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def BeginThreadsSimpleMarshal(numThreads, cookie): ret = [] for i in range(numThreads): hEvent = win32event.CreateEvent(None, 0, 0, None) thread.start_new(TestInterpInThread, (hEvent, cookie)) ret.append(hEvent) return ret
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creates multiple threads using simple marshalling .
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@set_database def get_download_youtube_ids(paths=None, downloaded=False, **kwargs): if paths: youtube_ids = dict() for path in paths: selector = (((Item.kind != 'Topic') & Item.path.contains(path)) & Item.youtube_id.is_null(False)) if downloaded: selector &= (Item.files_complete > 0) else: selector &= (Item.files_complete == 0) youtube_ids.update(dict([item for item in Item.select(Item.youtube_id, Item.title).where(selector).tuples() if item[0]])) return youtube_ids
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convenience function for taking a list of content ids and returning all associated youtube_ids for downloads .
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def precision_recall_curve(y_true, probas_pred, pos_label=None, sample_weight=None): (fps, tps, thresholds) = _binary_clf_curve(y_true, probas_pred, pos_label=pos_label, sample_weight=sample_weight) precision = (tps / (tps + fps)) recall = (tps / tps[(-1)]) last_ind = tps.searchsorted(tps[(-1)]) sl = slice(last_ind, None, (-1)) return (np.r_[(precision[sl], 1)], np.r_[(recall[sl], 0)], thresholds[sl])
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compute precision-recall pairs for different probability thresholds note: this implementation is restricted to the binary classification task .
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def find_value(stdout, key): match = _HORCM_PATTERNS[key]['pattern'].search(stdout) if match: if (_HORCM_PATTERNS[key]['type'] is list): return [value.strip() for value in LDEV_SEP_PATTERN.split(match.group(key))] return _HORCM_PATTERNS[key]['type'](match.group(key)) return None
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return the first match from the given raidcom command output .
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@treeio_login_required @handle_response_format def currency_delete(request, currency_id, response_format='html'): currency = get_object_or_404(Currency, pk=currency_id) if (not request.user.profile.has_permission(currency, mode='w')): return user_denied(request, "You don't have access to this Currency", response_format) if currency.is_default: return user_denied(request, 'You cannot delete the Base Currency', response_format) if request.POST: if ('delete' in request.POST): if ('trash' in request.POST): currency.trash = True currency.save() else: currency.delete() return HttpResponseRedirect(reverse('finance_settings_view')) elif ('cancel' in request.POST): return HttpResponseRedirect(reverse('finance_currency_view', args=[currency.id])) return render_to_response('finance/currency_delete', {'currency': currency}, context_instance=RequestContext(request), response_format=response_format)
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currency delete .
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def MakeUniformPmf(low, high, n): pmf = Pmf() for x in np.linspace(low, high, n): pmf.Set(x, 1) pmf.Normalize() return pmf
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make a uniform pmf .
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def change_process_owner(uid, gid): try: os.setgid(gid) os.setuid(uid) except Exception as exc: error = DaemonOSEnvironmentError(('Unable to change file creation mask (%(exc)s)' % vars())) raise error
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change the owning uid and gid of this process .
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def list_bucket_files(bucket_name, prefix, max_keys=1000): scope = 'https://www.googleapis.com/auth/devstorage.read_only' url = ('https://%s.commondatastorage.googleapis.com/?' % bucket_name) query = [('max-keys', max_keys)] if prefix: query.append(('prefix', prefix)) url += urllib.urlencode(query) (auth_token, _) = app_identity.get_access_token(scope) result = urlfetch.fetch(url, method=urlfetch.GET, headers={'Authorization': ('OAuth %s' % auth_token), 'x-goog-api-version': '2'}) if (result and (result.status_code == 200)): doc = xml.dom.minidom.parseString(result.content) return [node.childNodes[0].data for node in doc.getElementsByTagName('Key')] raise BackupValidationException('Request to Google Cloud Storage failed')
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returns a listing of of a bucket that matches the given prefix .
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def runmodule(name='__main__', **kw): main(defaultTest=name, **kw)
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collect and run tests in a single module only .
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def plot_histograms(ax, prng, nb_samples=10000): params = ((10, 10), (4, 12), (50, 12), (6, 55)) for (a, b) in params: values = prng.beta(a, b, size=nb_samples) ax.hist(values, histtype='stepfilled', bins=30, alpha=0.8, normed=True) ax.annotate('Annotation', xy=(0.25, 4.25), xycoords='data', xytext=(0.9, 0.9), textcoords='axes fraction', va='top', ha='right', bbox=dict(boxstyle='round', alpha=0.2), arrowprops=dict(arrowstyle='->', connectionstyle='angle,angleA=-95,angleB=35,rad=10')) return ax
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plot 4 histograms and a text annotation .
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def is_centos_or_rhel(distribution): return distribution.startswith(('centos-', 'rhel-'))
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determine whether the named distribution is a version of centos or rhel .
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def nested_view(request): c = Client() c.get('/no_template_view/') return render(request, 'base.html', {'nested': 'yes'})
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a view that uses test client to call another view .
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def id_(reset=False): if reset: id_pattern = re.compile('Monit id (?P<id>[^ ]+)') cmd = 'echo y|monit -r' out = __salt__['cmd.run_all'](cmd, python_shell=True) ret = id_pattern.search(out['stdout']).group('id') return (ret if ret else False) else: cmd = 'monit -i' out = __salt__['cmd.run'](cmd) ret = out.split(':')[(-1)].strip() return ret
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return the id .
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@pytest.fixture def small_push_dir(tmpdir): contents = ('abcdefghijlmnopqrstuvwxyz\n' * 10000) push_dir = tmpdir.join('push-from').ensure(dir=True) push_dir.join('arbitrary-file').write(contents) push_dir.join('pg_xlog').mksymlinkto('/tmp/wal-e-test-must-not-exist') push_dir.join('holy-smokes').ensure() return push_dir
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create a small pg data directory-alike .
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def test_found(): with pytest.raises(falcon.http_status.HTTPStatus) as redirect: hug.redirect.found('/') assert ('302' in redirect.value.status)
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test to ensure function causes a redirect with http 302 status code .
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def rgb_to_ints(rgb): if (len(rgb) == 6): return tuple([int(h, 16) for h in RE_RGB6.split(rgb)[1:4]]) else: return tuple([int((h * 2), 16) for h in RE_RGB3.split(rgb)[1:4]])
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converts an rgb string into a tuple of ints .
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def _process_quantiles(x, dim): x = np.asarray(x, dtype=float) if (x.ndim == 0): x = x[np.newaxis] elif (x.ndim == 1): if (dim == 1): x = x[:, np.newaxis] else: x = x[np.newaxis, :] return x
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adjust quantiles array so that last axis labels the components of each data point .
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def deserialize(collection, topological=True): datastruct = deserialize_raw(collection.collection_type()) if (topological and (type(datastruct) == list)): datastruct.sort(__depth_cmp) if (type(datastruct) == dict): collection.from_dict(datastruct) elif (type(datastruct) == list): collection.from_list(datastruct)
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deserialize any string or stream like object into a python data structure .
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def test_ada_sk_estimator(): check_estimator(ADASYN)
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test the sklearn estimator compatibility .
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def gcg(seq): try: seq = str(seq) except AttributeError: pass index = checksum = 0 for char in seq: index += 1 checksum += (index * ord(char.upper())) if (index == 57): index = 0 return (checksum % 10000)
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returns the gcg checksum for a sequence .
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def ramp(x=None, v_min=0, v_max=1, name=None): return tf.clip_by_value(x, clip_value_min=v_min, clip_value_max=v_max, name=name)
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the ramp activation function .
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def extract_line(text, index): p = (text.rfind('\n', 0, index) + 1) q = text.find('\n', index) if (q < 0): q = len(text) return text[p:q]
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text may be a multiline string; extract only the line containing the given character index .
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def parse_ref(container, refspec): refspec = to_bytes(refspec) possible_refs = [refspec, ('refs/' + refspec), ('refs/tags/' + refspec), ('refs/heads/' + refspec), ('refs/remotes/' + refspec), (('refs/remotes/' + refspec) + '/HEAD')] for ref in possible_refs: if (ref in container): return ref else: raise KeyError(refspec)
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parse a string referring to a reference .
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@newrelic.agent.function_trace() @allow_CORS_GET @prevent_indexing def _document_deleted(request, deletion_logs): if (request.user and request.user.has_perm('wiki.restore_document')): deletion_log = deletion_logs.order_by('-pk')[0] context = {'deletion_log': deletion_log} return render(request, 'wiki/deletion_log.html', context, status=404) raise Http404
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when a document has been deleted return a 404 .
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false
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def check_arg_errcode(result, func, cargs): check_err(arg_byref(cargs)) return result
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the error code is returned in the last argument .
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def ascent(): import pickle import os fname = os.path.join(os.path.dirname(__file__), 'ascent.dat') with open(fname, 'rb') as f: ascent = array(pickle.load(f)) return ascent
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get an 8-bit grayscale bit-depth .
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def addToNamePathDictionary(directoryPath, namePathDictionary): pluginFileNames = getPluginFileNamesFromDirectoryPath(directoryPath) for pluginFileName in pluginFileNames: namePathDictionary[pluginFileName.replace('_', '')] = os.path.join(directoryPath, pluginFileName)
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add to the name path dictionary .
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def _module(language): return _modules.setdefault(language, __import__(language, globals(), {}, [], (-1)))
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returns the given language module .
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def reset_ignored(): cmd = ['softwareupdate', '--reset-ignored'] salt.utils.mac_utils.execute_return_success(cmd) return (list_ignored() == [])
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make sure the ignored updates are not ignored anymore .
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def percentiles(a, pcts, axis=None): scores = [] try: n = len(pcts) except TypeError: pcts = [pcts] n = 0 for (i, p) in enumerate(pcts): if (axis is None): score = stats.scoreatpercentile(a.ravel(), p) else: score = np.apply_along_axis(stats.scoreatpercentile, axis, a, p) scores.append(score) scores = np.asarray(scores) if (not n): scores = scores.squeeze() return scores
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like scoreatpercentile but can take and return array of percentiles .
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def p_field_id(p): p[0] = p[1]
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field_id : intconstant : .
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def from_xml(xml): if (xml is None): return None deserializer = XmlDeserializer() return deserializer(xml)
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deserialize xml to a dictionary .
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def gamma_correct(cs, c): gamma = cs.gamma if (gamma == GAMMA_REC709): cc = 0.018 if (c < cc): c = (((1.099 * math.pow(cc, 0.45)) - 0.099) / cc) else: c = ((1.099 * math.pow(c, 0.45)) - 0.099) else: c = math.pow(c, (1.0 / gamma)) return c
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transform linear rgb values to nonlinear rgb values .
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def JoinLists(**args): out = {} for (key, val) in args.items(): if val: out[key] = StrJoin(JOIN_DELIMS[key], val) else: out[key] = '' return out
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take a dictionary of {long_name:values} .
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def safe_dump_all(documents, stream=None, **kwds): return dump_all(documents, stream, Dumper=SafeDumper, **kwds)
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serialize a sequence of python objects into a yaml stream .
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def find_bucket_key(s3_path): s3_components = s3_path.split('/') bucket = s3_components[0] s3_key = '' if (len(s3_components) > 1): s3_key = '/'.join(s3_components[1:]) return (bucket, s3_key)
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this is a helper function that given an s3 path such that the path is of the form: bucket/key it will return the bucket and the key represented by the s3 path .
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def _textlist(self, _addtail=False): result = [] if ((not _addtail) and (self.text is not None)): result.append(self.text) for elem in self: result.extend(elem.textlist(True)) if (_addtail and (self.tail is not None)): result.append(self.tail) return result
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returns a list of text strings contained within an element and its sub-elements .
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def columnate(l, prefix): if (not l): return '' l = l[:] clen = max((len(s) for s in l)) ncols = ((tty_width() - len(prefix)) / (clen + 2)) if (ncols <= 1): ncols = 1 clen = 0 cols = [] while (len(l) % ncols): l.append('') rows = (len(l) / ncols) for s in range(0, len(l), rows): cols.append(l[s:(s + rows)]) out = '' for row in zip(*cols): out += ((prefix + ''.join((('%-*s' % ((clen + 2), s)) for s in row))) + '\n') return out
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format elements of l in columns with prefix leading each line .
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def _pb_timestamp_to_datetime(timestamp_pb): return (_EPOCH + datetime.timedelta(seconds=timestamp_pb.seconds, microseconds=(timestamp_pb.nanos / 1000.0)))
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convert a timestamp protobuf to a datetime object .
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def get_effective_domain_id(request): default_domain = get_default_domain(request) domain_id = default_domain.get('id') domain_name = default_domain.get('name') return (None if (domain_name == DEFAULT_DOMAIN) else domain_id)
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gets the id of the default domain to use when creating identity objects .
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def mulmatmat(matlist1, matlist2, K): matcol = [list(i) for i in zip(*matlist2)] result = [] for row in matlist1: result.append([mulrowcol(row, col, K) for col in matcol]) return result
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multiplies two matrices by multiplying each row with each column at a time .
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def total_result_to_dict(total): return {'meta': extract_meta(total), 'duration': _get_duration(world), 'features': [extract_feature_data(feature_result) for feature_result in total.feature_results]}
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transform a totalresult to a json-serializable python dictionary .
train
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def test_determinism_even_sequences(): rng = np.random.RandomState(123) lengths = rng.randint(1, 10, 100) data = [(['w'] * l) for l in lengths] batch_size = 5 my_iter = EvenSequencesSubsetIterator(data, batch_size) visited1 = ([0] * len(data)) for (b_ind, ind_list) in enumerate(my_iter): assert [(len(data[i]) == len(data[ind_list[0]])) for i in ind_list] for i in ind_list: visited1[i] = b_ind my_iter = EvenSequencesSubsetIterator(data, batch_size) visited2 = ([0] * len(data)) for (b_ind, ind_list) in enumerate(my_iter): assert [(len(data[i]) == len(data[ind_list[0]])) for i in ind_list] for i in ind_list: visited2[i] = b_ind assert np.all((np.asarray(visited1) == np.asarray(visited2)))
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check that evensequencessubsetiterator deterministically visits entries of a dataset of sequence data .
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def visit_binary_product(fn, expr): stack = [] def visit(element): if isinstance(element, ScalarSelect): (yield element) elif ((element.__visit_name__ == 'binary') and operators.is_comparison(element.operator)): stack.insert(0, element) for l in visit(element.left): for r in visit(element.right): fn(stack[0], l, r) stack.pop(0) for elem in element.get_children(): visit(elem) else: if isinstance(element, ColumnClause): (yield element) for elem in element.get_children(): for e in visit(elem): (yield e) list(visit(expr))
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produce a traversal of the given expression .
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@pytest.fixture(autouse=True) def init_fake_clipboard(quteproc): quteproc.send_cmd(':debug-set-fake-clipboard')
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make sure the fake clipboard will be used .
train
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def mock_responses(resps): def wrapper(func): @responses.activate @functools.wraps(func) def wrapped(*args, **kwargs): for resp in resps: responses.add(*resp.args, **resp.kwargs) return func(*args, **kwargs) return wrapped return wrapper
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mock responses for sleepiq .
train
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def _restore_int(name, value): if ((name == 'http01_port') and (value == 'None')): logger.info('updating legacy http01_port value') return cli.flag_default('http01_port') try: return int(value) except ValueError: raise errors.Error('Expected a numeric value for {0}'.format(name))
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restores an integer key-value pair from a renewal config file .
train
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def context_get(stack, name): return stack.get(name)
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find and return a name from a contextstack instance .
train
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def _get_cols_m2m(cls, k, child, fk_left_col_name, fk_right_col_name, fk_left_deferrable, fk_left_initially, fk_right_deferrable, fk_right_initially, fk_left_ondelete, fk_left_onupdate, fk_right_ondelete, fk_right_onupdate): (col_info, left_col) = _get_col_o2m(cls, fk_left_col_name, ondelete=fk_left_ondelete, onupdate=fk_left_onupdate, deferrable=fk_left_deferrable, initially=fk_left_initially) right_col = _get_col_o2o(cls, k, child, fk_right_col_name, ondelete=fk_right_ondelete, onupdate=fk_right_onupdate, deferrable=fk_right_deferrable, initially=fk_right_initially) left_col.primary_key = right_col.primary_key = True return (left_col, right_col)
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gets the parent and child classes and returns foreign keys to both tables .
train
false
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def _valid_code(seed, drift=0): return totp(key=seed, t=(int(time.time()) + (drift * 30)))
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generate a valid code .
train
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def assert_equal_steps(test_case, expected, actual): expected_steps = getattr(expected, 'steps') actual_steps = getattr(actual, 'steps') if (None in (expected_steps, actual_steps)): test_case.assertEqual(expected, actual) else: mismatch_steps = [] missing_steps = [] index = 0 for (index, expected_step) in enumerate(expected_steps): try: actual_step = actual_steps[index] except IndexError: missing_steps = expected_steps[index:] break if (expected_step != actual_step): mismatch_steps.append('* expected: {} !=\n actual: {}'.format(expected_step, actual_step)) extra_steps = actual_steps[(index + 1):] if (mismatch_steps or missing_steps or extra_steps): test_case.fail('Step Mismatch\nMismatch:\n{}\nMissing:\n{}\nExtra:\n{}'.format('\n'.join(mismatch_steps), missing_steps, extra_steps))
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assert that the list of provided steps are the same .
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def set_global_options(options): global _global_options _global_options = dict(options)
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sets the global options used as defaults for web server execution .
train
false
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def get_pairs(all_files, read1_indicator, read2_indicator, match_barcodes=False, barcode_indicator='_I1_'): pairs = {} bc_pairs = {} read1_files = [] read2_files = [] bc_files = [] for curr_file in all_files: curr_file_string_r1 = curr_file.split(read1_indicator) curr_file_string_r2 = curr_file.split(read2_indicator) if match_barcodes: curr_file_string_bc = curr_file.split(barcode_indicator) if (len(curr_file_string_r1) == 2): read1_files.append(curr_file_string_r1) elif (len(curr_file_string_r2) == 2): read2_files.append(curr_file_string_r2) elif (match_barcodes and (len(curr_file_string_bc) == 2)): bc_files.append(curr_file_string_bc) else: raise ValueError, ((('Invalid filename found for splitting on input ' + ('for file %s, ' % curr_file)) + 'check input read1_indicator ') + 'and read2_indicator parameters as well.') for curr_read1 in read1_files: for curr_read2 in read2_files: if (curr_read1 == curr_read2): pairs[read1_indicator.join(curr_read1)] = read2_indicator.join(curr_read2) if match_barcodes: for curr_read1 in read1_files: for curr_bc in bc_files: if (curr_read1 == curr_bc): bc_pairs[read1_indicator.join(curr_read1)] = barcode_indicator.join(curr_bc) forward_reads = set(pairs.keys()) bc_reads = set(bc_pairs.keys()) non_matching_f_reads = (forward_reads - bc_reads) if non_matching_f_reads: raise ValueError, ('Found forward reads without matching barcodes file: %s' % non_matching_f_reads) return (pairs, bc_pairs)
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get the list of callback/errback pairs from the user .
train
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def _json_decode_datetime(d): return datetime.datetime.strptime(d['isostr'], _DATETIME_FORMAT)
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converts a dict of json primitives to a datetime object .
train
false
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def solow_jacobian(t, k, g, n, s, alpha, delta): jac = (((s * alpha) * (k ** (alpha - 1))) - ((g + n) + delta)) return jac
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jacobian matrix for the solow model .
train
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def _get_all_permissions(opts): builtin = _get_builtin_permissions(opts) custom = list(opts.permissions) return (builtin + custom)
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returns for all permissions in the given opts .
train
false
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def no_setting_conf_directly_in_tests(logical_line, filename): if ('nova/tests/' in filename): res = conf_attribute_set_re.match(logical_line) if res: (yield (0, 'N320: Setting CONF.* attributes directly in tests is forbidden. Use self.flags(option=value) instead'))
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check for setting conf .
train
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def prespi_AccountBroker_initialize(self, conn, put_timestamp, **kwargs): if (not self.account): raise ValueError('Attempting to create a new database with no account set') self.create_container_table(conn) self.create_account_stat_table(conn, put_timestamp)
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the accountbroker initialze() function before we added the policy stat table .
train
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def stats_aggregate(): return s3_rest_controller()
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restful crud controller .
train
false
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def numeric_mixing_matrix(G, attribute, nodes=None, normalized=True): d = attribute_mixing_dict(G, attribute, nodes) s = set(d.keys()) for (k, v) in d.items(): s.update(v.keys()) m = max(s) mapping = dict(zip(range((m + 1)), range((m + 1)))) a = dict_to_numpy_array(d, mapping=mapping) if normalized: a = (a / a.sum()) return a
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return numeric mixing matrix for attribute .
train
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def _write_proxy_conf(proxyfile): msg = 'Invalid value for proxy file provided!, Supplied value = {0}'.format(proxyfile) log.trace('Salt Proxy Module: write proxy conf') if proxyfile: log.debug('Writing proxy conf file') with salt.utils.fopen(proxyfile, 'w') as proxy_conf: proxy_conf.write('master = {0}'.format(__grains__['master'])) msg = 'Wrote proxy file {0}'.format(proxyfile) log.debug(msg) return msg
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write to file .
train
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def _get_config_errors(request, cache=True): global _CONFIG_ERROR_LIST if ((not cache) or (_CONFIG_ERROR_LIST is None)): error_list = [] for module in appmanager.DESKTOP_MODULES: try: validator = getattr(module.conf, CONFIG_VALIDATOR) except AttributeError: continue if (not callable(validator)): LOG.warn(('Auto config validation: %s.%s is not a function' % (module.conf.__name__, CONFIG_VALIDATOR))) continue try: for (confvar, error) in validator(request.user): error = {'name': (confvar if isinstance(confvar, str) else confvar.get_fully_qualifying_key()), 'message': error} if isinstance(confvar, BoundConfig): error['value'] = confvar.get() error_list.append(error) except Exception as ex: LOG.exception(('Error in config validation by %s: %s' % (module.nice_name, ex))) _CONFIG_ERROR_LIST = error_list return _CONFIG_ERROR_LIST
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returns a list of tuples .
train
false
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def find_room(name, api_url=None, api_key=None, api_version=None): rooms = list_rooms(api_url=api_url, api_key=api_key, api_version=api_version) if rooms: for x in range(0, len(rooms)): if (rooms[x]['name'] == name): return rooms[x] return False
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find a room by name and return it .
train
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def _get_object(obj_ref): return _db_content[obj_ref.type][obj_ref]
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helper function to retrieve objtype from pillars if objname is string_types .
train
false
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def country_codes(): create_country_codes() try: country_codes = tk.get_action('tag_list')(data_dict={'vocabulary_id': 'country_codes'}) return country_codes except tk.ObjectNotFound: return None
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return the list of country codes from the country codes vocabulary .
train
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def reset_orig(): mpl.rcParams.update(_orig_rc_params)
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restore all rc params to original settings .
train
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def run_network(filename, n, eta): random.seed(12345678) np.random.seed(12345678) (training_data, validation_data, test_data) = mnist_loader.load_data_wrapper() net = network2.Network([784, n, 10], cost=network2.CrossEntropyCost) print 'Train the network using the default starting weights.' (default_vc, default_va, default_tc, default_ta) = net.SGD(training_data, 30, 10, eta, lmbda=5.0, evaluation_data=validation_data, monitor_evaluation_accuracy=True) print 'Train the network using the large starting weights.' net.large_weight_initializer() (large_vc, large_va, large_tc, large_ta) = net.SGD(training_data, 30, 10, eta, lmbda=5.0, evaluation_data=validation_data, monitor_evaluation_accuracy=True) f = open(filename, 'w') json.dump({'default_weight_initialization': [default_vc, default_va, default_tc, default_ta], 'large_weight_initialization': [large_vc, large_va, large_tc, large_ta]}, f) f.close()
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train the network for num_epochs on training_set_size images .
train
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def barycenter_kneighbors_graph(X, n_neighbors, reg=0.001, n_jobs=1): knn = NearestNeighbors((n_neighbors + 1), n_jobs=n_jobs).fit(X) X = knn._fit_X n_samples = X.shape[0] ind = knn.kneighbors(X, return_distance=False)[:, 1:] data = barycenter_weights(X, X[ind], reg=reg) indptr = np.arange(0, ((n_samples * n_neighbors) + 1), n_neighbors) return csr_matrix((data.ravel(), ind.ravel(), indptr), shape=(n_samples, n_samples))
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computes the barycenter weighted graph of k-neighbors for points in x parameters x : {array-like .
train
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def staticfile(filename, root=None, match='', content_types=None, debug=False): request = cherrypy.serving.request if (request.method not in ('GET', 'HEAD')): if debug: cherrypy.log('request.method not GET or HEAD', 'TOOLS.STATICFILE') return False if (match and (not re.search(match, request.path_info))): if debug: cherrypy.log(('request.path_info %r does not match pattern %r' % (request.path_info, match)), 'TOOLS.STATICFILE') return False if (not os.path.isabs(filename)): if (not root): msg = ("Static tool requires an absolute filename (got '%s')." % filename) if debug: cherrypy.log(msg, 'TOOLS.STATICFILE') raise ValueError(msg) filename = os.path.join(root, filename) return _attempt(filename, content_types, debug=debug)
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serve a static resource from the given filename .
train
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@with_setup(prepare_stdout, registry.clear) def test_xunit_xml_output_with_no_errors(): called = [] def assert_correct_xml_output(filename, doc): called.append(True) expect(doc.toxml).when.called.doesnt.throw(UnicodeDecodeError) old = xunit_output.write_xml_doc xunit_output.write_xml_doc = assert_correct_xml_output runner = Runner(feature_name('xunit_unicode_and_bytestring_mixing'), enable_xunit=True) try: runner.run() finally: xunit_output.write_xml_doc = old
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test xunit doc xml output .
train
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def showHttpErrorCodes(): if kb.httpErrorCodes: warnMsg = 'HTTP error codes detected during run:\n' warnMsg += ', '.join((('%d (%s) - %d times' % (code, (httplib.responses[code] if (code in httplib.responses) else '?'), count)) for (code, count) in kb.httpErrorCodes.items())) logger.warn(warnMsg) if any((((str(_).startswith('4') or str(_).startswith('5')) and (_ != httplib.INTERNAL_SERVER_ERROR) and (_ != kb.originalCode)) for _ in kb.httpErrorCodes.keys())): msg = 'too many 4xx and/or 5xx HTTP error codes ' msg += 'could mean that some kind of protection is involved (e.g. WAF)' logger.debug(msg)
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shows all http error codes raised till now .
train
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def _imgLink(col, latex, model): txt = _latexFromHtml(col, latex) fname = ('latex-%s.png' % checksum(txt.encode('utf8'))) link = ('<img class=latex src="%s">' % fname) if os.path.exists(fname): return link elif (not build): return ('[latex]%s[/latex]' % latex) else: err = _buildImg(col, txt, fname, model) if err: return err else: return link
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return an img link for latex .
train
false
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def get_concept_classified(): ct._write_head() df = _get_type_data((ct.SINA_CONCEPTS_INDEX_URL % (ct.P_TYPE['http'], ct.DOMAINS['sf'], ct.PAGES['cpt']))) data = [] for row in df.values: rowDf = _get_detail(row[0]) rowDf['c_name'] = row[1] data.append(rowDf) data = pd.concat(data, ignore_index=True) return data
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return dataframe code :股票代码 name :股票名称 c_name :概念名称 .
train
false
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def assert_equal_in(logical_line): res = (asse_equal_in_start_with_true_or_false_re.search(logical_line) or asse_equal_in_end_with_true_or_false_re.search(logical_line)) if res: (yield (0, 'N338: Use assertIn/NotIn(A, B) rather than assertEqual(A in B, True/False) when checking collection contents.'))
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check for assertequal .
train
false
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def getUnpackedLoops(loops): if (len(loops) == 1): firstLoop = loops[0] if (firstLoop.__class__ == list): return firstLoop return loops
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get unpacked loops .
train
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def p_assignment_operator(t): pass
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assignment_operator : equals | timesequal | divequal | modequal | plusequal | minusequal | lshiftequal | rshiftequal | andequal | orequal | xorequal .
train
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@verbose def tfr_stockwell(inst, fmin=None, fmax=None, n_fft=None, width=1.0, decim=1, return_itc=False, n_jobs=1, verbose=None): data = _get_data(inst, return_itc) picks = pick_types(inst.info, meg=True, eeg=True) info = pick_info(inst.info, picks) data = data[:, picks, :] n_jobs = check_n_jobs(n_jobs) (power, itc, freqs) = _induced_power_stockwell(data, sfreq=info['sfreq'], fmin=fmin, fmax=fmax, n_fft=n_fft, width=width, decim=decim, return_itc=return_itc, n_jobs=n_jobs) times = inst.times[::decim].copy() nave = len(data) out = AverageTFR(info, power, times, freqs, nave, method='stockwell-power') if return_itc: out = (out, AverageTFR(deepcopy(info), itc, times.copy(), freqs.copy(), nave, method='stockwell-itc')) return out
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time-frequency representation using stockwell transform .
train
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def _prepare_report_dir(dir_name): dir_name.rmtree_p() dir_name.mkdir_p()
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sets a given directory to a created .
train
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def build_user_vars(registry, xml_parent, data): XML.SubElement(xml_parent, 'org.jenkinsci.plugins.builduser.BuildUser')
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yaml: build-user-vars set environment variables to the value of the user that started the build .
train
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def sum_expr(operators): return lo.LinOp(lo.SUM, operators[0].size, operators, None)
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add linear operators .
train
false
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def _get_service_endpoint(context, svc, region=None, public=True): region = _safe_region(region) context = (context or identity) url_type = {True: 'public', False: 'private'}[public] svc_obj = context.services.get(svc) if (not svc_obj): return None ep = svc_obj.endpoints.get(region, {}).get(url_type) if (not ep): ep = svc_obj.endpoints.get('ALL', {}).get(url_type) return ep
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parses the services dict to get the proper endpoint for the given service .
train
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def examplesfullTest(vm, prompt=Prompt): installPexpect(vm, prompt) vm.sendline('sudo -n python ~/mininet/examples/test/runner.py -v')
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full test of mininet examples .
train
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@testing.requires_testing_data def test_dipole_fitting_ctf(): raw_ctf = read_raw_ctf(fname_ctf).set_eeg_reference() events = make_fixed_length_events(raw_ctf, 1) evoked = Epochs(raw_ctf, events, 1, 0, 0, baseline=None).average() cov = make_ad_hoc_cov(evoked.info) sphere = make_sphere_model((0.0, 0.0, 0.0)) fit_dipole(evoked, cov, sphere)
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test dipole fitting with ctf data .
train
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def release(): return uname()[2]
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version should be a string like 0 .
train
false
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def net_if_addrs(): has_enums = (sys.version_info >= (3, 4)) if has_enums: import socket rawlist = _psplatform.net_if_addrs() rawlist.sort(key=(lambda x: x[1])) ret = collections.defaultdict(list) for (name, fam, addr, mask, broadcast, ptp) in rawlist: if has_enums: try: fam = socket.AddressFamily(fam) except ValueError: if (WINDOWS and (fam == (-1))): fam = _psplatform.AF_LINK elif (hasattr(_psplatform, 'AF_LINK') and (_psplatform.AF_LINK == fam)): fam = _psplatform.AF_LINK if (fam == _psplatform.AF_LINK): separator = (':' if POSIX else '-') while (addr.count(separator) < 5): addr += ('%s00' % separator) ret[name].append(_common.snic(fam, addr, mask, broadcast, ptp)) return dict(ret)
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return the addresses associated to each nic installed on the system as a dictionary whose keys are the nic names and value is a list of namedtuples for each address assigned to the nic .
train
false
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def testCount(): deleteMatches() deletePlayers() c = countPlayers() if (c == '0'): raise TypeError("countPlayers should return numeric zero, not string '0'.") if (c != 0): raise ValueError('After deletion, countPlayers should return zero.') print '1. countPlayers() returns 0 after initial deletePlayers() execution.' registerPlayer('Chandra Nalaar') c = countPlayers() if (c != 1): raise ValueError('After one player registers, countPlayers() should be 1. Got {c}'.format(c=c)) print '2. countPlayers() returns 1 after one player is registered.' registerPlayer('Jace Beleren') c = countPlayers() if (c != 2): raise ValueError('After two players register, countPlayers() should be 2. Got {c}'.format(c=c)) print '3. countPlayers() returns 2 after two players are registered.' deletePlayers() c = countPlayers() if (c != 0): raise ValueError('After deletion, countPlayers should return zero.') print '4. countPlayers() returns zero after registered players are deleted.\n5. Player records successfully deleted.'
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test for initial player count .
train
false
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def get_collectors_from_module(mod): for attrname in dir(mod): attr = getattr(mod, attrname) if (inspect.isclass(attr) and issubclass(attr, Collector) and (attr != Collector)): if attrname.startswith('parent_'): continue fqcn = '.'.join([mod.__name__, attrname]) try: cls = load_dynamic_class(fqcn, Collector) (yield (cls.__name__, cls)) except Exception: logger.error('Failed to load Collector: %s. %s', fqcn, traceback.format_exc()) continue
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locate all of the collector classes within a given module .
train
true
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def Instance2Str(o, d): if d.has_key(o.__class__): return d[o.__class__](o, d) cl = filter((lambda x, o=o: ((type(x) is types.ClassType) and isinstance(o, x))), d.keys()) if ((not cl) and hasattr(types, 'ObjectType')): cl = filter((lambda x, o=o: ((type(x) is types.TypeType) and isinstance(o, x) and (d[x] is not Instance2Str))), d.keys()) if (not cl): return d[types.StringType](o, d) d[o.__class__] = d[cl[0]] return d[cl[0]](o, d)
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convert an instance to a string representation .
train
true
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def search_by_build_id(hex_encoded_id): cache = ((cache_dir + '-libc.so.') + hex_encoded_id) if (os.path.exists(cache) and read(cache).startswith('\x7fELF')): log.info_once(('Using cached data from %r' % cache)) return cache log.info('Downloading data from GitHub') url_base = 'https://gitlab.com/libcdb/libcdb/raw/master/hashes/build_id/' url = urlparse.urljoin(url_base, hex_encoded_id) data = '' while (not data.startswith('\x7fELF')): data = wget(url) if (not data): return None if data.startswith('..'): url = (os.path.dirname(url) + '/') url = urlparse.urljoin(url, data) write(cache, data) return cache
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given a hex-encoded build id .
train
false
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def strtotuple(s): if (not match('^[,.0-9 ()\\[\\]]*$', s)): raise Exception('Invalid characters in string for tuple conversion') if (s.count('(') != s.count(')')): raise Exception('Invalid count of ( and )') if (s.count('[') != s.count(']')): raise Exception('Invalid count of [ and ]') r = eval(s) if (type(r) not in (list, tuple)): raise Exception('Conversion failed') return r
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convert a tuple string into a tuple with some security checks .
train
false
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def decodeName(name): decodedName = name hexNumbers = re.findall('#([0-9a-f]{2})', name, (re.DOTALL | re.IGNORECASE)) for hexNumber in hexNumbers: try: decodedName = decodedName.replace(('#' + hexNumber), chr(int(hexNumber, 16))) except: return ((-1), 'Error decoding name') return (0, decodedName)
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decode the given pdf name .
train
false
1,589
def RtlGetVersion(os_version_info_struct): rc = ctypes.windll.Ntdll.RtlGetVersion(ctypes.byref(os_version_info_struct)) if (rc != 0): raise exceptions.WindowsError('Getting Windows version failed.')
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wraps the lowlevel rtlgetversion routine .
train
false
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def parameter_banks(device, device_dict=DEVICE_DICT): if (device != None): if (device.class_name in device_dict.keys()): def names_to_params(bank): return map(partial(get_parameter_by_name, device), bank) return map(names_to_params, device_dict[device.class_name]) else: if (device.class_name in MAX_DEVICES): try: banks = device.get_bank_count() except: banks = 0 if (banks != 0): def _bank_parameters(bank_index): try: parameter_indices = device.get_bank_parameters(bank_index) except: parameter_indices = [] if (len(parameter_indices) != 8): return [None for i in range(0, 8)] else: return [(device.parameters[i] if (i != (-1)) else None) for i in parameter_indices] return map(_bank_parameters, range(0, banks)) return group(device_parameters_to_map(device), 8) return []
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determine the parameters to use for a device .
train
false
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def OpenQuickFixList(focus=False, autoclose=False): vim.command(u'botright copen') SetFittingHeightForCurrentWindow() if autoclose: vim.command(u'au WinLeave <buffer> q') if VariableExists(u'#User#YcmQuickFixOpened'): vim.command(u'doautocmd User YcmQuickFixOpened') if (not focus): JumpToPreviousWindow()
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open the quickfix list to full width at the bottom of the screen with its height automatically set to fit all entries .
train
false
1,592
def _extract_nested_case(case_node, stmts_list): if isinstance(case_node.stmts[0], (c_ast.Case, c_ast.Default)): stmts_list.append(case_node.stmts.pop()) _extract_nested_case(stmts_list[(-1)], stmts_list)
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recursively extract consecutive case statements that are made nested by the parser and add them to the stmts_list .
train
false
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def summarize_exit_codes(exit_codes): for ec in exit_codes: if (ec != 0): return ec return 0
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take a list of exit codes .
train
false
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def disable_key(key_id, region=None, key=None, keyid=None, profile=None): conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile) r = {} try: key = conn.disable_key(key_id) r['result'] = True except boto.exception.BotoServerError as e: r['result'] = False r['error'] = __utils__['boto.get_error'](e) return r
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mark key as disabled .
train
true
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def get_drone(hostname): if (hostname == 'localhost'): return _LocalDrone() try: return _RemoteDrone(hostname) except DroneUnreachable: return None
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use this factory method to get drone objects .
train
false
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def manage_accessed(wrapped): def accessed(session, *arg, **kw): session.accessed = now = int(time.time()) if (session._reissue_time is not None): if ((now - session.renewed) > session._reissue_time): session.changed() return wrapped(session, *arg, **kw) accessed.__doc__ = wrapped.__doc__ return accessed
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decorator which causes a cookie to be renewed when an accessor method is called .
train
false
1,600
def get_legal(state): feature = np.zeros((1, state.size, state.size)) for (x, y) in state.get_legal_moves(): feature[(0, x, y)] = 1 return feature
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zero at all illegal moves .
train
false