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def DEFINE_spaceseplist(name, default, help, flag_values=FLAGS, **args): parser = WhitespaceSeparatedListParser() serializer = ListSerializer(' ') DEFINE(parser, name, default, help, flag_values, serializer, **args)
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registers a flag whose value is a whitespace-separated list of strings .
train
false
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def _FinalElement(key): return key.path().element_list()[(-1)]
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return final element of a keys path .
train
false
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def check_resource_cleanup(rsrc, template_id, resource_data, engine_id, timeout, msg_queue): check_message = functools.partial(_check_for_message, msg_queue) rsrc.delete_convergence(template_id, resource_data, engine_id, timeout, check_message)
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delete the resource if appropriate .
train
false
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@sopel.module.nickname_commands(u'load') @sopel.module.priority(u'low') @sopel.module.thread(False) def f_load(bot, trigger): if (not trigger.admin): return name = trigger.group(2) path = u'' if (not name): return bot.reply(u'Load what?') if (name in sys.modules): return bot.reply(u'Module already loaded, use reload') mods = sopel.loader.enumerate_modules(bot.config) if (name not in mods): return bot.reply((u'Module %s not found' % name)) (path, type_) = mods[name] load_module(bot, name, path, type_)
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loads a module .
train
false
9,744
def slice_2d(X, rows, cols): return [X[i][j] for j in cols for i in rows]
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slices a 2d list to a flat array .
train
false
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def load_ndarray_transfer(name): assert (name in ['avicenna', 'harry', 'rita', 'sylvester', 'terry', 'ule']) fname = os.path.join(preprocess('${PYLEARN2_DATA_PATH}'), 'UTLC', 'filetensor', (name + '_transfer.ft')) transfer = load_filetensor(fname) return transfer
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load the transfer labels for the training set of data set name .
train
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def add_blob_owner(bucket_name, blob_name, user_email): storage_client = storage.Client() bucket = storage_client.bucket(bucket_name) blob = bucket.blob(blob_name) blob.acl.reload() blob.acl.user(user_email).grant_owner() blob.acl.save() print 'Added user {} as an owner on blob {} in bucket {}.'.format(user_email, blob_name, bucket_name)
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adds a user as an owner on the given blob .
train
false
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@world.absorb def wait_for_xmodule(): world.wait_for_js_variable_truthy('XModule') world.wait_for_js_variable_truthy('XBlock')
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wait until the xmodule javascript has loaded on the page .
train
false
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def getMin(value): return min(value)
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get the min .
train
false
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def exp_re(DE, r, k): RE = S.Zero g = DE.atoms(Function).pop() mini = None for t in Add.make_args(DE): (coeff, d) = t.as_independent(g) if isinstance(d, Derivative): j = (len(d.args) - 1) else: j = 0 if ((mini is None) or (j < mini)): mini = j RE += (coeff * r((k + j))) if mini: RE = RE.subs(k, (k - mini)) return RE
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converts a de with constant coefficients into a re .
train
false
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@permission_required([('Apps', 'ModerateReview')]) def queue_moderated(request): queues_helper = ReviewersQueuesHelper(request) qs = queues_helper.get_moderated_queue() page = paginate(request, qs, per_page=20) flags = dict(ReviewFlag.FLAGS) reviews_formset = ReviewFlagFormSet((request.POST or None), queryset=page.object_list, request=request) if reviews_formset.is_valid(): reviews_formset.save() return redirect(reverse('reviewers.apps.queue_moderated')) return render(request, 'reviewers/queue.html', context(request, reviews_formset=reviews_formset, tab='moderated', page=page, flags=flags))
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queue for reviewing app reviews .
train
false
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def cat_convert(cat): if (cat and (cat.lower() != 'none')): cats = config.get_ordered_categories() raw_cats = config.get_categories() for ucat in cats: try: indexer = raw_cats[ucat['name']].newzbin() if (not isinstance(indexer, list)): indexer = [indexer] except: indexer = [] for name in indexer: if re.search(('^%s$' % wildcard_to_re(name)), cat, re.I): if ('.' in name): logging.debug('Convert group "%s" to user-cat "%s"', cat, ucat['name']) else: logging.debug('Convert index site category "%s" to user-cat "%s"', cat, ucat['name']) return ucat['name'] for ucat in cats: if (cat.lower() == ucat['name'].lower()): logging.debug('Convert index site category "%s" to user-cat "%s"', cat, ucat['name']) return ucat['name'] for ucat in cats: if cat.lower().startswith(ucat['name'].lower()): logging.debug('Convert index site category "%s" to user-cat "%s"', cat, ucat['name']) return ucat['name'] return None
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convert indexers category/group-name to user categories .
train
false
9,755
def send_claim_registered_email(claimer, unclaimed_user, node, throttle=(24 * 3600)): unclaimed_record = unclaimed_user.get_unclaimed_record(node._primary_key) timestamp = unclaimed_record.get('last_sent') if (not throttle_period_expired(timestamp, throttle)): raise HTTPError(http.BAD_REQUEST, data=dict(message_long='User account can only be claimed with an existing user once every 24 hours')) verification_key = generate_verification_key(verification_type='claim') unclaimed_record['token'] = verification_key['token'] unclaimed_record['expires'] = verification_key['expires'] unclaimed_record['claimer_email'] = claimer.username unclaimed_user.save() referrer = User.load(unclaimed_record['referrer_id']) claim_url = web_url_for('claim_user_registered', uid=unclaimed_user._primary_key, pid=node._primary_key, token=unclaimed_record['token'], _external=True) mails.send_mail(referrer.username, mails.FORWARD_INVITE_REGISTERED, user=unclaimed_user, referrer=referrer, node=node, claim_url=claim_url, fullname=unclaimed_record['name']) unclaimed_record['last_sent'] = get_timestamp() unclaimed_user.save() mails.send_mail(claimer.username, mails.PENDING_VERIFICATION_REGISTERED, fullname=claimer.fullname, referrer=referrer, node=node)
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a registered user claiming the unclaimed user account as an contributor to a project .
train
false
9,756
def _makePipe(): (r, w) = pipe() return (_FDHolder(r), _FDHolder(w))
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create a pipe .
train
false
9,757
@parse_data @set_database def get_content_items(ids=None, **kwargs): if ids: values = Item.select().where(Item.id.in_(ids)) else: values = Item.select() return values
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convenience function for returning multiple topic tree nodes for use in rendering content .
train
false
9,758
def allocate_ids_async(model, size, **kwargs): return datastore.AllocateIdsAsync(_coerce_to_key(model), size=size, **kwargs)
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asynchronously allocates a range of ids .
train
false
9,759
def populate_entry_points(entry_points): for entry_point in entry_points: name = entry_point.name try: entry_point = entry_point.load() except Exception as e: warnings.warn(AstropyUserWarning(u'{type} error occurred in entry point {name}.'.format(type=type(e).__name__, name=name))) else: if (not inspect.isclass(entry_point)): warnings.warn(AstropyUserWarning(u'Modeling entry point {0} expected to be a Class.'.format(name))) elif issubclass(entry_point, Fitter): name = entry_point.__name__ globals()[name] = entry_point __all__.append(name) else: warnings.warn(AstropyUserWarning(u'Modeling entry point {0} expected to extend astropy.modeling.Fitter'.format(name)))
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this injects entry points into the astropy .
train
false
9,763
def _CompareTasksByEta(a, b): if (a.eta_usec() > b.eta_usec()): return 1 if (a.eta_usec() < b.eta_usec()): return (-1) return 0
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python sort comparator for tasks by estimated time of arrival .
train
false
9,766
def mask_hash(hash, show=6, char=u'*'): masked = hash[:show] masked += (char * len(hash[show:])) return masked
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returns the given hash .
train
true
9,767
def patch_get_utility(target='zope.component.getUtility'): return mock.patch(target, new_callable=_create_get_utility_mock)
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patch zope .
train
false
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def isSegmentCompletelyInAnIntersection(segment, xIntersections): for xIntersectionIndex in xrange(0, len(xIntersections), 2): surroundingXFirst = xIntersections[xIntersectionIndex] surroundingXSecond = xIntersections[(xIntersectionIndex + 1)] if euclidean.isSegmentCompletelyInX(segment, surroundingXFirst, surroundingXSecond): return True return False
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add sparse endpoints from a segment .
train
false
9,773
def _chkconfig_add(name): cmd = '/sbin/chkconfig --add {0}'.format(name) if (__salt__['cmd.retcode'](cmd, python_shell=False) == 0): log.info('Added initscript "{0}" to chkconfig'.format(name)) return True else: log.error('Unable to add initscript "{0}" to chkconfig'.format(name)) return False
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run chkconfig --add for a service whose script is installed in /etc/init .
train
true
9,775
def get_constr_constant(constraints): constants = [get_constant(c.expr) for c in constraints] return np.hstack(constants)
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returns the constant term for the constraints matrix .
train
false
9,776
def parse_fp(source, module_name, lexer=None, parser=None, enable_cache=True): if (not module_name.endswith('_thrift')): raise ThriftParserError("ThriftPy can only generate module with '_thrift' suffix") if (enable_cache and (module_name in thrift_cache)): return thrift_cache[module_name] if (not hasattr(source, 'read')): raise ThriftParserError("Except `source` to be a file-like object witha method named 'read'") if (lexer is None): lexer = lex.lex() if (parser is None): parser = yacc.yacc(debug=False, write_tables=0) data = source.read() thrift = types.ModuleType(module_name) setattr(thrift, '__thrift_file__', None) thrift_stack.append(thrift) lexer.lineno = 1 parser.parse(data) thrift_stack.pop() if enable_cache: thrift_cache[module_name] = thrift return thrift
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parse a file-like object to thrift module object .
train
false
9,777
def save_tweets(filename, tweets): if (len(tweets) == 0): return try: archive = open(filename, 'w') except IOError as e: err(('Cannot save tweets: %s' % str(e))) return for k in sorted(tweets.keys()): try: archive.write(('%i %s\n' % (k, tweets[k].encode('utf-8')))) except Exception as ex: err(('archiving tweet %s failed due to %s' % (k, unicode(ex)))) archive.close()
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save tweets from dict to file .
train
false
9,778
def autoscroll(sbar, first, last): (first, last) = (float(first), float(last)) if ((first <= 0) and (last >= 1)): sbar.grid_remove() else: sbar.grid() sbar.set(first, last)
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hide and show scrollbar as needed .
train
false
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def g_connect(method): def wrapped(self, *args, **kwargs): if (not self.initialized): display.vvvv(('Initial connection to galaxy_server: %s' % self._api_server)) server_version = self._get_server_api_version() if (server_version not in self.SUPPORTED_VERSIONS): raise AnsibleError(('Unsupported Galaxy server API version: %s' % server_version)) self.baseurl = ('%s/api/%s' % (self._api_server, server_version)) self.version = server_version display.vvvv(('Base API: %s' % self.baseurl)) self.initialized = True return method(self, *args, **kwargs) return wrapped
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wrapper to lazily initialize connection info to galaxy .
train
false
9,785
def POST(k, n): return _XXX(k, n, _POST)
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munging to turn a method name into a post-hook-method-name .
train
false
9,787
def make_assert(error): def openssl_assert(ok): '\n If *ok* is not True, retrieve the error from OpenSSL and raise it.\n ' if (ok is not True): exception_from_error_queue(error) return openssl_assert
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create an assert function that uses :func:exception_from_error_queue to raise an exception wrapped by *error* .
train
false
9,789
def ScaleData(data, old_min, old_max, new_min, new_max): def ScalePoint(x): if (x is None): return None return ((scale * x) + translate) if (old_min == old_max): scale = 1 else: scale = ((new_max - new_min) / float((old_max - old_min))) translate = (new_min - (scale * old_min)) return map(ScalePoint, data)
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scale the input data so that the range old_min-old_max maps to new_min-new_max .
train
false
9,790
def dump_object(header, obj): result = (header + '\n') for key in obj.hash: if ((key == 'afe') or (key == 'hash')): continue result += ('%20s: %s\n' % (key, obj.hash[key])) return result
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dump an objects attributes and methods kind of like dir() .
train
false
9,791
def create_theme(name, **extra_kwargs): kwargs = {'status': STATUS_PUBLIC, 'name': name, 'slug': slugify(name), 'bayesian_rating': random.uniform(1, 5), 'average_daily_users': random.randint(200, 2000), 'weekly_downloads': random.randint(200, 2000), 'created': datetime.now(), 'last_updated': datetime.now()} kwargs.update(extra_kwargs) theme = Addon.objects.create(type=ADDON_EXTENSION, **kwargs) generate_version(addon=theme) theme.update_version() theme.status = STATUS_PUBLIC theme.type = ADDON_PERSONA Persona.objects.create(addon=theme, popularity=theme.weekly_downloads, persona_id=0) theme.save() return theme
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create a theme with the given name .
train
false
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@app.route('/scans/<int:scan_id>/exceptions/', methods=['POST']) @requires_auth def exception_creator(scan_id): scan_info = get_scan_info_from_id(scan_id) if (scan_info is None): abort(404, 'Scan not found') current_status = FakeStatus(None) current_status.set_running_plugin('phase', 'plugin') current_status.set_current_fuzzable_request('phase', 'http://www.w3af.org/') try: raise Exception('unittest') except Exception as exception: exec_info = sys.exc_info() enabled_plugins = '' scan_info.w3af_core.exception_handler.write_crash_file = (lambda x: x) scan_info.w3af_core.exception_handler.handle(current_status, exception, exec_info, enabled_plugins) return (jsonify({'code': 201}), 201)
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mostly for testing .
train
false
9,793
def plot_2_and_1(images): fig = plt.figure() ax = fig.add_subplot(1, 2, 1) ax.matshow(images[5], cmap=matplotlib.cm.binary) plt.xticks(np.array([])) plt.yticks(np.array([])) ax = fig.add_subplot(1, 2, 2) ax.matshow(images[3], cmap=matplotlib.cm.binary) plt.xticks(np.array([])) plt.yticks(np.array([])) plt.show()
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plot a 2 and a 1 image from the mnist set .
train
false
9,796
def generate_random_edx_username(): allowable_chars = (string.ascii_letters + string.digits) username = '' for _index in range(30): username = (username + random.SystemRandom().choice(allowable_chars)) return username
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create a valid random edx user id .
train
false
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def run_mcr_job(job): log('Running a compiled Matlab job.\n') os.chdir(job.expt_dir) if os.environ.has_key('MATLAB'): mcr_loc = os.environ['MATLAB'] else: mcr_loc = MCR_LOCATION cmd = ('./run_%s.sh %s %s' % (job.name, mcr_loc, job_file_for(job))) log(("Executing command '%s'\n" % cmd)) sh(cmd)
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run a compiled matlab job .
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def iseia(r, valid_types=(E6, E12, E24)): if ((not isinstance(r, numbers.Number)) or (r < 0) or math.isnan(r) or math.isinf(r)): return False if (r == 0): return True while (r < 100): r = (r * 10) while (r >= 1000): r = (r / 10) if (abs((r - round(r))) > 0.01): return False r = int(round(r)) for type_list in valid_types: if (r in type_list): return True if ((int((r / 10.0)) in type_list) and ((r % 10) == 0)): return True return False
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check if a component is a valid eia value .
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def char_from_number(number): base = 26 rval = '' if (number == 0): rval = 'A' while (number != 0): remainder = (number % base) new_char = chr((ord('A') + remainder)) rval = (new_char + rval) number //= base return rval
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converts number to string by rendering it in base 26 using capital letters as digits .
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def tquery(query, con=None, cur=None): res = sql.execute(query, con=con, cur=cur).fetchall() if (res is None): return None else: return list(res)
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replace removed sql .
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def sob(unicode, encoding): if (encoding is None): return unicode else: return unicode.encode(encoding)
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returns either the given unicode string or its encoding .
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def getInsetSeparateLoopsFromLoops(loops, radius, thresholdRatio=0.9): if (radius == 0.0): return loops isInset = (radius > 0) insetSeparateLoops = [] arounds = getAroundsFromLoops(loops, abs(radius), thresholdRatio) for around in arounds: if (isInset == euclidean.getIsInFilledRegion(loops, around[0])): if isInset: around.reverse() insetSeparateLoops.append(around) return insetSeparateLoops
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get the separate inset loops .
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def transpose_inplace(x, **kwargs): dims = list(range((x.ndim - 1), (-1), (-1))) return elemwise.DimShuffle(x.broadcastable, dims, inplace=True)(x)
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perform a transpose on a tensor without copying the underlying storage .
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def getCylindrical(azimuthDegrees, radius=1.0, z=0.0): return getCylindricalByRadians(math.radians(azimuthDegrees), radius, z)
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get the cylindrical vector3 by degrees .
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def convoltuion_shape(img_height, img_width, filter_shape, stride, padding): height = ((((img_height + (2 * padding[0])) - filter_shape[0]) / float(stride[0])) + 1) width = ((((img_width + (2 * padding[1])) - filter_shape[1]) / float(stride[1])) + 1) assert ((height % 1) == 0) assert ((width % 1) == 0) return (int(height), int(width))
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calculate output shape for convolution layer .
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def create_youtube_string(module): youtube_ids = [module.youtube_id_0_75, module.youtube_id_1_0, module.youtube_id_1_25, module.youtube_id_1_5] youtube_speeds = ['0.75', '1.00', '1.25', '1.50'] return ','.join([':'.join(pair) for pair in zip(youtube_speeds, youtube_ids) if pair[1]])
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create a string of youtube ids from modules metadata attributes .
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def is_true(val): return ((val is True) or (val in ['True', 'true', 'T', 't']))
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returns true if input is a boolean and true or is a string and looks like a true value .
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def safe_range(*args): rng = range(*args) if (len(rng) > MAX_RANGE): raise OverflowError(('range too big, maximum size for range is %d' % MAX_RANGE)) return rng
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a range that cant generate ranges with a length of more than max_range items .
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def _dict_diff(a, b): if (set(a.keys()) - set(b.keys())): LOG.debug('metadata diff -- master has extra keys: %(keys)s', {'keys': ' '.join((set(a.keys()) - set(b.keys())))}) return True for key in a: if (str(a[key]) != str(b[key])): LOG.debug('metadata diff -- value differs for key %(key)s: master "%(master_value)s" vs slave "%(slave_value)s"', {'key': key, 'master_value': a[key], 'slave_value': b[key]}) return True return False
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a one way dictionary diff .
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def initializer_mock(request, cls): _patch = patch.object(cls, '__init__', return_value=None) request.addfinalizer(_patch.stop) return _patch.start()
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return a mock for the __init__ method on *cls* where the patch is reversed after pytest uses it .
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def RequestMock(response='', headers=None): res = mock.Mock() res.read.return_value = response res.contents = response res.text = response res.iter_lines.side_effect = (lambda chunk_size=1: response.split('\n').__iter__()) res.iter_content.side_effect = (lambda chunk_size=1: (response,).__iter__()) def lines(): return [(l + '\n') for l in response.split('\n')[:(-1)]] res.readlines.side_effect = lines res.iter_lines.side_effect = (lambda : lines().__iter__()) res.headers = (headers or {}) res.headers['content-length'] = len(response) return res
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mocks the request objects of urllib2 and requests modules .
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def getPointMinimum(firstPoint, secondPoint): return Vector3(min(firstPoint.x, secondPoint.x), min(firstPoint.y, secondPoint.y), min(firstPoint.z, secondPoint.z))
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get a point with each component the minimum of the respective components of a pair of vector3s .
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def get_filetype_icon(fname): ext = osp.splitext(fname)[1] if ext.startswith('.'): ext = ext[1:] return get_icon(('%s.png' % ext), ima.icon('FileIcon'))
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return file type icon .
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def RecurrenceOperators(base, generator): ring = RecurrenceOperatorAlgebra(base, generator) return (ring, ring.shift_operator)
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returns an algebra of recurrence operators and the operator for shifting i .
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def setup_authentication(config): config.include('pyramid_multiauth') settings = config.get_settings() policies = aslist(settings['multiauth.policies']) if ('basicauth' in policies): config.include('kinto.core.authentication') def on_policy_selected(event): authn_type = event.policy_name.lower() event.request.authn_type = authn_type event.request.selected_userid = event.userid logger.bind(uid=event.userid, authn_type=authn_type) config.add_subscriber(on_policy_selected, MultiAuthPolicySelected)
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register non-default auth methods .
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def b64e(s): return base64.b64encode(s)
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b64e(s) -> str base64 encodes a string example: .
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def cuda_set_device(dev_id): pass
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selects the cuda device with the given id .
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def _GivePropertiesFromGeneralToSpecific(handler_list): for (i, j) in itertools.combinations(xrange(len(handler_list)), 2): if handler_list[j].MatchesAll(handler_list[i]): if isinstance(handler_list[i], SimpleHandler): handler_list[i] = handler_list[i].CreateOverlappedHandler() handler_list[i].AddMatchingHandler(handler_list[j])
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makes sure that handlers have all properties of more general ones .
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def _get_deployment_flavor(flavor=None): if (not flavor): flavor = CONF.paste_deploy.flavor return ('' if (not flavor) else ('-' + flavor))
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retrieve the paste_deploy .
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def intensity_range(image, range_values='image', clip_negative=False): if (range_values == 'dtype'): range_values = image.dtype.type if (range_values == 'image'): i_min = np.min(image) i_max = np.max(image) elif (range_values in DTYPE_RANGE): (i_min, i_max) = DTYPE_RANGE[range_values] if clip_negative: i_min = 0 else: (i_min, i_max) = range_values return (i_min, i_max)
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return image intensity range based on desired value type .
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def get_diff_for_otu_maps(otu_map1, otu_map2): otus1 = set(otu_map1.keys()) otus2 = set(otu_map2.keys()) ids1 = set([x for otu in otus1 for x in otu_map1[otu]]) ids2 = set([x for otu in otus2 for x in otu_map2[otu]]) return ((ids1 - ids2), (ids2 - ids1))
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return reads in two otu_maps that are not shared otu_map1 .
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def check_known_inconsistencies(bill_data, bond_data): inconsistent_dates = bill_data.index.sym_diff(bond_data.index) known_inconsistencies = [pd.Timestamp('2006-09-04', tz='UTC'), pd.Timestamp('2010-02-15', tz='UTC'), pd.Timestamp('2013-07-25', tz='UTC')] unexpected_inconsistences = inconsistent_dates.drop(known_inconsistencies) if len(unexpected_inconsistences): in_bills = bill_data.index.difference(bond_data.index).difference(known_inconsistencies) in_bonds = bond_data.index.difference(bill_data.index).difference(known_inconsistencies) raise ValueError('Inconsistent dates for Canadian treasury bills vs bonds. \nDates with bills but not bonds: {in_bills}.\nDates with bonds but not bills: {in_bonds}.'.format(in_bills=in_bills, in_bonds=in_bonds))
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there are a couple quirks in the data provided by bank of canada .
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def kpsewhich(filename): try: find_cmd('kpsewhich') proc = subprocess.Popen(['kpsewhich', filename], stdout=subprocess.PIPE, stderr=subprocess.PIPE) (stdout, stderr) = proc.communicate() return stdout.strip().decode('utf8', 'replace') except FindCmdError: pass
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invoke kpsewhich command with an argument filename .
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def test_withall(tmpmod): with tmpmod.mkdir('xontrib').join('spameggs.py').open('w') as x: x.write("\n__all__ = 'spam', '_foobar'\nspam = 1\neggs = 2\n_foobar = 3\n") ctx = xontrib_context('spameggs') assert (ctx == {'spam': 1, '_foobar': 3})
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tests what gets exported from a module with __all__ .
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def test_approve_addons_approve_files(use_case, mozilla_user): (addon, file1, file2, review_type) = use_case approve_addons.approve_files([(file1, review_type), (file2, review_type)]) assert (file1.reload().status == amo.STATUS_PUBLIC) assert (file2.reload().status == amo.STATUS_PUBLIC) logs = AddonLog.objects.filter(addon=addon) assert (len(logs) == 2) (file1_log, file2_log) = logs assert (file1_log.activity_log.details['comments'] == u'bulk approval') assert (file1_log.activity_log.user == mozilla_user) assert (file2_log.activity_log.details['comments'] == u'bulk approval') assert (file2_log.activity_log.user == mozilla_user) assert (not ReviewerScore.objects.all())
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files are approved using the correct review type .
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def _get_participants(msg, excluded_emails=[]): participants = ((msg.to_addr + msg.cc_addr) + msg.bcc_addr) return sorted(list(set([email.lower() for (_, email) in participants if (email not in excluded_emails)])))
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returns an alphabetically sorted list of emails addresses that msg was sent to .
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def filter_form_field_choices(field, predicate, invert=False): if (not callable(predicate)): allowed_values = set(predicate) def predicate(pair): return (pair[0] in allowed_values) if invert: choices = [pair for pair in field.choices if (not predicate(pair))] else: choices = [pair for pair in field.choices if predicate(pair)] field.choices = field.widget.choices = choices
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filter choices of a form field and its widget by predicate .
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@testing.requires_testing_data def test_mixed_stc(): N = 90 T = 2 S = 3 data = rng.randn(N, T) vertno = (S * [np.arange((N // S))]) assert_raises(ValueError, MixedSourceEstimate, data=data, vertices=[np.arange(N)]) stc = MixedSourceEstimate(data, vertno, 0, 1) vol = read_source_spaces(fname_vsrc) assert_raises(ValueError, stc.plot_surface, src=vol)
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test source estimate from mixed source space .
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def unpack(source): (payload, symtab, radix, count) = _filterargs(source) if (count != len(symtab)): raise UnpackingError('Malformed p.a.c.k.e.r. symtab.') try: unbase = Unbaser(radix) except TypeError: raise UnpackingError('Unknown p.a.c.k.e.r. encoding.') def lookup(match): 'Look up symbols in the synthetic symtab.' word = match.group(0) return (symtab[unbase(word)] or word) source = re.sub('\\b\\w+\\b', lookup, payload) return _replacestrings(source)
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unpack data to tuple of length n .
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def _clone(requestedVersion): assert (not os.path.exists(VERSIONSDIR)), 'use `git fetch` not `git clone`' print(_translate('Downloading the PsychoPy Library from Github (may take a while)')) cmd = ('git clone -o github https://github.com/psychopy/versions ' + VER_SUBDIR) print(cmd) subprocess.check_output(cmd.split(), cwd=USERDIR) return _checkout(requestedVersion)
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download all versions .
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def decode_wait_status(sts): if os.WIFEXITED(sts): es = (os.WEXITSTATUS(sts) & 65535) msg = ('exit status %s' % es) return (es, msg) elif os.WIFSIGNALED(sts): sig = os.WTERMSIG(sts) msg = ('terminated by %s' % signame(sig)) if hasattr(os, 'WCOREDUMP'): iscore = os.WCOREDUMP(sts) else: iscore = (sts & 128) if iscore: msg += ' (core dumped)' return ((-1), msg) else: msg = ('unknown termination cause 0x%04x' % sts) return ((-1), msg)
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decode the status returned by wait() or waitpid() .
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@open_file(1, mode='wb') def write_gpickle(G, path, protocol=pickle.HIGHEST_PROTOCOL): pickle.dump(G, path, protocol)
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write graph in python pickle format .
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def module_exists(module_name): try: __import__(module_name) except ImportError: return False else: return True
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check to see if a module is installed or not .
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def ReverseBitsInt64(v): v = (((v >> 1) & 6148914691236517205) | ((v & 6148914691236517205) << 1)) v = (((v >> 2) & 3689348814741910323) | ((v & 3689348814741910323) << 2)) v = (((v >> 4) & 1085102592571150095) | ((v & 1085102592571150095) << 4)) v = (((v >> 8) & 71777214294589695) | ((v & 71777214294589695) << 8)) v = (((v >> 16) & 281470681808895) | ((v & 281470681808895) << 16)) v = int(((v >> 32) | ((v << 32) & 18446744073709551615L))) return v
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reverse the bits of a 64-bit integer .
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def flatten_dictionary(input, sep='.', prefix=None): for (name, value) in sorted(input.items()): fullname = sep.join(filter(None, [prefix, name])) if isinstance(value, dict): for result in flatten_dictionary(value, sep, fullname): (yield result) else: (yield (fullname, value))
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produces iterator of pairs where the first value is the joined key names and the second value is the value associated with the lowest level key .
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def app_has_custom(app, attr): return mro_lookup(app.__class__, attr, stop={Celery, object}, monkey_patched=[__name__])
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return true if app has customized method attr .
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def hostgroup_update(groupid, name=None, **connection_args): conn_args = _login(**connection_args) try: if conn_args: method = 'hostgroup.update' params = {'groupid': groupid} if name: params['name'] = name params = _params_extend(params, **connection_args) ret = _query(method, params, conn_args['url'], conn_args['auth']) return ret['result']['groupids'] else: raise KeyError except KeyError: return ret
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update existing hosts group .
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def test_multi_constructor_obj(): try: load('a: !obj:decimal.Decimal { 1 }') except TypeError as e: assert (str(e) == 'Received non string object (1) as key in mapping.') pass except Exception as e: error_msg = ('Got the unexpected error: %s' % e) reraise_as(ValueError(error_msg))
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tests whether multi_constructor_obj throws an exception when the keys in mapping are none .
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def parse_language(speaker, emote): emote = _RE_REF_LANG.sub('\\1', emote) errors = [] mapping = {} for (imatch, say_match) in enumerate(reversed(list(_RE_LANGUAGE.finditer(emote)))): (langname, saytext) = say_match.groups() (istart, iend) = (say_match.start(), say_match.end()) key = ('##%i' % imatch) emote = ((emote[:istart] + ('{%s}' % key)) + emote[iend:]) mapping[key] = (langname, saytext) if errors: raise LanguageError('\n'.join(errors)) return (emote, mapping)
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parse the emote for language .
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def label2rgb(label, image=None, colors=None, alpha=0.3, bg_label=(-1), bg_color=(0, 0, 0), image_alpha=1, kind='overlay'): if (kind == 'overlay'): return _label2rgb_overlay(label, image, colors, alpha, bg_label, bg_color, image_alpha) else: return _label2rgb_avg(label, image, bg_label, bg_color)
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return an rgb image where color-coded labels are painted over the image .
train
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def _get_server_type(doc): if (not doc.get('ok')): return SERVER_TYPE.Unknown if doc.get('isreplicaset'): return SERVER_TYPE.RSGhost elif doc.get('setName'): if doc.get('hidden'): return SERVER_TYPE.RSOther elif doc.get('ismaster'): return SERVER_TYPE.RSPrimary elif doc.get('secondary'): return SERVER_TYPE.RSSecondary elif doc.get('arbiterOnly'): return SERVER_TYPE.RSArbiter else: return SERVER_TYPE.RSOther elif (doc.get('msg') == 'isdbgrid'): return SERVER_TYPE.Mongos else: return SERVER_TYPE.Standalone
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determine the server type from an ismaster response .
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true
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def wotan2penntreebank(token, tag): for (k, v) in wotan.items(): if tag.startswith(k): for (a, b) in v: if (a in tag): return (token, b) return (token, tag)
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converts a wotan tag to a penn treebank ii tag .
train
false
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@pytest.mark.django_db def test_cross_sell_plugin_type(): shop = get_default_shop() supplier = get_default_supplier() product = create_product('test-sku', shop=shop, supplier=supplier, stock_behavior=StockBehavior.UNSTOCKED) context = get_jinja_context(product=product) type_counts = ((ProductCrossSellType.RELATED, 1), (ProductCrossSellType.RECOMMENDED, 2), (ProductCrossSellType.BOUGHT_WITH, 3)) for (type, count) in type_counts: _create_cross_sell_products(product, shop, supplier, type, count) assert (ProductCrossSell.objects.filter(product1=product, type=type).count() == count) for (type, count) in type_counts: assert (len(list(product_helpers.get_product_cross_sells(context, product, type, count))) == count)
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test that template helper returns correct number of cross sells when shop contains multiple relation types .
train
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def plot_images_together(images): fig = plt.figure() images = [image[:, 3:25] for image in images] image = np.concatenate(images, axis=1) ax = fig.add_subplot(1, 1, 1) ax.matshow(image, cmap=matplotlib.cm.binary) plt.xticks(np.array([])) plt.yticks(np.array([])) plt.show()
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plot a single image containing all six mnist images .
train
false
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def getNewRepository(): return ExportRepository()
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get the repository constructor .
train
false
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def fourier_transform(f, x, k, **hints): return FourierTransform(f, x, k).doit(**hints)
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compute the unitary .
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false
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def _PutSecret(io_loop, secret): _GetSecretsManager().PutSecret(secret, sys.stdin.read()) io_loop.stop()
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reads the new secret from stdin and writes to secrets subdir .
train
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def get_request_or_stub(): request = crum.get_current_request() if (request is None): log.warning('Could not retrieve the current request. A stub request will be created instead using settings.SITE_NAME. This should be used *only* in test cases, never in production!') full_url = 'http://{site_name}'.format(site_name=settings.SITE_NAME) parsed_url = urlparse(full_url) return RequestFactory(SERVER_NAME=parsed_url.hostname, SERVER_PORT=(parsed_url.port or 80)).get('/') else: return request
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return the current request or a stub request .
train
false
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def p_command_def_bad_arg(p): p[0] = 'BAD ARGUMENT IN DEF STATEMENT'
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command : def id lparen error rparen equals expr .
train
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def reject_spurious_dots(*items): for list in items: for tok in list: if ((tok == '.') and (type(tok) == HySymbol)): raise LexException('Malformed dotted list', tok.start_line, tok.start_column)
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reject the spurious dots from items .
train
false
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def json_view(f): @wraps(f) def _wrapped(req, *a, **kw): try: ret = f(req, *a, **kw) blob = json.dumps(ret) return http.HttpResponse(blob, content_type=JSON) except http.Http404 as e: blob = json.dumps({'success': False, 'error': 404, 'message': str(e)}) return http.HttpResponseNotFound(blob, content_type=JSON) except PermissionDenied as e: blob = json.dumps({'success': False, 'error': 403, 'message': str(e)}) return http.HttpResponseForbidden(blob, content_type=JSON) except Exception as e: blob = json.dumps({'success': False, 'error': 500, 'message': str(e)}) return http.HttpResponseServerError(blob, content_type=JSON) return _wrapped
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return some basic document info in a json blob .
train
false
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def write_proj(fname, projs): check_fname(fname, 'projection', ('-proj.fif', '-proj.fif.gz')) fid = io.write.start_file(fname) io.proj._write_proj(fid, projs) io.write.end_file(fid)
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write projections to a fif file .
train
false
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def text_date_synonym(name): def getter(self): return getattr(self, name) def setter(self, value): if isinstance(value, basestring): try: setattr(self, name, datetime.strptime(value, u'%Y-%m-%d')) except ValueError: setattr(self, name, None) else: setattr(self, name, value) return synonym(name, descriptor=property(getter, setter))
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converts y-m-d date strings into datetime objects .
train
false
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@gof.local_optimizer([T.AllocEmpty]) def local_alloc_empty_to_zeros(node): if isinstance(node.op, T.AllocEmpty): return [T.zeros(node.inputs, dtype=node.outputs[0].dtype)]
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this convert allocempty to alloc of 0 .
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@_get_client def image_member_update(client, memb_id, values): return client.image_member_update(memb_id=memb_id, values=values)
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update an imagemember object .
train
false
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def handle_data_class_factory(endog, exog): if data_util._is_using_ndarray_type(endog, exog): klass = ModelData elif data_util._is_using_pandas(endog, exog): klass = PandasData elif data_util._is_using_patsy(endog, exog): klass = PatsyData elif data_util._is_using_ndarray(endog, exog): klass = ModelData else: raise ValueError(('unrecognized data structures: %s / %s' % (type(endog), type(exog)))) return klass
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given inputs .
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def from_array_like(ary, stream=0, gpu_data=None): if (ary.ndim == 0): ary = ary.reshape(1) return DeviceNDArray(ary.shape, ary.strides, ary.dtype, writeback=ary, stream=stream, gpu_data=gpu_data)
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create a devicendarray object that is like ary .
train
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def bench_scikit_tree_classifier(X, Y): from sklearn.tree import DecisionTreeClassifier gc.collect() tstart = datetime.now() clf = DecisionTreeClassifier() clf.fit(X, Y).predict(X) delta = (datetime.now() - tstart) scikit_classifier_results.append((delta.seconds + (delta.microseconds / mu_second)))
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benchmark with scikit-learn decision tree classifier .
train
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def SetLevel(level): global _Level _Level = level
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set the current indentation level .
train
false
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def vb_get_box(): vb_get_manager() vbox = _virtualboxManager.vbox return vbox
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needed for certain operations in the sdk e .
train
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def prewitt_v(image, mask=None): assert_nD(image, 2) image = img_as_float(image) result = convolve(image, VPREWITT_WEIGHTS) return _mask_filter_result(result, mask)
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find the vertical edges of an image using the prewitt transform .
train
false