Instruction stringlengths 362 7.83k | output_code stringlengths 1 945 |
|---|---|
Given the code snippet: <|code_start|> my_env = os.environ.copy()
my_env["OCF_ROOT"] = config.path.ocf_root
for k, v in params.items():
my_env["OCF_RESKEY_" + k] = v
cmd = [os.path.join(config.path.ocf_root, "resource.d", agent.ra_provider, agent.ra_type), "validate-all"]
if options.regressio... | op stop timeout=100 \ |
Predict the next line after this snippet: <|code_start|> if msg.startswith("ERROR: "):
logger.error(msg[7:])
elif msg.startswith("WARNING: "):
logger.warning(msg[9:])
elif msg.startswith("INFO: "):
logger.info(msg[6:])
elif m... | primitive {id} ocf:heartbeat:LVM-activate \ |
Continue the code snippet: <|code_start|> print(".EXT", " ".join(cmd))
p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, env=my_env)
_out, _ = p.communicate()
out = to_ascii(_out)
p.wait()
if log is True:
for msg in out.splitlines():
if msg.start... | op start timeout=60 \ |
Given the code snippet: <|code_start|> 'release': release,
'version': version,
'machine': machine,
'processor': processor,
'distname': distname,
'user': get_user(),
'hostname': hostname,
'uptime': uptime[0],
'idle... | '/etc/csync2/key_hagroup', |
Using the snippet: <|code_start|># Copyright (C) 2013 Dejan Muhamedagic <dmuhamedagic@suse.de>
# See COPYING for license information.
logger = log.setup_logger(__name__)
# graphviz stuff
def _attr_str(attr_d):
return ','.join(['%s="%s"' % (k, v)
for k, v in attr_d.items()])
<|code_end|>
... | def _quoted(name): |
Given snippet: <|code_start|> #print out
out = self._parse('location loc-1 thing rule role=slave -inf: #uname eq madrid')
self.assertEqual(out.get('id'), 'loc-1')
self.assertEqual(out.get('rsc'), 'thing')
self.assertEqual(out.get('score'), None)
out = self._parse('locati... | out = self._parse('colocation col-1 10: ( bar wiz') |
Using the snippet: <|code_start|> #self.assertTrue(['sequential', 'false'] in out.resources[0][1])
self.assertEqual(out.get('id'), 'o1')
out = self._parse('order o1 Mandatory: A B C sequential=true')
self.assertEqual(1, len(out.xpath('/rsc_order/resource_set')))
#self.assertTrue(... | out = self._parse(inp) |
Based on the snippet: <|code_start|>
@mock.patch('logging.Logger.error')
def test_acl(self, mock_error):
out = self._parse('role user-1 error')
self.assertFalse(out)
out = self._parse('user user-1 role:user-1')
self.assertNotEqual(out, False)
out = self._parse("role bigd... | self.assertEqual(['a', 'b', 'c'], out.xpath('./role/@id')) |
Here is a snippet: <|code_start|> "ptest": "ptest",
"simulate": "crm_simulate",
}
meta_progs = ("crmd", "pengine", "stonithd", "cib")
meta_progs_20 = ("pacemaker-controld", "pacemaker-schedulerd", "pacemaker-fenced", "pacemaker-based")
# elide these properties from tab completion
crmd_metadata_do_not_complete ... | Default:1 |
Given the code snippet: <|code_start|> followed by some {{foo}}.{{wiz}}
and then some at the end"""
assert """Here's a line of text
followed by another line
followed by some a.b
and then some at the end""" == handles.parse(t, {'foo': "a", 'wiz': "b"})
def test_weird_chars():
t = "{{foo#_bar... | def test_result(): |
Here is a snippet: <|code_start|># Copyright (C) 2008-2011 Dejan Muhamedagic <dmuhamedagic@suse.de>
# Copyright (C) 2013-2016 Kristoffer Gronlund <kgronlund@suse.com>
# See COPYING for license information.
logger = log.setup_logger(__name__)
logger_utils = log.LoggerUtils(logger)
_NVPAIR_RE = re.compile(r'([^=@$][... | _RESOURCE_RE = re.compile(r'([a-z_#$][^=]*)$', re.IGNORECASE) |
Here is a snippet: <|code_start|># Copyright (C) 2008-2011 Dejan Muhamedagic <dmuhamedagic@suse.de>
# Copyright (C) 2013-2016 Kristoffer Gronlund <kgronlund@suse.com>
# See COPYING for license information.
logger = log.setup_logger(__name__)
logger_utils = log.LoggerUtils(logger)
_NVPAIR_RE = re.compile(r'([^=@$][... | _ALERT_PATH_RE = re.compile(r'(.*)$') |
Here is a snippet: <|code_start|># Copyright (C) 2008-2011 Dejan Muhamedagic <dmuhamedagic@suse.de>
# Copyright (C) 2013-2016 Kristoffer Gronlund <kgronlund@suse.com>
# See COPYING for license information.
logger = log.setup_logger(__name__)
logger_utils = log.LoggerUtils(logger)
_NVPAIR_RE = re.compile(r'([^=@$][... | _RESOURCE_RE = re.compile(r'([a-z_#$][^=]*)$', re.IGNORECASE) |
Next line prediction: <|code_start|> @mock.patch('crmsh.crash_test.utils.crmshutils.get_stdout_stderr')
def test_check_node_status(self, mock_run, mock_error):
output = """
1084783297 15sp2-1 member
1084783193 15sp2-2 lost
"""
mock_run.return_value = (0, output, None)
res = utils... | * Online: [ 15sp2-1 15sp2-2 ] |
Using the snippet: <|code_start|> err_output = """
==Dumping header on disk {}
==Header on disk {} NOT dumped
sbd failed; please check the logs.
""".format(dev, dev)
mock_os_path_exists.return_value = True
mock_sbd_check_header.return_value = (1, "==Dumping header on disk {}".format(dev),
... | Timeout (loop) : 1 |
Based on the snippet: <|code_start|>
def login_required(view_callable):
def check_login(request, *args, **kwargs):
if request.user.is_authenticated:
return view_callable(request, *args, **kwargs)
assert hasattr(request, 'session'), "Session middleware needed."
login_kwargs =... | return login(request, **login_kwargs) |
Next line prediction: <|code_start|># -*- coding: utf-8 -*-
USER_AGENT = (
'FeedHQ/%s (https://github.com/feedhq/feedhq; %%s; https://github.com/'
'feedhq/feedhq/wiki/fetcher; like FeedFetcher-Google)'
) % __version__
FAVICON_FETCHER = USER_AGENT % 'favicon fetcher'
LINK_CHECKER = USER_AGENT % 'ping'
<|co... | def is_feed(parsed): |
Here is a snippet: <|code_start|> FONT_PT_SANS = 'pt-sans'
FONT_UBUNTU_CONDENSED = 'ubuntu-condensed'
FONT_SOURCE_SANS_PRO = 'source-sans-pro'
FONTS = (
(
_('Serif'), (
(FONT_DROID_SERIF, 'Droid Serif'),
(FONT_GENTIUM_BASIC, 'Gentium Basic'),
... | is_staff = models.BooleanField(default=False) |
Predict the next line for this snippet: <|code_start|>
logger = get_logger(__name__)
def sentry_handler(job, *exc_info):
extra = {
<|code_end|>
with the help of current file imports:
import os
from raven import Client
from rq import Connection, Queue, Worker
from structlog import get_logger
from . import Sent... | 'job_id': job.id, |
Given the code snippet: <|code_start|>
logger = structlog.get_logger(__name__)
class Command(SentryCommand):
def handle_sentry(self, **options):
r = get_redis_connection()
prefix = 'rq:job:'
keys = (
"".join(chars) for chars in product('0123456789abcdef', repeat=1)
)
... | ) |
Predict the next line after this snippet: <|code_start|>
admin.autodiscover()
urlpatterns = [
url(r'^admin/rq/', include('django_rq_dashboard.urls')),
url(r'^admin/', admin.site.urls),
url(r'^subscriber/', include('django_push.subscriber.urls')),
url(r'^health/$', views.health, name='health'),
url... | level_overrides=settings.LOG_LEVEL_OVERRIDES, |
Here is a snippet: <|code_start|># -*- coding: utf-8 -*-
monkey.patch_html5lib()
monkey.patch_feedparser()
admin.autodiscover()
urlpatterns = [
url(r'^admin/rq/', include('django_rq_dashboard.urls')),
url(r'^admin/', admin.site.urls),
url(r'^subscriber/', include('django_push.subscriber.urls')),
ur... | url(r'^', include(('feedhq.feeds.urls', 'feeds'), namespace='feeds')), |
Predict the next line after this snippet: <|code_start|> User.query.filter_by(sid=sid).delete()
db.session.commit()
def user_from_sid(sid):
return User.query.filter_by(sid=sid).first()
def graph_all_posts():
posts = Post.query.all()
d = min(p.posted_date for p in posts)
while d < date.today():
... | print(e.posted_date) |
Next line prediction: <|code_start|>
def delete_user(sid):
Post.query.filter_by(user_sid=sid).delete()
User.query.filter_by(sid=sid).delete()
db.session.commit()
def user_from_sid(sid):
return User.query.filter_by(sid=sid).first()
def graph_all_posts():
posts = Post.query.all()
d = min(p.poste... | upairs = users.items() |
Given the following code snippet before the placeholder: <|code_start|>
def delete_user(sid):
Post.query.filter_by(user_sid=sid).delete()
User.query.filter_by(sid=sid).delete()
db.session.commit()
def user_from_sid(sid):
return User.query.filter_by(sid=sid).first()
def graph_all_posts():
posts = P... | upairs.sort() |
Based on the snippet: <|code_start|># -*- coding: utf-8 -*-
from __future__ import unicode_literals
class MockParser(object):
def __init__(self, main_sheet, sub_sheets):
self.main_sheet = Sheet(main_sheet)
self.sub_sheets = {k: Sheet(v) for k, v in sub_sheets.items()}
def test_spreadsheetoupu... | ), |
Predict the next line after this snippet: <|code_start|>
def child_to_xml(parent_el, tagname, child, toplevel=False, nsmap=None):
if hasattr(child, "items"):
child_el = dict_to_xml(child, tagname, toplevel=False, nsmap=nsmap)
if child_el is not None:
parent_el.append(child_el)
else:... | def dict_to_xml(data, tagname, toplevel=True, nsmap=None): |
Here is a snippet: <|code_start|> USING_LXML = True
# Note that lxml is now "required" - it's listed as a requirement in
# setup.py and is needed for the tests to pass.
# However, stdlib etree still exists as an unsupported feature.
except ImportError:
USING_LXML = False
warn("Using stdlib etree... | if USING_LXML and ":" in attr_name: |
Here is a snippet: <|code_start|>
# 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... | 'footerbgcolor' : '#183828', |
Here is a snippet: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
data = generate_data(200) # np.random.randint(2, size=(1... | initial_vmap = { rbm.v: T.matrix('v') } |
Predict the next line after this snippet: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
data = generate_data(200) # np.ra... | umap = {} |
Using the snippet: <|code_start|> # mean_field_for_stats is a list of units for which 'mean_field' should be used to compute statistics, rather than 'sample'.
# complete units lists
visible_units = rbm.complete_units_list(visible_units)
hidden_units = rbm.complete_units_list(hidden_units)
context_un... | v1_in_vmap.update(context_vmap) # add context |
Here is a snippet: <|code_start|>emodel_train_so_far = []
edata_so_far = []
emodel_so_far = []
for epoch in range(epochs):
monitoring_data_train = [(cost, energy_data, energy_model) for cost, energy_data, energy_model in train({ rbm.v: train_set_x })]
mses_train, edata_train_list, emodel_train_list = zip(*moni... | plt.legend() |
Continue the code snippet: <|code_start|>precision_variables = [rbm.Wp.var, rbm.bvp.var]
umap = {}
for var in variables:
pu = var + (learning_rate/mb_size) * updaters.CDUpdater(rbm, var, s) # the learning rate is 0.001
if var in precision_variables:
pu = updaters.BoundUpdater(pu, bound=0, type='upper')... | plt.imshow(d.reshape((28,28)), interpolation='gaussian') |
Here is a snippet: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# load data
print ">> Loading dataset..."
f = gzip.open('datasets/mnist.... | n_visible = train_set_x.shape[1] |
Predict the next line for this snippet: <|code_start|>edata_train_so_far = []
emodel_train_so_far = []
edata_so_far = []
emodel_so_far = []
for epoch in range(epochs):
monitoring_data_train = [(cost, energy_data, energy_model) for cost, energy_data, energy_model in train({ rbm.v: train_set_x })]
mses_train, ed... | plt.title("MSE") |
Predict the next line for this snippet: <|code_start|>print ">> Training for %d epochs..." % epochs
mses_train_so_far = []
mses_valid_so_far = []
edata_train_so_far = []
emodel_train_so_far = []
edata_so_far = []
emodel_so_far = []
for epoch in range(epochs):
monitoring_data_train = [(cost, energy_data, energy_mo... | plt.figure(1) |
Given the following code snippet before the placeholder: <|code_start|>
def sample_evolution(start, ns=100): # start = start data
sample = t.compile_function(initial_vmap, mb_size=1, monitors=[m_model], name='evaluate', train=False, mode=mode)
data = start
plot_data(data)
while True:
... | edata_train_so_far = [] |
Given the following code snippet before the placeholder: <|code_start|> sample = t.compile_function(initial_vmap, mb_size=1, monitors=[m_model], name='evaluate', train=False, mode=mode)
data = start
plot_data(data)
while True:
for k in range(ns):
for x in sample({ rbm.v: da... | edata_so_far = [] |
Here is a snippet: <|code_start|> plt.figure(1)
plt.clf()
plt.plot(mses_train_so_far, label='train')
plt.plot(mses_valid_so_far, label='validation')
plt.title("MSE")
plt.legend()
plt.draw()
plt.figure(4)
plt.clf()
plt.plot(edata_so_far, label='validation / data')
plt.plot... | print "training set: MSE = %.6f, data energy = %.2f, model energy = %.2f" % (mse_train, edata_train, emodel_train) |
Predict the next line after this snippet: <|code_start|> sample = t.compile_function(initial_vmap, mb_size=1, monitors=[m_model], name='evaluate', train=False, mode=mode)
data = start
plot_data(data)
while True:
for k in range(ns):
for x in sample({ rbm.v: data }): # draw a... | edata_so_far = [] |
Predict the next line for this snippet: <|code_start|>
# TRAINING
print ">> Training for %d epochs..." % epochs
mses_train_so_far = []
mses_valid_so_far = []
edata_train_so_far = []
emodel_train_so_far = []
edata_so_far = []
emodel_so_far = []
for epoch in range(epochs):
monitoring_data_train = [(cost, ... | edata_valid = np.mean(edata) |
Using the snippet: <|code_start|>
# tensordot = T.tensordot # use theano implementation
class FixedBiasParameters(Parameters):
# Bias fixed at -1, which is useful for some energy functions (like Gaussian with fixed variance, Beta)
def __init__(self, rbm, units, name=None):
super(FixedBiasParameters, s... | self.terms[self.hu] = lambda vmap: T.dot(vmap[self.vu], W) |
Continue the code snippet: <|code_start|># train = t.compile_function(initial_vmap, mb_size=32, monitors=[m], name='train', mode=mode)
train = t.compile_function(initial_vmap, mb_size=100, monitors=[m, m_model], name='train', mode=mode)
evaluate = t.compile_function(initial_vmap, mb_size=100, monitors=[m, m_model], nam... | epochs = 200 |
Given the code snippet: <|code_start|> sample = t.compile_function(initial_vmap, mb_size=1, monitors=[m_model], name='evaluate', train=False, mode=mode)
data = start
plot_data(data)
while True:
for k in range(ns):
for x in sample({ rbm.v: data }): # draw a new sample
... | for epoch in range(epochs): |
Using the snippet: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# load data
print ">> Loading dataset..."
f = gzip.open('datasets/mnist... | train_set_x, train_set_y = train_set |
Predict the next line for this snippet: <|code_start|>
def sample_evolution(start, ns=100): # start = start data
sample = t.compile_function(initial_vmap, mb_size=1, monitors=[m_model], name='evaluate', train=False, mode=mode)
data = start
plot_data(data)
while True:
for k in range... | mses_train_so_far = [] |
Here is a snippet: <|code_start|>
def sample_evolution(start, ns=100): # start = start data
sample = t.compile_function(initial_vmap, mb_size=1, monitors=[m_model], name='evaluate', train=False, mode=mode)
data = start
plot_data(data)
while True:
for k in range(ns):
for x ... | mact_valid_so_far = [] |
Predict the next line for this snippet: <|code_start|>
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# load data
print ">> Loading dataset..."
f = gzip.open('dataset... | learning_rate = 0.1 |
Next line prediction: <|code_start|>valid_set_x, valid_set_y = valid_set
test_set_x, test_set_y = test_set
# TODO DEBUG
# train_set_x = train_set_x[:10000]
valid_set_x = valid_set_x[:1000]
n_visible = train_set_x.shape[1]
n_hidden = 500
mb_size = 20
k = 15
learning_rate = 0.1
epochs = 15
print ">> Constructing RB... | umap[var] = pu |
Based on the snippet: <|code_start|>edata_so_far = []
emodel_so_far = []
for epoch in range(epochs):
monitoring_data_train = [(cost, energy_data, energy_model) for cost, energy_data, energy_model in train({ rbm.v: train_set_x })]
mses_train, edata_train_list, emodel_train_list = zip(*monitoring_data_train)
... | plt.draw() |
Given the code snippet: <|code_start|>m_model = s['model'][rbm.v]
e_data = rbm.energy(s['data']).mean()
e_model = rbm.energy(s['model']).mean()
# train = t.compile_function(initial_vmap, mb_size=32, monitors=[m], name='train', mode=mode)
train = t.compile_function(initial_vmap, mb_size=mb_size, monitors=[m, e_data, e_... | data = x[0] |
Predict the next line for this snippet: <|code_start|> edata_train = np.mean(edata_train_list)
emodel_train = np.mean(emodel_train_list)
monitoring_data = [(cost, data, model, energy_data, energy_model) for cost, data, model, energy_data, energy_model in evaluate({ rbm.v: valid_set_x })]
mses_valid,... | plt.plot(emodel_train_so_far, label='train / model') |
Given the code snippet: <|code_start|>
class SelfUpdater(Updater):
def get_update(self):
return self.variable
DecayUpdater = SelfUpdater
# weight decay: the update == the parameter values themselves
# (decay constant is taken care of by ScaleUpdater)
class MomentumUpdater(Updater):
<|code... | def __init__(self, pu, momentum, variable_shape): |
Given the following code snippet before the placeholder: <|code_start|>mode = None
# generate data
data = generate_data(200)
# use the predefined binary-binary RBM, which has visible units (rbm.v), hidden units (rbm.h),
# a weight matrix W connecting them (rbm.W), and visible and hidden biases (rbm.bv and rbm.bh).
n_... | for epoch in range(epochs): |
Given snippet: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
data = generate_data(200)
# use the predefined binary-binary... | n_visible = data.shape[1] |
Predict the next line after this snippet: <|code_start|>
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
data = generate_data(200)
# use the predefined binary-binary ... | epochs = 50 |
Given the code snippet: <|code_start|>data = generate_data(200)
# use the predefined binary-binary RBM, which has visible units (rbm.v), hidden units (rbm.h),
# a weight matrix W connecting them (rbm.W), and visible and hidden biases (rbm.bv and rbm.bh).
n_visible = data.shape[1]
n_hidden = 100
rbm = rbms.BinaryBinary... | print "MSE = %.4f" % np.mean(costs) |
Based on the snippet: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
data = generate_data(200)
# use the predefined binary... | for var in rbm.variables: |
Here is a snippet: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
data = generate_data(200)
# use the predefined binary-bi... | initial_vmap = { rbm.v: T.matrix('v') } |
Predict the next line for this snippet: <|code_start|># mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
data = generate_data(200)
# use the predefined binary-binary RBM... | epochs = 50 |
Next line prediction: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
print ">> Generating dataset..."
data = generate_data(... | n_context = data_context.shape[1] |
Given the following code snippet before the placeholder: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
print ">> Generatin... | data_train = data[:-1000, :] |
Given snippet: <|code_start|># mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
print ">> Generating dataset..."
data = generate_data(1000) # np.random.randint(2, size=(10000, n_visible))
data_context = get_context(data)
data_train = data[:-1000, :]
dat... | umap[var] = pu |
Predict the next line after this snippet: <|code_start|>
plt.ion()
# DEBUGGING
# mode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
# mode = theano.compile.DebugMode(check_py_code=False, require_matching_strides=False)
mode = None
# generate data
print ">> Generating dataset..."
d... | data_train = data[:-1000, :] |
Based on the snippet: <|code_start|>
loop = get_loop()
@asyncio.coroutine
def run(cmd, **kwargs):
transport, protocol = yield from async_execute_process(
create_protocol(), cmd, **kwargs)
<|code_end|>
, predict the immediate next line with the help of imports:
from osrf_pycommon.process_utils import asy... | retcode = yield from protocol.complete |
Given snippet: <|code_start|># 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 exp... | stderr_master, stderr_slave = pty.openpty() |
Based on the snippet: <|code_start|># Copyright 2014 Open Source Robotics Foundation, 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
#... | stderr_master, stderr_slave = None, None |
Using the snippet: <|code_start|>
class BatchJob:
def __init__(self, *, python_interpreter=None):
self.run = run
self.run_history = []
self.python = sys.executable if python_interpreter is None else python_interpreter
self.python_history = []
def pre(self):
raise NotI... | raise RuntimeError("Called pop_run with an empty run history.") |
Predict the next line for this snippet: <|code_start|># Copyright 2014 Open Source Robotics Foundation, 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/lice... | def execute_process(cmd, cwd=None, env=None, shell=False, emulate_tty=False): |
Predict the next line for this snippet: <|code_start|>
def _pack_attrs(foreground, background, style):
return foreground + (background * 16) + style
def _win_reset(handle, attrs):
SetConsoleTextAttribute(handle, attrs)
return attrs
def _win_style(style, handle, attrs):
attrs_list = _unpack_attrs(at... | attrs = _pack_attrs(*attrs_list) |
Given the code snippet: <|code_start|>#!/usr/bin/env python3
# Copyright 2015 Open Source Robotics Foundation, 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.o... | "ros2_batch_job was imported from somewhere other than the local directory of this script" |
Predict the next line for this snippet: <|code_start|> ):
loop = get_loop()
# Create the PTY's
stdout_master, stdout_slave = pty.openpty()
if stderr_to_stdout:
stderr_master, stderr_slave = stdout_master, stdout_slave
else:
stderr_master, stderr_slave =... | def connection_made(self, transport): |
Continue the code snippet: <|code_start|> # create an archive
folder_name = 'ros2-' + args.os
if args.os == 'linux' or args.os == 'osx':
if args.os == 'osx':
machine = platform.machine()
else:
machine = sys.implementation._multiarch.split('-', 1)[0]
archive_pat... | else: |
Given the code snippet: <|code_start|>#!/usr/bin/python3
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation; either version 3 of the License, or (at your option) any
# later version. See ht... | @unittest.skipUnless(have_nmcli, 'nmcli not installed') |
Based on the snippet: <|code_start|>#!/usr/bin/python3
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation; either version 3 of the License, or (at your option) any
# later version. See http... | @unittest.skipUnless(have_nmcli, 'nmcli not installed') |
Given snippet: <|code_start|>#!/usr/bin/python3
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation; either version 3 of the License, or (at your option) any
# later version. See http://www.... | @unittest.skipUnless(have_nmcli, 'nmcli not installed') |
Here is a snippet: <|code_start|>###############################################################################
#
# apogee.tools.download: download APOGEE data files
#
###############################################################################
_DR10_URL= 'http://data.sdss3.org/sas/dr10'
_DR12_URL= 'http://data.s... | def allStar(dr=None,lite=False,mjd=58104): |
Using the snippet: <|code_start|>###############################################################################
# apogee.spec.lsf: Utilities to work with APOGEE LSFs
###############################################################################
try:
fitsread = fitsio.read
except ImportError:
<|code_end|>
, determ... | fitsread= pyfits.getdata |
Given snippet: <|code_start|>###############################################################################
# apogee.spec.lsf: Utilities to work with APOGEE LSFs
###############################################################################
try:
fitsread = fitsio.read
except ImportError:
fitsread= pyfits.getd... | lsf=None,xlsf=None,dxlsf=None,fiber='combo', |
Using the snippet: <|code_start|># coding: utf-8
from __future__ import division, print_function, unicode_literals, \
absolute_import
class GeneratorTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data = np.random.rand(100, 3) * 10 - 5
cls.df = pd.DataFrame(cls.data, co... | np.testing.assert_array_equal(np.sum(self.data, axis=1), |
Predict the next line after this snippet: <|code_start|> results = self.generator.describe(self.df)
np.testing.assert_array_equal(np.sin(self.data),
results[["sin x", "sin y", "sin z"]])
np.testing.assert_array_equal(np.sum(self.data, axis=1),
... | self.assertAlmostEqual(df.iloc[0]["exp 8c-Z"], np.exp(3)) |
Using the snippet: <|code_start|># coding: utf-8
from __future__ import division, print_function, unicode_literals, \
absolute_import
class GeneratorTest(unittest.TestCase):
<|code_end|>
, determine the next line of code. You have imports:
import unittest
import os
import json
import numpy as np
import pand... | @classmethod |
Continue the code snippet: <|code_start|>
file_path = os.path.dirname(__file__)
def test_func():
return 1
class TestMetrics(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.x1 = np.array([1, 2, 3])
cls.x2 = np.array([4, 5, 6])
cls.x3 = np.array([1, 1, 1])
cls.x4 ... | def test_mse(self): |
Here is a snippet: <|code_start|>
file_path = os.path.dirname(__file__)
def test_func():
return 1
class TestMetrics(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.x1 = np.array([1, 2, 3])
<|code_end|>
. Write the next line using the current file imports:
import unittest
import numpy ... | cls.x2 = np.array([4, 5, 6]) |
Given the following code snippet before the placeholder: <|code_start|>
file_path = os.path.dirname(__file__)
def test_func():
return 1
class TestMetrics(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.x1 = np.array([1, 2, 3])
cls.x2 = np.array([4, 5, 6])
cls.x3 = np.ar... | def test_mse(self): |
Given snippet: <|code_start|>
def test_func():
return 1
class DummyClass:
def __init__(self):
self.name = 'dummy'
def get_config(self):
return {"config": "Dummyclass config"}
class TestGeneralUtil(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.x1 = np.array([1,... | "config": {}}, module_objects=globals()), DummyClass) |
Predict the next line for this snippet: <|code_start|>
# now the kernels are defined as functions
# For future development the kernel should be class
# with tunable parameters, this is particular useful for Bayesian methods
def rbf(x1, x2, sigma):
d_squared = np.sum((x1[:, None, :] - x2[None, :, :]) ** 2, axis=2)
... | 'metric function identifier:', identifier) |
Predict the next line for this snippet: <|code_start|> for d in self.test_pool:
self.test_structures.append(d['structure'])
self.test_energies.append(d['outputs']['energy'])
self.test_forces.append(d['outputs']['forces'])
self.test_stresses.append(d['outputs']['vir... | for stress1, stress2 in zip(test_stresses, np.array(self.test_stresses).ravel()): |
Next line prediction: <|code_start|># coding: utf-8
# Copyright (c) Materials Virtual Lab
# Distributed under the terms of the BSD License.
from __future__ import division, print_function, unicode_literals, \
absolute_import
CWD = os.getcwd()
test_datapool = loadfn(os.path.join(os.path.dirname(__file__), 'datap... | self.test_stresses = [] |
Here is a snippet: <|code_start|> :return: list, a list of species string
"""
return [i.specie.name for i in self.structure if i.specie.name in self.species_map.values()]
def copy(self):
"""
Copy a new StateStructure
:return: StateStructure
"""
return ... | def append(self, state_dict): |
Here is a snippet: <|code_start|> def to_specie_list(self):
"""
Convert the spin list to species list using the species_map
:return: list, a list of species string
"""
return [i.specie.name for i in self.structure if i.specie.name in self.species_map.values()]
def copy(se... | self.length = 0 |
Here is a snippet: <|code_start|>
file_path = os.path.dirname(__file__)
def test_func():
return 1
class TestKernel(unittest.TestCase):
@classmethod
<|code_end|>
. Write the next line using the current file imports:
import unittest
import numpy as np
import os
from veidt.kernel import rbf, get_kernel
and c... | def setUpClass(cls): |
Given the following code snippet before the placeholder: <|code_start|>
file_path = os.path.dirname(__file__)
def test_func():
return 1
class TestKernel(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.x1 = np.array([[1, 2], [1, 2]])
cls.x2 = np.array([[2, 3], [2, 3]])
c... | self.assertAlmostEqual(np.sum(test_dict).item(), |
Here is a snippet: <|code_start|>
class TestMultiSpeciesNN(unittest.TestCase):
def test_create_atomic_nn(self):
keras_input = Input(shape=(None, 3))
keras_output = create_atomic_nn(keras_input, [3, 10, 1])
model = Model(inputs=keras_input, outputs=keras_output)
model.compile(loss='m... | model.fit(x, y, epochs=100, verbose=False) |
Continue the code snippet: <|code_start|>
class TestMultiSpeciesNN(unittest.TestCase):
def test_create_atomic_nn(self):
keras_input = Input(shape=(None, 3))
keras_output = create_atomic_nn(keras_input, [3, 10, 1])
model = Model(inputs=keras_input, outputs=keras_output)
model.compile... | model.fit(features, outputs, epochs=100, verbose=0) |
Next line prediction: <|code_start|>
def binary_accuracy(y_true, y_pred):
return np.mean(np.array(y_true).ravel() == np.array(y_pred).ravel())
mse = MSE = mean_squared_error
mae = MAE = mean_absolute_error
def serialize(metric):
return serialize_veidt_object(metric)
def deserialize(config):
return d... | return identifier |
Using the snippet: <|code_start|> self.model.fit(x, y)
return self
def predict(self, x):
return self.model.predict(x)
class TestDescrber(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.dd = DummyDescriber()
def test_fit(self):
dd2 = self.dd.fit([1, 2... | def setUpClass(cls): |
Next line prediction: <|code_start|> def predict(self, x):
return self.model.predict(x)
class TestDescrber(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.dd = DummyDescriber()
def test_fit(self):
dd2 = self.dd.fit([1, 2, 3])
self.assertEqual(dd2, self.dd)
... | def test_fit(self): |
Given snippet: <|code_start|># Generated by Django 2.2.8 on 2019-12-23 18:00
def synopsis(pr, make_searchable=False):
self = pr
def verbosify(val, units=None, pre=None, pre_whitespace=True, post=None, post_whitespace=True):
elaborated = ""
if val is not None and val != '':
<|code_end|>
, con... | try: |
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