,
is_comment_too_deep,
prepare_content
)
from django_comment_common.signals import (
comment_created,
comment_deleted,
comment_edited,
comment_endorsed,
comment_voted,
thread_created,
thread_deleted,
thread_edited,
thread_voted
)
from django_comment_common.utils import ThreadContext
from eventtracking import tracker
from lms.djangoapps.courseware.exceptions import CourseAccessRedirect
from util.file import store_uploaded_file
log = logging.getLogger(__name__)
TRACKING_MAX_FORUM_BODY = 2000
TRACKING_MAX_FORUM_TITLE = 1000
_EVENT_NAME_TEMPLATE = 'edx.forum.{obj_type}.{action_name}'
def track_forum_event(request, event_name, course, obj, data, id_map=None):
"""
Send out an analytics event when a forum event happens. Works for threads,
responses to threads, and comments on those responses.
"""
user = request.user
data['id'] = obj.id
commentable_id = data['commentable_id']
team = get_team(commentable_id)
if team is not None:
data.update(team_id=team.team_id)
if id_map is None:
id_map = get_cached_discussion_id_map(course, [commentable_id], user)
if commentable_id in id_map:
data['category_name'] = id_map[commentable_id]["title"]
data['category_id'] = commentable_id
data['url'] = request.META.get('HTTP_REFERER', '')
data['user_forums_roles'] = [
role.name for role in user.roles.filter(course_id=course.id)
]
data['user_course_roles'] = [
role.role for role in user.courseaccessrole_set.filter(course_id=course.id)
]
tracker.emit(event_name, data)
def track_created_event(request, event_name, course, obj, data):
"""
Send analytics event for a newly created thread, response or comment.
"""
if len(obj.body) > TRACKING_MAX_FORUM_BODY:
data['truncated'] = True
else:
data['truncated'] = False
data['body'] = obj.body[:TRACKING_MAX_FORUM_BODY]
track_forum_event(request, event_name, course, obj, data)
def add_truncated_title_to_event_data(event_data, full_title):
event_data['title_truncated'] = (len(full_title) > TRACKING_MAX_FORUM_TITLE)
event_data['title'] = full_title[:TRACKING_MAX_FORUM_TITLE]
def track_thread_created_event(request, course, thread, followed):
"""
Send analytics event for a newly created thread.
"""
event_name = _EVENT_NAME_TEMPLATE.format(obj_type='thread', action_name='created')
event_data = {
'commentable_id': thread.commentable_id,
'group_id': thread.get("group_id"),
'thread_type': thread.thread_type,
'anonymous': thread.anonymous,
'anonymous_to_peers': thread.anonymous_to_peers,
'options': {'followed': followed},
# There is a stated desire for an 'origin' property that will state
# whether this thread was created via courseware or the forum.
# However, the view does not contain that data, and including it will
# likely require changes elsewhere.
}
add_truncated_title_to_event_data(event_data, thread.title)
track_created_event(request, event_name, course, thread, event_data)
def track_comment_created_event(request, course, comment, commentable_id, followed):
"""
Send analytics event for a newly created response or comment.
"""
obj_type = 'comment' if comment.get("parent_id") else 'response'
event_name = _EVENT_NAME_TEMPLATE.format(obj_type=obj_type, action_name='created')
event_data = {
'discussion': {'id': comment.thread_id},
'commentable_id': commentable_id,
'options': {'followed': followed},
}
parent_id = comment.get('parent_id')
if parent_id:
event_data['response'] = {'id': parent_id}
track_created_event(request, event_name, course, comment, event_data)
def track_voted_event(request, course, obj, vote_value, undo_vote=False):
"""
Send analytics event for a vote on a thread or response.
"""
if isinstance(obj, cc.Thread):
obj_type = 'thread'
else:
obj_type = 'response'
event_name = _EVENT_NAME_TEMPLATE.format(obj_type=obj_type, action_name='voted')
event_data = {
'commentable_id': obj.commentable_id,
'target_username': obj.get('username'),
'undo_vote': undo_vote,
'vote_value': vote_value,
}
track_forum_event(request, event_name, course, obj, event_data)
def permitted(func):
"""
View decorator to verify the user is authorized to access this endpoint.
"""
@functools.wraps(func)
def wrapper(request, *args, **kwargs):
"""
Wrapper for the view that only calls the view if the user is authorized.
"""
def fetch_content():
"""
Extract the forum object from the keyword arguments to the view.
"""
if "thread_id" in kwargs:
content = cc.Thread.find(kwargs["thread_id"]).to_dict()
elif "comment_id" in kwargs:
content = cc.Comment.find(kwargs["comment_id"]).to_dict()
elif "commentable_id" in kwargs:
content = cc.Commentable.find(kwargs["commentable_id"]).to_dict()
else:
content = None
return content
course_key = CourseKey.from_string(kwargs['course_id'])
if check_permissions_by_view(request.user, course_key, fetch_content(), request.view_name):
return func(request, *args, **kwargs)
else:
return JsonError("unauthorized", status=401)
return wrapper
def ajax_content_response(request, course_key, content):
"""
Standard AJAX response returning the content hierarchy of the current thread.
"""
user_info = cc.User.from_django_user(request.user).to_dict()
annotated_content_info = get_annotated_content_info(course_key, content, request.user, user_info)
return JsonResponse({
'content': prepare_content(content, course_key),
'annotated_content_info': annotated_content_info,
})
@require_POST
@login_required
@permitted
def create_thread(request, course_id, commentable_id):
"""
Given a course and commentble ID, create the thread
"""
log.debug("Creating new thread in %r, id %r", course_id, commentable_id)
course_key = CourseKey.from_string(course_id)
course = get_course_with_access(request.user, 'load', course_key)
post = request.POST
user = request.user
if course.allow_anonymous:
anonymous = post.get('anonymous', 'false').lower() == 'true'
else:
anonymous = False
if course.allow_anonymous_to_peers:
anonymous_to_peers = post.get('anonymous_to_peers', 'false').lower() == 'true'
else:
anonymous_to_peers = False
if 'title' not in post or not post['title'].strip():
return JsonError(_("Title can't be empty"))
if 'body' not in post or not post['body'].strip():
return JsonError(_("Body can't be empty"))
params = {
'anonymous': anonymous,
'anonymous_to_peers': anonymous_to_peers,
'commentable_id': commentable_id,
'course_id': course_key.to_deprecated_string(),
'user_id': user.id,
'thread_type': post["thread_type"],
'body': post["body"],
'title': post["title"],
}
# Check for whether this commentable belongs to a team, and add the right context
if get_team(commentable_id) is not None:
params['context'] = ThreadContext.STANDALONE
else:
params['context'] = ThreadContext.COURSE
thread = cc.Thread(**params)
# Divide the thread if required
try:
group_id = get_group_id_for_comments_service(request, course_key, commentable_id)
except ValueError:
return HttpResponseServerError("Invalid group id for commentable")
if group_id is not None:
thread.group_id = group_id
thread.save()
thread_created.send(sender=None, user=user, post=thread)
# patch for backward compatibility to comments service
if 'pinned' not in thread.attributes:
thread['pinned'] = False
follow = post.get('auto_subscribe', 'false').lower() == 'true'
if follow:
cc_user = cc.User.from_django_user(user)
cc_user.follow(thread)
data = thread.to_dict()
add_courseware_context([data], course, user)
track_thread_created_event(request, course, thread, follow)
if request.is_ajax():
return ajax_content_response(request, course_key, data)
else:
return JsonResponse(prepare_content(data, course_key))
@require_POST
@login_required
@permitted
def update_thread(request, course_id, thread_id):
"""
Given a course id and thread id, update a existing thread, used for both static and ajax submissions
"""
if 'title' not in request.POST or not request.POST['title'].strip():
return JsonError(_("Title can't be empty"))
if 'body' not in request.POST or not request.POST['body'].strip():
return JsonError(_("Body can't be empty"))
course_key = CourseKey.from_string(course_id)
thread = cc.Thread.find(thread_id)
# Get thread context first in order to be safe from reseting the values of thread object later
thread_context = getattr(thread, "context", "course")
thread.body = request.POST["body"]
thread.title = request.POST["title"]
user = request.user
# The following checks should avoid issues we've seen during deploys, where end users are hitting an updated server
# while their browser still has the old client code. This will avoid erasing present values in those cases.
if "thread_type" in request.POST:
thread.thread_type = request.POST["thread_type"]
if "commentable_id" in request.POST:
commentable_id = request.POST["commentable_id"]
course = get_course_with_access(user, 'load', course_key)
if thread_context == "course" and not discussion_category_id_access(course, user, commentable_id):
return JsonError(_("Topic doesn't exist"))
else:
thread.commentable_id = commentable_id
thread.save()
thread_edited.send(sender=None, user=user, post=thread)
if request.is_ajax():
return ajax_content_response(request, course_key, thread.to_dict())
else:
return JsonResponse(prepare_content(thread.to_dict(), course_key))
def _create_comment(request, course_key, thread_id=None, parent_id=None):
"""
given a course_key, thread_id, and parent_id, create a comment,
called from create_comment to do the actual creation
"""
assert isinstance(course_key, CourseKey)
post = request.POST
user = request.user
if 'body' not in post or not post['body'].strip():
return JsonError(_("Body can't be empty"))
course = get_course_with_access(user, 'load', course_key)
if course.allow_anonymous:
anonymous = post.get('anonymous', 'false').lower() == 'true'
else:
anonymous = False
if course.allow_anonymous_to_peers:
anonymous_to_peers = post.get('anonymous_to_peers', 'false').lower() == 'true'
else:
anonymous_to_peers = False
comment = cc.Comment(
anonymous=anonymous,
anonymous_to_peers=anonymous_to_peers,
user_id=user.id,
course_id=course_key.to_deprecated_string(),
thread_id=thread_id,
parent_id=parent_id,
body=post["body"]
)
comment.save()
comment_created.send(sender=None, user=user, post=comment)
followed = post.get('auto_subscribe', 'false').lower() == 'true'
if followed:
cc_user = cc.User.from_django_user(request.user)
cc_user.follow(comment.thread)
track_comment_created_event(request, course, comment, comment.thread.commentable_id, followed)
if request.is_ajax():
return ajax_content_response(request, course_key, comment.to_dict())
else:
return JsonResponse(prepare_content(comment.to_dict(), course.id))
@require_POST
@login_required
@permitted
def create_comment(request, course_id, thread_id):
"""
given a course_id and thread_id, test for comment depth. if not too deep,
call _create_comment to create the actual comment.
"""
if is_comment_too_deep(parent=None):
return JsonError(_("Comment level too deep"))
return _create_comment(request, CourseKey.from_string(course_id), thread_id=thread_id)
@require_POST
@login_required
@permitted
def delete_thread(request, course_id, thread_id):
"""
given a course_id and thread_id, delete this thread
this is ajax only
"""
course_key = CourseKey.from_string(course_id)
thread = cc.Thread.find(thread_id)
thread.delete()
thread_deleted.send(sender=None, user=request.user, post=thread)
return JsonResponse(prepare_content(thread.to_dict(), course_key))
@require_POST
@login_required
@permitted
def update_comment(request, course_id, comment_id):
"""
given a course_id and comment_id, update the comment with payload attributes
handles static and ajax submissions
"""
course_key = CourseKey.from_string(course_id)
comment = cc.Comment.find(comment_id)
if 'body' not in request.POST or not request.POST['body'].strip():
return JsonError(_("Body can't be empty"))
comment.body = request.POST["body"]
comment.save()
comment_edited.send(sender=None, user=request.user, post=comment)
if request.is_ajax():
return ajax_content_response(request, course_key, comment.to_dict())
else:
return JsonResponse(prepare_content(comment.to_dict(), course_key))
@require_POST
@login_required
@permitted
def endorse_comment(request, course_id, comment_id):
"""
given a course_id and comment_id, toggle the endorsement of this comment,
ajax only
"""
course_key = CourseKey.from_string(course_id)
comment = cc.Comment.find(comment_id)
user = request.user
comment.endorsed = request.POST.get('endorsed', 'false').lower() == 'true'
comment.endorsement_user_id = user.id
comment.save()
comment_endorsed.send(sender=None, user=user, post=comment)
return JsonResponse(prepare_content(comment.to_dict(), course_key))
@require_POST
@login_required
@permitted
def openclose_thread(request, course_id, thread_id):
"""
given a course_id and thread_id, toggle the status of this thread
ajax only
"""
course_key = CourseKey.from_string(course_id)
thread = cc.Thread.find(thread_id)
thread.closed = request.POST.get('closed', 'false').lower() == 'true'
thread.save()
return JsonResponse({
'content': prepare_content(thread.to_dict(), course_key),
'ability': get_ability(course_key, thread.to_dict(), request.user),
})
@require_POST
@login_required
@permitted
def create_sub_comment(request, course_id, comment_id):
"""
given a course_id and comment_id, create a response to a comment
after checking the max depth allowed, if allowed
"""
if is_comment_too_deep(parent=cc.Comment(comment_id)):
return JsonError(_("Comment level too deep"))
return _create_comment(request, CourseKey.from_string(course_id), parent_id=comment_id)
@require_POST
@login_required
@permitted
def delete_comment(request, course_id, comment_id):
"""
given a course_id and comment_id delete this comment
ajax only
"""
course_key = CourseKey.from_string(course_id)
comment = cc.Comment.find(comment_id)
comment.delete()
comment_deleted.send(sender=None, user=request.user, post=comment)
return JsonResponse(prepare_content(comment.to_dict(), course_key))
def _vote_or_unvote(request, course_id, obj, value='up', undo_vote=False):
"""
Vote or unvote for a thread or a response.
"""
course_key = CourseKey.from_string(course_id)
course = get_course_with_access(request.user, 'load', course_key)
user = cc.User.from_django_user(request.user)
if undo_vote:
user.unvote(obj)
# TODO(smarnach): Determine the value of the vote that is undone. Currently, you can
# only cast upvotes in the user interface, so it is assumed that the vote value is 'up'.
# (People could theoretically downvote by handcrafting AJAX requests.)
else:
user.vote(obj, value)
track_voted_event(request, course, obj, value, undo_vote)
return JsonResponse(prepare_content(obj.to_dict(), course_key))
@require_POST
@login_required
@permitted
def vote_for_comment(request, course_id, comment_id, value):
"""
Given a course_id and comment_id, vote for this response. AJAX only.
"""
comment = cc.Comment.find(comment_id)
result = _vote_or_unvote(request, course_id, comment, value)
comment_voted.send(sender=None, user=request.user, post=comment)
return result
@require_POST
@login_required
@permitted
def undo_vote_for_comment(request, course_id, comment_id):
"""
given a course id and comment id, remove vote
ajax only
"""
return _vote_or_unvote(request, course_id, cc.Comment.find(comment_id), undo_vote=True)
@require_POST
@login_required
@permitted
def vote_for_thread(request, course_id, thread_id, value):
"""
given a course id and thread id vote for this thread
ajax only
"""
thread = cc.Thread.find(thread_id)
result = _vote_or_unvote(request, course_id, thread, value)
thread_voted.send(sender=None, user=request.user, post=thread)
return result
@require_POST
@login_required
@permitted
def undo_vote_for_thread(request, course_id, thread_id):
"""
given a course id and thread id, remove users vote for thread
ajax only
"""
return _vote_or_unvote(request, course_id, cc.Thread.find(thread_id), undo_vote=True)
@require_POST
@login_required
@permitted
def flag_abuse_for_thread(request, course_id, thread_id):
"""
given a course_id and thread_id flag this thread for abuse
ajax only
"""
course_key = CourseKey.from_string(course_id)
user = cc.User.from_django_user(request.user)
thread = cc.Thread.find(thread_id)
thread.flagAbuse(user, thread)
return JsonResponse(prepare_content(thread.to_dict(), course_key))
@require_POST
@login_required
@permitted
def un_flag_abuse_for_thread(request, course_id, thread_id):
"""
given a course id and thread id, remove abuse flag for this thread
ajax only
"""
user = cc.User.from_django_user(request.user)
course_key = CourseKey.from_string(course_id)
course = get_course_by_id(course_key)
thread = cc.Thread.find(thread_id)
remove_all = bool(
has_permission(request.user, 'openclose_thread', course_key) or
has_access(request.user, 'staff', course)
)
thread.unFlagAbuse(user, thread, remove_all)
return JsonResponse(prepare_content(thread.to_dict(), course_key))
@require_POST
@login_required
@permitted
def flag_abuse_for_comment(request, course_id, comment_id):
"""
given a course and comment id, flag comment for abuse
ajax only
"""
course_key = CourseKey.from_string(course_id)
user = cc.User.from_django_user(request.user)
comment = cc.Comment.find(comment_id)
comment.flagAbuse(user, comment)
return JsonResponse(prepare_content(comment.to_dict(), course_key))
@require_POST
@login_required
@permitted
def un_flag_abuse_for_comment(request, course_id, comment_id):
"""
given a course_id and comment id, unflag comment for abuse
ajax only
"""
user = cc.User.from_django_user(request.user)
course_key = CourseKey.from_string(course_id)
course = get_course_by_id(course_key)
remove_all = bool(
has_permission(request.user, 'openclose_thread', course_key) or
has_access(request.user, 'staff', course)
)
comment = cc.Comment.find(comment_id)
comment.unFlagAbuse(user, comment, remove_all)
return JsonResponse(prepare_content(comment.to_dict(), course_key))
@require_POST
@login_required
@permitted
def pin_thread(request, course_id, thread_id):
"""
given a course id and thread id, pin this thread
ajax only
"""
course_key = CourseKey.from_string(course_id)
user = cc.User.from_django_user(request.user)
thread = cc.Thread.find(thread_id)
thread.pin(user, thread_id)
return JsonResponse(prepare_content(thread.to_dict(), course_key))
@require_POST
@login_required
@permitted
def un_pin_thread(request, course_id, thread_id):
"""
given a course id and thread id, remove pin from this thread
ajax only
"""
course_key = CourseKey.from_string(course_id)
user = cc.User.from_django_user(request.user)
thread = cc.Thread.find(thread_id)
thread.un_pin(user, thread_id)
return JsonResponse(prepare_content(thread.to_dict(), course_key))
@require_POST
@login_required
@permitted
def follow_thread(request, course_id, thread_id):
user = cc.User.from_django_user(request.user)
thread = cc.Thread.find(thread_id)
user.follow(thread)
return JsonResponse({})
@require_POST
@login_required
@permitted
def follow_commentable(request, course_id, commentable_id):
"""
given a course_id and commentable id, follow this commentable
ajax only
"""
user = cc.User.from_django_user(request.user)
commentable = cc.Commentable.find(commentable_id)
user.follow(commentable)
return JsonResponse({})
@require_POST
@login_required
@permitted
def unfollow_thread(request, course_id, thread_id):
"""
given a course id and thread id, stop following this thread
ajax only
"""
user = cc.User.from_django_user(request.user)
thread = cc.Thread.find(thread_id)
user.unfollow(thread)
return JsonResponse({})
@require_POST
@login_required
@permitted
def unfollow_commentable(request, course_id, commentable_id):
"""
given a course id and commentable id stop following commentable
ajax only
"""
user = cc.User.from_django_user(request.user)
commentable = cc.Commentable.find(commentable_id)
user.unfollow(commentable)
return JsonResponse({})
@require_POST
@login_required
@csrf.csrf_exempt
def upload(request, course_id): # ajax upload file to a question or answer
"""view that handles file upload via Ajax
"""
# check upload permission
error = ''
new_file_name = ''
try:
# TODO authorization
#may raise exceptions.PermissionDenied
#if request.user.is_anonymous():
# msg = _('Sorry, anonymous users cannot upload files')
# raise exceptions.PermissionDenied(msg)
#request.user.assert_can_upload_file()
base_file_name = str(time.time()).replace('.', str(random.randint(0, 100000)))
file_storage, new_file_name = store_uploaded_file(
request, 'file-upload', cc_settings.ALLOWED_UPLOAD_FILE_TYPES, base_file_name,
max_file_size=cc_settings.MAX_UPLOAD_FILE_SIZE
)
except exceptions.PermissionDenied, err:
error = unicode(err)
except Exception, err:
print err
logging.critical(unicode(err))
error = _('Error uploading file. Please contact the site administrator. Thank you.')
if error == '':
result = _('Good')
file_url = file_storage.url(new_file_name)
parsed_url = urlparse.urlparse(file_url)
file_url = urlparse.urlunparse(
urlparse.ParseResult(
parsed_url.scheme,
parsed_url.netloc,
parsed_url.path,
'', '', ''
)
)
else:
result = ''
file_url = ''
# Using content-type of text/plain here instead of JSON because
# IE doesn't know how to handle the JSON response and prompts the
# user to save the JSON as a file instead of passing it to the callback.
return HttpResponse(json.dumps({
'result': {
'msg': result,
'error': error,
'file_url': file_url,
}
}), content_type="text/plain")
@require_GET
@login_required
def users(request, course_id):
"""
Given a `username` query parameter, find matches for users in the forum for this course.
Only exact matches are supported here, so the length of the result set will either be 0 or 1.
"""
course_key = CourseKey.from_string(course_id)
try:
get_course_overview_with_access(request.user, 'load', course_key, check_if_enrolled=True)
except Http404:
# course didn't exist, or requesting user does not have access to it.
return JsonError(status=404)
except CourseAccessRedirect:
# user does not have access to the course.
return JsonError(status=404)
try:
username = request.GET['username']
except KeyError:
# 400 is default status for JsonError
return JsonError(["username parameter is required"])
user_objs = []
try:
matched_user = User.objects.get(username=username)
cc_user = cc.User.from_django_user(matched_user)
cc_user.course_id = course_key
cc_user.retrieve(complete=False)
if (cc_user['threads_count'] + cc_user['comments_count']) > 0:
user_objs.append({
'id': matched_user.id,
'username': matched_user.username,
})
except User.DoesNotExist:
pass
return JsonResponse({"users": user_objs})
|
Garrett-R/scikit-learn | refs/heads/master | examples/bicluster/plot_spectral_coclustering.py | 276 | """
==============================================
A demo of the Spectral Co-Clustering algorithm
==============================================
This example demonstrates how to generate a dataset and bicluster it
using the the Spectral Co-Clustering algorithm.
The dataset is generated using the ``make_biclusters`` function, which
creates a matrix of small values and implants bicluster with large
values. The rows and columns are then shuffled and passed to the
Spectral Co-Clustering algorithm. Rearranging the shuffled matrix to
make biclusters contiguous shows how accurately the algorithm found
the biclusters.
"""
print(__doc__)
# Author: Kemal Eren <kemal@kemaleren.com>
# License: BSD 3 clause
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import make_biclusters
from sklearn.datasets import samples_generator as sg
from sklearn.cluster.bicluster import SpectralCoclustering
from sklearn.metrics import consensus_score
data, rows, columns = make_biclusters(
shape=(300, 300), n_clusters=5, noise=5,
shuffle=False, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Original dataset")
data, row_idx, col_idx = sg._shuffle(data, random_state=0)
plt.matshow(data, cmap=plt.cm.Blues)
plt.title("Shuffled dataset")
model = SpectralCoclustering(n_clusters=5, random_state=0)
model.fit(data)
score = consensus_score(model.biclusters_,
(rows[:, row_idx], columns[:, col_idx]))
print("consensus score: {:.3f}".format(score))
fit_data = data[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
plt.matshow(fit_data, cmap=plt.cm.Blues)
plt.title("After biclustering; rearranged to show biclusters")
plt.show()
|
dan1/horizon-x509 | refs/heads/master | openstack_dashboard/dashboards/identity/users/urls.py | 57 | # Copyright 2012 United States Government as represented by the
# Administrator of the National Aeronautics and Space Administration.
# All Rights Reserved.
#
# Copyright 2012 Nebula, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from django.conf.urls import patterns
from django.conf.urls import url
from openstack_dashboard.dashboards.identity.users import views
VIEWS_MOD = 'openstack_dashboard.dashboards.identity.users.views'
urlpatterns = patterns(
VIEWS_MOD,
url(r'^$', views.IndexView.as_view(), name='index'),
url(r'^(?P<user_id>[^/]+)/update/$',
views.UpdateView.as_view(), name='update'),
url(r'^create/$', views.CreateView.as_view(), name='create'),
url(r'^(?P<user_id>[^/]+)/detail/$',
views.DetailView.as_view(), name='detail'),
url(r'^(?P<user_id>[^/]+)/change_password/$',
views.ChangePasswordView.as_view(), name='change_password'))
|
lmprice/ansible | refs/heads/devel | lib/ansible/plugins/callback/actionable.py | 8 | # (c) 2015, Andrew Gaffney <andrew@agaffney.org>
# (c) 2017 Ansible Project
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
# Make coding more python3-ish
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
DOCUMENTATION = '''
callback: actionable
type: stdout
short_description: shows only items that need attention
description:
- Use this callback when you dont care about OK nor Skipped.
- This callback suppresses any non Failed or Changed status.
version_added: "2.1"
extends_documentation_fragment:
- default_callback
requirements:
- set as stdout callback in configuration
'''
from ansible import constants as C
from ansible.plugins.callback.default import CallbackModule as CallbackModule_default
class CallbackModule(CallbackModule_default):
CALLBACK_VERSION = 2.0
CALLBACK_TYPE = 'stdout'
CALLBACK_NAME = 'actionable'
def __init__(self):
self.super_ref = super(CallbackModule, self)
self.super_ref.__init__()
self.last_task = None
self.last_task_banner = None
self.shown_title = False
def v2_playbook_on_handler_task_start(self, task):
self.super_ref.v2_playbook_on_handler_task_start(task)
self.shown_title = True
def v2_playbook_on_task_start(self, task, is_conditional):
self.last_task = task
self.last_task_banner = self._get_task_banner(task)
self.shown_title = False
def display_task_banner(self):
if not self.shown_title:
self.super_ref.v2_playbook_on_task_start(self.last_task, None)
self.shown_title = True
def _print_task_banner(self, task):
self._display.banner(self.last_task_banner)
self._print_task_path(self.last_task)
self._last_task_banner = self.last_task._uuid
def _print_task_path(self, task):
if self._display.verbosity >= 2:
path = task.get_path()
if path:
self._display.display(u"task path: %s" % path, color=C.COLOR_DEBUG)
def _get_task_banner(self, task):
# args can be specified as no_log in several places: in the task or in
# the argument spec. We can check whether the task is no_log but the
# argument spec can't be because that is only run on the target
# machine and we haven't run it thereyet at this time.
#
# So we give people a config option to affect display of the args so
# that they can secure this if they feel that their stdout is insecure
# (shoulder surfing, logging stdout straight to a file, etc).
args = ''
if not task.no_log and C.DISPLAY_ARGS_TO_STDOUT:
args = u', '.join(u'%s=%s' % a for a in task.args.items())
args = u' %s' % args
return u"TASK [%s%s]" % (task.get_name().strip(), args)
def v2_runner_on_failed(self, result, ignore_errors=False):
self.display_task_banner()
self.super_ref.v2_runner_on_failed(result, ignore_errors)
def v2_runner_on_ok(self, result):
if result._result.get('changed', False):
self.display_task_banner()
self.super_ref.v2_runner_on_ok(result)
def v2_runner_on_unreachable(self, result):
self.display_task_banner()
self.super_ref.v2_runner_on_unreachable(result)
def v2_runner_on_skipped(self, result):
pass
def v2_playbook_on_include(self, included_file):
pass
def v2_runner_item_on_ok(self, result):
if result._result.get('changed', False):
self.display_task_banner()
self.super_ref.v2_runner_item_on_ok(result)
def v2_runner_item_on_skipped(self, result):
pass
def v2_runner_item_on_failed(self, result):
self.display_task_banner()
self.super_ref.v2_runner_item_on_failed(result)
|
matuu/simpleai | refs/heads/master | samples/search/hello_world.py | 4 | # coding=utf-8
from simpleai.search import SearchProblem, astar
GOAL = 'HELLO WORLD'
class HelloProblem(SearchProblem):
def actions(self, state):
if len(state) < len(GOAL):
return list(' ABCDEFGHIJKLMNOPQRSTUVWXYZ')
else:
return []
def result(self, state, action):
return state + action
def is_goal(self, state):
return state == GOAL
def heuristic(self, state):
# how far are we from the goal?
wrong = sum([1 if state[i] != GOAL[i] else 0
for i in range(len(state))])
missing = len(GOAL) - len(state)
return wrong + missing
problem = HelloProblem(initial_state='')
result = astar(problem)
print result.state
print result.path()
|
MichaelNedzelsky/intellij-community | refs/heads/master | python/testData/inspections/AddSelfFunction_after.py | 83 | class A:
def get_a(self):
pass
def foo(self):
self.get_a()
|
pcncadcache/cachesystem | refs/heads/master | extern/redis-binlog/extern/rocksdb/coverage/parse_gcov_output.py | 101 | import optparse
import re
import sys
from optparse import OptionParser
# the gcov report follows certain pattern. Each file will have two lines
# of report, from which we can extract the file name, total lines and coverage
# percentage.
def parse_gcov_report(gcov_input):
per_file_coverage = {}
total_coverage = None
for line in sys.stdin:
line = line.strip()
# --First line of the coverage report (with file name in it)?
match_obj = re.match("^File '(.*)'$", line)
if match_obj:
# fetch the file name from the first line of the report.
current_file = match_obj.group(1)
continue
# -- Second line of the file report (with coverage percentage)
match_obj = re.match("^Lines executed:(.*)% of (.*)", line)
if match_obj:
coverage = float(match_obj.group(1))
lines = int(match_obj.group(2))
if current_file is not None:
per_file_coverage[current_file] = (coverage, lines)
current_file = None
else:
# If current_file is not set, we reach the last line of report,
# which contains the summarized coverage percentage.
total_coverage = (coverage, lines)
continue
# If the line's pattern doesn't fall into the above categories. We
# can simply ignore them since they're either empty line or doesn't
# find executable lines of the given file.
current_file = None
return per_file_coverage, total_coverage
def get_option_parser():
usage = "Parse the gcov output and generate more human-readable code " +\
"coverage report."
parser = OptionParser(usage)
parser.add_option(
"--interested-files", "-i",
dest="filenames",
help="Comma separated files names. if specified, we will display " +
"the coverage report only for interested source files. " +
"Otherwise we will display the coverage report for all " +
"source files."
)
return parser
def display_file_coverage(per_file_coverage, total_coverage):
# To print out auto-adjustable column, we need to know the longest
# length of file names.
max_file_name_length = max(
len(fname) for fname in per_file_coverage.keys()
)
# -- Print header
# size of separator is determined by 3 column sizes:
# file name, coverage percentage and lines.
header_template = \
"%" + str(max_file_name_length) + "s\t%s\t%s"
separator = "-" * (max_file_name_length + 10 + 20)
print header_template % ("Filename", "Coverage", "Lines")
print separator
# -- Print body
# template for printing coverage report for each file.
record_template = "%" + str(max_file_name_length) + "s\t%5.2f%%\t%10d"
for fname, coverage_info in per_file_coverage.items():
coverage, lines = coverage_info
print record_template % (fname, coverage, lines)
# -- Print footer
if total_coverage:
print separator
print record_template % ("Total", total_coverage[0], total_coverage[1])
def report_coverage():
parser = get_option_parser()
(options, args) = parser.parse_args()
interested_files = set()
if options.filenames is not None:
interested_files = set(f.strip() for f in options.filenames.split(','))
# To make things simple, right now we only read gcov report from the input
per_file_coverage, total_coverage = parse_gcov_report(sys.stdin)
# Check if we need to display coverage info for interested files.
if len(interested_files):
per_file_coverage = dict(
(fname, per_file_coverage[fname]) for fname in interested_files
if fname in per_file_coverage
)
# If we only interested in several files, it makes no sense to report
# the total_coverage
total_coverage = None
if not len(per_file_coverage):
print >> sys.stderr, "Cannot find coverage info for the given files."
return
display_file_coverage(per_file_coverage, total_coverage)
if __name__ == "__main__":
report_coverage()
|
sxend/FrameworkBenchmarks | refs/heads/master | frameworks/Python/responder/responder_conf.py | 20 | import multiprocessing
import os
_is_travis = os.environ.get('TRAVIS') == 'true'
workers = multiprocessing.cpu_count()
if _is_travis:
workers = 2
bind = "0.0.0.0:8080"
keepalive = 120
errorlog = '-'
pidfile = '/tmp/responder.pid'
loglevel = 'error'
|
andmos/ansible | refs/heads/devel | lib/ansible/module_utils/network/netvisor/pn_nvos.py | 18 | # Copyright: (c) 2018, Pluribus Networks
# Simplified BSD License (see licenses/simplified_bsd.txt or https://opensource.org/licenses/BSD-2-Clause)
#
from __future__ import absolute_import, division, print_function
__metaclass__ = type
import shlex
def pn_cli(module, switch=None, username=None, password=None, switch_local=None):
"""
Method to generate the cli portion to launch the Netvisor cli.
:param module: The Ansible module to fetch username and password.
:return: The cli string for further processing.
"""
cli = '/usr/bin/cli --quiet -e --no-login-prompt '
if username and password:
cli += '--user "%s":"%s" ' % (username, password)
if switch:
cli += ' switch ' + switch
if switch_local:
cli += ' switch-local '
return cli
def booleanArgs(arg, trueString, falseString):
if arg is True:
return " %s " % trueString
elif arg is False:
return " %s " % falseString
else:
return ""
def run_cli(module, cli, state_map):
"""
This method executes the cli command on the target node(s) and returns the
output. The module then exits based on the output.
:param cli: the complete cli string to be executed on the target node(s).
:param state_map: Provides state of the command.
:param module: The Ansible module to fetch command
"""
state = module.params['state']
command = state_map[state]
cmd = shlex.split(cli)
result, out, err = module.run_command(cmd)
remove_cmd = '/usr/bin/cli --quiet -e --no-login-prompt'
results = dict(
command=' '.join(cmd).replace(remove_cmd, ''),
msg="%s operation completed" % command,
changed=True
)
# Response in JSON format
if result != 0:
module.exit_json(
command=' '.join(cmd).replace(remove_cmd, ''),
stderr=err.strip(),
msg="%s operation failed" % command,
changed=False
)
if out:
results['stdout'] = out.strip()
module.exit_json(**results)
|
phobson/bokeh | refs/heads/master | bokeh/server/task.py | 17 | '''
'''
from __future__ import absolute_import
# TODO (havocp) this class may be unused
class ServerTask(object):
'''
'''
pass
|
Yelp/paasta | refs/heads/master | paasta_tools/firewall_logging.py | 1 | import argparse
import logging
import os
import signal
import socket
import socketserver
import sys
import syslogmp
from paasta_tools.firewall import services_running_here
from paasta_tools.utils import _log
from paasta_tools.utils import configure_log
from paasta_tools.utils import load_system_paasta_config
DEFAULT_NUM_WORKERS = 5
log = logging.getLogger(__name__)
class SyslogUDPHandler(socketserver.BaseRequestHandler):
def setup(self):
configure_log()
self.cluster = load_system_paasta_config().get_cluster()
def handle(self):
data, socket = self.request
syslog_to_paasta_log(data, self.cluster)
def syslog_to_paasta_log(data, cluster):
iptables_log = parse_syslog(data)
if iptables_log is None:
return
service, instance = lookup_service_instance_by_ip(iptables_log["SRC"])
if service is None or instance is None:
return
# prepend hostname
log_line = iptables_log["hostname"] + ": " + iptables_log["message"]
_log(
service=service,
component="security",
level="debug",
cluster=cluster,
instance=instance,
line=log_line,
)
def parse_syslog(data):
parsed_data = syslogmp.parse(data)
try:
full_message = parsed_data.message.decode()
except UnicodeDecodeError:
return None
if not full_message.startswith("kernel: ["):
# Not a kernel message
return None
close_bracket = full_message.find("]")
if close_bracket == -1:
return None
iptables_message = full_message[close_bracket + 1 :].strip()
parts = iptables_message.split(" ")
# parts[0] is the log-prefix
# parts[1..] is either KEY=VALUE or just KEY
if not parts[1].startswith("IN="):
# not an iptables message
return None
fields = {k: v for k, _, v in (field.partition("=") for field in parts[1:])}
fields["hostname"] = parsed_data.hostname
fields["message"] = iptables_message
return fields
def lookup_service_instance_by_ip(ip_lookup):
for service, instance, mac, ip in services_running_here():
if ip == ip_lookup:
return (service, instance)
log.info(f"Unable to find container for ip {ip_lookup}")
return (None, None)
def parse_args(argv=None):
parser = argparse.ArgumentParser(
description="Adapts iptables syslog messages into scribe"
)
parser.add_argument(
"-v", "--verbose", action="store_true", dest="verbose", default=False
)
parser.add_argument(
"-l", "--listen-host", help="Default %(default)s", default="127.0.0.1"
)
parser.add_argument(
"-p", "--listen-port", type=int, help="Default %(default)s", default=1516
)
parser.add_argument(
"-w",
"--num-workers",
type=int,
help="Default %(default)s",
default=DEFAULT_NUM_WORKERS,
)
args = parser.parse_args(argv)
return args
def setup_logging(verbose):
if verbose:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.WARNING)
class MultiUDPServer(socketserver.UDPServer):
# UDPServer with SO_REUSEPORT enabled so that incoming packets are
# load-balanced across listeners
def server_bind(self):
self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1)
# UDPServer is old-style class so can't use super
socketserver.UDPServer.server_bind(self)
def run_server(listen_host, listen_port):
server = MultiUDPServer((listen_host, listen_port), SyslogUDPHandler)
server.serve_forever()
def main(argv=None):
args = parse_args(argv)
setup_logging(args.verbose)
assert args.num_workers > 0
# start n-1 separate processes, then run_server() on this one
num_forks = args.num_workers - 1
for x in range(num_forks):
if os.fork() == 0:
run_server(args.listen_host, args.listen_port)
# propagate SIGTERM to all my children then exit
signal.signal(
signal.SIGTERM, lambda signum, _: os.killpg(os.getpid(), signum) or sys.exit(1)
)
run_server(args.listen_host, args.listen_port)
|
srisankethu/babel | refs/heads/master | babel/support.py | 10 | # -*- coding: utf-8 -*-
"""
babel.support
~~~~~~~~~~~~~
Several classes and functions that help with integrating and using Babel
in applications.
.. note: the code in this module is not used by Babel itself
:copyright: (c) 2013 by the Babel Team.
:license: BSD, see LICENSE for more details.
"""
import gettext
import locale
from babel.core import Locale
from babel.dates import format_date, format_datetime, format_time, \
format_timedelta
from babel.numbers import format_number, format_decimal, format_currency, \
format_percent, format_scientific
from babel._compat import PY2, text_type, text_to_native
class Format(object):
"""Wrapper class providing the various date and number formatting functions
bound to a specific locale and time-zone.
>>> from babel.util import UTC
>>> from datetime import date
>>> fmt = Format('en_US', UTC)
>>> fmt.date(date(2007, 4, 1))
u'Apr 1, 2007'
>>> fmt.decimal(1.2345)
u'1.234'
"""
def __init__(self, locale, tzinfo=None):
"""Initialize the formatter.
:param locale: the locale identifier or `Locale` instance
:param tzinfo: the time-zone info (a `tzinfo` instance or `None`)
"""
self.locale = Locale.parse(locale)
self.tzinfo = tzinfo
def date(self, date=None, format='medium'):
"""Return a date formatted according to the given pattern.
>>> from datetime import date
>>> fmt = Format('en_US')
>>> fmt.date(date(2007, 4, 1))
u'Apr 1, 2007'
"""
return format_date(date, format, locale=self.locale)
def datetime(self, datetime=None, format='medium'):
"""Return a date and time formatted according to the given pattern.
>>> from datetime import datetime
>>> from pytz import timezone
>>> fmt = Format('en_US', tzinfo=timezone('US/Eastern'))
>>> fmt.datetime(datetime(2007, 4, 1, 15, 30))
u'Apr 1, 2007, 11:30:00 AM'
"""
return format_datetime(datetime, format, tzinfo=self.tzinfo,
locale=self.locale)
def time(self, time=None, format='medium'):
"""Return a time formatted according to the given pattern.
>>> from datetime import datetime
>>> from pytz import timezone
>>> fmt = Format('en_US', tzinfo=timezone('US/Eastern'))
>>> fmt.time(datetime(2007, 4, 1, 15, 30))
u'11:30:00 AM'
"""
return format_time(time, format, tzinfo=self.tzinfo, locale=self.locale)
def timedelta(self, delta, granularity='second', threshold=.85,
format='medium', add_direction=False):
"""Return a time delta according to the rules of the given locale.
>>> from datetime import timedelta
>>> fmt = Format('en_US')
>>> fmt.timedelta(timedelta(weeks=11))
u'3 months'
"""
return format_timedelta(delta, granularity=granularity,
threshold=threshold,
format=format, add_direction=add_direction,
locale=self.locale)
def number(self, number):
"""Return an integer number formatted for the locale.
>>> fmt = Format('en_US')
>>> fmt.number(1099)
u'1,099'
"""
return format_number(number, locale=self.locale)
def decimal(self, number, format=None):
"""Return a decimal number formatted for the locale.
>>> fmt = Format('en_US')
>>> fmt.decimal(1.2345)
u'1.234'
"""
return format_decimal(number, format, locale=self.locale)
def currency(self, number, currency):
"""Return a number in the given currency formatted for the locale.
"""
return format_currency(number, currency, locale=self.locale)
def percent(self, number, format=None):
"""Return a number formatted as percentage for the locale.
>>> fmt = Format('en_US')
>>> fmt.percent(0.34)
u'34%'
"""
return format_percent(number, format, locale=self.locale)
def scientific(self, number):
"""Return a number formatted using scientific notation for the locale.
"""
return format_scientific(number, locale=self.locale)
class LazyProxy(object):
"""Class for proxy objects that delegate to a specified function to evaluate
the actual object.
>>> def greeting(name='world'):
... return 'Hello, %s!' % name
>>> lazy_greeting = LazyProxy(greeting, name='Joe')
>>> print(lazy_greeting)
Hello, Joe!
>>> u' ' + lazy_greeting
u' Hello, Joe!'
>>> u'(%s)' % lazy_greeting
u'(Hello, Joe!)'
This can be used, for example, to implement lazy translation functions that
delay the actual translation until the string is actually used. The
rationale for such behavior is that the locale of the user may not always
be available. In web applications, you only know the locale when processing
a request.
The proxy implementation attempts to be as complete as possible, so that
the lazy objects should mostly work as expected, for example for sorting:
>>> greetings = [
... LazyProxy(greeting, 'world'),
... LazyProxy(greeting, 'Joe'),
... LazyProxy(greeting, 'universe'),
... ]
>>> greetings.sort()
>>> for greeting in greetings:
... print(greeting)
Hello, Joe!
Hello, universe!
Hello, world!
"""
__slots__ = ['_func', '_args', '_kwargs', '_value', '_is_cache_enabled']
def __init__(self, func, *args, **kwargs):
is_cache_enabled = kwargs.pop('enable_cache', True)
# Avoid triggering our own __setattr__ implementation
object.__setattr__(self, '_func', func)
object.__setattr__(self, '_args', args)
object.__setattr__(self, '_kwargs', kwargs)
object.__setattr__(self, '_is_cache_enabled', is_cache_enabled)
object.__setattr__(self, '_value', None)
@property
def value(self):
if self._value is None:
value = self._func(*self._args, **self._kwargs)
if not self._is_cache_enabled:
return value
object.__setattr__(self, '_value', value)
return self._value
def __contains__(self, key):
return key in self.value
def __nonzero__(self):
return bool(self.value)
def __dir__(self):
return dir(self.value)
def __iter__(self):
return iter(self.value)
def __len__(self):
return len(self.value)
def __str__(self):
return str(self.value)
def __unicode__(self):
return unicode(self.value)
def __add__(self, other):
return self.value + other
def __radd__(self, other):
return other + self.value
def __mod__(self, other):
return self.value % other
def __rmod__(self, other):
return other % self.value
def __mul__(self, other):
return self.value * other
def __rmul__(self, other):
return other * self.value
def __call__(self, *args, **kwargs):
return self.value(*args, **kwargs)
def __lt__(self, other):
return self.value < other
def __le__(self, other):
return self.value <= other
def __eq__(self, other):
return self.value == other
def __ne__(self, other):
return self.value != other
def __gt__(self, other):
return self.value > other
def __ge__(self, other):
return self.value >= other
def __delattr__(self, name):
delattr(self.value, name)
def __getattr__(self, name):
return getattr(self.value, name)
def __setattr__(self, name, value):
setattr(self.value, name, value)
def __delitem__(self, key):
del self.value[key]
def __getitem__(self, key):
return self.value[key]
def __setitem__(self, key, value):
self.value[key] = value
def __copy__(self):
return LazyProxy(
self._func,
enable_cache=self._is_cache_enabled,
*self._args,
**self._kwargs
)
def __deepcopy__(self, memo):
from copy import deepcopy
return LazyProxy(
deepcopy(self._func, memo),
enable_cache=deepcopy(self._is_cache_enabled, memo),
*deepcopy(self._args, memo),
**deepcopy(self._kwargs, memo)
)
class NullTranslations(gettext.NullTranslations, object):
DEFAULT_DOMAIN = None
def __init__(self, fp=None):
"""Initialize a simple translations class which is not backed by a
real catalog. Behaves similar to gettext.NullTranslations but also
offers Babel's on *gettext methods (e.g. 'dgettext()').
:param fp: a file-like object (ignored in this class)
"""
# These attributes are set by gettext.NullTranslations when a catalog
# is parsed (fp != None). Ensure that they are always present because
# some *gettext methods (including '.gettext()') rely on the attributes.
self._catalog = {}
self.plural = lambda n: int(n != 1)
super(NullTranslations, self).__init__(fp=fp)
self.files = list(filter(None, [getattr(fp, 'name', None)]))
self.domain = self.DEFAULT_DOMAIN
self._domains = {}
def dgettext(self, domain, message):
"""Like ``gettext()``, but look the message up in the specified
domain.
"""
return self._domains.get(domain, self).gettext(message)
def ldgettext(self, domain, message):
"""Like ``lgettext()``, but look the message up in the specified
domain.
"""
return self._domains.get(domain, self).lgettext(message)
def udgettext(self, domain, message):
"""Like ``ugettext()``, but look the message up in the specified
domain.
"""
return self._domains.get(domain, self).ugettext(message)
# backward compatibility with 0.9
dugettext = udgettext
def dngettext(self, domain, singular, plural, num):
"""Like ``ngettext()``, but look the message up in the specified
domain.
"""
return self._domains.get(domain, self).ngettext(singular, plural, num)
def ldngettext(self, domain, singular, plural, num):
"""Like ``lngettext()``, but look the message up in the specified
domain.
"""
return self._domains.get(domain, self).lngettext(singular, plural, num)
def udngettext(self, domain, singular, plural, num):
"""Like ``ungettext()`` but look the message up in the specified
domain.
"""
return self._domains.get(domain, self).ungettext(singular, plural, num)
# backward compatibility with 0.9
dungettext = udngettext
# Most of the downwards code, until it get's included in stdlib, from:
# http://bugs.python.org/file10036/gettext-pgettext.patch
#
# The encoding of a msgctxt and a msgid in a .mo file is
# msgctxt + "\x04" + msgid (gettext version >= 0.15)
CONTEXT_ENCODING = '%s\x04%s'
def pgettext(self, context, message):
"""Look up the `context` and `message` id in the catalog and return the
corresponding message string, as an 8-bit string encoded with the
catalog's charset encoding, if known. If there is no entry in the
catalog for the `message` id and `context` , and a fallback has been
set, the look up is forwarded to the fallback's ``pgettext()``
method. Otherwise, the `message` id is returned.
"""
ctxt_msg_id = self.CONTEXT_ENCODING % (context, message)
missing = object()
tmsg = self._catalog.get(ctxt_msg_id, missing)
if tmsg is missing:
if self._fallback:
return self._fallback.pgettext(context, message)
return message
# Encode the Unicode tmsg back to an 8-bit string, if possible
if self._output_charset:
return text_to_native(tmsg, self._output_charset)
elif self._charset:
return text_to_native(tmsg, self._charset)
return tmsg
def lpgettext(self, context, message):
"""Equivalent to ``pgettext()``, but the translation is returned in the
preferred system encoding, if no other encoding was explicitly set with
``bind_textdomain_codeset()``.
"""
ctxt_msg_id = self.CONTEXT_ENCODING % (context, message)
missing = object()
tmsg = self._catalog.get(ctxt_msg_id, missing)
if tmsg is missing:
if self._fallback:
return self._fallback.lpgettext(context, message)
return message
if self._output_charset:
return tmsg.encode(self._output_charset)
return tmsg.encode(locale.getpreferredencoding())
def npgettext(self, context, singular, plural, num):
"""Do a plural-forms lookup of a message id. `singular` is used as the
message id for purposes of lookup in the catalog, while `num` is used to
determine which plural form to use. The returned message string is an
8-bit string encoded with the catalog's charset encoding, if known.
If the message id for `context` is not found in the catalog, and a
fallback is specified, the request is forwarded to the fallback's
``npgettext()`` method. Otherwise, when ``num`` is 1 ``singular`` is
returned, and ``plural`` is returned in all other cases.
"""
ctxt_msg_id = self.CONTEXT_ENCODING % (context, singular)
try:
tmsg = self._catalog[(ctxt_msg_id, self.plural(num))]
if self._output_charset:
return text_to_native(tmsg, self._output_charset)
elif self._charset:
return text_to_native(tmsg, self._charset)
return tmsg
except KeyError:
if self._fallback:
return self._fallback.npgettext(context, singular, plural, num)
if num == 1:
return singular
else:
return plural
def lnpgettext(self, context, singular, plural, num):
"""Equivalent to ``npgettext()``, but the translation is returned in the
preferred system encoding, if no other encoding was explicitly set with
``bind_textdomain_codeset()``.
"""
ctxt_msg_id = self.CONTEXT_ENCODING % (context, singular)
try:
tmsg = self._catalog[(ctxt_msg_id, self.plural(num))]
if self._output_charset:
return tmsg.encode(self._output_charset)
return tmsg.encode(locale.getpreferredencoding())
except KeyError:
if self._fallback:
return self._fallback.lnpgettext(context, singular, plural, num)
if num == 1:
return singular
else:
return plural
def upgettext(self, context, message):
"""Look up the `context` and `message` id in the catalog and return the
corresponding message string, as a Unicode string. If there is no entry
in the catalog for the `message` id and `context`, and a fallback has
been set, the look up is forwarded to the fallback's ``upgettext()``
method. Otherwise, the `message` id is returned.
"""
ctxt_message_id = self.CONTEXT_ENCODING % (context, message)
missing = object()
tmsg = self._catalog.get(ctxt_message_id, missing)
if tmsg is missing:
if self._fallback:
return self._fallback.upgettext(context, message)
return text_type(message)
return tmsg
def unpgettext(self, context, singular, plural, num):
"""Do a plural-forms lookup of a message id. `singular` is used as the
message id for purposes of lookup in the catalog, while `num` is used to
determine which plural form to use. The returned message string is a
Unicode string.
If the message id for `context` is not found in the catalog, and a
fallback is specified, the request is forwarded to the fallback's
``unpgettext()`` method. Otherwise, when `num` is 1 `singular` is
returned, and `plural` is returned in all other cases.
"""
ctxt_message_id = self.CONTEXT_ENCODING % (context, singular)
try:
tmsg = self._catalog[(ctxt_message_id, self.plural(num))]
except KeyError:
if self._fallback:
return self._fallback.unpgettext(context, singular, plural, num)
if num == 1:
tmsg = text_type(singular)
else:
tmsg = text_type(plural)
return tmsg
def dpgettext(self, domain, context, message):
"""Like `pgettext()`, but look the message up in the specified
`domain`.
"""
return self._domains.get(domain, self).pgettext(context, message)
def udpgettext(self, domain, context, message):
"""Like `upgettext()`, but look the message up in the specified
`domain`.
"""
return self._domains.get(domain, self).upgettext(context, message)
# backward compatibility with 0.9
dupgettext = udpgettext
def ldpgettext(self, domain, context, message):
"""Equivalent to ``dpgettext()``, but the translation is returned in the
preferred system encoding, if no other encoding was explicitly set with
``bind_textdomain_codeset()``.
"""
return self._domains.get(domain, self).lpgettext(context, message)
def dnpgettext(self, domain, context, singular, plural, num):
"""Like ``npgettext``, but look the message up in the specified
`domain`.
"""
return self._domains.get(domain, self).npgettext(context, singular,
plural, num)
def udnpgettext(self, domain, context, singular, plural, num):
"""Like ``unpgettext``, but look the message up in the specified
`domain`.
"""
return self._domains.get(domain, self).unpgettext(context, singular,
plural, num)
# backward compatibility with 0.9
dunpgettext = udnpgettext
def ldnpgettext(self, domain, context, singular, plural, num):
"""Equivalent to ``dnpgettext()``, but the translation is returned in
the preferred system encoding, if no other encoding was explicitly set
with ``bind_textdomain_codeset()``.
"""
return self._domains.get(domain, self).lnpgettext(context, singular,
plural, num)
if not PY2:
ugettext = gettext.NullTranslations.gettext
ungettext = gettext.NullTranslations.ngettext
class Translations(NullTranslations, gettext.GNUTranslations):
"""An extended translation catalog class."""
DEFAULT_DOMAIN = 'messages'
def __init__(self, fp=None, domain=None):
"""Initialize the translations catalog.
:param fp: the file-like object the translation should be read from
:param domain: the message domain (default: 'messages')
"""
super(Translations, self).__init__(fp=fp)
self.domain = domain or self.DEFAULT_DOMAIN
if not PY2:
ugettext = gettext.GNUTranslations.gettext
ungettext = gettext.GNUTranslations.ngettext
@classmethod
def load(cls, dirname=None, locales=None, domain=None):
"""Load translations from the given directory.
:param dirname: the directory containing the ``MO`` files
:param locales: the list of locales in order of preference (items in
this list can be either `Locale` objects or locale
strings)
:param domain: the message domain (default: 'messages')
"""
if locales is not None:
if not isinstance(locales, (list, tuple)):
locales = [locales]
locales = [str(locale) for locale in locales]
if not domain:
domain = cls.DEFAULT_DOMAIN
filename = gettext.find(domain, dirname, locales)
if not filename:
return NullTranslations()
with open(filename, 'rb') as fp:
return cls(fp=fp, domain=domain)
def __repr__(self):
return '<%s: "%s">' % (type(self).__name__,
self._info.get('project-id-version'))
def add(self, translations, merge=True):
"""Add the given translations to the catalog.
If the domain of the translations is different than that of the
current catalog, they are added as a catalog that is only accessible
by the various ``d*gettext`` functions.
:param translations: the `Translations` instance with the messages to
add
:param merge: whether translations for message domains that have
already been added should be merged with the existing
translations
"""
domain = getattr(translations, 'domain', self.DEFAULT_DOMAIN)
if merge and domain == self.domain:
return self.merge(translations)
existing = self._domains.get(domain)
if merge and existing is not None:
existing.merge(translations)
else:
translations.add_fallback(self)
self._domains[domain] = translations
return self
def merge(self, translations):
"""Merge the given translations into the catalog.
Message translations in the specified catalog override any messages
with the same identifier in the existing catalog.
:param translations: the `Translations` instance with the messages to
merge
"""
if isinstance(translations, gettext.GNUTranslations):
self._catalog.update(translations._catalog)
if isinstance(translations, Translations):
self.files.extend(translations.files)
return self
|
nkgilley/home-assistant | refs/heads/dev | homeassistant/components/hisense_aehw4a1/climate.py | 21 | """Pyaehw4a1 platform to control of Hisense AEH-W4A1 Climate Devices."""
import logging
from pyaehw4a1.aehw4a1 import AehW4a1
import pyaehw4a1.exceptions
from homeassistant.components.climate import ClimateEntity
from homeassistant.components.climate.const import (
FAN_AUTO,
FAN_HIGH,
FAN_LOW,
FAN_MEDIUM,
HVAC_MODE_COOL,
HVAC_MODE_DRY,
HVAC_MODE_FAN_ONLY,
HVAC_MODE_HEAT,
HVAC_MODE_OFF,
PRESET_BOOST,
PRESET_ECO,
PRESET_NONE,
PRESET_SLEEP,
SUPPORT_FAN_MODE,
SUPPORT_PRESET_MODE,
SUPPORT_SWING_MODE,
SUPPORT_TARGET_TEMPERATURE,
SWING_BOTH,
SWING_HORIZONTAL,
SWING_OFF,
SWING_VERTICAL,
)
from homeassistant.const import (
ATTR_TEMPERATURE,
PRECISION_WHOLE,
TEMP_CELSIUS,
TEMP_FAHRENHEIT,
)
from . import CONF_IP_ADDRESS, DOMAIN
SUPPORT_FLAGS = (
SUPPORT_TARGET_TEMPERATURE
| SUPPORT_FAN_MODE
| SUPPORT_SWING_MODE
| SUPPORT_PRESET_MODE
)
MIN_TEMP_C = 16
MAX_TEMP_C = 32
MIN_TEMP_F = 61
MAX_TEMP_F = 90
HVAC_MODES = [
HVAC_MODE_OFF,
HVAC_MODE_HEAT,
HVAC_MODE_COOL,
HVAC_MODE_DRY,
HVAC_MODE_FAN_ONLY,
]
FAN_MODES = [
"mute",
FAN_LOW,
FAN_MEDIUM,
FAN_HIGH,
FAN_AUTO,
]
SWING_MODES = [
SWING_OFF,
SWING_VERTICAL,
SWING_HORIZONTAL,
SWING_BOTH,
]
PRESET_MODES = [
PRESET_NONE,
PRESET_ECO,
PRESET_BOOST,
PRESET_SLEEP,
"sleep_2",
"sleep_3",
"sleep_4",
]
AC_TO_HA_STATE = {
"0001": HVAC_MODE_HEAT,
"0010": HVAC_MODE_COOL,
"0011": HVAC_MODE_DRY,
"0000": HVAC_MODE_FAN_ONLY,
}
HA_STATE_TO_AC = {
HVAC_MODE_OFF: "off",
HVAC_MODE_HEAT: "mode_heat",
HVAC_MODE_COOL: "mode_cool",
HVAC_MODE_DRY: "mode_dry",
HVAC_MODE_FAN_ONLY: "mode_fan",
}
AC_TO_HA_FAN_MODES = {
"00000000": FAN_AUTO, # fan value for heat mode
"00000001": FAN_AUTO,
"00000010": "mute",
"00000100": FAN_LOW,
"00000110": FAN_MEDIUM,
"00001000": FAN_HIGH,
}
HA_FAN_MODES_TO_AC = {
"mute": "speed_mute",
FAN_LOW: "speed_low",
FAN_MEDIUM: "speed_med",
FAN_HIGH: "speed_max",
FAN_AUTO: "speed_auto",
}
AC_TO_HA_SWING = {
"00": SWING_OFF,
"10": SWING_VERTICAL,
"01": SWING_HORIZONTAL,
"11": SWING_BOTH,
}
_LOGGER = logging.getLogger(__name__)
def _build_entity(device):
_LOGGER.debug("Found device at %s", device)
return ClimateAehW4a1(device)
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the AEH-W4A1 climate platform."""
# Priority 1: manual config
if hass.data[DOMAIN].get(CONF_IP_ADDRESS):
devices = hass.data[DOMAIN][CONF_IP_ADDRESS]
else:
# Priority 2: scanned interfaces
devices = await AehW4a1().discovery()
entities = [_build_entity(device) for device in devices]
async_add_entities(entities, True)
class ClimateAehW4a1(ClimateEntity):
"""Representation of a Hisense AEH-W4A1 module for climate device."""
def __init__(self, device):
"""Initialize the climate device."""
self._unique_id = device
self._device = AehW4a1(device)
self._hvac_modes = HVAC_MODES
self._fan_modes = FAN_MODES
self._swing_modes = SWING_MODES
self._preset_modes = PRESET_MODES
self._available = None
self._on = None
self._temperature_unit = None
self._current_temperature = None
self._target_temperature = None
self._hvac_mode = None
self._fan_mode = None
self._swing_mode = None
self._preset_mode = None
self._previous_state = None
async def async_update(self):
"""Pull state from AEH-W4A1."""
try:
status = await self._device.command("status_102_0")
except pyaehw4a1.exceptions.ConnectionError as library_error:
_LOGGER.warning(
"Unexpected error of %s: %s", self._unique_id, library_error
)
self._available = False
return
self._available = True
self._on = status["run_status"]
if status["temperature_Fahrenheit"] == "0":
self._temperature_unit = TEMP_CELSIUS
else:
self._temperature_unit = TEMP_FAHRENHEIT
self._current_temperature = int(status["indoor_temperature_status"], 2)
if self._on == "1":
device_mode = status["mode_status"]
self._hvac_mode = AC_TO_HA_STATE[device_mode]
fan_mode = status["wind_status"]
self._fan_mode = AC_TO_HA_FAN_MODES[fan_mode]
swing_mode = f'{status["up_down"]}{status["left_right"]}'
self._swing_mode = AC_TO_HA_SWING[swing_mode]
if self._hvac_mode in (HVAC_MODE_COOL, HVAC_MODE_HEAT):
self._target_temperature = int(status["indoor_temperature_setting"], 2)
else:
self._target_temperature = None
if status["efficient"] == "1":
self._preset_mode = PRESET_BOOST
elif status["low_electricity"] == "1":
self._preset_mode = PRESET_ECO
elif status["sleep_status"] == "0000001":
self._preset_mode = PRESET_SLEEP
elif status["sleep_status"] == "0000010":
self._preset_mode = "sleep_2"
elif status["sleep_status"] == "0000011":
self._preset_mode = "sleep_3"
elif status["sleep_status"] == "0000100":
self._preset_mode = "sleep_4"
else:
self._preset_mode = PRESET_NONE
else:
self._hvac_mode = HVAC_MODE_OFF
self._fan_mode = None
self._swing_mode = None
self._target_temperature = None
self._preset_mode = None
@property
def available(self):
"""Return True if entity is available."""
return self._available
@property
def name(self):
"""Return the name of the climate device."""
return self._unique_id
@property
def temperature_unit(self):
"""Return the unit of measurement."""
return self._temperature_unit
@property
def current_temperature(self):
"""Return the current temperature."""
return self._current_temperature
@property
def target_temperature(self):
"""Return the temperature we are trying to reach."""
return self._target_temperature
@property
def hvac_mode(self):
"""Return hvac target hvac state."""
return self._hvac_mode
@property
def hvac_modes(self):
"""Return the list of available operation modes."""
return self._hvac_modes
@property
def fan_mode(self):
"""Return the fan setting."""
return self._fan_mode
@property
def fan_modes(self):
"""Return the list of available fan modes."""
return self._fan_modes
@property
def preset_mode(self):
"""Return the preset mode if on."""
return self._preset_mode
@property
def preset_modes(self):
"""Return the list of available preset modes."""
return self._preset_modes
@property
def swing_mode(self):
"""Return swing operation."""
return self._swing_mode
@property
def swing_modes(self):
"""Return the list of available fan modes."""
return self._swing_modes
@property
def min_temp(self):
"""Return the minimum temperature."""
if self._temperature_unit == TEMP_CELSIUS:
return MIN_TEMP_C
return MIN_TEMP_F
@property
def max_temp(self):
"""Return the maximum temperature."""
if self._temperature_unit == TEMP_CELSIUS:
return MAX_TEMP_C
return MAX_TEMP_F
@property
def precision(self):
"""Return the precision of the system."""
return PRECISION_WHOLE
@property
def target_temperature_step(self):
"""Return the supported step of target temperature."""
return 1
@property
def supported_features(self):
"""Return the list of supported features."""
return SUPPORT_FLAGS
async def async_set_temperature(self, **kwargs):
"""Set new target temperatures."""
if self._on != "1":
_LOGGER.warning(
"AC at %s is off, could not set temperature", self._unique_id
)
return
temp = kwargs.get(ATTR_TEMPERATURE)
if temp is not None:
_LOGGER.debug("Setting temp of %s to %s", self._unique_id, temp)
if self._preset_mode != PRESET_NONE:
await self.async_set_preset_mode(PRESET_NONE)
if self._temperature_unit == TEMP_CELSIUS:
await self._device.command(f"temp_{int(temp)}_C")
else:
await self._device.command(f"temp_{int(temp)}_F")
async def async_set_fan_mode(self, fan_mode):
"""Set new fan mode."""
if self._on != "1":
_LOGGER.warning("AC at %s is off, could not set fan mode", self._unique_id)
return
if self._hvac_mode in (HVAC_MODE_COOL, HVAC_MODE_FAN_ONLY) and (
self._hvac_mode != HVAC_MODE_FAN_ONLY or fan_mode != FAN_AUTO
):
_LOGGER.debug("Setting fan mode of %s to %s", self._unique_id, fan_mode)
await self._device.command(HA_FAN_MODES_TO_AC[fan_mode])
async def async_set_swing_mode(self, swing_mode):
"""Set new target swing operation."""
if self._on != "1":
_LOGGER.warning(
"AC at %s is off, could not set swing mode", self._unique_id
)
return
_LOGGER.debug("Setting swing mode of %s to %s", self._unique_id, swing_mode)
swing_act = self._swing_mode
if swing_mode == SWING_OFF and swing_act != SWING_OFF:
if swing_act in (SWING_HORIZONTAL, SWING_BOTH):
await self._device.command("hor_dir")
if swing_act in (SWING_VERTICAL, SWING_BOTH):
await self._device.command("vert_dir")
if swing_mode == SWING_BOTH and swing_act != SWING_BOTH:
if swing_act in (SWING_OFF, SWING_HORIZONTAL):
await self._device.command("vert_swing")
if swing_act in (SWING_OFF, SWING_VERTICAL):
await self._device.command("hor_swing")
if swing_mode == SWING_VERTICAL and swing_act != SWING_VERTICAL:
if swing_act in (SWING_OFF, SWING_HORIZONTAL):
await self._device.command("vert_swing")
if swing_act in (SWING_BOTH, SWING_HORIZONTAL):
await self._device.command("hor_dir")
if swing_mode == SWING_HORIZONTAL and swing_act != SWING_HORIZONTAL:
if swing_act in (SWING_BOTH, SWING_VERTICAL):
await self._device.command("vert_dir")
if swing_act in (SWING_OFF, SWING_VERTICAL):
await self._device.command("hor_swing")
async def async_set_preset_mode(self, preset_mode):
"""Set new preset mode."""
if self._on != "1":
if preset_mode == PRESET_NONE:
return
await self.async_turn_on()
_LOGGER.debug("Setting preset mode of %s to %s", self._unique_id, preset_mode)
if preset_mode == PRESET_ECO:
await self._device.command("energysave_on")
self._previous_state = preset_mode
elif preset_mode == PRESET_BOOST:
await self._device.command("turbo_on")
self._previous_state = preset_mode
elif preset_mode == PRESET_SLEEP:
await self._device.command("sleep_1")
self._previous_state = self._hvac_mode
elif preset_mode == "sleep_2":
await self._device.command("sleep_2")
self._previous_state = self._hvac_mode
elif preset_mode == "sleep_3":
await self._device.command("sleep_3")
self._previous_state = self._hvac_mode
elif preset_mode == "sleep_4":
await self._device.command("sleep_4")
self._previous_state = self._hvac_mode
elif self._previous_state is not None:
if self._previous_state == PRESET_ECO:
await self._device.command("energysave_off")
elif self._previous_state == PRESET_BOOST:
await self._device.command("turbo_off")
elif self._previous_state in HA_STATE_TO_AC:
await self._device.command(HA_STATE_TO_AC[self._previous_state])
self._previous_state = None
async def async_set_hvac_mode(self, hvac_mode):
"""Set new operation mode."""
_LOGGER.debug("Setting operation mode of %s to %s", self._unique_id, hvac_mode)
if hvac_mode == HVAC_MODE_OFF:
await self.async_turn_off()
else:
await self._device.command(HA_STATE_TO_AC[hvac_mode])
if self._on != "1":
await self.async_turn_on()
async def async_turn_on(self):
"""Turn on."""
_LOGGER.debug("Turning %s on", self._unique_id)
await self._device.command("on")
async def async_turn_off(self):
"""Turn off."""
_LOGGER.debug("Turning %s off", self._unique_id)
await self._device.command("off")
|
tear44/Cinnamon | refs/heads/master | docs/search-providers-examples/apt@cinnamon.org/search_provider.py | 21 | # -*- coding=utf-8 -*-
import subprocess
import sys
import gettext
import json
gettext.install("cinnamon", "/usr/share/locale")
if __name__ == "__main__":
results = []
packages = subprocess.check_output(["apt-cache", "search"] + sys.argv[1].split(" ")).splitlines()[:10]
for p in packages:
i = p.index(" - ")
name = p[:i]
description = p[i+3:]
results.append({'id': name, 'label': _("Install package : ") + name, 'description': description})
print json.dumps(results)
|
greyfenrir/taurus | refs/heads/master | scripts/installer/bzt_win.py | 1 | #!python3.6
import os, sys
sys.path.insert(0, 'pkgs')
os.environ["PATH"] += os.pathsep + os.path.join(os.path.dirname(sys.executable), 'Scripts')
def main():
sys.exit(os.system("cmd /k bzt --help"))
|
fitnr/twitter_markov | refs/heads/master | twitter_markov/__main__.py | 1 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2014-2016 Neil Freeman contact@fakeisthenewreal.org
# This program 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.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from __future__ import unicode_literals, print_function
import os
import sys
from signal import signal, SIGPIPE, SIG_DFL
import argparse
import twitter_bot_utils as tbu
from . import TwitterMarkov
from . import checking
from . import __version__ as version
TWEETER_DESC = 'Post markov chain ("ebooks") tweets to Twitter'
LEARNER_DESC = 'Turn a twitter archive into a twitter_markov-ready text file'
def main():
parser = argparse.ArgumentParser(
'twittermarkov', description='Tweet with a markov bot, or teach it from a twitter archive.')
tbu.args.add_default_args(parser, version, ())
subparsers = parser.add_subparsers()
tweeter = subparsers.add_parser('tweet', description=TWEETER_DESC, usage='%(prog)s [options]')
tbu.args.add_default_args(tweeter, include=('user', 'config', 'dry-run', 'verbose', 'quiet'))
tweeter.add_argument('-r', '--reply', action='store_const', const='reply',
dest='action', help='tweet responses to recent mentions')
tweeter.add_argument('--corpus', dest='corpus', metavar='corpus', type=str,
help='text file, one sentence per line')
tweeter.add_argument('--max-len', type=int, default=140, help='maximum output length. default: 140')
tweeter.add_argument('--state-size', type=int, help='model state size. default: 2')
tweeter.add_argument('--no-learn', dest='learn', action='store_false',
help='skip learning (by default, recent tweets from the "parent" account are added to corpus)')
tweeter.set_defaults(subparser='tweet', func=tweet_func, action='tweet')
learner = subparsers.add_parser('corpus', description=LEARNER_DESC, usage="%(prog)s [options] archive corpus")
learner.add_argument('-o', type=str, dest='output', metavar='corpus',
help='output text file (defaults to stdout)', default='/dev/stdout')
learner.add_argument('--no-retweets', action='store_true', help='skip retweets')
learner.add_argument('--no-replies', action='store_true', help='filter out replies')
learner.add_argument('--no-mentions', action='store_true', help='filter out mentions')
learner.add_argument('--no-urls', action='store_true', help='filter out urls')
learner.add_argument('--no-media', action='store_true', help='filter out media')
learner.add_argument('--no-hashtags', action='store_true', help='filter out hashtags')
learner.add_argument('-q', '--quiet', action='store_true', help='run quietly')
learner.add_argument('archive', type=str, metavar='archive',
default=os.getcwd(), help='archive csv file (e.g. tweets.csv found in Twitter archive)')
learner.set_defaults(subparser='learn', func=learn_func, action='learn')
args = parser.parse_args()
try:
func = args.func
except AttributeError:
parser.parse_args(['--help'])
argdict = vars(args)
del argdict['func']
if args.subparser == 'tweet':
func(**argdict)
elif args.subparser == 'learn':
func(**argdict)
def tweet_func(action, max_len=None, **kwargs):
tm = TwitterMarkov(**kwargs)
try:
if action == 'tweet':
tm.log.debug('tweeting')
tm.tweet(max_len=max_len)
elif action == 'reply':
tm.log.debug('replying')
tm.reply_all(max_len=max_len)
except RuntimeError:
tm.log.error('model was unable to compose a tweet')
return
def learn_func(**kwargs):
if not kwargs['quiet']:
print("Reading " + kwargs['archive'], file=sys.stderr)
archive = tbu.archive.read_csv(kwargs.get('archive'))
gen = checking.generator(archive, **kwargs)
tweets = (tweet.replace(u'\n', u' ') + '\n' for tweet in gen)
if kwargs['output'] in ('-', '/dev/stdout'):
signal(SIGPIPE, SIG_DFL)
sys.stdout.writelines(tweets)
else:
if not kwargs['quiet']:
print("Writing " + kwargs['output'], file=sys.stderr)
with open(kwargs.get('output'), 'w') as f:
f.writelines(tweets)
if __name__ == '__main__':
main()
|
imitrichev/cantera | refs/heads/master | interfaces/cython/cantera/mixmaster/ThermoProp.py | 4 | import sys
if sys.version_info[0] == 3:
from tkinter import *
else:
from Tkinter import *
from .UnitChooser import UnitVar
_tv = ['Temperature','Internal Energy','Enthalpy']
_pv = ['Pressure', 'Density']
def badpair(a,b):
if a.name in _tv:
if not b.name in _pv:
return 1
else:
if not b.name in _tv:
return 1
class ThermoProp:
def __init__(self, master, thermoframe, row, name, value, units, defaultunit=0):
self.value = DoubleVar()
self.thermoframe = thermoframe
self.entry = UnitVar(master,units,defaultunit)
self.entry.grid(column=1,row=row,sticky=W)
self.entry.v.config(state=DISABLED,bg='lightgray')
self.checked=IntVar()
self.checked.set(0)
self.name = name
self.c=Checkbutton(master,
text=name,
variable=self.checked,
onvalue=1,
offvalue=0,
command=self.check
)
self.c.grid(column=0,row=row, sticky=W+N)
def check(self):
if self == self.thermoframe.last1:
self.checked.set(1)
return
elif self == self.thermoframe.last2:
self.checked.set(1)
self.thermoframe.last2 = self.thermoframe.last1
self.thermoframe.last1 = self
return
# elif badpair(self, self.thermoframe.last1):
# self.checked.set(0)
# return
self._check()
self.thermoframe.last2.checked.set(0)
self.thermoframe.last2._check()
self.thermoframe.last2 = self.thermoframe.last1
self.thermoframe.last1 = self
def _check(self):
if self.isChecked():
self.entry.v.config(state=NORMAL,bg='white')
else:
self.entry.v.config(state=DISABLED,bg='lightgray')
def isChecked(self):
return self.checked.get()
def set(self, value):
self.entry.set(value)
def get(self):
return self.entry.get()
|
lin-credible/scikit-learn | refs/heads/master | sklearn/manifold/t_sne.py | 106 | # Author: Alexander Fabisch -- <afabisch@informatik.uni-bremen.de>
# License: BSD 3 clause (C) 2014
# This is the standard t-SNE implementation. There are faster modifications of
# the algorithm:
# * Barnes-Hut-SNE: reduces the complexity of the gradient computation from
# N^2 to N log N (http://arxiv.org/abs/1301.3342)
# * Fast Optimization for t-SNE:
# http://cseweb.ucsd.edu/~lvdmaaten/workshops/nips2010/papers/vandermaaten.pdf
import numpy as np
from scipy import linalg
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
from ..base import BaseEstimator
from ..utils import check_array
from ..utils import check_random_state
from ..utils.extmath import _ravel
from ..decomposition import RandomizedPCA
from ..metrics.pairwise import pairwise_distances
from . import _utils
MACHINE_EPSILON = np.finfo(np.double).eps
def _joint_probabilities(distances, desired_perplexity, verbose):
"""Compute joint probabilities p_ij from distances.
Parameters
----------
distances : array, shape (n_samples * (n_samples-1) / 2,)
Distances of samples are stored as condensed matrices, i.e.
we omit the diagonal and duplicate entries and store everything
in a one-dimensional array.
desired_perplexity : float
Desired perplexity of the joint probability distributions.
verbose : int
Verbosity level.
Returns
-------
P : array, shape (n_samples * (n_samples-1) / 2,)
Condensed joint probability matrix.
"""
# Compute conditional probabilities such that they approximately match
# the desired perplexity
conditional_P = _utils._binary_search_perplexity(
distances, desired_perplexity, verbose)
P = conditional_P + conditional_P.T
sum_P = np.maximum(np.sum(P), MACHINE_EPSILON)
P = np.maximum(squareform(P) / sum_P, MACHINE_EPSILON)
return P
def _kl_divergence(params, P, alpha, n_samples, n_components):
"""t-SNE objective function: KL divergence of p_ijs and q_ijs.
Parameters
----------
params : array, shape (n_params,)
Unraveled embedding.
P : array, shape (n_samples * (n_samples-1) / 2,)
Condensed joint probability matrix.
alpha : float
Degrees of freedom of the Student's-t distribution.
n_samples : int
Number of samples.
n_components : int
Dimension of the embedded space.
Returns
-------
kl_divergence : float
Kullback-Leibler divergence of p_ij and q_ij.
grad : array, shape (n_params,)
Unraveled gradient of the Kullback-Leibler divergence with respect to
the embedding.
"""
X_embedded = params.reshape(n_samples, n_components)
# Q is a heavy-tailed distribution: Student's t-distribution
n = pdist(X_embedded, "sqeuclidean")
n += 1.
n /= alpha
n **= (alpha + 1.0) / -2.0
Q = np.maximum(n / (2.0 * np.sum(n)), MACHINE_EPSILON)
# Optimization trick below: np.dot(x, y) is faster than
# np.sum(x * y) because it calls BLAS
# Objective: C (Kullback-Leibler divergence of P and Q)
kl_divergence = 2.0 * np.dot(P, np.log(P / Q))
# Gradient: dC/dY
grad = np.ndarray((n_samples, n_components))
PQd = squareform((P - Q) * n)
for i in range(n_samples):
np.dot(_ravel(PQd[i]), X_embedded[i] - X_embedded, out=grad[i])
grad = grad.ravel()
c = 2.0 * (alpha + 1.0) / alpha
grad *= c
return kl_divergence, grad
def _gradient_descent(objective, p0, it, n_iter, n_iter_without_progress=30,
momentum=0.5, learning_rate=1000.0, min_gain=0.01,
min_grad_norm=1e-7, min_error_diff=1e-7, verbose=0,
args=None):
"""Batch gradient descent with momentum and individual gains.
Parameters
----------
objective : function or callable
Should return a tuple of cost and gradient for a given parameter
vector.
p0 : array-like, shape (n_params,)
Initial parameter vector.
it : int
Current number of iterations (this function will be called more than
once during the optimization).
n_iter : int
Maximum number of gradient descent iterations.
n_iter_without_progress : int, optional (default: 30)
Maximum number of iterations without progress before we abort the
optimization.
momentum : float, within (0.0, 1.0), optional (default: 0.5)
The momentum generates a weight for previous gradients that decays
exponentially.
learning_rate : float, optional (default: 1000.0)
The learning rate should be extremely high for t-SNE! Values in the
range [100.0, 1000.0] are common.
min_gain : float, optional (default: 0.01)
Minimum individual gain for each parameter.
min_grad_norm : float, optional (default: 1e-7)
If the gradient norm is below this threshold, the optimization will
be aborted.
min_error_diff : float, optional (default: 1e-7)
If the absolute difference of two successive cost function values
is below this threshold, the optimization will be aborted.
verbose : int, optional (default: 0)
Verbosity level.
args : sequence
Arguments to pass to objective function.
Returns
-------
p : array, shape (n_params,)
Optimum parameters.
error : float
Optimum.
i : int
Last iteration.
"""
if args is None:
args = []
p = p0.copy().ravel()
update = np.zeros_like(p)
gains = np.ones_like(p)
error = np.finfo(np.float).max
best_error = np.finfo(np.float).max
best_iter = 0
for i in range(it, n_iter):
new_error, grad = objective(p, *args)
error_diff = np.abs(new_error - error)
error = new_error
grad_norm = linalg.norm(grad)
if error < best_error:
best_error = error
best_iter = i
elif i - best_iter > n_iter_without_progress:
if verbose >= 2:
print("[t-SNE] Iteration %d: did not make any progress "
"during the last %d episodes. Finished."
% (i + 1, n_iter_without_progress))
break
if min_grad_norm >= grad_norm:
if verbose >= 2:
print("[t-SNE] Iteration %d: gradient norm %f. Finished."
% (i + 1, grad_norm))
break
if min_error_diff >= error_diff:
if verbose >= 2:
print("[t-SNE] Iteration %d: error difference %f. Finished."
% (i + 1, error_diff))
break
inc = update * grad >= 0.0
dec = np.invert(inc)
gains[inc] += 0.05
gains[dec] *= 0.95
np.clip(gains, min_gain, np.inf)
grad *= gains
update = momentum * update - learning_rate * grad
p += update
if verbose >= 2 and (i + 1) % 10 == 0:
print("[t-SNE] Iteration %d: error = %.7f, gradient norm = %.7f"
% (i + 1, error, grad_norm))
return p, error, i
def trustworthiness(X, X_embedded, n_neighbors=5, precomputed=False):
"""Expresses to what extent the local structure is retained.
The trustworthiness is within [0, 1]. It is defined as
.. math::
T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1}
\sum_{j \in U^{(k)}_i (r(i, j) - k)}
where :math:`r(i, j)` is the rank of the embedded datapoint j
according to the pairwise distances between the embedded datapoints,
:math:`U^{(k)}_i` is the set of points that are in the k nearest
neighbors in the embedded space but not in the original space.
* "Neighborhood Preservation in Nonlinear Projection Methods: An
Experimental Study"
J. Venna, S. Kaski
* "Learning a Parametric Embedding by Preserving Local Structure"
L.J.P. van der Maaten
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
matrix. Otherwise it contains a sample per row.
X_embedded : array, shape (n_samples, n_components)
Embedding of the training data in low-dimensional space.
n_neighbors : int, optional (default: 5)
Number of neighbors k that will be considered.
precomputed : bool, optional (default: False)
Set this flag if X is a precomputed square distance matrix.
Returns
-------
trustworthiness : float
Trustworthiness of the low-dimensional embedding.
"""
if precomputed:
dist_X = X
else:
dist_X = pairwise_distances(X, squared=True)
dist_X_embedded = pairwise_distances(X_embedded, squared=True)
ind_X = np.argsort(dist_X, axis=1)
ind_X_embedded = np.argsort(dist_X_embedded, axis=1)[:, 1:n_neighbors + 1]
n_samples = X.shape[0]
t = 0.0
ranks = np.zeros(n_neighbors)
for i in range(n_samples):
for j in range(n_neighbors):
ranks[j] = np.where(ind_X[i] == ind_X_embedded[i, j])[0][0]
ranks -= n_neighbors
t += np.sum(ranks[ranks > 0])
t = 1.0 - t * (2.0 / (n_samples * n_neighbors *
(2.0 * n_samples - 3.0 * n_neighbors - 1.0)))
return t
class TSNE(BaseEstimator):
"""t-distributed Stochastic Neighbor Embedding.
t-SNE [1] is a tool to visualize high-dimensional data. It converts
similarities between data points to joint probabilities and tries
to minimize the Kullback-Leibler divergence between the joint
probabilities of the low-dimensional embedding and the
high-dimensional data. t-SNE has a cost function that is not convex,
i.e. with different initializations we can get different results.
It is highly recommended to use another dimensionality reduction
method (e.g. PCA for dense data or TruncatedSVD for sparse data)
to reduce the number of dimensions to a reasonable amount (e.g. 50)
if the number of features is very high. This will suppress some
noise and speed up the computation of pairwise distances between
samples. For more tips see Laurens van der Maaten's FAQ [2].
Read more in the :ref:`User Guide <t_sne>`.
Parameters
----------
n_components : int, optional (default: 2)
Dimension of the embedded space.
perplexity : float, optional (default: 30)
The perplexity is related to the number of nearest neighbors that
is used in other manifold learning algorithms. Larger datasets
usually require a larger perplexity. Consider selcting a value
between 5 and 50. The choice is not extremely critical since t-SNE
is quite insensitive to this parameter.
early_exaggeration : float, optional (default: 4.0)
Controls how tight natural clusters in the original space are in
the embedded space and how much space will be between them. For
larger values, the space between natural clusters will be larger
in the embedded space. Again, the choice of this parameter is not
very critical. If the cost function increases during initial
optimization, the early exaggeration factor or the learning rate
might be too high.
learning_rate : float, optional (default: 1000)
The learning rate can be a critical parameter. It should be
between 100 and 1000. If the cost function increases during initial
optimization, the early exaggeration factor or the learning rate
might be too high. If the cost function gets stuck in a bad local
minimum increasing the learning rate helps sometimes.
n_iter : int, optional (default: 1000)
Maximum number of iterations for the optimization. Should be at
least 200.
metric : string or callable, optional
The metric to use when calculating distance between instances in a
feature array. If metric is a string, it must be one of the options
allowed by scipy.spatial.distance.pdist for its metric parameter, or
a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
If metric is "precomputed", X is assumed to be a distance matrix.
Alternatively, if metric is a callable function, it is called on each
pair of instances (rows) and the resulting value recorded. The callable
should take two arrays from X as input and return a value indicating
the distance between them. The default is "euclidean" which is
interpreted as squared euclidean distance.
init : string, optional (default: "random")
Initialization of embedding. Possible options are 'random' and 'pca'.
PCA initialization cannot be used with precomputed distances and is
usually more globally stable than random initialization.
verbose : int, optional (default: 0)
Verbosity level.
random_state : int or RandomState instance or None (default)
Pseudo Random Number generator seed control. If None, use the
numpy.random singleton. Note that different initializations
might result in different local minima of the cost function.
Attributes
----------
embedding_ : array-like, shape (n_samples, n_components)
Stores the embedding vectors.
training_data_ : array-like, shape (n_samples, n_features)
Stores the training data.
Examples
--------
>>> import numpy as np
>>> from sklearn.manifold import TSNE
>>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
>>> model = TSNE(n_components=2, random_state=0)
>>> model.fit_transform(X) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
array([[ 887.28..., 238.61...],
[ -714.79..., 3243.34...],
[ 957.30..., -2505.78...],
[-1130.28..., -974.78...])
References
----------
[1] van der Maaten, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data
Using t-SNE. Journal of Machine Learning Research 9:2579-2605, 2008.
[2] van der Maaten, L.J.P. t-Distributed Stochastic Neighbor Embedding
http://homepage.tudelft.nl/19j49/t-SNE.html
"""
def __init__(self, n_components=2, perplexity=30.0,
early_exaggeration=4.0, learning_rate=1000.0, n_iter=1000,
metric="euclidean", init="random", verbose=0,
random_state=None):
if init not in ["pca", "random"]:
raise ValueError("'init' must be either 'pca' or 'random'")
self.n_components = n_components
self.perplexity = perplexity
self.early_exaggeration = early_exaggeration
self.learning_rate = learning_rate
self.n_iter = n_iter
self.metric = metric
self.init = init
self.verbose = verbose
self.random_state = random_state
def fit(self, X, y=None):
"""Fit the model using X as training data.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
matrix. Otherwise it contains a sample per row.
"""
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'], dtype=np.float64)
random_state = check_random_state(self.random_state)
if self.early_exaggeration < 1.0:
raise ValueError("early_exaggeration must be at least 1, but is "
"%f" % self.early_exaggeration)
if self.n_iter < 200:
raise ValueError("n_iter should be at least 200")
if self.metric == "precomputed":
if self.init == 'pca':
raise ValueError("The parameter init=\"pca\" cannot be used "
"with metric=\"precomputed\".")
if X.shape[0] != X.shape[1]:
raise ValueError("X should be a square distance matrix")
distances = X
else:
if self.verbose:
print("[t-SNE] Computing pairwise distances...")
if self.metric == "euclidean":
distances = pairwise_distances(X, metric=self.metric, squared=True)
else:
distances = pairwise_distances(X, metric=self.metric)
# Degrees of freedom of the Student's t-distribution. The suggestion
# alpha = n_components - 1 comes from "Learning a Parametric Embedding
# by Preserving Local Structure" Laurens van der Maaten, 2009.
alpha = max(self.n_components - 1.0, 1)
n_samples = X.shape[0]
self.training_data_ = X
P = _joint_probabilities(distances, self.perplexity, self.verbose)
if self.init == 'pca':
pca = RandomizedPCA(n_components=self.n_components,
random_state=random_state)
X_embedded = pca.fit_transform(X)
elif self.init == 'random':
X_embedded = None
else:
raise ValueError("Unsupported initialization scheme: %s"
% self.init)
self.embedding_ = self._tsne(P, alpha, n_samples, random_state,
X_embedded=X_embedded)
return self
def _tsne(self, P, alpha, n_samples, random_state, X_embedded=None):
"""Runs t-SNE."""
# t-SNE minimizes the Kullback-Leiber divergence of the Gaussians P
# and the Student's t-distributions Q. The optimization algorithm that
# we use is batch gradient descent with three stages:
# * early exaggeration with momentum 0.5
# * early exaggeration with momentum 0.8
# * final optimization with momentum 0.8
# The embedding is initialized with iid samples from Gaussians with
# standard deviation 1e-4.
if X_embedded is None:
# Initialize embedding randomly
X_embedded = 1e-4 * random_state.randn(n_samples,
self.n_components)
params = X_embedded.ravel()
# Early exaggeration
P *= self.early_exaggeration
params, error, it = _gradient_descent(
_kl_divergence, params, it=0, n_iter=50, momentum=0.5,
min_grad_norm=0.0, min_error_diff=0.0,
learning_rate=self.learning_rate, verbose=self.verbose,
args=[P, alpha, n_samples, self.n_components])
params, error, it = _gradient_descent(
_kl_divergence, params, it=it + 1, n_iter=100, momentum=0.8,
min_grad_norm=0.0, min_error_diff=0.0,
learning_rate=self.learning_rate, verbose=self.verbose,
args=[P, alpha, n_samples, self.n_components])
if self.verbose:
print("[t-SNE] Error after %d iterations with early "
"exaggeration: %f" % (it + 1, error))
# Final optimization
P /= self.early_exaggeration
params, error, it = _gradient_descent(
_kl_divergence, params, it=it + 1, n_iter=self.n_iter,
momentum=0.8, learning_rate=self.learning_rate,
verbose=self.verbose, args=[P, alpha, n_samples,
self.n_components])
if self.verbose:
print("[t-SNE] Error after %d iterations: %f" % (it + 1, error))
X_embedded = params.reshape(n_samples, self.n_components)
return X_embedded
def fit_transform(self, X, y=None):
"""Transform X to the embedded space.
Parameters
----------
X : array, shape (n_samples, n_features) or (n_samples, n_samples)
If the metric is 'precomputed' X must be a square distance
matrix. Otherwise it contains a sample per row.
Returns
-------
X_new : array, shape (n_samples, n_components)
Embedding of the training data in low-dimensional space.
"""
self.fit(X)
return self.embedding_
|
SofiaReis/django-cms | refs/heads/develop | menus/menu_pool.py | 23 | # -*- coding: utf-8 -*-
from logging import getLogger
from django.conf import settings
from django.contrib import messages
from django.contrib.sites.models import Site
from django.core.cache import cache
from django.core.exceptions import ValidationError
from django.core.urlresolvers import NoReverseMatch
from django.utils.translation import get_language
from django.utils.translation import ugettext_lazy as _
from cms.utils import get_cms_setting
from cms.utils.django_load import load
from menus.base import Menu
from menus.exceptions import NamespaceAlreadyRegistered
from menus.models import CacheKey
import copy
logger = getLogger('menus')
def _build_nodes_inner_for_one_menu(nodes, menu_class_name):
'''
This is an easier to test "inner loop" building the menu tree structure
for one menu (one language, one site)
'''
done_nodes = {} # Dict of node.id:Node
final_nodes = []
# This is to prevent infinite loops - we need to compare the number of
# times we see a specific node to "something", and for the time being,
# it's the total number of nodes
list_total_length = len(nodes)
while nodes:
# For when the node has a parent_id but we haven't seen it yet.
# We must not append it to the final list in this case!
should_add_to_final_list = True
node = nodes.pop(0)
# Increment the "seen" counter for this specific node.
node._counter = getattr(node, '_counter', 0) + 1
# Implicit namespacing by menu.__name__
if not node.namespace:
node.namespace = menu_class_name
if node.namespace not in done_nodes:
# We need to create the namespace dict to avoid KeyErrors
done_nodes[node.namespace] = {}
# If we have seen the parent_id already...
if node.parent_id in done_nodes[node.namespace]:
# Implicit parent namespace by menu.__name__
if not node.parent_namespace:
node.parent_namespace = menu_class_name
parent = done_nodes[node.namespace][node.parent_id]
parent.children.append(node)
node.parent = parent
# If it has a parent_id but we haven't seen it yet...
elif node.parent_id:
# We check for infinite loops here, by comparing the number of
# times we "saw" this node to the number of nodes in the list
if node._counter < list_total_length:
nodes.append(node)
# Never add this node to the final list until it has a real
# parent (node.parent)
should_add_to_final_list = False
if should_add_to_final_list:
final_nodes.append(node)
# add it to the "seen" list
done_nodes[node.namespace][node.id] = node
return final_nodes
class MenuPool(object):
def __init__(self):
self.menus = {}
self.modifiers = []
self.discovered = False
self._expanded = False
def discover_menus(self):
if self.discovered:
return
# FIXME: Remove in 3.4
load('menu')
load('cms_menus')
from menus.modifiers import register
register()
self.discovered = True
self._expanded = False
def _expand_menus(self):
"""
Expands the menu_pool by converting any found CMSAttachMenu entries to
one entry for each instance they are attached to. and instantiates menus
from the existing menu classes.
"""
# Ideally, this would have been done in discover_menus(), but the pages
# aren't loaded when that executes. This private method is used to
# perform the expansion and instantiate the menus classes into menu-
# instances just before any menus are built.
if self._expanded:
return
expanded_menus = {}
for menu_class_name, menu_cls in self.menus.items():
# In order to be eligible for "expansion", the menu_cls must, in
# fact, be an instantiable class. We are lenient about this here,
# though, because the CMS has previously allowed attaching
# CMSAttachMenu's as objects rather than classes.
if isinstance(menu_cls, Menu):
# A Menu **instance** was registered, this is non-standard, but
# acceptable. However, it cannot be "expanded", so, just add it
# as-is to the list of expanded_menus.
menu_cls = menu_cls.__class__
if hasattr(menu_cls, "get_instances"):
# It quacks like a CMSAttachMenu, expand away!
# If a menu exists but has no instances,
# it's included in the available menus as is
instances = menu_cls.get_instances()
if not instances:
expanded_menus[menu_class_name] = menu_cls()
else:
for instance in instances:
namespace = "{0}:{1}".format(
menu_class_name, instance.pk)
menu_inst = menu_cls()
menu_inst.instance = instance
expanded_menus[namespace] = menu_inst
elif hasattr(menu_cls, "get_nodes"):
# This is another type of Menu, cannot be expanded, but must be
# instantiated, none-the-less.
expanded_menus[menu_class_name] = menu_cls()
else:
raise ValidationError(
"Something was registered as a menu, but isn't.")
self._expanded = True
self.menus = expanded_menus
def clear(self, site_id=None, language=None, all=False):
'''
This invalidates the cache for a given menu (site_id and language)
'''
if all:
cache_keys = CacheKey.objects.get_keys()
else:
cache_keys = CacheKey.objects.get_keys(site_id, language)
to_be_deleted = cache_keys.distinct().values_list('key', flat=True)
if to_be_deleted:
cache.delete_many(to_be_deleted)
cache_keys.delete()
def register_menu(self, menu_cls):
import warnings
if menu_cls.__module__.split('.')[-1] == 'menu':
warnings.warn('menu.py filename is deprecated, '
'and it will be removed in version 3.4; '
'please rename it to cms_menus.py', DeprecationWarning)
from menus.base import Menu
assert issubclass(menu_cls, Menu)
# If we should register a menu after we've already expanded the existing
# ones, we need to mark it as such.
self._expanded = False
if menu_cls.__name__ in self.menus.keys():
raise NamespaceAlreadyRegistered(
"[{0}] a menu with this name is already registered".format(
menu_cls.__name__))
# Note: menu_cls should still be the menu CLASS at this point. It will
# be instantiated in self._expand_menus().
self.menus[menu_cls.__name__] = menu_cls
def register_modifier(self, modifier_class):
import os
import inspect
import warnings
source_file = os.path.basename(inspect.stack()[1][1])
if source_file == 'menu.py':
warnings.warn('menu.py filename is deprecated, '
'and it will be removed in version 3.4; '
'please rename it to cms_menus.py', DeprecationWarning)
from menus.base import Modifier
assert issubclass(modifier_class, Modifier)
if modifier_class not in self.modifiers:
self.modifiers.append(modifier_class)
def _build_nodes(self, request, site_id):
"""
This is slow. Caching must be used.
One menu is built per language and per site.
Namespaces: they are ID prefixes to avoid node ID clashes when plugging
multiple trees together.
- We iterate on the list of nodes.
- We store encountered nodes in a dict (with namespaces):
done_nodes[<namespace>][<node's id>] = node
- When a node has a parent defined, we lookup that parent in done_nodes
if it's found:
set the node as the node's parent's child (re-read this)
else:
the node is put at the bottom of the list
"""
# Before we do anything, make sure that the menus are expanded.
self._expand_menus()
# Cache key management
lang = get_language()
prefix = getattr(settings, "CMS_CACHE_PREFIX", "menu_cache_")
key = "%smenu_nodes_%s_%s" % (prefix, lang, site_id)
if request.user.is_authenticated():
key += "_%s_user" % request.user.pk
cached_nodes = cache.get(key, None)
if cached_nodes:
return cached_nodes
final_nodes = []
for menu_class_name in self.menus:
menu = self.menus[menu_class_name]
try:
if isinstance(menu, type):
menu = menu()
nodes = menu.get_nodes(request)
except NoReverseMatch:
# Apps might raise NoReverseMatch if an apphook does not yet
# exist, skip them instead of crashing
nodes = []
toolbar = getattr(request, 'toolbar', None)
if toolbar and toolbar.is_staff:
messages.error(request,
_('Menu %s cannot be loaded. Please, make sure all '
'its urls exist and can be resolved.') %
menu_class_name)
logger.error("Menu %s could not be loaded." %
menu_class_name, exc_info=True)
# nodes is a list of navigation nodes (page tree in cms + others)
final_nodes += _build_nodes_inner_for_one_menu(
nodes, menu_class_name)
cache.set(key, final_nodes, get_cms_setting('CACHE_DURATIONS')['menus'])
# We need to have a list of the cache keys for languages and sites that
# span several processes - so we follow the Django way and share through
# the database. It's still cheaper than recomputing every time!
# This way we can selectively invalidate per-site and per-language,
# since the cache shared but the keys aren't
CacheKey.objects.get_or_create(key=key, language=lang, site=site_id)
return final_nodes
def apply_modifiers(self, nodes, request, namespace=None, root_id=None,
post_cut=False, breadcrumb=False):
if not post_cut:
nodes = self._mark_selected(request, nodes)
for cls in self.modifiers:
inst = cls()
nodes = inst.modify(
request, nodes, namespace, root_id, post_cut, breadcrumb)
return nodes
def get_nodes(self, request, namespace=None, root_id=None, site_id=None,
breadcrumb=False):
self.discover_menus()
if not site_id:
site_id = Site.objects.get_current().pk
nodes = self._build_nodes(request, site_id)
nodes = copy.deepcopy(nodes)
nodes = self.apply_modifiers(nodes, request, namespace, root_id,
post_cut=False, breadcrumb=breadcrumb)
return nodes
def _mark_selected(self, request, nodes):
# There /may/ be two nodes that get marked with selected. A published
# and a draft version of the node. We'll mark both, later, the unused
# one will be removed anyway.
sel = []
for node in nodes:
node.sibling = False
node.ancestor = False
node.descendant = False
node_abs_url = node.get_absolute_url()
if node_abs_url == request.path[:len(node_abs_url)]:
if sel:
if len(node_abs_url) > len(sel[0].get_absolute_url()):
sel = [node]
elif len(node_abs_url) == len(sel[0].get_absolute_url()):
sel.append(node)
else:
sel = [node]
for node in nodes:
node.selected = (node in sel)
return nodes
def get_menus_by_attribute(self, name, value):
"""
Returns the list of menus that match the name/value criteria provided.
"""
# Note that we are limiting the output to only single instances of any
# specific menu class. This is to address issue (#4041) which has
# cropped-up in 3.0.13/3.0.0.
self.discover_menus()
self._expand_menus()
return sorted(list(set([(menu.__class__.__name__, menu.name)
for menu_class_name, menu in self.menus.items()
if getattr(menu, name, None) == value])))
def get_nodes_by_attribute(self, nodes, name, value):
return [node for node in nodes if node.attr.get(name, None) == value]
menu_pool = MenuPool()
|
unifycore/ryu | refs/heads/master | ryu/lib/of_config/__init__.py | 7 | # Copyright (C) 2013 Nippon Telegraph and Telephone Corporation.
# Copyright (C) 2013 Isaku Yamahata <yamahata at private email ne jp>
#
# 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 glob
import os.path
import sys
SCHEMA_DIR = os.path.dirname(__file__)
_PREFIX = 'of-config-'
_SUFFIX = '.xsd'
_files = glob.glob(os.path.join(SCHEMA_DIR, 'of-config-*.xsd'))
OF_CONFIG_XSD_FILES = dict(
(os.path.basename(f)[len(_PREFIX):-len(_SUFFIX)], f) for f in _files)
# For convinience
# OF_CONFIG_1_0_XSD = os.path.join(SCHEMA_DIR, 'of-config-1.0.xsd')
# and so on
_this_module = sys.modules[__name__]
for (version, xsd_file) in OF_CONFIG_XSD_FILES.items():
setattr(_this_module,
'OF_CONFIG_%s_XSD' % version.replace('.', '_'), xsd_file)
OFCONFIG_1_1_CONFIG = 'urn:onf:params:xml:ns:onf:of12:config'
OFCONFIG_1_1_YANG = 'urn:onf:of12:config:yang'
# LINC specific?
OFCONFIG_1_1_1_YANG = 'urn:onf:of111:config:yang'
OFCONFIG_YANG_NAMESPACES = {
'1.1': OFCONFIG_1_1_YANG,
'1.1.1': OFCONFIG_1_1_1_YANG,
}
|
cedadev/cis | refs/heads/master | cis/test/integration/base_integration_test.py | 2 | from netCDF4 import Dataset
import os
import unittest
from hamcrest import assert_that, greater_than_or_equal_to, less_than_or_equal_to, is_
from functools import reduce
class BaseIntegrationTest(unittest.TestCase):
OUTPUT_FILENAME = "test_integration_out.nc"
def setUp(self):
# Set force overwrite in case working files are still present
os.environ['CIS_FORCE_OVERWRITE'] = "True"
self.clean_output()
def tearDown(self):
# Pop off the environemnt variable
os.environ.pop('CIS_FORCE_OVERWRITE')
self.clean_output()
def clean_output(self):
if hasattr(self, 'ds') and self.ds.isopen():
self.ds.close()
if os.path.exists(self.OUTPUT_FILENAME):
os.remove(self.OUTPUT_FILENAME)
def check_output_contains_variables(self, output_path, var_names):
self.ds = Dataset(output_path)
for var in var_names:
try:
var = self.ds.variables[var]
except KeyError:
raise AssertionError("Variable %s not found in output file" % var)
self.ds.close()
def check_output_file_variable_attribute_contains_string(self, output_path, variable, attribute, string):
self.ds = Dataset(output_path)
try:
var = self.ds.variables[variable]
except KeyError:
raise AssertionError("Variable %s not found in output file" % variable)
try:
att_string = getattr(var, attribute)
except AttributeError:
raise AssertionError("Attribute %s not found in variable" % attribute)
assert string in att_string
self.ds.close()
def check_latlon_subsetting(self, lat_max, lat_min, lon_max, lon_min, lat_var='latitude', lon_var='longitude'):
self.ds = Dataset(self.OUTPUT_FILENAME)
lat = self.ds.variables[lat_var][:]
lon = self.ds.variables[lon_var][:]
assert_that(min(lon), greater_than_or_equal_to(lon_min))
assert_that(max(lon), less_than_or_equal_to(lon_max))
assert_that(min(lat), greater_than_or_equal_to(lat_min))
assert_that(max(lat), less_than_or_equal_to(lat_max))
self.ds.close()
def check_alt_subsetting(self, alt_max, alt_min):
self.ds = Dataset(self.OUTPUT_FILENAME)
alt = self.ds.variables['altitude'][:]
assert_that(min(alt), greater_than_or_equal_to(alt_min))
assert_that(max(alt), less_than_or_equal_to(alt_max))
self.ds.close()
def check_pres_subsetting(self, pres_max, pres_min, pres_name='air_pressure'):
import numpy as np
self.ds = Dataset(self.OUTPUT_FILENAME)
pres = self.ds.variables[pres_name][:]
assert_that(np.min(pres), greater_than_or_equal_to(pres_min))
assert_that(np.max(pres), less_than_or_equal_to(pres_max))
self.ds.close()
@staticmethod
def _clean_sample_file_name(sample_file):
import re
return re.sub(r'([\\]):', r':', sample_file)
def check_output_col_grid(self, sample_file, sample_var, output_file, output_vars, expected_shape=None):
"""
Check that the output grid matches the sample grid in shape.
:param sample_file:
:param sample_var:
:param output_file:
:param output_vars:
:return:
"""
from cis import read_data
from operator import mul
if expected_shape is None:
sample_shape = read_data(self._clean_sample_file_name(sample_file), sample_var).data.shape
else:
sample_shape = expected_shape
self.ds = Dataset(self._clean_sample_file_name(output_file))
for output_var in output_vars:
output_shape = self.ds.variables[output_var].shape
# This copes with dims in different orders, length 1 values being taken out etc
assert_that(reduce(mul, sample_shape), is_(reduce(mul, output_shape)))
self.ds.close()
def check_output_vars_are_different(self, output_file, output_vars):
"""
Check that the output variables are NOT exactly the same
:param output_file:
:param output_vars:
:return:
"""
from itertools import combinations
import numpy as np
self.ds = Dataset(self._clean_sample_file_name(output_file))
# Loop over each possible pair of output var
for a, b in combinations(output_vars, 2):
a_data = self.ds.variables[a]
b_data = self.ds.variables[b]
assert not np.allclose(a_data, b_data)
self.ds.close() |
SilentCircle/sentry | refs/heads/master | src/sentry/migrations/0001_initial.py | 12 | # encoding: utf-8
import 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 'GroupedMessage'
db.create_table('sentry_groupedmessage', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('logger', self.gf('django.db.models.fields.CharField')(default='root', max_length=64, db_index=True, blank=True)),
('class_name', self.gf('django.db.models.fields.CharField')(db_index=True, max_length=128, null=True, blank=True)),
('level', self.gf('django.db.models.fields.PositiveIntegerField')(default=40, db_index=True, blank=True)),
('message', self.gf('django.db.models.fields.TextField')()),
('traceback', self.gf('django.db.models.fields.TextField')(null=True, blank=True)),
('view', self.gf('django.db.models.fields.CharField')(max_length=200, db_index=True)),
('url', self.gf('django.db.models.fields.URLField')(max_length=200, null=True, blank=True)),
('server_name', self.gf('django.db.models.fields.CharField')(max_length=128, db_index=True)),
('checksum', self.gf('django.db.models.fields.CharField')(max_length=32, db_index=True)),
('status', self.gf('django.db.models.fields.PositiveIntegerField')(default=0)),
('times_seen', self.gf('django.db.models.fields.PositiveIntegerField')(default=1)),
('last_seen', self.gf('django.db.models.fields.DateTimeField')(default=datetime.datetime.now, db_index=True)),
('first_seen', self.gf('django.db.models.fields.DateTimeField')(default=datetime.datetime.now, db_index=True)),
))
db.send_create_signal('sentry', ['GroupedMessage'])
# Adding unique constraint on 'GroupedMessage', fields ['logger', 'view', 'checksum']
db.create_unique('sentry_groupedmessage', ['logger', 'view', 'checksum'])
# Adding model 'Message'
db.create_table('sentry_message', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('logger', self.gf('django.db.models.fields.CharField')(default='root', max_length=64, db_index=True, blank=True)),
('class_name', self.gf('django.db.models.fields.CharField')(db_index=True, max_length=128, null=True, blank=True)),
('level', self.gf('django.db.models.fields.PositiveIntegerField')(default=40, db_index=True, blank=True)),
('message', self.gf('django.db.models.fields.TextField')()),
('traceback', self.gf('django.db.models.fields.TextField')(null=True, blank=True)),
('view', self.gf('django.db.models.fields.CharField')(max_length=200, db_index=True)),
('url', self.gf('django.db.models.fields.URLField')(max_length=200, null=True, blank=True)),
('server_name', self.gf('django.db.models.fields.CharField')(max_length=128, db_index=True)),
('checksum', self.gf('django.db.models.fields.CharField')(max_length=32, db_index=True)),
('datetime', self.gf('django.db.models.fields.DateTimeField')(default=datetime.datetime.now, db_index=True)),
('data', self.gf('django.db.models.fields.TextField')(null=True, blank=True)),
))
db.send_create_signal('sentry', ['Message'])
def backwards(self, orm):
# Deleting model 'GroupedMessage'
db.delete_table('sentry_groupedmessage')
# Removing unique constraint on 'GroupedMessage', fields ['logger', 'view', 'checksum']
db.delete_unique('sentry_groupedmessage', ['logger', 'view', 'checksum'])
# Deleting model 'Message'
db.delete_table('sentry_message')
models = {
'sentry.groupedmessage': {
'Meta': {'unique_together': "(('logger', 'view', 'checksum'),)", 'object_name': 'GroupedMessage'},
'checksum': ('django.db.models.fields.CharField', [], {'max_length': '32', 'db_index': 'True'}),
'class_name': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '128', 'null': 'True', 'blank': 'True'}),
'first_seen': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now', 'db_index': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_seen': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now', 'db_index': 'True'}),
'level': ('django.db.models.fields.PositiveIntegerField', [], {'default': '40', 'db_index': 'True', 'blank': 'True'}),
'logger': ('django.db.models.fields.CharField', [], {'default': "'root'", 'max_length': '64', 'db_index': 'True', 'blank': 'True'}),
'message': ('django.db.models.fields.TextField', [], {}),
'server_name': ('django.db.models.fields.CharField', [], {'max_length': '128', 'db_index': 'True'}),
'status': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'times_seen': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}),
'traceback': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'view': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'})
},
'sentry.message': {
'Meta': {'object_name': 'Message'},
'checksum': ('django.db.models.fields.CharField', [], {'max_length': '32', 'db_index': 'True'}),
'class_name': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '128', 'null': 'True', 'blank': 'True'}),
'data': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'datetime': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now', 'db_index': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'level': ('django.db.models.fields.PositiveIntegerField', [], {'default': '40', 'db_index': 'True', 'blank': 'True'}),
'logger': ('django.db.models.fields.CharField', [], {'default': "'root'", 'max_length': '64', 'db_index': 'True', 'blank': 'True'}),
'message': ('django.db.models.fields.TextField', [], {}),
'server_name': ('django.db.models.fields.CharField', [], {'max_length': '128', 'db_index': 'True'}),
'traceback': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'view': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'})
}
}
complete_apps = ['sentry']
|
epiphany27/NewsBlur | refs/heads/master | vendor/facebook.py | 16 | #!/usr/bin/env python
#
# Copyright 2010 Facebook
#
# 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.
"""Python client library for the Facebook Platform.
This client library is designed to support the Graph API and the official
Facebook JavaScript SDK, which is the canonical way to implement
Facebook authentication. Read more about the Graph API at
http://developers.facebook.com/docs/api. You can download the Facebook
JavaScript SDK at http://github.com/facebook/connect-js/.
If your application is using Google AppEngine's webapp framework, your
usage of this module might look like this:
user = facebook.get_user_from_cookie(self.request.cookies, key, secret)
if user:
graph = facebook.GraphAPI(user["access_token"])
profile = graph.get_object("me")
friends = graph.get_connections("me", "friends")
"""
import cgi
import time
import urllib
import urllib2
import hashlib
import hmac
import base64
import logging
# Find a JSON parser
try:
import simplejson as json
except ImportError:
try:
from django.utils import simplejson as json
except ImportError:
import json
_parse_json = json.loads
# Find a query string parser
try:
from urlparse import parse_qs
except ImportError:
from cgi import parse_qs
class GraphAPI(object):
"""A client for the Facebook Graph API.
See http://developers.facebook.com/docs/api for complete documentation
for the API.
The Graph API is made up of the objects in Facebook (e.g., people, pages,
events, photos) and the connections between them (e.g., friends,
photo tags, and event RSVPs). This client provides access to those
primitive types in a generic way. For example, given an OAuth access
token, this will fetch the profile of the active user and the list
of the user's friends:
graph = facebook.GraphAPI(access_token)
user = graph.get_object("me")
friends = graph.get_connections(user["id"], "friends")
You can see a list of all of the objects and connections supported
by the API at http://developers.facebook.com/docs/reference/api/.
You can obtain an access token via OAuth or by using the Facebook
JavaScript SDK. See http://developers.facebook.com/docs/authentication/
for details.
If you are using the JavaScript SDK, you can use the
get_user_from_cookie() method below to get the OAuth access token
for the active user from the cookie saved by the SDK.
"""
def __init__(self, access_token=None):
self.access_token = access_token
def get_object(self, id, **args):
"""Fetchs the given object from the graph."""
return self.request(id, args)
def get_objects(self, ids, **args):
"""Fetchs all of the given object from the graph.
We return a map from ID to object. If any of the IDs are invalid,
we raise an exception.
"""
args["ids"] = ",".join(ids)
return self.request("", args)
def get_connections(self, id, connection_name, **args):
"""Fetchs the connections for given object."""
return self.request(id + "/" + connection_name, args)
def put_object(self, parent_object, connection_name, **data):
"""Writes the given object to the graph, connected to the given parent.
For example,
graph.put_object("me", "feed", message="Hello, world")
writes "Hello, world" to the active user's wall. Likewise, this
will comment on a the first post of the active user's feed:
feed = graph.get_connections("me", "feed")
post = feed["data"][0]
graph.put_object(post["id"], "comments", message="First!")
See http://developers.facebook.com/docs/api#publishing for all of
the supported writeable objects.
Most write operations require extended permissions. For example,
publishing wall posts requires the "publish_stream" permission. See
http://developers.facebook.com/docs/authentication/ for details about
extended permissions.
"""
assert self.access_token, "Write operations require an access token"
return self.request(parent_object + "/" + connection_name, post_args=data)
def put_wall_post(self, message, attachment={}, profile_id="me"):
"""Writes a wall post to the given profile's wall.
We default to writing to the authenticated user's wall if no
profile_id is specified.
attachment adds a structured attachment to the status message being
posted to the Wall. It should be a dictionary of the form:
{"name": "Link name"
"link": "http://www.example.com/",
"caption": "{*actor*} posted a new review",
"description": "This is a longer description of the attachment",
"picture": "http://www.example.com/thumbnail.jpg"}
"""
return self.put_object(profile_id, "feed", message=message, **attachment)
def put_comment(self, object_id, message):
"""Writes the given comment on the given post."""
return self.put_object(object_id, "comments", message=message)
def put_like(self, object_id):
"""Likes the given post."""
return self.put_object(object_id, "likes")
def delete_object(self, id):
"""Deletes the object with the given ID from the graph."""
self.request(id, post_args={"method": "delete"})
def put_photo(self, image, message=None, album_id=None, **kwargs):
"""Uploads an image using multipart/form-data
image=File like object for the image
message=Caption for your image
album_id=None posts to /me/photos which uses or creates and uses
an album for your application.
"""
object_id = album_id or "me"
#it would have been nice to reuse self.request; but multipart is messy in urllib
post_args = {
'access_token': self.access_token,
'source': image,
'message': message
}
post_args.update(kwargs)
content_type, body = self._encode_multipart_form(post_args)
req = urllib2.Request("https://graph.facebook.com/%s/photos" % object_id, data=body)
req.add_header('Content-Type', content_type)
try:
data = urllib2.urlopen(req).read()
#For Python 3 use this:
#except urllib2.HTTPError as e:
except urllib2.HTTPError, e:
data = e.read() # Facebook sends OAuth errors as 400, and urllib2 throws an exception, we want a GraphAPIError
try:
response = _parse_json(data)
# Raise an error if we got one, but don't freak out if Facebook just gave us a Bool value
if response and isinstance(response, dict) and response.get("error"):
raise GraphAPIError(response["error"].get("code", 1),
response["error"]["message"])
except ValueError:
response = data
return response
# based on: http://code.activestate.com/recipes/146306/
def _encode_multipart_form(self, fields):
"""Fields are a dict of form name-> value
For files, value should be a file object.
Other file-like objects might work and a fake name will be chosen.
Return (content_type, body) ready for httplib.HTTP instance
"""
BOUNDARY = '----------ThIs_Is_tHe_bouNdaRY_$'
CRLF = '\r\n'
L = []
for (key, value) in fields.items():
logging.debug("Encoding %s, (%s)%s" % (key, type(value), value))
if not value:
continue
L.append('--' + BOUNDARY)
if hasattr(value, 'read') and callable(value.read):
filename = getattr(value,'name','%s.jpg' % key)
L.append('Content-Disposition: form-data; name="%s"; filename="%s"' % (key, filename))
L.append('Content-Type: image/jpeg')
value = value.read()
logging.debug(type(value))
else:
L.append('Content-Disposition: form-data; name="%s"' % key)
L.append('')
if isinstance(value, unicode):
logging.debug("Convert to ascii")
value = value.encode('ascii')
L.append(value)
L.append('--' + BOUNDARY + '--')
L.append('')
body = CRLF.join(L)
content_type = 'multipart/form-data; boundary=%s' % BOUNDARY
return content_type, body
def request(self, path, args=None, post_args=None):
"""Fetches the given path in the Graph API.
We translate args to a valid query string. If post_args is given,
we send a POST request to the given path with the given arguments.
"""
if not args: args = {}
if self.access_token:
if post_args is not None:
post_args["access_token"] = self.access_token
else:
args["access_token"] = self.access_token
post_data = None if post_args is None else urllib.urlencode(post_args)
try:
file = urllib2.urlopen("https://graph.facebook.com/" + path + "?" +
urllib.urlencode(args), post_data)
except urllib2.HTTPError, e:
response = _parse_json( e.read() )
raise GraphAPIError(response["error"]["type"],
response["error"]["message"])
try:
fileInfo = file.info()
if fileInfo.maintype == 'text':
response = _parse_json(file.read())
elif fileInfo.maintype == 'image':
mimetype = fileInfo['content-type']
response = {
"data": file.read(),
"mime-type": mimetype,
"url": file.url,
}
else:
raise GraphAPIError('Response Error', 'Maintype was not text or image')
finally:
file.close()
if response and isinstance(response, dict) and response.get("error"):
raise GraphAPIError(response["error"]["type"],
response["error"]["message"])
return response
def api_request(self, path, args=None, post_args=None):
"""Fetches the given path in the Graph API.
We translate args to a valid query string. If post_args is given,
we send a POST request to the given path with the given arguments.
"""
if not args: args = {}
if self.access_token:
if post_args is not None:
post_args["access_token"] = self.access_token
else:
args["access_token"] = self.access_token
if self.api_key:
if post_args is not None:
post_args["api_key"] = self.api_key
else:
args["api_key"] = self.api_key
if post_args is not None:
post_args["format"] = "json-strings"
else:
args["format"] = "json-strings"
post_data = None if post_args is None else urllib.urlencode(post_args)
file = urllib.urlopen("https://api.facebook.com/method/" + path + "?" +
urllib.urlencode(args), post_data)
try:
response = _parse_json(file.read())
finally:
file.close()
if response and response.get("error"):
raise GraphAPIError(response["error"]["type"],
response["error"]["message"])
return response
def fql(self, query, args=None, post_args=None):
"""FQL query.
Two reasons to have this method:
1. Graph api does not expose some info fields of a user, e.g.
a user's networks/affiliations, we have to fall back to old api.
2. FQL is a strong tool.
Example query: "SELECT affiliations FROM user WHERE uid = me()"
"""
if not args: args = {}
if self.access_token:
if post_args is not None:
post_args["access_token"] = self.access_token
else:
args["access_token"] = self.access_token
post_data = None if post_args is None else urllib.urlencode(post_args)
args["query"] = query
args["format"]="json"
file = urllib2.urlopen("https://api.facebook.com/method/fql.query?" +
urllib.urlencode(args), post_data)
try:
content = file.read()
response = _parse_json(content)
#Return a list if success, return a dictionary if failed
if type(response) is dict and "error_code" in response:
raise GraphAPIError(response["error_code"],response["error_msg"])
except Exception, e:
raise e
finally:
file.close()
return response
class GraphAPIError(Exception):
def __init__(self, type, message):
Exception.__init__(self, message)
self.type = type
def get_user_from_cookie(cookies, app_id, app_secret):
"""Parses the cookie set by the official Facebook JavaScript SDK.
cookies should be a dictionary-like object mapping cookie names to
cookie values.
If the user is logged in via Facebook, we return a dictionary with the
keys "uid" and "access_token". The former is the user's Facebook ID,
and the latter can be used to make authenticated requests to the Graph API.
If the user is not logged in, we return None.
Download the official Facebook JavaScript SDK at
http://github.com/facebook/connect-js/. Read more about Facebook
authentication at http://developers.facebook.com/docs/authentication/.
"""
cookie = cookies.get("fbsr_" + app_id, "")
if not cookie: return None
parsed_request = parse_signed_request(cookie, app_secret)
result = get_access_token_from_code(parsed_request["code"], "",
app_id, app_secret)
result["uid"] = parsed_request["user_id"]
return result
def parse_signed_request(signed_request, app_secret):
""" Return dictionary with signed request data.
We return a dictionary containing the information in the signed_request. This will
include a user_id if the user has authorised your application, as well as any
information requested in the scope.
If the signed_request is malformed or corrupted, False is returned.
"""
try:
l = signed_request.split('.', 2)
encoded_sig = str(l[0])
payload = str(l[1])
sig = base64.urlsafe_b64decode(encoded_sig + "=" * ((4 - len(encoded_sig) % 4) % 4))
data = base64.urlsafe_b64decode(payload + "=" * ((4 - len(payload) % 4) % 4))
except IndexError:
return False # raise ValueError('signed_request malformed')
except TypeError:
return False # raise ValueError('signed_request had corrupted payload')
data = _parse_json(data)
if data.get('algorithm', '').upper() != 'HMAC-SHA256':
return False # raise ValueError('signed_request used unknown algorithm')
expected_sig = hmac.new(app_secret, msg=payload, digestmod=hashlib.sha256).digest()
if sig != expected_sig:
return False # raise ValueError('signed_request had signature mismatch')
return data
def auth_url(app_id, canvas_url, perms = None):
url = "https://www.facebook.com/dialog/oauth?"
kvps = {'client_id': app_id, 'redirect_uri': canvas_url}
if perms:
kvps['scope'] = ",".join(perms)
return url + urllib.urlencode(kvps)
def get_access_token_from_code(code, redirect_uri, app_id, app_secret):
"""
Get a user-specific access token from the "code" returned from a Facebook
OAuth dialog. Returns a dict containing the access token and its expiration
date (if applicable).
"""
args = {
"code": code,
"redirect_uri": redirect_uri,
"client_id": app_id,
"client_secret": app_secret,
}
# We would use GraphAPI.request() here, except for that the fact that the
# response is a key-value pair, and not JSON.
response = urllib.urlopen("https://graph.facebook.com/oauth/access_token" +
"?" + urllib.urlencode(args)).read()
query_str = parse_qs(response)
if "access_token" in query_str:
result = {"access_token":query_str["access_token"][0]}
if "expires" in query_str:
result["expires"] = query_str["expires"][0]
return result
else:
response = json.loads(response)
raise GraphAPIError(response["error"]["type"],
response["error"]["message"])
def get_app_access_token(app_id, app_secret):
"""
Get the access_token for the app that can be used for insights and creating test users
app_id = retrieved from the developer page
app_secret = retrieved from the developer page
returns the application access_token
"""
# Get an app access token
args = {'grant_type':'client_credentials',
'client_id':app_id,
'client_secret':app_secret}
file = urllib2.urlopen("https://graph.facebook.com/oauth/access_token?" +
urllib.urlencode(args))
try:
result = file.read().split("=")[1]
finally:
file.close()
return result |
aselle/tensorflow | refs/heads/master | tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py | 10 | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for rnn module."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.contrib import rnn as rnn_lib
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops as ops_lib
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import rnn
from tensorflow.python.ops import rnn_cell
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables as variables_lib
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging
from tensorflow.python.util import nest
class Plus1RNNCell(rnn_lib.RNNCell):
"""RNN Cell generating (output, new_state) = (input + 1, state + 1)."""
@property
def output_size(self):
return 5
@property
def state_size(self):
return 5
def __call__(self, input_, state, scope=None):
return (input_ + 1, state + 1)
class DummyMultiDimensionalLSTM(rnn_lib.RNNCell):
"""LSTM Cell generating (output, new_state) = (input + 1, state + 1).
The input to this cell may have an arbitrary number of dimensions that follow
the preceding 'Time' and 'Batch' dimensions.
"""
def __init__(self, dims):
"""Initialize the Multi-dimensional LSTM cell.
Args:
dims: tuple that contains the dimensions of the output of the cell,
without including 'Time' or 'Batch' dimensions.
"""
if not isinstance(dims, tuple):
raise TypeError("The dimensions passed to DummyMultiDimensionalLSTM "
"should be a tuple of ints.")
self._dims = dims
self._output_size = tensor_shape.TensorShape(self._dims)
self._state_size = (tensor_shape.TensorShape(self._dims),
tensor_shape.TensorShape(self._dims))
@property
def output_size(self):
return self._output_size
@property
def state_size(self):
return self._state_size
def __call__(self, input_, state, scope=None):
h, c = state
return (input_ + 1, (h + 1, c + 1))
class NestedRNNCell(rnn_lib.RNNCell):
"""RNN Cell generating (output, new_state) = (input + 1, state + 1).
The input, output and state of this cell is a tuple of two tensors.
"""
@property
def output_size(self):
return (5, 5)
@property
def state_size(self):
return (6, 6)
def __call__(self, input_, state, scope=None):
h, c = state
x, y = input_
return ((x + 1, y + 1), (h + 1, c + 1))
class TestStateSaver(object):
def __init__(self, batch_size, state_size):
self._batch_size = batch_size
self._state_size = state_size
self.saved_state = {}
def state(self, name):
if isinstance(self._state_size, dict):
state_size = self._state_size[name]
else:
state_size = self._state_size
if isinstance(state_size, int):
state_size = (state_size,)
elif isinstance(state_size, tuple):
pass
else:
raise TypeError("state_size should either be an int or a tuple")
return array_ops.zeros((self._batch_size,) + state_size)
def save_state(self, name, state):
self.saved_state[name] = state
return array_ops.identity(state)
@property
def batch_size(self):
return self._batch_size
@property
def state_size(self):
return self._state_size
class TestStateSaverWithCounters(TestStateSaver):
"""Class wrapper around TestStateSaver.
A dummy class used for testing of static_state_saving_rnn. It helps test if
save_state and state functions got called same number of time when we
evaluate output of rnn cell and state or either of them separately. It
inherits from the TestStateSaver and adds the counters for calls of functions.
"""
def __init__(self, batch_size, state_size):
super(TestStateSaverWithCounters, self).__init__(batch_size, state_size)
self._num_state_calls = variables_lib.Variable(0)
self._num_save_state_calls = variables_lib.Variable(0)
def state(self, name):
with ops_lib.control_dependencies(
[state_ops.assign_add(self._num_state_calls, 1)]):
return super(TestStateSaverWithCounters, self).state(name)
def save_state(self, name, state):
with ops_lib.control_dependencies([state_ops.assign_add(
self._num_save_state_calls, 1)]):
return super(TestStateSaverWithCounters, self).save_state(name, state)
@property
def num_state_calls(self):
return self._num_state_calls
@property
def num_save_state_calls(self):
return self._num_save_state_calls
class RNNTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
def testInvalidSequenceLengthShape(self):
cell = Plus1RNNCell()
inputs = [array_ops.placeholder(dtypes.float32, shape=(3, 4))]
with self.assertRaisesRegexp(ValueError, "must be a vector"):
rnn.static_rnn(cell, inputs, dtype=dtypes.float32, sequence_length=4)
def testRNN(self):
cell = Plus1RNNCell()
batch_size = 2
input_size = 5
max_length = 8 # unrolled up to this length
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs), len(inputs))
for out, inp in zip(outputs, inputs):
self.assertEqual(out.get_shape(), inp.get_shape())
self.assertEqual(out.dtype, inp.dtype)
with self.test_session(use_gpu=True) as sess:
input_value = np.random.randn(batch_size, input_size)
values = sess.run(outputs + [state], feed_dict={inputs[0]: input_value})
# Outputs
for v in values[:-1]:
self.assertAllClose(v, input_value + 1.0)
# Final state
self.assertAllClose(values[-1],
max_length * np.ones(
(batch_size, input_size), dtype=np.float32))
def testDropout(self):
cell = Plus1RNNCell()
full_dropout_cell = rnn_cell.DropoutWrapper(
cell, input_keep_prob=1e-12, seed=0)
(name, dep), = full_dropout_cell._checkpoint_dependencies
self.assertIs(dep, cell)
self.assertEqual("cell", name)
batch_size = 2
input_size = 5
max_length = 8
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
with variable_scope.variable_scope("share_scope"):
outputs, state = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
with variable_scope.variable_scope("drop_scope"):
dropped_outputs, _ = rnn.static_rnn(
full_dropout_cell, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs), len(inputs))
for out, inp in zip(outputs, inputs):
self.assertEqual(out.get_shape().as_list(), inp.get_shape().as_list())
self.assertEqual(out.dtype, inp.dtype)
with self.test_session(use_gpu=True) as sess:
input_value = np.random.randn(batch_size, input_size)
values = sess.run(outputs + [state], feed_dict={inputs[0]: input_value})
full_dropout_values = sess.run(
dropped_outputs, feed_dict={
inputs[0]: input_value
})
for v in values[:-1]:
self.assertAllClose(v, input_value + 1.0)
for d_v in full_dropout_values[:-1]: # Add 1.0 to dropped_out (all zeros)
self.assertAllClose(d_v, np.ones_like(input_value))
def testDynamicCalculation(self):
cell = Plus1RNNCell()
sequence_length = array_ops.placeholder(dtypes.int64)
batch_size = 2
input_size = 5
max_length = 8
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
with variable_scope.variable_scope("drop_scope"):
dynamic_outputs, dynamic_state = rnn.static_rnn(
cell, inputs, sequence_length=sequence_length, dtype=dtypes.float32)
self.assertEqual(len(dynamic_outputs), len(inputs))
with self.test_session(use_gpu=True) as sess:
input_value = np.random.randn(batch_size, input_size)
dynamic_values = sess.run(
dynamic_outputs,
feed_dict={
inputs[0]: input_value,
sequence_length: [2, 3]
})
dynamic_state_value = sess.run(
[dynamic_state],
feed_dict={
inputs[0]: input_value,
sequence_length: [2, 3]
})
# outputs are fully calculated for t = 0, 1
for v in dynamic_values[:2]:
self.assertAllClose(v, input_value + 1.0)
# outputs at t = 2 are zero for entry 0, calculated for entry 1
self.assertAllClose(dynamic_values[2],
np.vstack((np.zeros((input_size)),
1.0 + input_value[1, :])))
# outputs at t = 3+ are zero
for v in dynamic_values[3:]:
self.assertAllEqual(v, np.zeros_like(input_value))
# the final states are:
# entry 0: the values from the calculation at t=1
# entry 1: the values from the calculation at t=2
self.assertAllEqual(dynamic_state_value[0],
np.vstack((1.0 * (1 + 1) * np.ones((input_size)),
1.0 * (2 + 1) * np.ones((input_size)))))
def _testScope(self, factory, prefix="prefix", use_outer_scope=True):
with self.test_session(use_gpu=True, graph=ops_lib.Graph()):
if use_outer_scope:
with variable_scope.variable_scope(prefix) as scope:
factory(scope)
else:
factory(prefix)
# check that all the variables names starts
# with the proper scope.
variables_lib.global_variables_initializer()
all_vars = variables_lib.global_variables()
prefix = prefix or "rnn"
scope_vars = [v for v in all_vars if v.name.startswith(prefix + "/")]
tf_logging.info("RNN with scope: %s (%s)" %
(prefix, "scope" if use_outer_scope else "str"))
for v in scope_vars:
tf_logging.info(v.name)
self.assertEqual(len(scope_vars), len(all_vars))
def testScope(self):
def factory(scope):
cell = Plus1RNNCell()
batch_size = 2
input_size = 5
max_length = 8 # unrolled up to this length
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
return rnn.static_rnn(cell, inputs, dtype=dtypes.float32, scope=scope)
self._testScope(factory, use_outer_scope=True)
self._testScope(factory, use_outer_scope=False)
self._testScope(factory, prefix=None, use_outer_scope=False)
class LSTMTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
def testDType(self):
# Test case for GitHub issue 16228
# Not passing dtype in constructor results in default float32
lstm = rnn_cell.LSTMCell(10)
input_tensor = array_ops.ones([10, 50])
lstm.build(input_tensor.get_shape())
self.assertEqual(lstm._bias.dtype, dtypes.float32_ref)
# Explicitly pass dtype in constructor
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
lstm = rnn_cell.LSTMCell(10, dtype=dtype)
input_tensor = array_ops.ones([10, 50])
lstm.build(input_tensor.get_shape())
self.assertEqual(lstm._bias.dtype, dtype._as_ref)
def testNoProjNoSharding(self):
num_units = 3
input_size = 5
batch_size = 2
max_length = 8
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
cell = rnn_cell.LSTMCell(
num_units, initializer=initializer, state_is_tuple=False)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs), len(inputs))
for out in outputs:
self.assertEqual(out.get_shape().as_list(), [batch_size, num_units])
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
sess.run(outputs, feed_dict={inputs[0]: input_value})
def testCellClipping(self):
num_units = 3
input_size = 5
batch_size = 2
max_length = 8
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
cell_clip=0.0,
initializer=initializer,
state_is_tuple=False)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs), len(inputs))
for out in outputs:
self.assertEqual(out.get_shape().as_list(), [batch_size, num_units])
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
values = sess.run(outputs, feed_dict={inputs[0]: input_value})
for value in values:
# if cell c is clipped to 0, tanh(c) = 0 => m==0
self.assertAllEqual(value, np.zeros((batch_size, num_units)))
def testNoProjNoShardingSimpleStateSaver(self):
num_units = 3
input_size = 5
batch_size = 2
max_length = 8
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
state_saver = TestStateSaver(batch_size, 2 * num_units)
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=False,
initializer=initializer,
state_is_tuple=False)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
with variable_scope.variable_scope("share_scope"):
outputs, state = rnn.static_state_saving_rnn(
cell, inputs, state_saver=state_saver, state_name="save_lstm")
self.assertEqual(len(outputs), len(inputs))
for out in outputs:
self.assertEqual(out.get_shape().as_list(), [batch_size, num_units])
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
(last_state_value, saved_state_value) = sess.run(
[state, state_saver.saved_state["save_lstm"]],
feed_dict={
inputs[0]: input_value
})
self.assertAllEqual(last_state_value, saved_state_value)
def testNoProjNoShardingTupleStateSaver(self):
num_units = 3
input_size = 5
batch_size = 2
max_length = 8
with self.test_session(graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
state_saver = TestStateSaver(batch_size, num_units)
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=False,
initializer=initializer,
state_is_tuple=True)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
with variable_scope.variable_scope("share_scope"):
outputs, state = rnn.static_state_saving_rnn(
cell, inputs, state_saver=state_saver, state_name=("c", "m"))
self.assertEqual(len(outputs), len(inputs))
for out in outputs:
self.assertEqual(out.get_shape().as_list(), [batch_size, num_units])
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
last_and_saved_states = sess.run(
state + (state_saver.saved_state["c"], state_saver.saved_state["m"]),
feed_dict={
inputs[0]: input_value
})
self.assertEqual(4, len(last_and_saved_states))
self.assertAllEqual(last_and_saved_states[:2], last_and_saved_states[2:])
def testNoProjNoShardingNestedTupleStateSaver(self):
num_units = 3
input_size = 5
batch_size = 2
max_length = 8
with self.test_session(graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
state_saver = TestStateSaver(
batch_size, {
"c0": num_units,
"m0": num_units,
"c1": num_units + 1,
"m1": num_units + 1,
"c2": num_units + 2,
"m2": num_units + 2,
"c3": num_units + 3,
"m3": num_units + 3
})
def _cell(i):
return rnn_cell.LSTMCell(
num_units + i,
use_peepholes=False,
initializer=initializer,
state_is_tuple=True)
# This creates a state tuple which has 4 sub-tuples of length 2 each.
cell = rnn_cell.MultiRNNCell(
[_cell(i) for i in range(4)], state_is_tuple=True)
self.assertEqual(len(cell.state_size), 4)
for i in range(4):
self.assertEqual(len(cell.state_size[i]), 2)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(batch_size, input_size))
]
state_names = (("c0", "m0"), ("c1", "m1"), ("c2", "m2"), ("c3", "m3"))
with variable_scope.variable_scope("share_scope"):
outputs, state = rnn.static_state_saving_rnn(
cell, inputs, state_saver=state_saver, state_name=state_names)
self.assertEqual(len(outputs), len(inputs))
# Final output comes from _cell(3) which has state size num_units + 3
for out in outputs:
self.assertEqual(out.get_shape().as_list(), [batch_size, num_units + 3])
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
last_states = sess.run(
list(nest.flatten(state)), feed_dict={
inputs[0]: input_value
})
saved_states = sess.run(
list(state_saver.saved_state.values()),
feed_dict={
inputs[0]: input_value
})
self.assertEqual(8, len(last_states))
self.assertEqual(8, len(saved_states))
flat_state_names = nest.flatten(state_names)
named_saved_states = dict(
zip(state_saver.saved_state.keys(), saved_states))
for i in range(8):
self.assertAllEqual(last_states[i],
named_saved_states[flat_state_names[i]])
def testProjNoSharding(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
max_length = 8
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None, input_size))
]
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
initializer=initializer,
state_is_tuple=False)
outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs), len(inputs))
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
sess.run(outputs, feed_dict={inputs[0]: input_value})
def _testStateTupleWithProjAndSequenceLength(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
max_length = 8
sequence_length = [4, 6]
with self.test_session(graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None, input_size))
]
cell_notuple = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
initializer=initializer,
state_is_tuple=False)
cell_tuple = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
initializer=initializer,
state_is_tuple=True)
with variable_scope.variable_scope("root") as scope:
outputs_notuple, state_notuple = rnn.static_rnn(
cell_notuple,
inputs,
dtype=dtypes.float32,
sequence_length=sequence_length,
scope=scope)
scope.reuse_variables()
# TODO(ebrevdo): For this test, we ensure values are identical and
# therefore the weights here are tied. In the future, we may consider
# making the state_is_tuple property mutable so we can avoid
# having to do this - especially if users ever need to reuse
# the parameters from different RNNCell instances. Right now,
# this seems an unrealistic use case except for testing.
cell_tuple._scope = cell_notuple._scope # pylint: disable=protected-access
outputs_tuple, state_tuple = rnn.static_rnn(
cell_tuple,
inputs,
dtype=dtypes.float32,
sequence_length=sequence_length,
scope=scope)
self.assertEqual(len(outputs_notuple), len(inputs))
self.assertEqual(len(outputs_tuple), len(inputs))
self.assertTrue(isinstance(state_tuple, tuple))
self.assertTrue(isinstance(state_notuple, ops_lib.Tensor))
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
outputs_notuple_v = sess.run(
outputs_notuple, feed_dict={
inputs[0]: input_value
})
outputs_tuple_v = sess.run(
outputs_tuple, feed_dict={
inputs[0]: input_value
})
self.assertAllEqual(outputs_notuple_v, outputs_tuple_v)
(state_notuple_v,) = sess.run(
(state_notuple,), feed_dict={
inputs[0]: input_value
})
state_tuple_v = sess.run(state_tuple, feed_dict={inputs[0]: input_value})
self.assertAllEqual(state_notuple_v, np.hstack(state_tuple_v))
def testProjSharding(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
num_proj_shards = 3
num_unit_shards = 2
max_length = 8
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None, input_size))
]
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
num_unit_shards=num_unit_shards,
num_proj_shards=num_proj_shards,
initializer=initializer,
state_is_tuple=False)
outputs, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs), len(inputs))
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
sess.run(outputs, feed_dict={inputs[0]: input_value})
def testDoubleInput(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
num_proj_shards = 3
num_unit_shards = 2
max_length = 8
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(-1, 1, seed=self._seed)
inputs = max_length * [
array_ops.placeholder(dtypes.float64, shape=(None, input_size))
]
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
num_unit_shards=num_unit_shards,
num_proj_shards=num_proj_shards,
initializer=initializer,
state_is_tuple=False)
outputs, _ = rnn.static_rnn(
cell,
inputs,
initial_state=cell.zero_state(batch_size, dtypes.float64))
self.assertEqual(len(outputs), len(inputs))
variables_lib.global_variables_initializer().run()
input_value = np.asarray(
np.random.randn(batch_size, input_size), dtype=np.float64)
values = sess.run(outputs, feed_dict={inputs[0]: input_value})
self.assertEqual(values[0].dtype, input_value.dtype)
def testShardNoShardEquivalentOutput(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
num_proj_shards = 3
num_unit_shards = 2
max_length = 8
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None, input_size))
]
initializer = init_ops.constant_initializer(0.001)
cell_noshard = rnn_cell.LSTMCell(
num_units,
num_proj=num_proj,
use_peepholes=True,
initializer=initializer,
num_unit_shards=num_unit_shards,
num_proj_shards=num_proj_shards,
state_is_tuple=False)
cell_shard = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
initializer=initializer,
num_proj=num_proj,
state_is_tuple=False)
with variable_scope.variable_scope("noshard_scope"):
outputs_noshard, state_noshard = rnn.static_rnn(
cell_noshard, inputs, dtype=dtypes.float32)
with variable_scope.variable_scope("shard_scope"):
outputs_shard, state_shard = rnn.static_rnn(
cell_shard, inputs, dtype=dtypes.float32)
self.assertEqual(len(outputs_noshard), len(inputs))
self.assertEqual(len(outputs_noshard), len(outputs_shard))
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
feeds = dict((x, input_value) for x in inputs)
values_noshard = sess.run(outputs_noshard, feed_dict=feeds)
values_shard = sess.run(outputs_shard, feed_dict=feeds)
state_values_noshard = sess.run([state_noshard], feed_dict=feeds)
state_values_shard = sess.run([state_shard], feed_dict=feeds)
self.assertEqual(len(values_noshard), len(values_shard))
self.assertEqual(len(state_values_noshard), len(state_values_shard))
for (v_noshard, v_shard) in zip(values_noshard, values_shard):
self.assertAllClose(v_noshard, v_shard, atol=1e-3)
for (s_noshard, s_shard) in zip(state_values_noshard, state_values_shard):
self.assertAllClose(s_noshard, s_shard, atol=1e-3)
def testDoubleInputWithDropoutAndDynamicCalculation(self):
"""Smoke test for using LSTM with doubles, dropout, dynamic calculation."""
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
num_proj_shards = 3
num_unit_shards = 2
max_length = 8
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
sequence_length = array_ops.placeholder(dtypes.int64)
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
inputs = max_length * [
array_ops.placeholder(dtypes.float64, shape=(None, input_size))
]
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
num_unit_shards=num_unit_shards,
num_proj_shards=num_proj_shards,
initializer=initializer,
state_is_tuple=False)
dropout_cell = rnn_cell.DropoutWrapper(cell, 0.5, seed=0)
outputs, state = rnn.static_rnn(
dropout_cell,
inputs,
sequence_length=sequence_length,
initial_state=cell.zero_state(batch_size, dtypes.float64))
self.assertEqual(len(outputs), len(inputs))
variables_lib.global_variables_initializer().run(feed_dict={
sequence_length: [2, 3]
})
input_value = np.asarray(
np.random.randn(batch_size, input_size), dtype=np.float64)
values = sess.run(
outputs, feed_dict={
inputs[0]: input_value,
sequence_length: [2, 3]
})
state_value = sess.run(
[state], feed_dict={
inputs[0]: input_value,
sequence_length: [2, 3]
})
self.assertEqual(values[0].dtype, input_value.dtype)
self.assertEqual(state_value[0].dtype, input_value.dtype)
def testSharingWeightsWithReuse(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
max_length = 8
with self.test_session(graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(-1, 1, seed=self._seed)
initializer_d = init_ops.random_uniform_initializer(
-1, 1, seed=self._seed + 1)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None, input_size))
]
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
initializer=initializer,
state_is_tuple=False)
cell_d = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
initializer=initializer_d,
state_is_tuple=False)
with variable_scope.variable_scope("share_scope"):
outputs0, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
with variable_scope.variable_scope("share_scope", reuse=True):
outputs1, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
with variable_scope.variable_scope("diff_scope"):
outputs2, _ = rnn.static_rnn(cell_d, inputs, dtype=dtypes.float32)
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
output_values = sess.run(
outputs0 + outputs1 + outputs2, feed_dict={
inputs[0]: input_value
})
outputs0_values = output_values[:max_length]
outputs1_values = output_values[max_length:2 * max_length]
outputs2_values = output_values[2 * max_length:]
self.assertEqual(len(outputs0_values), len(outputs1_values))
self.assertEqual(len(outputs0_values), len(outputs2_values))
for o1, o2, o3 in zip(outputs0_values, outputs1_values, outputs2_values):
# Same weights used by both RNNs so outputs should be the same.
self.assertAllEqual(o1, o2)
# Different weights used so outputs should be different.
self.assertTrue(np.linalg.norm(o1 - o3) > 1e-6)
def testSharingWeightsWithDifferentNamescope(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
max_length = 8
with self.test_session(graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(-1, 1, seed=self._seed)
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None, input_size))
]
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
initializer=initializer,
state_is_tuple=False)
with ops_lib.name_scope("scope0"):
with variable_scope.variable_scope("share_scope"):
outputs0, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
with ops_lib.name_scope("scope1"):
with variable_scope.variable_scope("share_scope", reuse=True):
outputs1, _ = rnn.static_rnn(cell, inputs, dtype=dtypes.float32)
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
output_values = sess.run(
outputs0 + outputs1, feed_dict={
inputs[0]: input_value
})
outputs0_values = output_values[:max_length]
outputs1_values = output_values[max_length:]
self.assertEqual(len(outputs0_values), len(outputs1_values))
for out0, out1 in zip(outputs0_values, outputs1_values):
self.assertAllEqual(out0, out1)
def testDynamicRNNAllowsUnknownTimeDimension(self):
inputs = array_ops.placeholder(dtypes.float32, shape=[1, None, 20])
cell = rnn_cell.GRUCell(30)
# Smoke test, this should not raise an error
rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32)
@test_util.run_in_graph_and_eager_modes
def testDynamicRNNWithTupleStates(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
max_length = 8
sequence_length = [4, 6]
in_graph_mode = not context.executing_eagerly()
with self.test_session(graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
if in_graph_mode:
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None, input_size))
]
else:
inputs = max_length * [
constant_op.constant(
np.random.randn(batch_size, input_size).astype(np.float32))
]
inputs_c = array_ops.stack(inputs)
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
num_proj=num_proj,
initializer=initializer,
state_is_tuple=True)
with variable_scope.variable_scope("root") as scope:
outputs_static, state_static = rnn.static_rnn(
cell,
inputs,
dtype=dtypes.float32,
sequence_length=sequence_length,
scope=scope)
scope.reuse_variables()
outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
cell,
inputs_c,
dtype=dtypes.float32,
time_major=True,
sequence_length=sequence_length,
scope=scope)
self.assertTrue(isinstance(state_static, rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(state_dynamic, rnn_cell.LSTMStateTuple))
self.assertEqual(state_static[0], state_static.c)
self.assertEqual(state_static[1], state_static.h)
self.assertEqual(state_dynamic[0], state_dynamic.c)
self.assertEqual(state_dynamic[1], state_dynamic.h)
if in_graph_mode:
variables_lib.global_variables_initializer().run()
input_value = np.random.randn(batch_size, input_size)
outputs_static = sess.run(
outputs_static, feed_dict={
inputs[0]: input_value
})
outputs_dynamic = sess.run(
outputs_dynamic, feed_dict={
inputs[0]: input_value
})
state_static = sess.run(
state_static, feed_dict={
inputs[0]: input_value
})
state_dynamic = sess.run(
state_dynamic, feed_dict={
inputs[0]: input_value
})
if in_graph_mode:
self.assertAllEqual(outputs_static, outputs_dynamic)
else:
self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic)
self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic))
@test_util.run_in_graph_and_eager_modes
def testDynamicRNNWithNestedTupleStates(self):
num_units = 3
input_size = 5
batch_size = 2
num_proj = 4
max_length = 8
sequence_length = [4, 6]
in_graph_mode = not context.executing_eagerly()
with self.test_session(graph=ops_lib.Graph()) as sess:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
if in_graph_mode:
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None, input_size))
]
else:
inputs = max_length * [
constant_op.constant(
np.random.randn(batch_size, input_size).astype(np.float32))
]
inputs_c = array_ops.stack(inputs)
def _cell(i):
return rnn_cell.LSTMCell(
num_units + i,
use_peepholes=True,
num_proj=num_proj + i,
initializer=initializer,
state_is_tuple=True)
# This creates a state tuple which has 4 sub-tuples of length 2 each.
cell = rnn_cell.MultiRNNCell(
[_cell(i) for i in range(4)], state_is_tuple=True)
self.assertEqual(len(cell.state_size), 4)
for i in range(4):
self.assertEqual(len(cell.state_size[i]), 2)
test_zero = cell.zero_state(1, dtypes.float32)
self.assertEqual(len(test_zero), 4)
for i in range(4):
self.assertEqual(test_zero[i][0].get_shape()[1], cell.state_size[i][0])
self.assertEqual(test_zero[i][1].get_shape()[1], cell.state_size[i][1])
with variable_scope.variable_scope("root") as scope:
outputs_static, state_static = rnn.static_rnn(
cell,
inputs,
dtype=dtypes.float32,
sequence_length=sequence_length,
scope=scope)
scope.reuse_variables()
outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
cell,
inputs_c,
dtype=dtypes.float32,
time_major=True,
sequence_length=sequence_length,
scope=scope)
if in_graph_mode:
input_value = np.random.randn(batch_size, input_size)
variables_lib.global_variables_initializer().run()
outputs_static = sess.run(
outputs_static, feed_dict={
inputs[0]: input_value
})
outputs_dynamic = sess.run(
outputs_dynamic, feed_dict={
inputs[0]: input_value
})
state_static = sess.run(
nest.flatten(state_static), feed_dict={
inputs[0]: input_value
})
state_dynamic = sess.run(
nest.flatten(state_dynamic), feed_dict={
inputs[0]: input_value
})
if in_graph_mode:
self.assertAllEqual(outputs_static, outputs_dynamic)
else:
self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic)
state_static = nest.flatten(state_static)
state_dynamic = nest.flatten(state_dynamic)
self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic))
def _testDynamicEquivalentToStaticRNN(self, use_sequence_length):
time_steps = 8
num_units = 3
num_proj = 4
input_size = 5
batch_size = 2
input_values = np.random.randn(time_steps, batch_size, input_size).astype(
np.float32)
if use_sequence_length:
sequence_length = np.random.randint(0, time_steps, size=batch_size)
else:
sequence_length = None
in_graph_mode = not context.executing_eagerly()
# TODO(b/68017812): Eager ignores operation seeds, so we need to create a
# single cell and reuse it across the static and dynamic RNNs. Remove this
# special case once is fixed.
if not in_graph_mode:
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
initializer=initializer,
num_proj=num_proj,
state_is_tuple=False)
########### Step 1: Run static graph and generate readouts
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
if in_graph_mode:
concat_inputs = array_ops.placeholder(
dtypes.float32, shape=(time_steps, batch_size, input_size))
else:
concat_inputs = constant_op.constant(input_values)
inputs = array_ops.unstack(concat_inputs)
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
# TODO(akshayka): Remove special case once b/68017812 is fixed.
if in_graph_mode:
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
initializer=initializer,
num_proj=num_proj,
state_is_tuple=False)
with variable_scope.variable_scope("dynamic_scope"):
outputs_static, state_static = rnn.static_rnn(
cell, inputs, sequence_length=sequence_length, dtype=dtypes.float32)
if in_graph_mode:
# Generate gradients and run sessions to obtain outputs
feeds = {concat_inputs: input_values}
# Initialize
variables_lib.global_variables_initializer().run(feed_dict=feeds)
# Generate gradients of sum of outputs w.r.t. inputs
static_gradients = gradients_impl.gradients(
outputs_static + [state_static], [concat_inputs])
# Generate gradients of individual outputs w.r.t. inputs
static_individual_gradients = nest.flatten([
gradients_impl.gradients(y, [concat_inputs])
for y in [outputs_static[0], outputs_static[-1], state_static]
])
# Generate gradients of individual variables w.r.t. inputs
trainable_variables = ops_lib.get_collection(
ops_lib.GraphKeys.TRAINABLE_VARIABLES)
assert len(trainable_variables) > 1, (
"Count of trainable variables: %d" % len(trainable_variables))
# pylint: disable=bad-builtin
static_individual_variable_gradients = nest.flatten([
gradients_impl.gradients(y, trainable_variables)
for y in [outputs_static[0], outputs_static[-1], state_static]
])
# Test forward pass
values_static = sess.run(outputs_static, feed_dict=feeds)
(state_value_static,) = sess.run((state_static,), feed_dict=feeds)
# Test gradients to inputs and variables w.r.t. outputs & final state
static_grad_values = sess.run(static_gradients, feed_dict=feeds)
static_individual_grad_values = sess.run(
static_individual_gradients, feed_dict=feeds)
static_individual_var_grad_values = sess.run(
static_individual_variable_gradients, feed_dict=feeds)
########## Step 2: Run dynamic graph and generate readouts
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
if in_graph_mode:
concat_inputs = array_ops.placeholder(
dtypes.float32, shape=(time_steps, batch_size, input_size))
else:
concat_inputs = constant_op.constant(input_values)
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
# TODO(akshayka): Remove this special case once b/68017812 is
# fixed.
if in_graph_mode:
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=True,
initializer=initializer,
num_proj=num_proj,
state_is_tuple=False)
with variable_scope.variable_scope("dynamic_scope"):
outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
cell,
inputs=concat_inputs,
sequence_length=sequence_length,
time_major=True,
dtype=dtypes.float32)
split_outputs_dynamic = array_ops.unstack(outputs_dynamic, time_steps)
if in_graph_mode:
feeds = {concat_inputs: input_values}
# Initialize
variables_lib.global_variables_initializer().run(feed_dict=feeds)
# Generate gradients of sum of outputs w.r.t. inputs
dynamic_gradients = gradients_impl.gradients(
split_outputs_dynamic + [state_dynamic], [concat_inputs])
# Generate gradients of several individual outputs w.r.t. inputs
dynamic_individual_gradients = nest.flatten([
gradients_impl.gradients(y, [concat_inputs])
for y in [
split_outputs_dynamic[0], split_outputs_dynamic[-1],
state_dynamic
]
])
# Generate gradients of individual variables w.r.t. inputs
trainable_variables = ops_lib.get_collection(
ops_lib.GraphKeys.TRAINABLE_VARIABLES)
assert len(trainable_variables) > 1, (
"Count of trainable variables: %d" % len(trainable_variables))
dynamic_individual_variable_gradients = nest.flatten([
gradients_impl.gradients(y, trainable_variables)
for y in [
split_outputs_dynamic[0], split_outputs_dynamic[-1],
state_dynamic
]
])
# Test forward pass
values_dynamic = sess.run(split_outputs_dynamic, feed_dict=feeds)
(state_value_dynamic,) = sess.run((state_dynamic,), feed_dict=feeds)
# Test gradients to inputs and variables w.r.t. outputs & final state
dynamic_grad_values = sess.run(dynamic_gradients, feed_dict=feeds)
dynamic_individual_grad_values = sess.run(
dynamic_individual_gradients, feed_dict=feeds)
dynamic_individual_var_grad_values = sess.run(
dynamic_individual_variable_gradients, feed_dict=feeds)
######### Step 3: Comparisons
if not in_graph_mode:
values_static = outputs_static
values_dynamic = split_outputs_dynamic
state_value_static = state_static
state_value_dynamic = state_dynamic
self.assertEqual(len(values_static), len(values_dynamic))
for (value_static, value_dynamic) in zip(values_static, values_dynamic):
self.assertAllEqual(value_static, value_dynamic)
self.assertAllEqual(state_value_static, state_value_dynamic)
if in_graph_mode:
self.assertAllEqual(static_grad_values, dynamic_grad_values)
self.assertEqual(
len(static_individual_grad_values),
len(dynamic_individual_grad_values))
self.assertEqual(
len(static_individual_var_grad_values),
len(dynamic_individual_var_grad_values))
for i, (a, b) in enumerate(
zip(static_individual_grad_values, dynamic_individual_grad_values)):
tf_logging.info("Comparing individual gradients iteration %d" % i)
self.assertAllEqual(a, b)
for i, (a, b) in enumerate(
zip(static_individual_var_grad_values,
dynamic_individual_var_grad_values)):
tf_logging.info(
"Comparing individual variable gradients iteration %d" % i)
self.assertAllEqual(a, b)
@test_util.run_in_graph_and_eager_modes
def testDynamicEquivalentToStaticRNN(self):
self._testDynamicEquivalentToStaticRNN(use_sequence_length=False)
self._testDynamicEquivalentToStaticRNN(use_sequence_length=False)
class BidirectionalRNNTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
def _createBidirectionalRNN(self, use_shape, use_sequence_length, scope=None):
num_units = 3
input_size = 5
batch_size = 2
max_length = 8
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
sequence_length = array_ops.placeholder(
dtypes.int64) if use_sequence_length else None
cell_fw = rnn_cell.LSTMCell(
num_units, input_size, initializer=initializer, state_is_tuple=False)
cell_bw = rnn_cell.LSTMCell(
num_units, input_size, initializer=initializer, state_is_tuple=False)
inputs = max_length * [
array_ops.placeholder(
dtypes.float32,
shape=(batch_size, input_size) if use_shape else (None, input_size))
]
outputs, state_fw, state_bw = rnn.static_bidirectional_rnn(
cell_fw,
cell_bw,
inputs,
dtype=dtypes.float32,
sequence_length=sequence_length,
scope=scope)
self.assertEqual(len(outputs), len(inputs))
for out in outputs:
self.assertEqual(out.get_shape().as_list(),
[batch_size if use_shape else None, 2 * num_units])
input_value = np.random.randn(batch_size, input_size)
outputs = array_ops.stack(outputs)
return input_value, inputs, outputs, state_fw, state_bw, sequence_length
def _testBidirectionalRNN(self, use_shape):
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
input_value, inputs, outputs, state_fw, state_bw, sequence_length = (
self._createBidirectionalRNN(use_shape, True))
variables_lib.global_variables_initializer().run()
# Run with pre-specified sequence length of 2, 3
out, s_fw, s_bw = sess.run(
[outputs, state_fw, state_bw],
feed_dict={
inputs[0]: input_value,
sequence_length: [2, 3]
})
# Since the forward and backward LSTM cells were initialized with the
# same parameters, the forward and backward output has to be the same,
# but reversed in time. The format is output[time][batch][depth], and
# due to depth concatenation (as num_units=3 for both RNNs):
# - forward output: out[][][depth] for 0 <= depth < 3
# - backward output: out[][][depth] for 4 <= depth < 6
#
# First sequence in batch is length=2
# Check that the time=0 forward output is equal to time=1 backward output
self.assertEqual(out[0][0][0], out[1][0][3])
self.assertEqual(out[0][0][1], out[1][0][4])
self.assertEqual(out[0][0][2], out[1][0][5])
# Check that the time=1 forward output is equal to time=0 backward output
self.assertEqual(out[1][0][0], out[0][0][3])
self.assertEqual(out[1][0][1], out[0][0][4])
self.assertEqual(out[1][0][2], out[0][0][5])
# Second sequence in batch is length=3
# Check that the time=0 forward output is equal to time=2 backward output
self.assertEqual(out[0][1][0], out[2][1][3])
self.assertEqual(out[0][1][1], out[2][1][4])
self.assertEqual(out[0][1][2], out[2][1][5])
# Check that the time=1 forward output is equal to time=1 backward output
self.assertEqual(out[1][1][0], out[1][1][3])
self.assertEqual(out[1][1][1], out[1][1][4])
self.assertEqual(out[1][1][2], out[1][1][5])
# Check that the time=2 forward output is equal to time=0 backward output
self.assertEqual(out[2][1][0], out[0][1][3])
self.assertEqual(out[2][1][1], out[0][1][4])
self.assertEqual(out[2][1][2], out[0][1][5])
# Via the reasoning above, the forward and backward final state should be
# exactly the same
self.assertAllClose(s_fw, s_bw)
def _testBidirectionalRNNWithoutSequenceLength(self, use_shape):
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
input_value, inputs, outputs, state_fw, state_bw, _ = (
self._createBidirectionalRNN(use_shape, False))
variables_lib.global_variables_initializer().run()
out, s_fw, s_bw = sess.run(
[outputs, state_fw, state_bw], feed_dict={
inputs[0]: input_value
})
# Since the forward and backward LSTM cells were initialized with the
# same parameters, the forward and backward output has to be the same,
# but reversed in time. The format is output[time][batch][depth], and
# due to depth concatenation (as num_units=3 for both RNNs):
# - forward output: out[][][depth] for 0 <= depth < 3
# - backward output: out[][][depth] for 4 <= depth < 6
#
# Both sequences in batch are length=8. Check that the time=i
# forward output is equal to time=8-1-i backward output
for i in xrange(8):
self.assertEqual(out[i][0][0], out[8 - 1 - i][0][3])
self.assertEqual(out[i][0][1], out[8 - 1 - i][0][4])
self.assertEqual(out[i][0][2], out[8 - 1 - i][0][5])
for i in xrange(8):
self.assertEqual(out[i][1][0], out[8 - 1 - i][1][3])
self.assertEqual(out[i][1][1], out[8 - 1 - i][1][4])
self.assertEqual(out[i][1][2], out[8 - 1 - i][1][5])
# Via the reasoning above, the forward and backward final state should be
# exactly the same
self.assertAllClose(s_fw, s_bw)
def testBidirectionalRNN(self):
self._testBidirectionalRNN(use_shape=False)
self._testBidirectionalRNN(use_shape=True)
def testBidirectionalRNNWithoutSequenceLength(self):
self._testBidirectionalRNNWithoutSequenceLength(use_shape=False)
self._testBidirectionalRNNWithoutSequenceLength(use_shape=True)
def _createBidirectionalDynamicRNN(self,
use_shape,
use_state_tuple,
use_time_major,
use_sequence_length,
scope=None):
num_units = 3
input_size = 5
batch_size = 2
max_length = 8
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
sequence_length = (
array_ops.placeholder(dtypes.int64) if use_sequence_length else None)
cell_fw = rnn_cell.LSTMCell(
num_units, initializer=initializer, state_is_tuple=use_state_tuple)
cell_bw = rnn_cell.LSTMCell(
num_units, initializer=initializer, state_is_tuple=use_state_tuple)
inputs = max_length * [
array_ops.placeholder(
dtypes.float32,
shape=(batch_size if use_shape else None, input_size))
]
inputs_c = array_ops.stack(inputs)
if not use_time_major:
inputs_c = array_ops.transpose(inputs_c, [1, 0, 2])
outputs, states = rnn.bidirectional_dynamic_rnn(
cell_fw,
cell_bw,
inputs_c,
sequence_length,
dtype=dtypes.float32,
time_major=use_time_major,
scope=scope)
outputs = array_ops.concat(outputs, 2)
state_fw, state_bw = states
outputs_shape = [None, max_length, 2 * num_units]
if use_shape:
outputs_shape[0] = batch_size
if use_time_major:
outputs_shape[0], outputs_shape[1] = outputs_shape[1], outputs_shape[0]
self.assertEqual(outputs.get_shape().as_list(), outputs_shape)
input_value = np.random.randn(batch_size, input_size)
return input_value, inputs, outputs, state_fw, state_bw, sequence_length
def _testBidirectionalDynamicRNN(self, use_shape, use_state_tuple,
use_time_major, use_sequence_length):
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
input_value, inputs, outputs, state_fw, state_bw, sequence_length = (
self._createBidirectionalDynamicRNN(
use_shape, use_state_tuple, use_time_major, use_sequence_length))
variables_lib.global_variables_initializer().run()
# Run with pre-specified sequence length of 2, 3
feed_dict = ({sequence_length: [2, 3]} if use_sequence_length else {})
feed_dict.update({inputs[0]: input_value})
if use_state_tuple:
out, c_fw, m_fw, c_bw, m_bw = sess.run(
[outputs, state_fw[0], state_fw[1], state_bw[0], state_bw[1]],
feed_dict=feed_dict)
s_fw = (c_fw, m_fw)
s_bw = (c_bw, m_bw)
else:
feed_dict.update({inputs[0]: input_value})
out, s_fw, s_bw = sess.run(
[outputs, state_fw, state_bw], feed_dict=feed_dict)
# Since the forward and backward LSTM cells were initialized with the
# same parameters, the forward and backward output has to be the same,
# but reversed in time. The format is output[time][batch][depth], and
# due to depth concatenation (as num_units=3 for both RNNs):
# - forward output: out[][][depth] for 0 <= depth < 3
# - backward output: out[][][depth] for 4 <= depth < 6
#
if not use_time_major:
out = np.swapaxes(out, 0, 1)
if use_sequence_length:
# First sequence in batch is length=2
# Check that the t=0 forward output is equal to t=1 backward output
self.assertEqual(out[0][0][0], out[1][0][3])
self.assertEqual(out[0][0][1], out[1][0][4])
self.assertEqual(out[0][0][2], out[1][0][5])
# Check that the t=1 forward output is equal to t=0 backward output
self.assertEqual(out[1][0][0], out[0][0][3])
self.assertEqual(out[1][0][1], out[0][0][4])
self.assertEqual(out[1][0][2], out[0][0][5])
# Second sequence in batch is length=3
# Check that the t=0 forward output is equal to t=2 backward output
self.assertEqual(out[0][1][0], out[2][1][3])
self.assertEqual(out[0][1][1], out[2][1][4])
self.assertEqual(out[0][1][2], out[2][1][5])
# Check that the t=1 forward output is equal to t=1 backward output
self.assertEqual(out[1][1][0], out[1][1][3])
self.assertEqual(out[1][1][1], out[1][1][4])
self.assertEqual(out[1][1][2], out[1][1][5])
# Check that the t=2 forward output is equal to t=0 backward output
self.assertEqual(out[2][1][0], out[0][1][3])
self.assertEqual(out[2][1][1], out[0][1][4])
self.assertEqual(out[2][1][2], out[0][1][5])
# Via the reasoning above, the forward and backward final state should
# be exactly the same
self.assertAllClose(s_fw, s_bw)
else: # not use_sequence_length
max_length = 8 # from createBidirectionalDynamicRNN
for t in range(max_length):
self.assertAllEqual(out[t, :, 0:3], out[max_length - t - 1, :, 3:6])
self.assertAllClose(s_fw, s_bw)
def testBidirectionalDynamicRNN(self):
# Generate 2^5 option values
# from [True, True, True, True, True] to [False, False, False, False, False]
options = itertools.product([True, False], repeat=4)
for option in options:
self._testBidirectionalDynamicRNN(
use_shape=option[0],
use_state_tuple=option[1],
use_time_major=option[2],
use_sequence_length=option[3])
def _testScope(self, factory, prefix="prefix", use_outer_scope=True):
# REMARKS: factory(scope) is a function accepting a scope
# as an argument, such scope can be None, a string
# or a VariableScope instance.
with self.test_session(use_gpu=True, graph=ops_lib.Graph()):
if use_outer_scope:
with variable_scope.variable_scope(prefix) as scope:
factory(scope)
else:
factory(prefix)
# check that all the variables names starts
# with the proper scope.
variables_lib.global_variables_initializer()
all_vars = variables_lib.global_variables()
prefix = prefix or "bidirectional_rnn"
scope_vars = [v for v in all_vars if v.name.startswith(prefix + "/")]
tf_logging.info("BiRNN with scope: %s (%s)" %
(prefix, "scope" if use_outer_scope else "str"))
for v in scope_vars:
tf_logging.info(v.name)
self.assertEqual(len(scope_vars), len(all_vars))
def testBidirectionalRNNScope(self):
def factory(scope):
return self._createBidirectionalRNN(
use_shape=True, use_sequence_length=True, scope=scope)
self._testScope(factory, use_outer_scope=True)
self._testScope(factory, use_outer_scope=False)
self._testScope(factory, prefix=None, use_outer_scope=False)
def testBidirectionalDynamicRNNScope(self):
def get_factory(use_time_major):
def factory(scope):
return self._createBidirectionalDynamicRNN(
use_shape=True,
use_state_tuple=True,
use_sequence_length=True,
use_time_major=use_time_major,
scope=scope)
return factory
self._testScope(get_factory(True), use_outer_scope=True)
self._testScope(get_factory(True), use_outer_scope=False)
self._testScope(get_factory(True), prefix=None, use_outer_scope=False)
self._testScope(get_factory(False), use_outer_scope=True)
self._testScope(get_factory(False), use_outer_scope=False)
self._testScope(get_factory(False), prefix=None, use_outer_scope=False)
class MultiDimensionalLSTMTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
def testMultiDimensionalLSTMAllRNNContainers(self):
feature_dims = (3, 4, 5)
input_size = feature_dims
batch_size = 2
max_length = 8
sequence_length = [4, 6]
with self.test_session(graph=ops_lib.Graph()) as sess:
inputs = max_length * [
array_ops.placeholder(dtypes.float32, shape=(None,) + input_size)
]
inputs_using_dim = max_length * [
array_ops.placeholder(
dtypes.float32, shape=(batch_size,) + input_size)
]
inputs_c = array_ops.stack(inputs)
# Create a cell for the whole test. This is fine because the cell has no
# variables.
cell = DummyMultiDimensionalLSTM(feature_dims)
state_saver = TestStateSaver(batch_size, input_size)
outputs_static, state_static = rnn.static_rnn(
cell, inputs, dtype=dtypes.float32, sequence_length=sequence_length)
outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
cell,
inputs_c,
dtype=dtypes.float32,
time_major=True,
sequence_length=sequence_length)
outputs_bid, state_fw, state_bw = rnn.static_bidirectional_rnn(
cell,
cell,
inputs_using_dim,
dtype=dtypes.float32,
sequence_length=sequence_length)
outputs_sav, state_sav = rnn.static_state_saving_rnn(
cell,
inputs_using_dim,
sequence_length=sequence_length,
state_saver=state_saver,
state_name=("h", "c"))
self.assertEqual(outputs_dynamic.get_shape().as_list(),
inputs_c.get_shape().as_list())
for out, inp in zip(outputs_static, inputs):
self.assertEqual(out.get_shape().as_list(), inp.get_shape().as_list())
for out, inp in zip(outputs_bid, inputs_using_dim):
input_shape_list = inp.get_shape().as_list()
# fwd and bwd activations are concatenated along the second dim.
input_shape_list[1] *= 2
self.assertEqual(out.get_shape().as_list(), input_shape_list)
variables_lib.global_variables_initializer().run()
input_total_size = (batch_size,) + input_size
input_value = np.random.randn(*input_total_size)
outputs_static_v = sess.run(
outputs_static, feed_dict={
inputs[0]: input_value
})
outputs_dynamic_v = sess.run(
outputs_dynamic, feed_dict={
inputs[0]: input_value
})
outputs_bid_v = sess.run(
outputs_bid, feed_dict={
inputs_using_dim[0]: input_value
})
outputs_sav_v = sess.run(
outputs_sav, feed_dict={
inputs_using_dim[0]: input_value
})
self.assertAllEqual(outputs_static_v, outputs_dynamic_v)
self.assertAllEqual(outputs_static_v, outputs_sav_v)
outputs_static_array = np.array(outputs_static_v)
outputs_static_array_double = np.concatenate(
(outputs_static_array, outputs_static_array), axis=2)
outputs_bid_array = np.array(outputs_bid_v)
self.assertAllEqual(outputs_static_array_double, outputs_bid_array)
state_static_v = sess.run(
state_static, feed_dict={
inputs[0]: input_value
})
state_dynamic_v = sess.run(
state_dynamic, feed_dict={
inputs[0]: input_value
})
state_bid_fw_v = sess.run(
state_fw, feed_dict={
inputs_using_dim[0]: input_value
})
state_bid_bw_v = sess.run(
state_bw, feed_dict={
inputs_using_dim[0]: input_value
})
state_sav_v = sess.run(
state_sav, feed_dict={
inputs_using_dim[0]: input_value
})
self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v))
self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_sav_v))
self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_fw_v))
self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_bw_v))
class NestedLSTMTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
def testNestedIOLSTMAllRNNContainers(self):
input_size = 5
batch_size = 2
state_size = 6
max_length = 8
sequence_length = [4, 6]
with self.test_session(graph=ops_lib.Graph()) as sess:
state_saver = TestStateSaver(batch_size, state_size)
single_input = (array_ops.placeholder(
dtypes.float32, shape=(None, input_size)),
array_ops.placeholder(
dtypes.float32, shape=(None, input_size)))
inputs = max_length * [single_input]
inputs_c = (array_ops.stack([input_[0] for input_ in inputs]),
array_ops.stack([input_[1] for input_ in inputs]))
single_input_using_dim = (array_ops.placeholder(
dtypes.float32, shape=(batch_size, input_size)),
array_ops.placeholder(
dtypes.float32,
shape=(batch_size, input_size)))
inputs_using_dim = max_length * [single_input_using_dim]
# Create a cell for the whole test. This is fine because the cell has no
# variables.
cell = NestedRNNCell()
outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
cell,
inputs_c,
dtype=dtypes.float32,
time_major=True,
sequence_length=sequence_length)
outputs_static, state_static = rnn.static_rnn(
cell, inputs, dtype=dtypes.float32, sequence_length=sequence_length)
outputs_bid, state_fw, state_bw = rnn.static_bidirectional_rnn(
cell,
cell,
inputs_using_dim,
dtype=dtypes.float32,
sequence_length=sequence_length)
outputs_sav, state_sav = rnn.static_state_saving_rnn(
cell,
inputs_using_dim,
sequence_length=sequence_length,
state_saver=state_saver,
state_name=("h", "c"))
def _assert_same_shape(input1, input2, double=False):
flat_input1 = nest.flatten(input1)
flat_input2 = nest.flatten(input2)
for inp1, inp2 in zip(flat_input1, flat_input2):
input_shape = inp1.get_shape().as_list()
if double:
input_shape[1] *= 2
self.assertEqual(input_shape, inp2.get_shape().as_list())
_assert_same_shape(inputs_c, outputs_dynamic)
_assert_same_shape(inputs, outputs_static)
_assert_same_shape(inputs_using_dim, outputs_sav)
_assert_same_shape(inputs_using_dim, outputs_bid, double=True)
variables_lib.global_variables_initializer().run()
input_total_size = (batch_size, input_size)
input_value = (np.random.randn(*input_total_size),
np.random.randn(*input_total_size))
outputs_dynamic_v = sess.run(
outputs_dynamic, feed_dict={
single_input: input_value
})
outputs_static_v = sess.run(
outputs_static, feed_dict={
single_input: input_value
})
outputs_sav_v = sess.run(
outputs_sav, feed_dict={
single_input_using_dim: input_value
})
outputs_bid_v = sess.run(
outputs_bid, feed_dict={
single_input_using_dim: input_value
})
self.assertAllEqual(outputs_static_v,
np.transpose(outputs_dynamic_v, (1, 0, 2, 3)))
self.assertAllEqual(outputs_static_v, outputs_sav_v)
outputs_static_array = np.array(outputs_static_v)
outputs_static_array_double = np.concatenate(
(outputs_static_array, outputs_static_array), axis=3)
outputs_bid_array = np.array(outputs_bid_v)
self.assertAllEqual(outputs_static_array_double, outputs_bid_array)
state_dynamic_v = sess.run(
state_dynamic, feed_dict={
single_input: input_value
})
state_static_v = sess.run(
state_static, feed_dict={
single_input: input_value
})
state_bid_fw_v = sess.run(
state_fw, feed_dict={
single_input_using_dim: input_value
})
state_bid_bw_v = sess.run(
state_bw, feed_dict={
single_input_using_dim: input_value
})
state_sav_v = sess.run(
state_sav, feed_dict={
single_input_using_dim: input_value
})
self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v))
self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_sav_v))
self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_fw_v))
self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_bw_v))
class StateSaverRNNTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
def _factory(self, scope, state_saver):
num_units = state_saver.state_size // 2
batch_size = state_saver.batch_size
input_size = 5
max_length = 8
initializer = init_ops.random_uniform_initializer(
-0.01, 0.01, seed=self._seed)
cell = rnn_cell.LSTMCell(
num_units,
use_peepholes=False,
initializer=initializer,
state_is_tuple=False)
inputs = max_length * [
array_ops.zeros(dtype=dtypes.float32, shape=(batch_size, input_size))
]
out, state = rnn.static_state_saving_rnn(
cell,
inputs,
state_saver=state_saver,
state_name="save_lstm",
scope=scope)
return out, state, state_saver
def _testScope(self, prefix="prefix", use_outer_scope=True):
num_units = 3
batch_size = 2
state_saver = TestStateSaver(batch_size, 2 * num_units)
with self.test_session(use_gpu=True, graph=ops_lib.Graph()):
if use_outer_scope:
with variable_scope.variable_scope(prefix) as scope:
self._factory(scope=scope, state_saver=state_saver)
else:
self._factory(scope=prefix, state_saver=state_saver)
variables_lib.global_variables_initializer()
# check that all the variables names starts
# with the proper scope.
all_vars = variables_lib.global_variables()
prefix = prefix or "rnn"
scope_vars = [v for v in all_vars if v.name.startswith(prefix + "/")]
tf_logging.info("RNN with scope: %s (%s)" %
(prefix, "scope" if use_outer_scope else "str"))
for v in scope_vars:
tf_logging.info(v.name)
self.assertEqual(len(scope_vars), len(all_vars))
def testStateSaverRNNScope(self):
self._testScope(use_outer_scope=True)
self._testScope(use_outer_scope=False)
self._testScope(prefix=None, use_outer_scope=False)
def testStateSaverCallsSaveState(self):
"""Test that number of calls to state and save_state is equal.
Test if the order of actual evaluating or skipping evaluation of out,
state tensors, which are the output tensors from static_state_saving_rnn,
have influence on number of calls to save_state and state methods of
state_saver object (the number of calls should be same.)
"""
num_units = 3
batch_size = 2
state_saver = TestStateSaverWithCounters(batch_size, 2 * num_units)
out, state, state_saver = self._factory(scope=None, state_saver=state_saver)
with self.test_session() as sess:
sess.run(variables_lib.global_variables_initializer())
sess.run(variables_lib.local_variables_initializer())
_, _, num_state_calls, num_save_state_calls = sess.run([
out,
state,
state_saver.num_state_calls,
state_saver.num_save_state_calls])
self.assertEqual(num_state_calls, num_save_state_calls)
_, num_state_calls, num_save_state_calls = sess.run([
out,
state_saver.num_state_calls,
state_saver.num_save_state_calls])
self.assertEqual(num_state_calls, num_save_state_calls)
_, num_state_calls, num_save_state_calls = sess.run([
state,
state_saver.num_state_calls,
state_saver.num_save_state_calls])
self.assertEqual(num_state_calls, num_save_state_calls)
class GRUTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
def testDynamic(self):
time_steps = 8
num_units = 3
input_size = 5
batch_size = 2
input_values = np.random.randn(time_steps, batch_size, input_size)
sequence_length = np.random.randint(0, time_steps, size=batch_size)
with self.test_session(use_gpu=True, graph=ops_lib.Graph()) as sess:
concat_inputs = array_ops.placeholder(
dtypes.float32, shape=(time_steps, batch_size, input_size))
cell = rnn_cell.GRUCell(num_units=num_units)
with variable_scope.variable_scope("dynamic_scope"):
outputs_dynamic, state_dynamic = rnn.dynamic_rnn(
cell,
inputs=concat_inputs,
sequence_length=sequence_length,
time_major=True,
dtype=dtypes.float32)
feeds = {concat_inputs: input_values}
# Initialize
variables_lib.global_variables_initializer().run(feed_dict=feeds)
sess.run([outputs_dynamic, state_dynamic], feed_dict=feeds)
def _testScope(self, factory, prefix="prefix", use_outer_scope=True):
with self.test_session(use_gpu=True, graph=ops_lib.Graph()):
if use_outer_scope:
with variable_scope.variable_scope(prefix) as scope:
factory(scope)
else:
factory(prefix)
variables_lib.global_variables_initializer()
# check that all the variables names starts
# with the proper scope.
all_vars = variables_lib.global_variables()
prefix = prefix or "rnn"
scope_vars = [v for v in all_vars if v.name.startswith(prefix + "/")]
tf_logging.info("RNN with scope: %s (%s)" %
(prefix, "scope" if use_outer_scope else "str"))
for v in scope_vars:
tf_logging.info(v.name)
self.assertEqual(len(scope_vars), len(all_vars))
def testDynamicScope(self):
time_steps = 8
num_units = 3
input_size = 5
batch_size = 2
sequence_length = np.random.randint(0, time_steps, size=batch_size)
def factory(scope):
concat_inputs = array_ops.placeholder(
dtypes.float32, shape=(time_steps, batch_size, input_size))
cell = rnn_cell.GRUCell(num_units=num_units)
return rnn.dynamic_rnn(
cell,
inputs=concat_inputs,
sequence_length=sequence_length,
time_major=True,
dtype=dtypes.float32,
scope=scope)
self._testScope(factory, use_outer_scope=True)
self._testScope(factory, use_outer_scope=False)
self._testScope(factory, prefix=None, use_outer_scope=False)
class RawRNNTest(test.TestCase):
def setUp(self):
self._seed = 23489
np.random.seed(self._seed)
def _testRawRNN(self, max_time):
with self.test_session(graph=ops_lib.Graph()) as sess:
batch_size = 16
input_depth = 4
num_units = 3
inputs = array_ops.placeholder(
shape=(max_time, batch_size, input_depth), dtype=dtypes.float32)
sequence_length = array_ops.placeholder(
shape=(batch_size,), dtype=dtypes.int32)
inputs_ta = tensor_array_ops.TensorArray(
dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
inputs_ta = inputs_ta.unstack(inputs)
cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
def loop_fn(time_, cell_output, cell_state, unused_loop_state):
emit_output = cell_output # == None for time == 0
if cell_output is None: # time == 0
next_state = cell.zero_state(batch_size, dtypes.float32)
else:
next_state = cell_state # copy state through
elements_finished = (time_ >= sequence_length)
finished = math_ops.reduce_all(elements_finished)
# For the very final iteration, we must emit a dummy input
next_input = control_flow_ops.cond(
finished,
lambda: array_ops.zeros([batch_size, input_depth], dtype=dtypes.float32),
lambda: inputs_ta.read(time_))
return (elements_finished, next_input, next_state, emit_output, None)
reuse_scope = variable_scope.get_variable_scope()
outputs_ta, final_state, _ = rnn.raw_rnn(cell, loop_fn, scope=reuse_scope)
outputs = outputs_ta.stack()
reuse_scope.reuse_variables()
outputs_dynamic_rnn, final_state_dynamic_rnn = rnn.dynamic_rnn(
cell,
inputs,
time_major=True,
dtype=dtypes.float32,
sequence_length=sequence_length,
scope=reuse_scope)
variables = variables_lib.trainable_variables()
gradients = gradients_impl.gradients([outputs, final_state],
[inputs] + variables)
gradients_dynamic_rnn = gradients_impl.gradients(
[outputs_dynamic_rnn, final_state_dynamic_rnn], [inputs] + variables)
variables_lib.global_variables_initializer().run()
rand_input = np.random.randn(max_time, batch_size, input_depth)
if max_time == 0:
rand_seq_len = np.zeros(batch_size)
else:
rand_seq_len = np.random.randint(max_time, size=batch_size)
# To ensure same output lengths for dynamic_rnn and raw_rnn
rand_seq_len[0] = max_time
(outputs_val, outputs_dynamic_rnn_val, final_state_val,
final_state_dynamic_rnn_val) = sess.run(
[outputs, outputs_dynamic_rnn, final_state, final_state_dynamic_rnn],
feed_dict={
inputs: rand_input,
sequence_length: rand_seq_len
})
self.assertAllClose(outputs_dynamic_rnn_val, outputs_val)
self.assertAllClose(final_state_dynamic_rnn_val, final_state_val)
# NOTE: Because with 0 time steps, raw_rnn does not have shape
# information about the input, it is impossible to perform
# gradients comparisons as the gradients eval will fail. So
# this case skips the gradients test.
if max_time > 0:
self.assertEqual(len(gradients), len(gradients_dynamic_rnn))
gradients_val = sess.run(
gradients,
feed_dict={
inputs: rand_input,
sequence_length: rand_seq_len
})
gradients_dynamic_rnn_val = sess.run(
gradients_dynamic_rnn,
feed_dict={
inputs: rand_input,
sequence_length: rand_seq_len
})
self.assertEqual(len(gradients_val), len(gradients_dynamic_rnn_val))
input_gradients_val = gradients_val[0]
input_gradients_dynamic_rnn_val = gradients_dynamic_rnn_val[0]
self.assertAllClose(input_gradients_val,
input_gradients_dynamic_rnn_val)
for i in range(1, len(gradients_val)):
self.assertAllClose(gradients_dynamic_rnn_val[i], gradients_val[i])
def testRawRNNZeroLength(self):
# NOTE: Because with 0 time steps, raw_rnn does not have shape
# information about the input, it is impossible to perform
# gradients comparisons as the gradients eval will fail. So this
# case skips the gradients test.
self._testRawRNN(max_time=0)
def testRawRNN(self):
self._testRawRNN(max_time=10)
def testLoopState(self):
with self.test_session(graph=ops_lib.Graph()):
max_time = 10
batch_size = 16
input_depth = 4
num_units = 3
inputs = np.random.randn(max_time, batch_size, input_depth)
inputs_ta = tensor_array_ops.TensorArray(
dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
inputs_ta = inputs_ta.unstack(inputs)
cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
def loop_fn(time_, cell_output, cell_state, loop_state):
if cell_output is None:
loop_state = constant_op.constant([0])
next_state = cell.zero_state(batch_size, dtypes.float32)
else:
loop_state = array_ops.stack([array_ops.squeeze(loop_state) + 1])
next_state = cell_state
emit_output = cell_output # == None for time == 0
elements_finished = array_ops.tile([time_ >= max_time], [batch_size])
finished = math_ops.reduce_all(elements_finished)
# For the very final iteration, we must emit a dummy input
next_input = control_flow_ops.cond(
finished,
lambda: array_ops.zeros([batch_size, input_depth], dtype=dtypes.float32),
lambda: inputs_ta.read(time_))
return (elements_finished, next_input, next_state, emit_output,
loop_state)
r = rnn.raw_rnn(cell, loop_fn)
loop_state = r[-1]
self.assertEqual([10], loop_state.eval())
def testLoopStateWithTensorArray(self):
with self.test_session(graph=ops_lib.Graph()):
max_time = 4
batch_size = 16
input_depth = 4
num_units = 3
inputs = np.random.randn(max_time, batch_size, input_depth)
inputs_ta = tensor_array_ops.TensorArray(
dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
inputs_ta = inputs_ta.unstack(inputs)
cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
def loop_fn(time_, cell_output, cell_state, loop_state):
if cell_output is None:
loop_state = tensor_array_ops.TensorArray(
dynamic_size=True,
size=0,
dtype=dtypes.int32,
clear_after_read=False)
loop_state = loop_state.write(0, 1)
next_state = cell.zero_state(batch_size, dtypes.float32)
else:
loop_state = loop_state.write(time_,
loop_state.read(time_ - 1) + time_)
next_state = cell_state
emit_output = cell_output # == None for time == 0
elements_finished = array_ops.tile([time_ >= max_time], [batch_size])
finished = math_ops.reduce_all(elements_finished)
# For the very final iteration, we must emit a dummy input
next_input = control_flow_ops.cond(
finished,
lambda: array_ops.zeros([batch_size, input_depth], dtype=dtypes.float32),
lambda: inputs_ta.read(time_))
return (elements_finished, next_input, next_state, emit_output,
loop_state)
r = rnn.raw_rnn(cell, loop_fn)
loop_state = r[-1]
loop_state = loop_state.stack()
self.assertAllEqual([1, 2, 2 + 2, 4 + 3, 7 + 4], loop_state.eval())
def testEmitDifferentStructureThanCellOutput(self):
with self.test_session(graph=ops_lib.Graph()) as sess:
max_time = 10
batch_size = 16
input_depth = 4
num_units = 3
inputs = np.random.randn(max_time, batch_size, input_depth)
inputs_ta = tensor_array_ops.TensorArray(
dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
inputs_ta = inputs_ta.unstack(inputs)
# Verify emit shapes may be unknown by feeding a placeholder that
# determines an emit shape.
unknown_dim = array_ops.placeholder(dtype=dtypes.int32)
cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
def loop_fn(time_, cell_output, cell_state, _):
if cell_output is None:
emit_output = (array_ops.zeros([2, 3], dtype=dtypes.int32),
array_ops.zeros([unknown_dim], dtype=dtypes.int64))
next_state = cell.zero_state(batch_size, dtypes.float32)
else:
emit_output = (array_ops.ones([batch_size, 2, 3], dtype=dtypes.int32),
array_ops.ones(
[batch_size, unknown_dim], dtype=dtypes.int64))
next_state = cell_state
elements_finished = array_ops.tile([time_ >= max_time], [batch_size])
finished = math_ops.reduce_all(elements_finished)
# For the very final iteration, we must emit a dummy input
next_input = control_flow_ops.cond(
finished,
lambda: array_ops.zeros([batch_size, input_depth], dtype=dtypes.float32),
lambda: inputs_ta.read(time_))
return (elements_finished, next_input, next_state, emit_output, None)
r = rnn.raw_rnn(cell, loop_fn)
output_ta = r[0]
self.assertEqual(2, len(output_ta))
self.assertEqual([dtypes.int32, dtypes.int64],
[ta.dtype for ta in output_ta])
output = [ta.stack() for ta in output_ta]
output_vals = sess.run(output, feed_dict={unknown_dim: 1})
self.assertAllEqual(
np.ones((max_time, batch_size, 2, 3), np.int32), output_vals[0])
self.assertAllEqual(
np.ones((max_time, batch_size, 1), np.int64), output_vals[1])
def _testScope(self, factory, prefix="prefix", use_outer_scope=True):
with self.test_session(use_gpu=True, graph=ops_lib.Graph()):
if use_outer_scope:
with variable_scope.variable_scope(prefix) as scope:
factory(scope)
else:
factory(prefix)
variables_lib.global_variables_initializer()
# check that all the variables names starts
# with the proper scope.
all_vars = variables_lib.global_variables()
prefix = prefix or "rnn"
scope_vars = [v for v in all_vars if v.name.startswith(prefix + "/")]
tf_logging.info("RNN with scope: %s (%s)" %
(prefix, "scope" if use_outer_scope else "str"))
for v in scope_vars:
tf_logging.info(v.name)
self.assertEqual(len(scope_vars), len(all_vars))
def testRawRNNScope(self):
max_time = 10
batch_size = 16
input_depth = 4
num_units = 3
def factory(scope):
inputs = array_ops.placeholder(
shape=(max_time, batch_size, input_depth), dtype=dtypes.float32)
sequence_length = array_ops.placeholder(
shape=(batch_size,), dtype=dtypes.int32)
inputs_ta = tensor_array_ops.TensorArray(
dtype=dtypes.float32, size=array_ops.shape(inputs)[0])
inputs_ta = inputs_ta.unstack(inputs)
cell = rnn_cell.LSTMCell(num_units, state_is_tuple=True)
def loop_fn(time_, cell_output, cell_state, unused_loop_state):
emit_output = cell_output # == None for time == 0
if cell_output is None: # time == 0
next_state = cell.zero_state(batch_size, dtypes.float32)
else:
next_state = cell_state
elements_finished = (time_ >= sequence_length)
finished = math_ops.reduce_all(elements_finished)
# For the very final iteration, we must emit a dummy input
next_input = control_flow_ops.cond(
finished,
lambda: array_ops.zeros([batch_size, input_depth], dtype=dtypes.float32),
lambda: inputs_ta.read(time_))
return (elements_finished, next_input, next_state, emit_output, None)
return rnn.raw_rnn(cell, loop_fn, scope=scope)
self._testScope(factory, use_outer_scope=True)
self._testScope(factory, use_outer_scope=False)
self._testScope(factory, prefix=None, use_outer_scope=False)
class DeviceWrapperCell(rnn_cell.RNNCell):
"""Class to ensure cell calculation happens on a specific device."""
def __init__(self, cell, device):
self._cell = cell
self._device = device
@property
def output_size(self):
return self._cell.output_size
@property
def state_size(self):
return self._cell.state_size
def __call__(self, input_, state, scope=None):
if self._device is not None:
with ops_lib.device(self._device):
return self._cell(input_, state, scope=scope)
else:
return self._cell(input_, state, scope=scope)
class TensorArrayOnCorrectDeviceTest(test.TestCase):
def _execute_rnn_on(self,
rnn_device=None,
cell_device=None,
input_device=None):
batch_size = 3
time_steps = 7
input_size = 5
num_units = 10
cell = rnn_cell.LSTMCell(num_units, use_peepholes=True)
gpu_cell = DeviceWrapperCell(cell, cell_device)
inputs = np.random.randn(batch_size, time_steps, input_size).astype(
np.float32)
sequence_length = np.random.randint(0, time_steps, size=batch_size)
if input_device is not None:
with ops_lib.device(input_device):
inputs = constant_op.constant(inputs)
if rnn_device is not None:
with ops_lib.device(rnn_device):
outputs, _ = rnn.dynamic_rnn(
gpu_cell,
inputs,
sequence_length=sequence_length,
dtype=dtypes.float32)
else:
outputs, _ = rnn.dynamic_rnn(
gpu_cell,
inputs,
sequence_length=sequence_length,
dtype=dtypes.float32)
with self.test_session(use_gpu=True) as sess:
opts = config_pb2.RunOptions(trace_level=config_pb2.RunOptions.FULL_TRACE)
run_metadata = config_pb2.RunMetadata()
variables_lib.global_variables_initializer().run()
sess.run(outputs, options=opts, run_metadata=run_metadata)
return run_metadata
def _retrieve_cpu_gpu_stats(self, run_metadata):
cpu_stats = None
gpu_stats = None
step_stats = run_metadata.step_stats
for ds in step_stats.dev_stats:
if "cpu:0" in ds.device[-5:].lower():
cpu_stats = ds.node_stats
if "gpu:0" == ds.device[-5:].lower():
gpu_stats = ds.node_stats
return cpu_stats, gpu_stats
def testRNNOnCPUCellOnGPU(self):
if not test.is_gpu_available():
return # Test requires access to a GPU
gpu_dev = test.gpu_device_name()
run_metadata = self._execute_rnn_on(
rnn_device="/cpu:0", cell_device=gpu_dev)
cpu_stats, gpu_stats = self._retrieve_cpu_gpu_stats(run_metadata)
def _assert_in(op_str, in_stats, out_stats):
self.assertTrue(any(op_str in s.node_name for s in in_stats))
self.assertFalse(any(op_str in s.node_name for s in out_stats))
# Writes happen at output of RNN cell
_assert_in("TensorArrayWrite", gpu_stats, cpu_stats)
# Gather happens on final TensorArray
_assert_in("TensorArrayGather", gpu_stats, cpu_stats)
# Reads happen at input to RNN cell
_assert_in("TensorArrayRead", cpu_stats, gpu_stats)
# Scatters happen to get initial input into TensorArray
_assert_in("TensorArrayScatter", cpu_stats, gpu_stats)
def testRNNOnCPUCellOnCPU(self):
if not test.is_gpu_available():
return # Test requires access to a GPU
gpu_dev = test.gpu_device_name()
run_metadata = self._execute_rnn_on(
rnn_device="/cpu:0", cell_device="/cpu:0", input_device=gpu_dev)
cpu_stats, gpu_stats = self._retrieve_cpu_gpu_stats(run_metadata)
def _assert_in(op_str, in_stats, out_stats):
self.assertTrue(any(op_str in s.node_name for s in in_stats))
self.assertFalse(any(op_str in s.node_name for s in out_stats))
# All TensorArray operations happen on CPU
_assert_in("TensorArray", cpu_stats, gpu_stats)
def testInputOnGPUCellNotDeclared(self):
if not test.is_gpu_available():
return # Test requires access to a GPU
gpu_dev = test.gpu_device_name()
run_metadata = self._execute_rnn_on(input_device=gpu_dev)
cpu_stats, gpu_stats = self._retrieve_cpu_gpu_stats(run_metadata)
def _assert_in(op_str, in_stats, out_stats):
self.assertTrue(any(op_str in s.node_name for s in in_stats))
self.assertFalse(any(op_str in s.node_name for s in out_stats))
# Everything happens on GPU
_assert_in("TensorArray", gpu_stats, cpu_stats)
if __name__ == "__main__":
test.main()
|
NL66278/odoo | refs/heads/8.0 | addons/point_of_sale/controllers/main.py | 43 | # -*- coding: utf-8 -*-
import logging
import simplejson
import os
import openerp
import time
import random
from openerp import http
from openerp.http import request
from openerp.addons.web.controllers.main import module_boot, login_redirect
_logger = logging.getLogger(__name__)
class PosController(http.Controller):
@http.route('/pos/web', type='http', auth='user')
def a(self, debug=False, **k):
cr, uid, context, session = request.cr, request.uid, request.context, request.session
if not session.uid:
return login_redirect()
PosSession = request.registry['pos.session']
pos_session_ids = PosSession.search(cr, uid, [('state','=','opened'),('user_id','=',session.uid)], context=context)
PosSession.login(cr,uid,pos_session_ids,context=context)
modules = simplejson.dumps(module_boot(request.db))
init = """
var wc = new s.web.WebClient();
wc.show_application = function(){
wc.action_manager.do_action("pos.ui");
};
wc.setElement($(document.body));
wc.start();
"""
html = request.registry.get('ir.ui.view').render(cr, session.uid,'point_of_sale.index',{
'modules': modules,
'init': init,
})
return html
|
hcsturix74/django | refs/heads/master | tests/utils_tests/test_os_utils.py | 482 | import os
import unittest
from django.core.exceptions import SuspiciousFileOperation
from django.utils._os import safe_join
class SafeJoinTests(unittest.TestCase):
def test_base_path_ends_with_sep(self):
drive, path = os.path.splitdrive(safe_join("/abc/", "abc"))
self.assertEqual(
path,
"{0}abc{0}abc".format(os.path.sep)
)
def test_root_path(self):
drive, path = os.path.splitdrive(safe_join("/", "path"))
self.assertEqual(
path,
"{}path".format(os.path.sep),
)
drive, path = os.path.splitdrive(safe_join("/", ""))
self.assertEqual(
path,
os.path.sep,
)
def test_parent_path(self):
with self.assertRaises(SuspiciousFileOperation):
safe_join("/abc/", "../def")
|
Rustem/toptal-blog-celery-toy-ex | refs/heads/master | docs/conf.py | 1 | # Celery Uncovered documentation build configuration file, created by
# sphinx-quickstart.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import os
import sys
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
# sys.path.insert(0, os.path.abspath('.'))
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = []
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
# source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = 'Celery Uncovered'
copyright = """2017, xepa4ep"""
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.1'
# The full version, including alpha/beta/rc tags.
release = '0.1'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
# language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
# today = ''
# Else, today_fmt is used as the format for a strftime call.
# today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all documents.
# default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
# add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
# add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
# show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
# modindex_common_prefix = []
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
# html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
# html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
# html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
# html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
# html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
# html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
# html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
# html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
# html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
# html_additional_pages = {}
# If false, no module index is generated.
# html_domain_indices = True
# If false, no index is generated.
# html_use_index = True
# If true, the index is split into individual pages for each letter.
# html_split_index = False
# If true, links to the reST sources are added to the pages.
# html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
# html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
# html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
# html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
# html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'celery_uncovereddoc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
# 'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index',
'celery_uncovered.tex',
'Celery Uncovered Documentation',
"""xepa4ep""", 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
# latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
# latex_use_parts = False
# If true, show page references after internal links.
# latex_show_pagerefs = False
# If true, show URL addresses after external links.
# latex_show_urls = False
# Documents to append as an appendix to all manuals.
# latex_appendices = []
# If false, no module index is generated.
# latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'celery_uncovered', 'Celery Uncovered Documentation',
["""xepa4ep"""], 1)
]
# If true, show URL addresses after external links.
# man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'celery_uncovered', 'Celery Uncovered Documentation',
"""xepa4ep""", 'Celery Uncovered',
"""A short description of the project.""", 'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
# texinfo_appendices = []
# If false, no module index is generated.
# texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
# texinfo_show_urls = 'footnote'
|
asimshankar/tensorflow | refs/heads/master | tensorflow/python/eager/tensor_test.py | 5 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Unit tests for TensorFlow "Eager" Mode's Tensor class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import re
import sys
import numpy as np
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.eager import context
from tensorflow.python.eager import core
from tensorflow.python.eager import test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import io_ops
def _create_tensor(value, device=None, dtype=None):
ctx = context.context()
if device is None:
device = ctx.device_name
if dtype is not None:
dtype = dtype.as_datatype_enum
try:
return ops.EagerTensor(
value, context=ctx._handle, device=device, dtype=dtype)
except core._NotOkStatusException as e: # pylint: disable=protected-access
raise core._status_to_exception(e.code, e.message)
class TFETensorTest(test_util.TensorFlowTestCase):
def testScalarTensor(self):
t = _create_tensor(3, dtype=dtypes.int32)
self.assertAllEqual(t, _create_tensor(np.array(3)))
self.assertEqual(dtypes.int32, t.dtype)
self.assertEqual(0, t.shape.ndims)
self.assertAllEqual([], t.shape.as_list())
self.assertIn("tf.Tensor", str(t))
self.assertIn("tf.Tensor", repr(t))
def testBadConstructorArgs(self):
ctx = context.context()
handle = ctx._handle
device = ctx.device_name
# Missing context.
with self.assertRaisesRegexp(
TypeError, r"Required argument 'context' \(pos 2\) not found"):
ops.EagerTensor(1, device=device)
# Missing device.
with self.assertRaisesRegexp(
TypeError, r"Required argument 'device' \(pos 3\) not found"):
ops.EagerTensor(1, context=handle)
# Bad dtype type.
with self.assertRaisesRegexp(TypeError,
"Expecting a DataType value for dtype. Got"):
ops.EagerTensor(1, context=handle, device=device, dtype="1")
# Following errors happen when trying to copy to GPU.
if not context.context().num_gpus():
self.skipTest("No GPUs found")
with ops.device("/device:GPU:0"):
device = ctx.device_name
# Bad context.
with self.assertRaisesRegexp(
TypeError, "Expecting a PyCapsule encoded context handle. Got"):
ops.EagerTensor(1.0, context=1, device=device)
# Bad device.
with self.assertRaisesRegexp(
TypeError, "Error parsing device argument to CopyToDevice"):
ops.EagerTensor(1.0, context=handle, device=1)
def testNumpyValue(self):
values = np.array([3.0])
t = _create_tensor(values)
self.assertAllEqual(values, t)
@test_util.assert_no_new_pyobjects_executing_eagerly
def testNumpyDtypeSurvivesThroughTensorConversion(self):
scalar_creators = [np.int32, np.int64, np.float32, np.float64]
conversion_functions = [ops.convert_to_tensor, constant_op.constant]
for scalar_creator in scalar_creators:
for conversion_function in conversion_functions:
np_val = scalar_creator(3)
tensor_val = conversion_function(np_val)
self.assertEqual(tensor_val.numpy().dtype, np_val.dtype)
self.assertEqual(tensor_val.numpy(), np_val)
def testNumpyValueWithCast(self):
values = np.array([3.0], dtype=np.float32)
t = _create_tensor(values, dtype=dtypes.float64)
self.assertAllEqual(values, t)
ctx = context.context()
# Bad dtype value.
with self.assertRaisesRegexp(TypeError, "Invalid dtype argument value"):
ops.EagerTensor(
values, context=ctx._handle, device=ctx.device_name, dtype=12345)
def testNumpyOrderHandling(self):
n = np.array([[1, 2], [3, 4]], order="F")
t = _create_tensor(n)
self.assertAllEqual([[1, 2], [3, 4]], t)
def testNumpyArrayDtype(self):
tensor = constant_op.constant([1.0, 2.0, 3.0])
numpy_tensor = np.asarray(tensor, dtype=np.int32)
self.assertAllEqual(numpy_tensor, [1, 2, 3])
def testNdimsAgreesWithNumpy(self):
numpy_tensor = np.asarray(1.0)
tensor = constant_op.constant(numpy_tensor)
self.assertAllEqual(numpy_tensor.ndim, tensor.ndim)
numpy_tensor = np.asarray([1.0, 2.0, 3.0])
tensor = constant_op.constant(numpy_tensor)
self.assertAllEqual(numpy_tensor.ndim, tensor.ndim)
numpy_tensor = np.asarray([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
tensor = constant_op.constant(numpy_tensor)
self.assertAllEqual(numpy_tensor.ndim, tensor.ndim)
def testLenAgreesWithNumpy(self):
numpy_tensor = np.asarray(1.0)
tensor = constant_op.constant(numpy_tensor)
with self.assertRaises(TypeError):
len(numpy_tensor)
with self.assertRaisesRegexp(
TypeError, r"Scalar tensor has no `len[(][)]`"):
len(tensor)
numpy_tensor = np.asarray([1.0, 2.0, 3.0])
tensor = constant_op.constant(numpy_tensor)
self.assertAllEqual(len(numpy_tensor), len(tensor))
numpy_tensor = np.asarray([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
tensor = constant_op.constant(numpy_tensor)
self.assertAllEqual(len(numpy_tensor), len(tensor))
def testCopy(self):
t = constant_op.constant(1.0)
tt = copy.copy(t)
self.assertAllEqual(tt, 1.0)
del tt
tt = copy.deepcopy(t)
self.assertAllEqual(tt, 1.0)
del tt
self.assertAllEqual(t, 1.0)
def testConstantDtype(self):
self.assertEqual(
constant_op.constant(1, dtype=np.int64).dtype, dtypes.int64)
def testTensorAndNumpyMatrix(self):
expected = np.array([[1.0, 2.0], [3.0, 4.0]], np.float32)
actual = _create_tensor([[1.0, 2.0], [3.0, 4.0]])
self.assertAllEqual(expected, actual)
self.assertEqual(np.float32, actual.dtype)
self.assertEqual(dtypes.float32, actual.dtype)
self.assertAllEqual([2, 2], actual.shape.as_list())
def testFloatDowncast(self):
# Unless explicitly specified, float64->float32
t = _create_tensor(3.0)
self.assertEqual(dtypes.float32, t.dtype)
t = _create_tensor(3.0, dtype=dtypes.float64)
self.assertEqual(dtypes.float64, t.dtype)
def testBool(self):
self.assertFalse(bool(_create_tensor(False)))
self.assertFalse(bool(_create_tensor([False])))
self.assertFalse(bool(_create_tensor([[False]])))
self.assertFalse(bool(_create_tensor([0])))
self.assertFalse(bool(_create_tensor([0.])))
self.assertTrue(bool(_create_tensor([1])))
self.assertTrue(bool(_create_tensor([1.])))
def testIntDowncast(self):
t = _create_tensor(3)
self.assertEqual(dtypes.int32, t.dtype)
t = _create_tensor(3, dtype=dtypes.int64)
self.assertEqual(dtypes.int64, t.dtype)
t = _create_tensor(2**33)
self.assertEqual(dtypes.int64, t.dtype)
def testTensorCreationFailure(self):
with self.assertRaises(ValueError):
# Should fail because the each row of the Python object has a different
# number of columns.
self.assertEqual(None, _create_tensor([[1], [1, 2]]))
def testMultiLineTensorStr(self):
t = _create_tensor(np.eye(3))
tensor_str = str(t)
self.assertIn("shape=%s, dtype=%s" % (t.shape, t.dtype.name), tensor_str)
self.assertIn(str(t), tensor_str)
def testMultiLineTensorRepr(self):
t = _create_tensor(np.eye(3))
tensor_repr = repr(t)
self.assertTrue(tensor_repr.startswith("<"))
self.assertTrue(tensor_repr.endswith(">"))
self.assertIn("id=%d, shape=%s, dtype=%s, numpy=\n%r" %
(t._id, t.shape, t.dtype.name, t.numpy()), tensor_repr)
def testTensorStrReprObeyNumpyPrintOptions(self):
orig_threshold = np.get_printoptions()["threshold"]
orig_edgeitems = np.get_printoptions()["edgeitems"]
np.set_printoptions(threshold=2, edgeitems=1)
t = _create_tensor(np.arange(10, dtype=np.int32))
self.assertTrue(re.match(r".*\[.*0.*\.\.\..*9.*\]", str(t)))
self.assertTrue(re.match(r".*\[.*0.*\.\.\..*9.*\]", repr(t)))
# Clean up: reset to previous printoptions.
np.set_printoptions(threshold=orig_threshold, edgeitems=orig_edgeitems)
def testZeroDimTensorStr(self):
t = _create_tensor(42)
self.assertIn("42, shape=(), dtype=int32", str(t))
def testZeroDimTensorRepr(self):
t = _create_tensor(42)
self.assertTrue(repr(t).startswith("<"))
self.assertTrue(repr(t).endswith(">"))
self.assertIn("id=%d, shape=(), dtype=int32, numpy=42" % t._id, repr(t))
def testZeroSizeTensorStr(self):
t = _create_tensor(np.zeros(0, dtype=np.float32))
self.assertIn("[], shape=(0,), dtype=float32", str(t))
def testZeroSizeTensorRepr(self):
t = _create_tensor(np.zeros(0, dtype=np.float32))
self.assertTrue(repr(t).startswith("<"))
self.assertTrue(repr(t).endswith(">"))
self.assertIn("id=%d, shape=(0,), dtype=float32, numpy=%r" % (t._id,
t.numpy()),
repr(t))
def testStringTensor(self):
t_np_orig = np.array([[b"a", b"ab"], [b"abc", b"abcd"]])
t = _create_tensor(t_np_orig)
t_np = t.numpy()
self.assertTrue(np.all(t_np == t_np_orig), "%s vs %s" % (t_np, t_np_orig))
def testIterateOverTensor(self):
l = [[1, 2], [3, 4]]
t = _create_tensor(l)
for list_element, tensor_element in zip(l, t):
self.assertAllEqual(list_element, tensor_element.numpy())
def testStringTensorOnGPU(self):
if not context.context().num_gpus():
self.skipTest("No GPUs found")
with ops.device("/device:GPU:0"):
with self.assertRaisesRegexp(
RuntimeError, "Can't copy Tensor with type string to device"):
_create_tensor("test string")
def testInvalidUTF8ProducesReasonableError(self):
if sys.version_info[0] < 3:
self.skipTest("Test is only valid in python3.")
with self.assertRaises(UnicodeDecodeError):
io_ops.read_file(b"\xff")
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorPreferredDtypeIsRespected(self):
self.assertEqual(
ops.convert_to_tensor(0.5, preferred_dtype=dtypes.int32).dtype,
dtypes.float32)
self.assertEqual(
ops.convert_to_tensor(0.5, preferred_dtype=dtypes.float64).dtype,
dtypes.float64)
@test_util.run_in_graph_and_eager_modes
def testCompatibility(self):
integer_types = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]
# Floats are not compatible with ints
for t in integer_types:
with self.assertRaises(TypeError):
constant_op.constant(0.5, dtype=t)
# Ints compatible with floats
self.assertEqual(
self.evaluate(constant_op.constant(5, dtype=dtypes.float16)), 5.0)
self.assertEqual(
self.evaluate(constant_op.constant(5, dtype=dtypes.float32)), 5.0)
self.assertEqual(
self.evaluate(constant_op.constant(5, dtype=dtypes.float64)), 5.0)
self.assertEqual(
self.evaluate(constant_op.constant(5, dtype=dtypes.bfloat16)), 5.0)
# Ints and floats are compatible with complex types
self.assertEqual(
constant_op.constant([[1.0]], dtype=dtypes.complex128).dtype,
dtypes.complex128)
self.assertEqual(
constant_op.constant([[1]], dtype=dtypes.complex128).dtype,
dtypes.complex128)
# Quantized types are not compatible with floats
quantized_types = [dtypes.qint16, dtypes.qint32, dtypes.qint8,
dtypes.quint16, dtypes.quint8]
for t in quantized_types:
with self.assertRaises(TypeError):
constant_op.constant(0.5, dtype=t)
# TODO(b/118402529): quantized types are broken in eager.
@test_util.run_in_graph_and_eager_modes
def testCConvertToTensor(self):
with self.assertRaises(TypeError):
_ = constant_op.constant(0) < 0.5
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorAllowsOverflow(self):
_ = ops.convert_to_tensor(123456789, dtype=dtypes.uint8)
def testEagerTensorError(self):
with self.assertRaisesRegexp(
TypeError,
"Cannot convert provided value to EagerTensor. "
"Provided value.*Requested dtype.*"):
_ = ops.convert_to_tensor(1., dtype=dtypes.int32)
class TFETensorUtilTest(test_util.TensorFlowTestCase):
def testListOfThree(self):
t1 = _create_tensor([[1, 2], [3, 4], [5, 6]], dtype=dtypes.int32)
t2 = _create_tensor([[1, 2, 5], [3, 4, 5]], dtype=dtypes.int32)
t3 = _create_tensor([[1], [3], [5], [6]], dtype=dtypes.int32)
r = pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1, t2, t3], 0)
self.assertAllEqual(np.array([3, 2, 4]), r.numpy())
r = pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1, t2, t3], 1)
self.assertAllEqual(np.array([2, 3, 1]), r.numpy())
def testEmptyTensorList(self):
a = pywrap_tensorflow.TFE_Py_TensorShapeSlice([], 0)
self.assertTrue(isinstance(a, ops.EagerTensor))
self.assertEqual(0, a.numpy().size)
def testTensorListContainsNonTensors(self):
t1 = _create_tensor([1, 2], dtype=dtypes.int32)
with self.assertRaisesRegexp(
TypeError,
r"Expected a list of EagerTensors but element 1 has type \"str\""):
pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1, "abc"], 0)
with self.assertRaisesRegexp(
TypeError,
r"Expected a list of EagerTensors but element 0 has type \"int\""):
pywrap_tensorflow.TFE_Py_TensorShapeSlice([2, t1], 0)
def testTensorListNotList(self):
t1 = _create_tensor([1, 2], dtype=dtypes.int32)
with self.assertRaisesRegexp(
TypeError,
r"tensors argument must be a list or a tuple. Got.*EagerTensor"):
pywrap_tensorflow.TFE_Py_TensorShapeSlice(t1, -2)
def testNegativeSliceDim(self):
t1 = _create_tensor([1, 2], dtype=dtypes.int32)
with self.assertRaisesRegexp(
ValueError,
r"Slice dimension must be non-negative. Got -2"):
pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1], -2)
def testUnicode(self):
self.assertEqual(constant_op.constant(u"asdf").numpy(), b"asdf")
def testFloatTensor(self):
self.assertEqual(dtypes.float64, _create_tensor(np.float64()).dtype)
self.assertEqual(dtypes.float32, _create_tensor(np.float32()).dtype)
self.assertEqual(dtypes.float16, _create_tensor(np.float16()).dtype)
self.assertEqual(dtypes.float32, _create_tensor(0.0).dtype)
def testSliceDimOutOfRange(self):
t1 = _create_tensor([[1, 2], [3, 4], [5, 6]], dtype=dtypes.int32)
t2 = _create_tensor([1, 2], dtype=dtypes.int32)
t3 = _create_tensor(2, dtype=dtypes.int32)
with self.assertRaisesRegexp(
IndexError,
r"Slice dimension \(2\) must be smaller than rank of all tensors, "
"but tensor at index 0 has rank 2"):
pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1], 2)
with self.assertRaisesRegexp(
IndexError,
r"Slice dimension \(1\) must be smaller than rank of all tensors, "
"but tensor at index 0 has rank 1"):
pywrap_tensorflow.TFE_Py_TensorShapeSlice([t2], 1)
with self.assertRaisesRegexp(
IndexError,
r"Slice dimension \(1\) must be smaller than rank of all tensors, "
"but tensor at index 1 has rank 1"):
pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1, t2], 1)
with self.assertRaisesRegexp(
IndexError,
r"Slice dimension \(0\) must be smaller than rank of all tensors, "
"but tensor at index 0 has rank 0"):
pywrap_tensorflow.TFE_Py_TensorShapeSlice([t3], 0)
with self.assertRaisesRegexp(
IndexError,
r"Slice dimension \(0\) must be smaller than rank of all tensors, "
"but tensor at index 2 has rank 0"):
pywrap_tensorflow.TFE_Py_TensorShapeSlice([t2, t1, t3], 0)
@test_util.assert_no_new_pyobjects_executing_eagerly
def testTensorDir(self):
t = array_ops.zeros(1)
t.test_attr = "Test"
instance_dir = dir(t)
type_dir = dir(ops.EagerTensor)
# Monkey patched attributes should show up in dir(t)
self.assertIn("test_attr", instance_dir)
instance_dir.remove("test_attr")
self.assertEqual(instance_dir, type_dir)
def testNonRectangularPackAsConstant(self):
l = [array_ops.zeros((10, 1)).numpy(), array_ops.zeros(1).numpy()]
with self.assertRaisesRegexp(
ValueError, "non-rectangular Python sequence"):
constant_op.constant(l)
if __name__ == "__main__":
test.main()
|
Flexget/Flexget | refs/heads/develop | flexget/tests/api_tests/test_server_api.py | 2 | import json
import os
from unittest.mock import patch
import pytest
from flexget import __version__
from flexget.api.app import __version__ as __api_version__
from flexget.api.app import base_message
from flexget.api.core.server import ObjectsContainer as OC
from flexget.manager import Manager
from flexget.tests.conftest import MockManager
from flexget.utils.tools import get_latest_flexget_version_number
class TestServerAPI:
config = """
tasks:
test:
rss:
url: http://test/rss
mock:
- title: entry 1
"""
def test_pid(self, api_client, schema_match):
rsp = api_client.get('/server/pid/', headers={})
assert rsp.status_code == 200
data = json.loads(rsp.get_data(as_text=True))
errors = schema_match(OC.pid_object, data)
assert not errors
assert data['pid'] == os.getpid()
def test_reload(self, api_client, schema_match):
with patch.object(MockManager, 'load_config') as mocked_load_config:
payload = {'operation': 'reload'}
rsp = api_client.json_post('/server/manage/', data=json.dumps(payload))
assert rsp.status_code == 200
data = json.loads(rsp.get_data(as_text=True))
errors = schema_match(base_message, data)
assert not errors
assert mocked_load_config.called
def test_shutdown(self, api_client, schema_match):
with patch.object(MockManager, 'shutdown') as mocked_shutdown:
payload = {'operation': 'shutdown'}
rsp = api_client.json_post('/server/manage/', data=json.dumps(payload))
assert rsp.status_code == 200
data = json.loads(rsp.get_data(as_text=True))
errors = schema_match(base_message, data)
assert not errors
assert mocked_shutdown.called
def test_get_config(self, api_client, schema_match):
rsp = api_client.get('/server/config/')
assert rsp.status_code == 200
data = json.loads(rsp.get_data(as_text=True))
errors = schema_match({'type': 'object'}, data)
assert not errors
assert data == {
'tasks': {
'test': {
'mock': [{'title': 'entry 1'}],
'rss': {
'url': 'http://test/rss',
'group_links': False,
'ascii': False,
'escape': False,
'silent': False,
'all_entries': True,
},
}
}
}
def test_get_raw_config(self, manager, api_client, schema_match):
manager.config_path = os.path.join(os.path.dirname(__file__), 'raw_config.yml')
rsp = api_client.get('/server/raw_config/')
assert rsp.status_code == 200
data = json.loads(rsp.get_data(as_text=True))
errors = schema_match(OC.raw_config_object, data)
assert not errors
assert (
data['raw_config']
== 'dGFza3M6CiAgdGVzdDoKICAgIHJzczoKICAgICAgdXJsOiBodHRwOi8vdGVzdC9yc3MKICAgIG1'
'vY2s6CiAgICAgIC0gdGl0bGU6IGVudHJ5IDE='
)
@pytest.mark.online
def test_version(self, api_client, schema_match):
latest = get_latest_flexget_version_number()
rsp = api_client.get('/server/version/')
assert rsp.status_code == 200
data = json.loads(rsp.get_data(as_text=True))
errors = schema_match(OC.version_object, data)
assert not errors
assert data == {
'flexget_version': __version__,
'api_version': __api_version__,
'latest_version': latest,
}
def test_crash_logs_without_crash_log(self, api_client, schema_match):
rsp = api_client.get('/server/crash_logs')
assert rsp.status_code == 200
data = json.loads(rsp.get_data(as_text=True))
errors = schema_match(OC.crash_logs, data)
assert not errors
assert not data
def test_crash_logs_with_crashes(self, api_client, schema_match, manager):
manager.config_base = os.path.join(os.path.dirname(__file__))
rsp = api_client.get('/server/crash_logs')
assert rsp.status_code == 200
data = json.loads(rsp.get_data(as_text=True))
errors = schema_match(OC.crash_logs, data)
assert not errors
assert len(data) == 2
|
MichaelNedzelsky/intellij-community | refs/heads/master | python/testData/resolve/multiFile/fromQualifiedPackageImportFile/FromQualifiedPackageImportFile.py | 83 | from mypackage.child import testfile
# <ref> |
YaningX/open-hackathon | refs/heads/master | open-hackathon-server/src/hackathon/hazure/service_adapter.py | 6 | # -*- coding: utf-8 -*-
"""
Copyright (c) Microsoft Open Technologies (Shanghai) Co. Ltd. All rights reserved.
The MIT License (MIT)
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.
"""
__author__ = "rapidhere"
__all__ = ["ServiceAdapter"]
from hackathon import Component
class ServiceAdapter(Component):
"""the abstract ServiceAdapter with proxy pattern
this adapter delegate the method and properties to the inner proxy, so make
the adpater has all functions that adaptee has
"""
def __init__(self, service):
self.service = service
def __getattr__(self, name):
return getattr(self.service, name)
|
wallyworld/juju | refs/heads/develop | acceptancetests/repository/trusty/haproxy/hooks/charmhelpers/fetch/giturl.py | 6 | # Copyright 2014-2015 Canonical Limited.
#
# This file is part of charm-helpers.
#
# charm-helpers 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.
#
# charm-helpers 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 charm-helpers. If not, see <http://www.gnu.org/licenses/>.
import os
from charmhelpers.fetch import (
BaseFetchHandler,
UnhandledSource
)
from charmhelpers.core.host import mkdir
import six
if six.PY3:
raise ImportError('GitPython does not support Python 3')
try:
from git import Repo
except ImportError:
from charmhelpers.fetch import apt_install
apt_install("python-git")
from git import Repo
from git.exc import GitCommandError # noqa E402
class GitUrlFetchHandler(BaseFetchHandler):
"""Handler for git branches via generic and github URLs"""
def can_handle(self, source):
url_parts = self.parse_url(source)
# TODO (mattyw) no support for ssh git@ yet
if url_parts.scheme not in ('http', 'https', 'git'):
return False
else:
return True
def clone(self, source, dest, branch):
if not self.can_handle(source):
raise UnhandledSource("Cannot handle {}".format(source))
repo = Repo.clone_from(source, dest)
repo.git.checkout(branch)
def install(self, source, branch="master", dest=None):
url_parts = self.parse_url(source)
branch_name = url_parts.path.strip("/").split("/")[-1]
if dest:
dest_dir = os.path.join(dest, branch_name)
else:
dest_dir = os.path.join(os.environ.get('CHARM_DIR'), "fetched",
branch_name)
if not os.path.exists(dest_dir):
mkdir(dest_dir, perms=0o755)
try:
self.clone(source, dest_dir, branch)
except GitCommandError as e:
raise UnhandledSource(e.message)
except OSError as e:
raise UnhandledSource(e.strerror)
return dest_dir
|
staslev/beam | refs/heads/master | sdks/python/apache_beam/io/textio.py | 5 | #
# 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.
#
"""A source and a sink for reading from and writing to text files."""
from __future__ import absolute_import
import logging
from functools import partial
from apache_beam.coders import coders
from apache_beam.io import filebasedsink
from apache_beam.io import filebasedsource
from apache_beam.io import iobase
from apache_beam.io.filebasedsource import ReadAllFiles
from apache_beam.io.filesystem import CompressionTypes
from apache_beam.io.iobase import Read
from apache_beam.io.iobase import Write
from apache_beam.transforms import PTransform
from apache_beam.transforms.display import DisplayDataItem
__all__ = ['ReadFromText', 'ReadAllFromText', 'WriteToText']
class _TextSource(filebasedsource.FileBasedSource):
r"""A source for reading text files.
Parses a text file as newline-delimited elements. Supports newline delimiters
'\n' and '\r\n.
This implementation only supports reading text encoded using UTF-8 or
ASCII.
"""
DEFAULT_READ_BUFFER_SIZE = 8192
class ReadBuffer(object):
# A buffer that gives the buffered data and next position in the
# buffer that should be read.
def __init__(self, data, position):
self._data = data
self._position = position
@property
def data(self):
return self._data
@data.setter
def data(self, value):
assert isinstance(value, bytes)
self._data = value
@property
def position(self):
return self._position
@position.setter
def position(self, value):
assert isinstance(value, (int, long))
if value > len(self._data):
raise ValueError('Cannot set position to %d since it\'s larger than '
'size of data %d.', value, len(self._data))
self._position = value
def __init__(self,
file_pattern,
min_bundle_size,
compression_type,
strip_trailing_newlines,
coder,
buffer_size=DEFAULT_READ_BUFFER_SIZE,
validate=True,
skip_header_lines=0):
super(_TextSource, self).__init__(file_pattern, min_bundle_size,
compression_type=compression_type,
validate=validate)
self._strip_trailing_newlines = strip_trailing_newlines
self._compression_type = compression_type
self._coder = coder
self._buffer_size = buffer_size
if skip_header_lines < 0:
raise ValueError('Cannot skip negative number of header lines: %d',
skip_header_lines)
elif skip_header_lines > 10:
logging.warning(
'Skipping %d header lines. Skipping large number of header '
'lines might significantly slow down processing.')
self._skip_header_lines = skip_header_lines
def display_data(self):
parent_dd = super(_TextSource, self).display_data()
parent_dd['strip_newline'] = DisplayDataItem(
self._strip_trailing_newlines,
label='Strip Trailing New Lines')
parent_dd['buffer_size'] = DisplayDataItem(
self._buffer_size,
label='Buffer Size')
parent_dd['coder'] = DisplayDataItem(
self._coder.__class__,
label='Coder')
return parent_dd
def read_records(self, file_name, range_tracker):
start_offset = range_tracker.start_position()
read_buffer = _TextSource.ReadBuffer('', 0)
next_record_start_position = -1
def split_points_unclaimed(stop_position):
return (0 if stop_position <= next_record_start_position
else iobase.RangeTracker.SPLIT_POINTS_UNKNOWN)
range_tracker.set_split_points_unclaimed_callback(split_points_unclaimed)
with self.open_file(file_name) as file_to_read:
position_after_skipping_header_lines = self._skip_lines(
file_to_read, read_buffer,
self._skip_header_lines) if self._skip_header_lines else 0
start_offset = max(start_offset, position_after_skipping_header_lines)
if start_offset > position_after_skipping_header_lines:
# Seeking to one position before the start index and ignoring the
# current line. If start_position is at beginning if the line, that line
# belongs to the current bundle, hence ignoring that is incorrect.
# Seeking to one byte before prevents that.
file_to_read.seek(start_offset - 1)
read_buffer = _TextSource.ReadBuffer('', 0)
sep_bounds = self._find_separator_bounds(file_to_read, read_buffer)
if not sep_bounds:
# Could not find a separator after (start_offset - 1). This means that
# none of the records within the file belongs to the current source.
return
_, sep_end = sep_bounds
read_buffer.data = read_buffer.data[sep_end:]
next_record_start_position = start_offset - 1 + sep_end
else:
next_record_start_position = position_after_skipping_header_lines
while range_tracker.try_claim(next_record_start_position):
record, num_bytes_to_next_record = self._read_record(file_to_read,
read_buffer)
# For compressed text files that use an unsplittable OffsetRangeTracker
# with infinity as the end position, above 'try_claim()' invocation
# would pass for an empty record at the end of file that is not
# followed by a new line character. Since such a record is at the last
# position of a file, it should not be a part of the considered range.
# We do this check to ignore such records.
if len(record) == 0 and num_bytes_to_next_record < 0: # pylint: disable=len-as-condition
break
# Record separator must be larger than zero bytes.
assert num_bytes_to_next_record != 0
if num_bytes_to_next_record > 0:
next_record_start_position += num_bytes_to_next_record
yield self._coder.decode(record)
if num_bytes_to_next_record < 0:
break
def _find_separator_bounds(self, file_to_read, read_buffer):
# Determines the start and end positions within 'read_buffer.data' of the
# next separator starting from position 'read_buffer.position'.
# Currently supports following separators.
# * '\n'
# * '\r\n'
# This method may increase the size of buffer but it will not decrease the
# size of it.
current_pos = read_buffer.position
while True:
if current_pos >= len(read_buffer.data):
# Ensuring that there are enough bytes to determine if there is a '\n'
# at current_pos.
if not self._try_to_ensure_num_bytes_in_buffer(
file_to_read, read_buffer, current_pos + 1):
return
# Using find() here is more efficient than a linear scan of the byte
# array.
next_lf = read_buffer.data.find('\n', current_pos)
if next_lf >= 0:
if next_lf > 0 and read_buffer.data[next_lf - 1] == '\r':
# Found a '\r\n'. Accepting that as the next separator.
return (next_lf - 1, next_lf + 1)
else:
# Found a '\n'. Accepting that as the next separator.
return (next_lf, next_lf + 1)
current_pos = len(read_buffer.data)
def _try_to_ensure_num_bytes_in_buffer(
self, file_to_read, read_buffer, num_bytes):
# Tries to ensure that there are at least num_bytes bytes in the buffer.
# Returns True if this can be fulfilled, returned False if this cannot be
# fulfilled due to reaching EOF.
while len(read_buffer.data) < num_bytes:
read_data = file_to_read.read(self._buffer_size)
if not read_data:
return False
read_buffer.data += read_data
return True
def _skip_lines(self, file_to_read, read_buffer, num_lines):
"""Skip num_lines from file_to_read, return num_lines+1 start position."""
if file_to_read.tell() > 0:
file_to_read.seek(0)
position = 0
for _ in range(num_lines):
_, num_bytes_to_next_record = self._read_record(file_to_read, read_buffer)
if num_bytes_to_next_record < 0:
# We reached end of file. It is OK to just break here
# because subsequent _read_record will return same result.
break
position += num_bytes_to_next_record
return position
def _read_record(self, file_to_read, read_buffer):
# Returns a tuple containing the current_record and number of bytes to the
# next record starting from 'read_buffer.position'. If EOF is
# reached, returns a tuple containing the current record and -1.
if read_buffer.position > self._buffer_size:
# read_buffer is too large. Truncating and adjusting it.
read_buffer.data = read_buffer.data[read_buffer.position:]
read_buffer.position = 0
record_start_position_in_buffer = read_buffer.position
sep_bounds = self._find_separator_bounds(file_to_read, read_buffer)
read_buffer.position = sep_bounds[1] if sep_bounds else len(
read_buffer.data)
if not sep_bounds:
# Reached EOF. Bytes up to the EOF is the next record. Returning '-1' for
# the starting position of the next record.
return (read_buffer.data[record_start_position_in_buffer:], -1)
if self._strip_trailing_newlines:
# Current record should not contain the separator.
return (read_buffer.data[record_start_position_in_buffer:sep_bounds[0]],
sep_bounds[1] - record_start_position_in_buffer)
else:
# Current record should contain the separator.
return (read_buffer.data[record_start_position_in_buffer:sep_bounds[1]],
sep_bounds[1] - record_start_position_in_buffer)
class _TextSink(filebasedsink.FileBasedSink):
"""A sink to a GCS or local text file or files."""
def __init__(self,
file_path_prefix,
file_name_suffix='',
append_trailing_newlines=True,
num_shards=0,
shard_name_template=None,
coder=coders.ToStringCoder(),
compression_type=CompressionTypes.AUTO,
header=None):
"""Initialize a _TextSink.
Args:
file_path_prefix: The file path to write to. The files written will begin
with this prefix, followed by a shard identifier (see num_shards), and
end in a common extension, if given by file_name_suffix. In most cases,
only this argument is specified and num_shards, shard_name_template, and
file_name_suffix use default values.
file_name_suffix: Suffix for the files written.
append_trailing_newlines: indicate whether this sink should write an
additional newline char after writing each element.
num_shards: The number of files (shards) used for output. If not set, the
service will decide on the optimal number of shards.
Constraining the number of shards is likely to reduce
the performance of a pipeline. Setting this value is not recommended
unless you require a specific number of output files.
shard_name_template: A template string containing placeholders for
the shard number and shard count. When constructing a filename for a
particular shard number, the upper-case letters 'S' and 'N' are
replaced with the 0-padded shard number and shard count respectively.
This argument can be '' in which case it behaves as if num_shards was
set to 1 and only one file will be generated. The default pattern used
is '-SSSSS-of-NNNNN' if None is passed as the shard_name_template.
coder: Coder used to encode each line.
compression_type: Used to handle compressed output files. Typical value
is CompressionTypes.AUTO, in which case the final file path's
extension (as determined by file_path_prefix, file_name_suffix,
num_shards and shard_name_template) will be used to detect the
compression.
header: String to write at beginning of file as a header. If not None and
append_trailing_newlines is set, '\n' will be added.
Returns:
A _TextSink object usable for writing.
"""
super(_TextSink, self).__init__(
file_path_prefix,
file_name_suffix=file_name_suffix,
num_shards=num_shards,
shard_name_template=shard_name_template,
coder=coder,
mime_type='text/plain',
compression_type=compression_type)
self._append_trailing_newlines = append_trailing_newlines
self._header = header
def open(self, temp_path):
file_handle = super(_TextSink, self).open(temp_path)
if self._header is not None:
file_handle.write(self._header)
if self._append_trailing_newlines:
file_handle.write('\n')
return file_handle
def display_data(self):
dd_parent = super(_TextSink, self).display_data()
dd_parent['append_newline'] = DisplayDataItem(
self._append_trailing_newlines,
label='Append Trailing New Lines')
return dd_parent
def write_encoded_record(self, file_handle, encoded_value):
"""Writes a single encoded record."""
file_handle.write(encoded_value)
if self._append_trailing_newlines:
file_handle.write('\n')
def _create_text_source(
file_pattern=None, min_bundle_size=None, compression_type=None,
strip_trailing_newlines=None, coder=None, skip_header_lines=None):
return _TextSource(
file_pattern=file_pattern, min_bundle_size=min_bundle_size,
compression_type=compression_type,
strip_trailing_newlines=strip_trailing_newlines,
coder=coder, validate=False, skip_header_lines=skip_header_lines)
class ReadAllFromText(PTransform):
"""A ``PTransform`` for reading a ``PCollection`` of text files.
Reads a ``PCollection`` of text files or file patterns and and produces a
``PCollection`` of strings.
Parses a text file as newline-delimited elements, by default assuming
UTF-8 encoding. Supports newline delimiters '\\n' and '\\r\\n'.
This implementation only supports reading text encoded using UTF-8 or ASCII.
This does not support other encodings such as UTF-16 or UTF-32.
"""
DEFAULT_DESIRED_BUNDLE_SIZE = 64 * 1024 * 1024 # 64MB
def __init__(
self,
min_bundle_size=0,
desired_bundle_size=DEFAULT_DESIRED_BUNDLE_SIZE,
compression_type=CompressionTypes.AUTO,
strip_trailing_newlines=True,
coder=coders.StrUtf8Coder(),
skip_header_lines=0,
**kwargs):
"""Initialize the ``ReadAllFromText`` transform.
Args:
min_bundle_size: Minimum size of bundles that should be generated when
splitting this source into bundles. See ``FileBasedSource`` for more
details.
desired_bundle_size: Desired size of bundles that should be generated when
splitting this source into bundles. See ``FileBasedSource`` for more
details.
compression_type: Used to handle compressed input files. Typical value
is ``CompressionTypes.AUTO``, in which case the underlying file_path's
extension will be used to detect the compression.
strip_trailing_newlines: Indicates whether this source should remove
the newline char in each line it reads before decoding that line.
validate: flag to verify that the files exist during the pipeline
creation time.
skip_header_lines: Number of header lines to skip. Same number is skipped
from each source file. Must be 0 or higher. Large number of skipped
lines might impact performance.
coder: Coder used to decode each line.
"""
super(ReadAllFromText, self).__init__(**kwargs)
source_from_file = partial(
_create_text_source, min_bundle_size=min_bundle_size,
compression_type=compression_type,
strip_trailing_newlines=strip_trailing_newlines, coder=coder,
skip_header_lines=skip_header_lines)
self._desired_bundle_size = desired_bundle_size
self._min_bundle_size = min_bundle_size
self._compression_type = compression_type
self._read_all_files = ReadAllFiles(
True, compression_type, desired_bundle_size, min_bundle_size,
source_from_file)
def expand(self, pvalue):
return pvalue | 'ReadAllFiles' >> self._read_all_files
class ReadFromText(PTransform):
r"""A :class:`~apache_beam.transforms.ptransform.PTransform` for reading text
files.
Parses a text file as newline-delimited elements, by default assuming
``UTF-8`` encoding. Supports newline delimiters ``\n`` and ``\r\n``.
This implementation only supports reading text encoded using ``UTF-8`` or
``ASCII``.
This does not support other encodings such as ``UTF-16`` or ``UTF-32``.
"""
def __init__(
self,
file_pattern=None,
min_bundle_size=0,
compression_type=CompressionTypes.AUTO,
strip_trailing_newlines=True,
coder=coders.StrUtf8Coder(),
validate=True,
skip_header_lines=0,
**kwargs):
"""Initialize the :class:`ReadFromText` transform.
Args:
file_pattern (str): The file path to read from as a local file path or a
GCS ``gs://`` path. The path can contain glob characters
(``*``, ``?``, and ``[...]`` sets).
min_bundle_size (int): Minimum size of bundles that should be generated
when splitting this source into bundles. See
:class:`~apache_beam.io.filebasedsource.FileBasedSource` for more
details.
compression_type (str): Used to handle compressed input files.
Typical value is :attr:`CompressionTypes.AUTO
<apache_beam.io.filesystem.CompressionTypes.AUTO>`, in which case the
underlying file_path's extension will be used to detect the compression.
strip_trailing_newlines (bool): Indicates whether this source should
remove the newline char in each line it reads before decoding that line.
validate (bool): flag to verify that the files exist during the pipeline
creation time.
skip_header_lines (int): Number of header lines to skip. Same number is
skipped from each source file. Must be 0 or higher. Large number of
skipped lines might impact performance.
coder (~apache_beam.coders.coders.Coder): Coder used to decode each line.
"""
super(ReadFromText, self).__init__(**kwargs)
self._source = _TextSource(
file_pattern, min_bundle_size, compression_type,
strip_trailing_newlines, coder, validate=validate,
skip_header_lines=skip_header_lines)
def expand(self, pvalue):
return pvalue.pipeline | Read(self._source)
class WriteToText(PTransform):
"""A :class:`~apache_beam.transforms.ptransform.PTransform` for writing to
text files."""
def __init__(
self,
file_path_prefix,
file_name_suffix='',
append_trailing_newlines=True,
num_shards=0,
shard_name_template=None,
coder=coders.ToStringCoder(),
compression_type=CompressionTypes.AUTO,
header=None):
r"""Initialize a :class:`WriteToText` transform.
Args:
file_path_prefix (str): The file path to write to. The files written will
begin with this prefix, followed by a shard identifier (see
**num_shards**), and end in a common extension, if given by
**file_name_suffix**. In most cases, only this argument is specified and
**num_shards**, **shard_name_template**, and **file_name_suffix** use
default values.
file_name_suffix (str): Suffix for the files written.
append_trailing_newlines (bool): indicate whether this sink should write
an additional newline char after writing each element.
num_shards (int): The number of files (shards) used for output.
If not set, the service will decide on the optimal number of shards.
Constraining the number of shards is likely to reduce
the performance of a pipeline. Setting this value is not recommended
unless you require a specific number of output files.
shard_name_template (str): A template string containing placeholders for
the shard number and shard count. Currently only ``''`` and
``'-SSSSS-of-NNNNN'`` are patterns accepted by the service.
When constructing a filename for a particular shard number, the
upper-case letters ``S`` and ``N`` are replaced with the ``0``-padded
shard number and shard count respectively. This argument can be ``''``
in which case it behaves as if num_shards was set to 1 and only one file
will be generated. The default pattern used is ``'-SSSSS-of-NNNNN'``.
coder (~apache_beam.coders.coders.Coder): Coder used to encode each line.
compression_type (str): Used to handle compressed output files.
Typical value is :class:`CompressionTypes.AUTO
<apache_beam.io.filesystem.CompressionTypes.AUTO>`, in which case the
final file path's extension (as determined by **file_path_prefix**,
**file_name_suffix**, **num_shards** and **shard_name_template**) will
be used to detect the compression.
header (str): String to write at beginning of file as a header.
If not :data:`None` and **append_trailing_newlines** is set, ``\n`` will
be added.
"""
self._sink = _TextSink(file_path_prefix, file_name_suffix,
append_trailing_newlines, num_shards,
shard_name_template, coder, compression_type, header)
def expand(self, pcoll):
return pcoll | Write(self._sink)
|
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